Precipitation Anomaly and Dengue Outbreaks in South East Asia: 2015-2016

This visualization with corresponding data dashboard shows the relationship between vegetation index anomalies and outbreaks of Rift Valley fever (RVF) during 2008 and 2011 in the South Africa region. The sequence starts in 2007 looking at the entire continent of Africa and zooms in the region of South Africa to take a closer look at the patterns between ENSO events (El Niño and La Niña), above normal vegetaion over land (green) and RVF outbreak locations (orange pins). This version of the visualization is similar to the one above, except it does not include the labels for the ENSO events (El Niño and La Niña) in the 2008-2011 timeline.This visualization with corresponding data dashboard shows the relationship between vegetation index anomalies and outbreaks of Rift Valley fever (RVF) during 2009 and 2011 in the South Africa region. The sequence starts in 2007 looking at the entire continent of Africa and zooms in the region of South Africa to take a closer look at the patterns between ENSO events (El Niño and La Niña), above normal vegetaion over land (green) and RVF outbreak locations (orange pins). In this version of the visualization the data dashboard is visible during the timeline of 2008-2011.This visualization with corresponding data dashboard shows the relationship between vegetation index anomalies and outbreaks of Rift Valley fever (RVF) during 2009 and 2011 in the South Africa region. The sequence starts in 2007 looking at the entire continent of Africa and zooms in the region of South Africa to take a closer look at the patterns between ENSO events (El Niño and La Niña), above normal vegetaion over land (green) and RVF outbreak locations (orange pins). Vegetation Index Anomalies (NDVI) data and Rift Valley fever (RVF) outbreak locations over land for the period 2007-2011. This sequence is provided with dates and colorbar. Frames of NDVI Anomaly and Rift Valley fever (RVF) outbreak locations over land for the entire duration of the visualization. This set of frames seves as a layer and is provided with transparency. Frames of NDVI Anomaly and Rift Valley fever (RVF) outbreak locations for the entire duration of the visualization, with the gray line indicating the region of South Africa over land with extents (15E-33E, 26S-35S). This set of frames seves as layer and is provided with transparency. This set of frames provides the dates and data dashboard timeline for the period 2008-2011. The dashboard tracks and visualizes indicators from two different data sources:Number of Rift Valley fever (RVF) disease outbreak locations in the South Africa region with extents (15E-33E, 26S-35S).Vegetation Index (NDVI) Anomalies over land in the South Africa region with extents (15E-33E, 26S-35S).Frames include the labels for El Niño, La Niña and High-risk RVF periods during 2009, 2010, 2011 and are provided with transparency. This set of frames provides the dates and data dashboard timeline for the period 2008-2011, without labels for the El Niño, La Niña and High-risk RVF periods. Frames are provided with transparency. Still image of the dashboard timeline for 2008-2011. Image is provided with transparency to serve as a layer. This set of frames serves as the dates layer for the period of 2008-2011. Frames are provided with transparency. Still image of label highlighting the El Niño period for the data dashboard. Image is provided with transparency. Still image/label of the La Niña period for the data dashboard. Image is provided with transparency Still image/ of abel highlighting the High-risk RVf period for 2010. Image is provided with transparency. Still image of label higlighting the High-risk RVF period for 2011. Image is provided with transparency. Precipitation Anomaly and Rift Valley fever (RVF) colorbar Normalized Difference Vegetation Index (NDVI) anomaly colorbar. During the period of 2009-2011, a series of Rift Valley fever (RVF) outbreaks occurred in South Africa and neighboring countries. The visualization featured on this page showcases the relationship between El Niño-Southern Oscillation (ENSO) induced anomalous vegetation and Rift Valley fever outbreaks in the region of South Africa. ENSO is an irregularly recurring climate pattern characterized by warmer (El Niño) and colder (La Niña) than usual ocean temperatures in the equatorial Pacific, which creates a ripple effect of anticipated weather changes in far-spread regions of our planet. Weather changes associated with the ENSO phenomenon result in climate anomalies related to each other, such as rainfall, and vegetation anomaly conditions that trigger outbreaks of infectious diseases of public health concern in different regions around the world. These distant weather effects are called teleconnections. Therefore, the effects of ENSO are called ENSO teleconnections, highlighting that warmer or colder than usual ocean temperatures in equatorial pacific with extents (5N-5S, 120W-170W) affect areas far from the source typically 2-3 months after. These relationships have been visualized with data in the entry ENSO Teleconnections and Rift Valley fever (RVF) outbreaks. During the last 20 years NASA scientist Dr. Assaf Anyamba and colleagues have been studying ENSO teleconnections by monitoring various climate datasets, among them Sea Surface Temperature and precipitation anomaly datasets from NASA and National Oceanic and Atmospheric Administration (NOAA) and vegetation data from NASA’s Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra (EOS AM-1) spacecraft. At the same time, the science team has been collecting, cataloguing and analyzing patterns and sources of infectious disease outbreaks worldwide (for example, see data visualization Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (4K version)). Furthermore, the science team has been carrying out long-term comprehensive research studies in South Africa to gain a better understating of all components of Rift Valley fever including livestock, wildlife, human and collecting data on mosquito vectors from different sites.Rift Valley fever (RVF) is an acute viral disease of domestic animals (cattle, buffalo, sheep, goats, and camels), but it also affects humans. It is spread by the bite of various Aedes and Culex mosquito species that carry the Rift Valley fever virus (RVFV). Large outbreaks termed epizootics (in livestock) and epidemics (humans) occur during periods of above normal and persistent rainfall. Such rainfall, floods mosquito habitats called dambos or pans leading to emergence of large numbers of RVF mosquito vectors. Rift Valley fever disease causes severe mortalities (80-100%) and abortion in livestock affecting the trade economy in the regions it occurs (for ex. ~$60M loss in East Africa during the 2006-2007 outbreak, ~$17.8M loss in South Africa, during the 2010 outbreak). In humans, it is manifests as mild influenza-like illness to severe hemorrhagic manifestations and hepatitis; retinitis (inflammation of the retina) and encephalitis (inflammation of the brain). The mortality in humans varies but ranges between 1-35% of those infected. It is classified as a cross-over pathogen of national and international security and of public health importance.The visualization featured on this page starts in 2007 looking at Vegetaion Index anomalies (NDVI) data (low/brown to hig/green) and Rift Valley fever outbreak locations (orange pins) over land in the Middle East and the entire continent of Africa. The sequence continues by zooming in slowly in the region of South Africa to take a closer look at the series of events and their patterns. A gray line with the label South Africa region indicates the area of interest over land with extents (15E-33E, 26S-35S). On the same time a synchronized data dashboard appears to track and visualize indicators from two different data sources. These two indicators are:Number of Rift Valley fever (RVF) disease outbreak locations in the South Africa region with extents (15E-33E, 26S-35S).Normalized Difference Vegetation Index (NDVI) Anomaly over land in the South Africa region with extents (15E-33E, 26S-35S).As the timeline unfolds, labels reveal the ENSO events (El Nino and La Nina periods) and the high-risk RVF periods to help guide the viewer on the patterns and teleconnections.We can see a spike of RVF outbreaks during February-May 2010 and another one during February-May 2011. These outbreaks are linked to ENSO teleconnections during the period of 2008-2011. The relationship between ENSO induced anomalous vegetation and disease outbreaks is clearly illustrated by outbreak patterns of Rift Valley fever in South Africa. During the La Niña phase of ENSO, Southern Africa receives persistent and above normal rainfall (for example, see Precipitation Anomaly and Rift Valley fever (RVF) outbreaks in South Africa: 2008-2011), which floods habitats of RVF mosquito vectors triggering hatching of RVF virus infected eggs. The above-normal rainfall is followed by an increase in vegetation creating appropriate habitats for the mosquito vectors setting the stage for RVF outbreak activity, which in simple terms means an uptick in mosquito populations that cause infections of domestic livestock and human populations with the RVF virus. However, in rare cases there is a departure from this canonical response, as we can observe in 2009-2010 when a mild El Niño event resulted in above normal vegetaion and a significant RVF outbreak in central South Africa.To explain a bit further the weather patterns and teleconnections, lets take a closer look at the sequence of events and their timelines. During 2009-2010, the mild El Niño event (May 2009-March 2010) was followed by an increase in rainfall in South Africa. This cumulative precipitation resulted in an increase of vegetation that manifested on October 2009. This increase of vegetation was followed by a spike of RVF outbreaks starting 3 months after (January/February 2010). The strong relationship between ENSO events and disease outbreaks underscores the importance of seasonal forecasts. Since disease outbreaks typically manifest 2-3 months after the start of El Niño and La Nina events, early and regular climate monitoring, paired with the use of monthly and seasonal climate forecasts become significant tools for disease control and prevention. Findings of an earlier scientific study by Dr. Assaf Anyamba et al. published in the journal Nature Scientific Reports with the title Global Disease Outbreaks Associated with the 2015-2016 El Niño event suggests that by monitoring monthly climate datasets, country public health agencies and organizations such as the United Nations' World Health Organization and Food and Agriculture Organizations, can utilize early warning forecasts to undertake preventive measures to minimize the spread of ecologically coupled diseases.Data Sources:Disease Outbreak data were collected from the Program for Monitoring Emerging Diseases (ProMED) and from the World Organisation for Animal Health/Organisation mondiale de la santé animale (OIE).Normalized Difference Vegetation Index (NDVI) dataset is derived from NASA’s Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra (EOS AM-1) spacecraft. We used the MODIS global monthly Climate Modeling Grid (CMG) products with a spatial resolution of 0.05° x 0.05° (~5.5 x 5.5 km). The data is available here. Reference: Townshend, J. & Justice, Christopher. (2002). Towards operational monitoring of terrestrial systems by moderate-resolution remote sensing. Remote Sensing of Environment. Volume 83, Issues 1-2, Pages: 351-359. 10.1016/S0034-4257(02)00082-2. ENSO events (El Niño and La Niña) are identified by using SST ENSO index (Niño 3.4) data obtained from the NOAA National Center for Climate Prediction on-line archives. The warm (El Niño) and cold (La Niña) periods of ENSO events were determined using the Oceanic Niño Index (ONI) threshold of +/- 0.5oC based on centered 30-year base periods updated every 5 years. The ONI is a 3-month running mean of Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4) SST anomalies in the Niño 3.4 region (5 N-5 S, 120W-170W).Supported with funding from the Defense Health Agency-Armed Forces Health Surveillance Branch (AFHSB) Global Emerging Infections Surveillance and Response System (GEIS) under Project #P0072_19_NSThe rest of this webpage offers additional versions, colorbar information, frames and layers associated with the development of this data-driven visualization. Related pages

This visualization with corresponding data dashboard shows the relationship between precipitation anomalies and outbreaks of Rift Valley fever (RVF) during 2008 and 2011 in the South Africa region. The sequence starts in 2007 looking at the entire continent of Africa and zooms in the region of South Africa to take a closer look at the patterns between ENSO events (El Niño and La Niña), above normal precipitation over land (blue) and RVF outbreak locations (orange pins). This version of the visualization does not include the labels for the ENSO events (El Niño and La Niña) in the 2008-2011 timeline.The visualization with corresponding data dashboard featured in this group shows the relationship between precipitation anomalies and outbreaks of Rift Valley fever (RVF) during 2008 and 2011 in the South Africa region. The sequence starts in 2007 looking at the entire continent of Africa and zooms in the region of South Africa to take a closer look at the patterns between above normal precipitation over land (blue) and RVF outbreak locations (orange pins). This version of the visualization unveils the data dashboard upfront for the entire period 2008-2011.The visualization with corresponding data dashboard featured in this group shows the relationship between precipitation anomalies and outbreaks of Rift Valley fever (RVF) during 2008 and 2011 in the South Africa region. The sequence starts in 2007 looking at the entire continent of Africa and zooms in the region of South Africa to take a closer look at the patterns between above normal precipitation over land (blue) and RVF outbreak locations (orange pins). Precipitation Anomaly data and Rift Valley fever (RVF) outbreak locations over land for the period 2007-2011. This sequence is provided with dates and colorbar. Frames of Precipitation Anomaly and Rift Valley fever (RVF) outbreak locations over land for the entire duration of the visualization. This set of frames seves as a layer and is provided with transparency. Frames of Precipitation Anomaly and Rift Valley fever (RVF) outbreak locations for the entire duration of the visualization, with the gray line indicating the region of South Africa over land with extents (15E-33E, 26S-35S). This set of frames seves as layer and is provided with transparency. This set of frames provides the dates and data dashboard timeline for the period 2008-2011. The dashboard tracks and visualizes indicators from two different data sources:Number of Rift Valley fever (RVF) disease outbreak locations in the South Africa region with extents (15E-33E, 26S-35S).Precipitation Anomaly over land in the South Africa region with extents (15E-33E, 26S-35S).Frames include the labels for El Niño, La Niña and High-risk RVF periods during 2009, 2010, 2011 and are provided with transparency. This set of frames provides the dates nad data dashboard timeline for the period 2008-2011, without labels for the El Niño, La Niña and High-risk RVF periods. Frames are provided with transparency. Still image of the dashboard timeline for 2008-2011. Image is provided with transparency to serve as a layer. This set of frames serves as the dates layer for the period of 2008-2011. Frames are provided with transparency. Still image of label highlighting the El Niño period for the data dashboard. Image is provided with transparency. Still image/label of the La Niña period for the data dashboard. Image is provided with transparency. Still image/label of the High-risk RVf period for 2010. Image is provided with transparency. Still image of labeel higlighting the High-risk RVF period for 2011. Image is provided with transparency. Precipitation Anomaly and Rift Valley fever (RVF) colorbar Precipitation Anomaly Colorbar During the period of 2009-2011, a series of Rift Valley fever (RVF) outbreaks occurred in South Africa and neighboring countries. The visualization featured on this page showcases the relationship between El Niño-Southern Oscillation (ENSO) induced anomalous rainfall and RVF outbreaks in the region of South Africa. ENSO is an irregularly recurring climate pattern characterized by warmer (El Niño) and colder (La Niña) than usual ocean temperatures in the equatorial Pacific, which creates a ripple effect of anticipated weather changes in far-spread regions of Earth. Weather changes associated with the ENSO phenomenon result in climate anomalies related to each other, such as rainfall, and vegetation anomaly conditions that trigger outbreaks of infectious diseases of public health concern in different regions around the world. These distant weather effects are called teleconnections. Therefore, the effects of ENSO are called ENSO teleconnections, highlighting that warmer or colder than usual ocean temperatures in equatorial pacific with extents (5N-5S, 120W-170W) affect areas far from the source typically 2-3 months after. These relationships have been visualized with data in the entry ENSO Teleconnections and Rift Valley fever (RVF) Outbreaks.During the last 20 years NASA scientist Dr. Assaf Anyamba and colleagues have been studying ENSO teleconnections by monitoring various climate datasets, among them Sea Surface Temperature and Precipitation Anomaly datasets from NASA and National Oceanic and Atmospheric Administration (NOAA) and Vegetation Index Anomalies from NASA’s Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra (EOS AM-1) spacecraft. At the same time, the science team has been collecting, cataloguing and analyzing patterns and sources of infectious disease outbreaks worldwide (for example, see data visualization Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (4K version)). Furthermore, the science team has been carrying out long-term comprehensive research studies in South Africa to gain a better understating of all components of Rift Valley fever including livestock, wildlife, human and collecting data on mosquito vectors from different sites.Rift Valley fever (RVF) is an acute viral disease of domestic animals (cattle, buffalo, sheep, goats, and camels), but it also affects humans. It is spread by the bite of various Aedes and Culex mosquito species that carry the Rift Valley fever virus (RVFV). Large outbreaks termed epizootics (in livestock) and epidemics (humans) occur during periods of above normal and persistent rainfall. Such rainfall, floods mosquito habitats called dambos or pans leading to emergence of large numbers of RVF mosquito vectors. Rift Valley fever disease causes severe mortalities (80-100%) and abortion in livestock affecting the trade economy in the regions it occurs (for ex. ~$60M loss in East Africa during the 2006-2007 outbreak, ~$17.8M loss in South Africa, during the 2010 outbreak). In humans, it is manifests as mild influenza-like illness to severe hemorrhagic manifestations and hepatitis; retinitis (inflammation of the retina) and encephalitis (inflammation of the brain). The mortality in humans varies but ranges between 1-35% of those infected. It is classified as a cross-over pathogen of national and international security and of public health importance.The visualization featured on this page starts in 2007 looking at Precipitation Anomaly data (dry/brown to wet/blue) and Rift Valley fever outbreak locations (orange pins) over land for Middle East and the entire continent of Africa. The sequence continues by zooming in slowly in the region of South Africa to take a closer look at the series of events and their patterns. A gray line with the label South Africa region indicates the area of interest/study over land with extents (15E-33E, 26S-35S). On the same time a synchronized data dashboard appears to track and visualize indicators from two different data sources. These two indicators are: Number of Rift Valley fever (RVF) outbreak locations in the South Africa region with extents (15E-33E, 26S-35S).Precipitation Anomaly over land in the South Africa region with extents (15E-33E, 26S-35S).As the timeline unfolds, labels reveal the ENSO events (El Niño and La Niña periods) and the duration of high-risk RVF periods to help guide the viewer on the patterns and teleconnections.We can see a spike of RVF outbreaks during February-May 2010 and another one during February-May 2011. These outbreaks are linked to ENSO teleconnections during the period of 2008-2011. The relationship between ENSO induced anomalous rainfall and disease outbreaks is clearly illustrated by outbreak patterns of Rift Valley fever in South Africa. During the La Niña phase of ENSO, Southern Africa receives persistent and above normal rainfall, which floods habitats of RVF mosquito vectors triggering hatching of RVF virus infected eggs. The above-normal rainfall is followed by an increase in vegetation (see Vegetation Index Anomalies and Rift Valley fever (RVF) outbreaks in South Africa region: 2008-2011)creating appropriate habitats for the mosquito vectors setting the stage for RVF outbreak activity, which in simple terms means an uptick in mosquito populations that cause infections of domestic livestock and human populations with the RVF virus. However, in rare cases there is a departure from this canonical response, as we can observe in 2009-2010, when a mild El Niño event resulted in above normal rainfall and a large RVF outbreak in central South Africa. To explain a bit further the weather patterns and teleconnections, lets take a closer look at the sequence of events and their timelines. During 2009-2010 the El Niño event (May 2009-March 2010) was followed by an increase in rainfall in South Africa. This cumulative precipitation resulted in an increase of vegetation that manifested on October 2009. This increase of vegetation was followed by a spike of RVF outbreaks starting 3 months after (January/February 2010).The strong relationship between ENSO events and disease outbreaks underscores the importance of seasonal forecasts. Since disease outbreaks typically manifest 2-3 months after the start of El Niño and La Nina events, early and regular climate monitoring, paired with the use of monthly and seasonal climate forecasts become significant tools for disease control and prevention. Findings of an earlier scientific study by Dr. Assaf Anyamba et al. published in the journal Nature Scientific Reports with the title Global Disease Outbreaks Associated with the 2015-2016 El Niño event suggests that by monitoring monthly climate datasets, country public health agencies and organizations such as the United Nations' World Health Organization and Food and Agriculture Organizations, can utilize early warning forecasts to undertake preventive measures to minimize the spread of ecologically coupled diseases.Data Sources:Disease Outbreak data were collected from the Program for Monitoring Emerging Diseases (ProMED) and from the World Organisation for Animal Health/Organisation mondiale de la santé animale (OIE).Rainfall/Precipitation anomaly data: African Rainfall Climatology (ARC) dataset from the National Oceanic and Atmospheric Administration (NOAA) – Climate Prediction Center (CPC) archives. The dataset is available over Africa at 0.1° x 0.1° spatial resolution from 1983 to present. Reference: Novella, N.S. and W.M. Thiaw, 2016: A Seasonal Rainfall Performance Probability Tool for Famine Early Warning Systems. J. Appl. Meteor. Climatol., 55, 2575–2586, 10.1175/JAMC-D-16-0111.1ENSO events (El Nino and La Nina) are identified by using SST ENSO index (Niño 3.4) data obtained from the NOAA National Center for Climate Prediction online archives. The warm (El Niño) and cold (La Niña) periods of ENSO events were determined using the Oceanic Niño Index (ONI) threshold of +/- 0.5°C based on centered 30-year base periods updated every 5 years. The ONI is a 3-month running mean of Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4) SST anomalies in the Niño 3.4 region (5 N-5 S, 120W-170W).Supported with funding from the Defense Health Agency-Armed Forces Health Surveillance Branch (AFHSB) Global Emerging Infections Surveillance and Response System (GEIS) under Project #P0072_19_NSThe rest of this webpage offers additional versions, colorbar information, frames and layers associated with the development of this data-driven visualization. Related pages

Data visualization featuring vegetation index anomalies over Africa and Middle East and locations of Rift Valley Fever (RVF) outbreaks (orange pins) during the period of 2000-2018. Frames are provided in 4K resolution. This set of frames provides the layer of monthly NDVI Anomaly data with Rift Valley fever locations (orange pins) of outbreaks for the period of 2000-2018. Frames are provided in 4K resolution with alpha channel for compositing purposes. This set of frames provides the dates layer for the NDVI Anomaly with Rift Valley Fever location outbreaks visualization for the period of 2000-2018. Frames are provided in 4K resolution with alpha channel. Colorbar for NDVI Anomaly The visualization featured on this page showcases the relationship between increase in vegetation and outbreaks of Rift Valley fever (RVF) over Middle East and the African continent for the period of 2000-2018. The data visualized in this animation are: a) Normalized Difference Vegetation Index (NDVI) (low/brown to high/green) over Africa and Middle East and b) locations of Rift Valley fever outbreaks (orange pins). We can see spikes of RVF outbreaks in various regions such as in Middle East, West, East and South Africa. These outbreaks are linked to El Niño-Southern Oscillation (ENSO) teleconnections. ENSO is an irregularly recurring climate pattern characterized by warmer (El Niño) and colder (La Niña) than usual ocean temperatures in the equatorial Pacific, which creates a ripple effect of anticipated weather changes in far-spread regions of our planet. Weather changes associated with the El Niño-Southern Oscillation phenomenon result in climate anomalies related to each other, such as rainfall, and vegetation anomaly conditions that trigger outbreaks of infectious diseases of public health concern in different regions around the world. These distant weather effects are called teleconnections. Therefore the effects of ENSO are called ENSO teleconnections, highlighting that warmer or colder than usual ocean temperatures in equatorial pacific with extents (5N-5S, 120W-170W) affect areas far from the source typically 2-3 months after.During the last 20 years NASA scientist Dr. Assaf Anyamba and colleagues have been studying ENSO teleconnections by monitoring various climate datasets, among them Sea Surface Temperature and precipitation anomaly datasets from NASA and National Oceanic and Atmospheric Administration (NOAA) and vegetation data from NASA’s Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra (EOS AM-1) spacecraft. At the same time, the science team has been collecting, cataloguing and analyzing patterns and sources of disease outbreaks worldwide. To learn more, please refer to entry: Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (4K version)The relationship between ENSO induced anomalous rainfall and disease outbreaks is clearly illustrated by outbreak patterns of Rift Valley fever (RVF). Typically, during El Niño (Eastern Africa) and La Niña (Southern Africa) events, Africa receives persistent and above normal rainfall, which floods habitats of RVF mosquito vectors triggering hatching of RVF virus infected eggs. The above-normal rainfall is followed by an increase in vegetation creating appropriate habitats for the mosquito vectors setting the stage for RVF outbreak activity, which in simple terms means an uptick in mosquito populations that cause infections of domestic livestock and human populations with the RVF virus.To learn more about the impacts of weather patterns and teleconnections for the region of South Africa during 2008-2011, please see the following data visualizations:ENSO Teleconnections and Rift Valley fever (RVF) OutbreaksPrecipitation Anomaly and Rift Valley fever (RVF) outbreaks in South Africa: 2008-2011Vegetation Index Anomalies and Rift Valley fever (RVF) outbreaks in South Africa region: 2008-2011Data Sources:Disease Outbreak data were collected from the Program for Monitoring Emerging Diseases (ProMED) and the World Organisation for Animal Health/Organisation mondiale de la santé animale (OIE).Normalized Difference Vegetation Index (NDVI) dataset is derived from NASA’s Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra (EOS AM-1) spacecraft. We used the MODIS global monthly Climate Modeling Grid (CMG) products with a spatial resolution of 0.05° x 0.05° (~5.5 x 5.5 km). The data is available here. Reference: Townshend, J. & Justice, Christopher. (2002). Towards operational monitoring of terrestrial systems by moderate-resolution remote sensing. Remote Sensing of Environment. Volume 83, Issues 1-2, Pages: 351-359. 10.1016/S0034-4257(02)00082-2. This visualization was created on October 2019, to support Dr. Assaf Anyamba’s interview and filming of the upcoming documentary titled Connected produced by Netflix. [Citation: "Clouds". Connected - The Hidden Science of Everything, Season 1, Episode 5, 2 Aug. 2020, Netflix]Supported with funding from the Defense Health Agency-Armed Forces Health Surveillance Branch (AFHSB) Global Emerging Infections Surveillance and Response System (GEIS) under Project #P0072_19_NSThe rest of this webpage offers frames, layers and colorbar information associated with the development of this data-driven visualization. Related pages

This visualization shows the relationship between vegetation index anomalies (Normalized Difference Vegetation Index - NDVI) data and outbreak locations of Rift Valley fever (RVf) during 2008 and 2011. The sequence starts in 2007 looking at the entire continent of Africa and zooms in the region of South Africa slowly to take a closer look at the above normal vegetation (green) and RVF outbreak locations (orange pins). Frames are provided in 4K resolution. This set of frames provides the layer of Normalized Difference Vegetation Index (NDVI) data with Rift Valley Fever outbreak location over land. Frames are provided in 4K resolution with alpha channel for compositing purposes. This set of frames provides the dates layer for the NDVI Anomaly data with Rift Valley Fever locations of outbreaks for the period of 2007-2011. Frames are provided in 4K resolution with alpha channel. Colorbar for NDVI Anomaly During the period of 2009-2011, a series of Rift Valley fever (RVF) outbreaks occured in South Africa and neighboring countries. The visualization featured on this page showcases the relationship between increase in vegetation and the RVF outbreak events. The visualization starts in 2007 looking at the entire continent of Africa and zooms slowly to the region of South Africa, so that we can take a closer look at the outbreak locations. The data visualized in this animation are: a) Normalized Difference Vegetation Index (NDVI) over Africa and Middle East and b) locations of Rift Valley fever outbreaks (orange pins). We can see clearly a spike of RVF outbreaks during February-May 2010 and again during February-May 2011. These outbreaks are linked to El Niño-Southern Oscillation (ENSO) Teleconnections during the period of 2009-2011.ENSO is an irregularly recurring climate pattern characterized by warmer (El Niño) and colder (La Niña) than usual ocean temperatures in the equatorial Pacific, which creates a ripple effect of anticipated weather changes in far-spread regions of our planet. Weather changes associated with the El Niño-Southern Oscillation phenomenon result in climate anomalies related to each other, such as rainfall, and vegetation anomaly conditions that trigger outbreaks of infectious diseases of public health concern in different regions around the world. These distant weather effects are called teleconnections. Therefore the effects of ENSO are called ENSO teleconnections, highlighting that warmer or colder than usual ocean temperatures in equatorial pacific with extents (5N-5S, 120W-170W) affect areas far from the source typically 2-3 months after. These relationships have been visualized with data for the region of South Africa in the entry ENSO Teleconnections and Rift Valley fever (RVF) Outbreaks.During the last 20 years NASA scientist Dr. Assaf Anyamba and colleagues have been studying ENSO teleconnections by monitoring various climate datasets, among them Sea Surface Temperature and precipitation anomaly datasets from NASA and National Oceanic and Atmospheric Administration (NOAA) and vegetation data from NASA’s Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra (EOS AM-1) spacecraft. At the same time, the science team has been collecting, cataloguing and analyzing patterns and sources of disease outbreaks worldwide. The relationship between ENSO induced anomalous rainfall and disease outbreaks is clearly illustrated by outbreak patterns of Rift Valley fever in South Africa. During the La Niña phase of ENSO, Southern Africa receives persistent and above normal rainfall, which floods habitats of RVF mosquito vectors triggering hatching of RVF virus infected eggs. The above-normal rainfall is followed by an increase in vegetation creating appropriate habitats for the mosquito vectors setting the stage for RVF outbreak activity, which in simple terms means an uptick in mosquito populations that cause infections of domestic livestock and human populations with the RVF virus. However, in rare cases there is a departure from this canonical response, as we can observe in 2009-2010 when a mild El Niño event resulted in above normal vegetaion and a significant RVF outbreak in central South Africa.To learn more about the weather patterns, teleconnections and sequence of events, please see the following data visualizations:Precipitation Anomaly and Rift Valley fever (RVF) outbreaks in South Africa: 2008-2011Vegetation Index Anomalies and Rift Valley fever (RVF) outbreaks in South Africa region: 2008-2011Data Sources:Disease Outbreak data were collected from the Program for Monitoring Emerging Diseases (ProMED) and the World Organisation for Animal Health/Organisation mondiale de la santé animale (OIE).Normalized Difference Vegetation Index (NDVI) dataset is derived from NASA’s Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra (EOS AM-1) spacecraft. We used the MODIS global monthly Climate Modeling Grid (CMG) products with a spatial resolution of 0.05° x 0.05° (~5.5 x 5.5 km). The data is available here. Reference: Townshend, J. & Justice, Christopher. (2002). Towards operational monitoring of terrestrial systems by moderate-resolution remote sensing. Remote Sensing of Environment. Volume 83, Issues 1-2, Pages: 351-359. 10.1016/S0034-4257(02)00082-2. This visualization was created on October 2019, to support Dr. Assaf Anyamba’s interview and filming of the upcoming documentary titled Connected produced by Netflix. [Citation: "Clouds". Connected - The Hidden Science of Everything, Season 1, Episode 5, 2 Aug. 2020, Netflix]Supported with funding from the Defense Health Agency-Armed Forces Health Surveillance Branch (AFHSB) Global Emerging Infections Surveillance and Response System (GEIS) under Project #P0072_19_NSThe rest of this webpage offers frames, layers and colorbar information associated with the development of this data-driven visualization. Related pages

El Niño-Southern Oscillation (ENSO) is an irregularly recurring climate pattern characterized by warmer (El Niño) and colder (La Niña) than usual ocean temperatures in the equatorial Pacific, which creates a ripple effect of anticipated weather changes in far-spread regions of our planet. Weather changes associated with the ENSO phenomenon result in climate anomalies related to each other, such as rainfall, and vegetation anomaly conditions that trigger outbreaks of infectious diseases of public health concern in different regions around the world. These distant weather effects are called teleconnections. Therefore the effects of ENSO are called ENSO teleconnections, highlighting that warmer or colder than usual ocean temperatures in equatorial pacific with extents (5N-5S, 120W-170W) affect areas far from the source typically 2-3 months after.During the last 20 years NASA scientist Dr. Assaf Anyamba and colleagues have been studying ENSO teleconnections by monitoring various climate datasets, among them Sea Surface Temperature and Precipitation Anomaly datasets from NASA and National Oceanic and Atmospheric Administration (NOAA) and Vegetation Index Anomalies from NASA’s Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra (EOS AM-1) spacecraft. At the same time, the science team has been collecting, cataloguing and analyzing patterns and sources of infectious disease outbreaks worldwide (see data visualization Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (4K version)). Furthermore, the science team has been collecting data on mosquito vectors from different sites and carrying out long-term comprehensive research studies in South Africa to gain a better understanding of all components of Rift Valley fever including livestock, wildlife and humans.Rift Valley fever (RVF) is an acute viral disease of domestic animals (cattle, buffalo, sheep, goats, and camels), but it also affects humans. It is spread by the bite of various Aedes and Culex mosquito species that carry the Rift Valley fever virus (RVFV). Large outbreaks termed epizootics (in livestock) and epidemics (humans) occur during periods of above normal and persistent rainfall. Such rainfall, floods mosquito habitats called dambos or pans leading to emergence of large numbers of RVF mosquito vectors. Rift Valley fever disease causes severe mortalities (80-100%) and abortion in livestock affecting the trade economy in the regions it occurs (for ex. ~$60M loss in East Africa during the 2006-2007 outbreak, ~$17.8M loss in South Africa, during the 2010 outbreak). In humans, RVF can manifest in various ways. For example, it can vary from a mild influenza-like illness and it can even cause severe hemorrhage, but also it can turn into hepatitis, retinitis (inflammation of the retina) and encephalitis (inflammation of the brain). The mortality in humans varies and ranges between 1-35% of those infected. It is classified as a cross-over pathogen of national and international security and of public health importance.The data visualization featured on this page showcases the relationship between ENSO teleconnections and Rift Valley fever outbreaks in South Africa during the period 2008-2011. The visualization comprises of two parts:Top:On the top part we can see three separate spheres representing the earth with three distinct datasets. On the left, Sea Surface Temperature (SST) anomaly data are mapped on water and over the equatorial Pacific, the Nino 3.4 Index SST region is highlighted. In the middle Precipitation anomaly data has been mapped over the land in the African continent. A grey inset highlights the South Africa region of interest. On the right vegetation index anomalies and Rift Valley fever outbreak locations have been mapped in the African continent and a grey inset highlights the region of interest. The region of interest is the area where RVF outbreaks peak with extents (15E-33E, 26S-35S).Bottom:On the bottom, a synchronized data dashboard for the same period, tracks and visualizes indicators from the four different data sources represented in the top part. These four indicators are:Number of Rift Valley fever (RVF) disease outbreak locations in the South Africa region with extents (15E-33E, 26S-35S)Normalized Difference Vegetation Index (NDVI) Anomaly in the South Africa region with extents 1(5E-33E, 26S-35S)Precipitation Anomaly in the South Africa region with extents (15E-33E, 26S-35S)Nino 3.4 Index Region Sea Surface Temperature Anomaly (SST) with extents (5N-5S, 120W-170W)As the timeline unfolds, labels reveal the El Niño, La Niña periods and the high-risk RVF periods to help guide the viewer on the patterns and teleconnections.We can see a spike of RVF outbreaks during February-May 2010 and another one during February-May 2011. These outbreaks are linked to ENSO teleconnections during the period of 2008-2011. The relationship between ENSO induced anomalous rainfall and disease outbreaks is clearly illustrated by outbreak patterns of Rift Valley fever in South Africa. During the La Niña phase of ENSO, Southern Africa receives persistent and above normal rainfall, which floods habitats of RVF mosquito vectors triggering hatching of RVF virus infected eggs. The above-normal rainfall is followed by an increase in vegetation creating appropriate habitats for the mosquito vectors setting the stage for RVF outbreak activity, which in simple terms means an uptick in mosquito populations that cause infections of domestic livestock and human populations with the RVF virus. However, in rare cases there is a departure from this canonical response, as we can observe in 2009-2010, when a mild El Niño event resulted in above normal rainfall and a large RVF outbreak in central South Africa.To explain a bit further the weather patterns and teleconnections, lets take a closer look at the sequence of events and their timelines. During 2009-2010 the El Niño event (May 2009-March 2010) was followed by an increase in rainfall in South Africa. This cumulative precipitation resulted in an increase of vegetation that manifested on October 2009. This increase of vegetation was followed by a spike of RVF outbreaks starting 3 months after (January/February 2010). The impacts of precipitation and vegetation on the outbreaks of RVF in the region of South Africa have been visualized with data on the entries: Precipitation Anomaly and Rift Valley fever (RVF) outbreaks in South Africa: 2008-2011 and Vegetation Index Anomalies and Rift Valley fever (RVF) outbreaks in South Africa region: 2008-2011The strong relationship between ENSO events and disease outbreaks underscores the importance of seasonal forecasts. Since disease outbreaks typically manifest 2-3 months after the start of El Niño and La Nina events, early and regular climate monitoring, paired with the use of monthly and seasonal climate forecasts become significant tools for disease control and prevention. Findings of an earlier scientific study by Dr. Assaf Anyamba et al. published in the journal Nature Scientific Reports with the title Global Disease Outbreaks Associated with the 2015-2016 El Niño event suggests that by monitoring monthly climate datasets, country public health agencies and organizations such as the United Nations' World Health Organization and Food and Agriculture Organizations, can utilize early warning forecasts to undertake preventive measures to minimize the spread of ecologically coupled diseases.Data Sources:Sea Surface Temperature (SST) data: The SST known as the NOAA OI.v2 SST monthly fields are derived by a linear interpolation of the weekly optimum interpolation (OI) version 2 fields to daily fields then averaging the daily values over a month. The analysis uses in situ and satellite SST's plus SST's simulated by sea-ice cover. Before the analysis is computed, the satellite data is adjusted for biases using the method of Reynolds (1988) and Reynolds and Marsico (1993). The SST dataset is available here.SST ENSO index (Niño 3.4) for the period 2009-2018 is obtained from the NOAA National Center for Climate Prediction online archives. The warm (El Niño) and cold (La Niña) periods of ENSO events were determined using the Oceanic Niño Index (ONI) threshold of +/- 0.5 degress Celcius based on centered 30-year base periods updated every 5 years. The ONI is a 3-month running mean of Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4) SST anomalies in the Niño 3.4 region (5 N-5 S, 120W-170W).Disease Outbreak data were collected from the Program for Monitoring Emerging Diseases (ProMED) and the World Organisation for Animal Health/Organisation mondiale de la santé animale (OIE).Rainfall/Precipitation anomaly data: African Rainfall Climatology (ARC) dataset from the National Oceanic and Atmospheric Administration (NOAA) – Climate Prediction Center (CPC) archives. The dataset is available over Africa at 0.1° x 0.1° spatial resolution from 1983 to present. Reference: Novella, N.S. and W.M. Thiaw, 2016: A Seasonal Rainfall Performance Probability Tool for Famine Early Warning Systems. J. Appl. Meteor. Climatol., 55, 2575–2586, 10.1175/JAMC-D-16-0111.1Normalized Difference Vegetation Index (NDVI) dataset is derived from NASA’s Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra (EOS AM-1) spacecraft. We used the MODIS global monthly Climate Modeling Grid (CMG) products with a spatial resolution of 0.05° x 0.05° (~5.5 x 5.5 km). The data is available through this portal. Reference: Townshend, J. & Justice, Christopher. (2002). Towards operational monitoring of terrestrial systems by moderate-resolution remote sensing. Remote Sensing of Environment. Volume 83, Issues 1-2, Pages: 351-359. 10.1016/S0034-4257(02)00082-2. Supported with funding from the Defense Health Agency-Armed Forces Health Surveillance Branch (AFHSB) Global Emerging Infections Surveillance and Response System (GEIS) under Project #P0072_19_NSThe rest of this webpage offers additional versions, colorbar information, frames and layers associated with the development of this data-driven visualization. During the 2008-2011 period, ENSO events brought changes to weather conditions across the globe that triggered infectious disease outbreaks, such as mosquito-borne Rift Valley fever (RVF) in South Africa. This visualization with corresponding data dashboard shows how Sea Surface Temperature (SST) anomalies in the equatorial Pacific Ocean (left) gave rise to Precipitation (center) and Vegetation (right) Index Anomalies in South Africa. During La Niña events, Southern Africa receives persistent and above normal rainfall, which floods habitats of RVF mosquito vectors triggering hatching of RVF virus infected eggs. The above-normal rainfall is followed by an increase in vegetation creating appropriate habitats for the mosquito vectors setting the stage for RVF outbreak activity, which in simple terms means an uptick in mosquito populations that cause infections of domestic livestock and human populations with the RVF virus. However, in rare cases there is a departure from this canonical response, as we can observe in 2009-2010, when a mild El Niño event resulted in above normal vegetaton and a large RVF outbreak in South Africa. This visualization is similar to the one above, except the data dashboard is unveiled for the entire period 2008-2011.During the 2008-2011 period, ENSO events brought changes to weather conditions across the globe that triggered infectious disease outbreaks, such as mosquito-borne Rift Valley fever (RVF) in South Africa. This visualization with corresponding data dashboard shows how Sea Surface Temperature (SST) anomalies in the equatorial Pacific Ocean (left) gave rise to Precipitation (center) and Vegetation (right) Anomalies in South Africa. During La Niña events, Southern Africa receives persistent and above normal rainfall, which floods habitats of RVF mosquito vectors triggering hatching of RVF virus infected eggs. The above-normal rainfall is followed by an increase in vegetation creating appropriate habitats for the mosquito vectors setting the stage for RVF outbreak activity, which in simple terms means an uptick in mosquito populations that cause infections of domestic livestock and human populations with the RVF virus. However, in rare cases there is a departure from this canonical response, as we can observe in 2009-2010, when a mild El Niño event resulted in above normal vegetaton and a large RVF outbreak in South Africa. This set of frames provides the data dashboard timeline for the period 2008-2011. The dashboard tracks and visualizes indicators from four different data sources: 1. Number of Rift Valley fever (RVF) disease outbreaks for the South Africa region, with extents (15E-33E, 26S-35S)2. Normalized Difference Vegetation Index (NDVI) Anomaly for the South Africa region with extents (15E-33E, 26S-35S)3. Preiciptation Anomaly for the South Africa region with extents (15E-33E, 26S-35S)4. Nino 3.4 Index Region Sea Surface Temperature Anomaly (SST), with extents (5N-5S, 120W-170W)This set of frames include the labels for the El Niño, La Niña and High-risk RVF periods during 2009, 2010 and 2011. Frames are provided with transparency. Sea Surface Temperature monthly Anomaly data over the ocean for the period of 2008-2011 with dates and colorbar information. The Nino 3.4 Index Region with extents (5N-5S, 120W-170W) over the equatorial Pacific is highligthed with a gray box. Sea Surface Temperature Anomalies for the period of 2008-2011. The frames are provided with transparency to serve as a layer for compositing purposes. Still image/Layer of the Niño 3.4 Index Region Sea Surface Temperature Anomaly (SST) with transparency. The coordinates of the Niño 3.4 Index Region are: 5N-5S, 120W-170W. This still image can be overlaid on top of the Sea Surface Temperature (SST) Anomaly sequence provided above to create a composite. Monthly Precipitation Anomaly data in Africa and middle East over the land for the period 2008-2011 with dates and colorbar information. This set of frames visualizes monthly Precipitation Anomaly data in Africa for the period of 2008-2011. Still Image/Layer of the Sourth Africa region of interest with extents (15E-33E, 26S-35S). This layer is provided with transparency to be overlaid on top of the Precipitation Anomaly set of frames. Monthly Normalized Difference Vegetation Index (NDVI) data in Africa and Middle East and locations of Rift Valley fever (RVF) outbreaks (orange pins) for the period of 2008-2011. The region of Southern Africa with extents (15E-33E, 26S-35S) is highlighted in a gray box. This set of frames visualizes monthly Normalized Difference Vegetation Index (NDVI) data and Rift Valley fever (RVf) outbreak locations (orange particles) in Africa for the period of 2008-2011. The region of South Africa with extents (15E-33E, 26S-35S) is highlighted with a gray box.Frames are provided with transparency. This set of frames provides the corresponding Dates Layer for the period of 2008-2011. The frames are provided with transparency. This set of frames provides the data dashboard timeline for the period 2008-2011, without labels for the El Niño, La Niña and High-risk RVF periods. Frames are provided with transparency. Still image of label highlighting the El Niño period for the data dashboard. Still image/label of the La Nina period for the data dashboard (provided with transparency). Still image/label of the High-risk RVf period for 2010. Still image/label of the High-risk RVF period for 2011. Sea Surface Temperature Anomaly colorbar Precipitation Anomaly colorbar Normalized Difference Vegetation Index (NDVI) anomaly colorbar Related pages

This visualization shows the variability in global sea surface temperature anomalies, the associated ENSO index timeline and locations of infectious disease outbreaks over the global land surface. Content is available in 4K resolution. This visualization shows the variability in global sea surface temperature anomalies, the associated ENSO index timeline and locations of infectious disease outbreaks over the global land surface. Content is available in 4K resolution.The content available in this group of frames and movies does not include the label Strong El Niño Period in the ENSO index timeline for the highlighted period 2015-2016. This visualization captures monthly Sea Surface Temperature (SST) anomalies around the world from 2009-2018, along with locations of global disease outbreaks. This version does not include the timeline of the Niño 3.4 index. Frames are available in 4K resolution. Frames of monthly Sea Surface Temperature Anomalies and locations of Global Disease Outbreaks for the period of 2009-2018. Frames are provided with alpha channel. Still image of the global diseases panel and their associated symbols (provided with transparency). The 11 infectious diseases visualized are: Chinkungunya, Cholera, Dengue virusl, Hantavirus, Plague, Respiratory Illness, Rift Valley fever, Ross River virus, St. Louis Encephalitis, Tularemia and Zika virus. Still image of latitude axes with equator line, provided with transparency. This still image can be overlaid on top of the Sea Surface Temperature (SST) Anomaly with Disease frames and it is provided to develop a composite. Still image of Niño 3.4 region with transparency. The coordinates of the Niño 3.4 region are: 5N-5S, 120W-170W. This still image can be overlaid on top of the Sea Surface Temperature (SST) Anomaly with Diseases frames and it is provided to develop a composite. This set of frames provides the corresponding ENSO 3.4 plot timeline for the period of 2009-2018 and it includes the label Strong El Niño Period in the highlighted 2015-2016 area. The frames contain transparency so that they can be overlaid as a layer. This set of frames provides the corresponding ENSO 3.4 plot timeline for the period of 2009-2018. The frames contain transparency so that they can be overlaid as a layer. This set of frames provides the corresponding Dates Layer for the period of 2009-2018. Frames are provided with transparency. Sea Surface Temperature (SST) Anomaly colorbar. This webpage provides the 4K version of: Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (updated), released on January 6, 2020.Content has been created for 4K display systems that can handle finer resolution and details. It is recommended to use content from this version for HD (1920x1080) and lower resolutions. The El Niño-Southern Oscillation (ENSO) phenomenon is an irregularly recurring climate pattern characterized by warmer (El Niño) and colder (La Niña) than usual ocean temperatures in the equatorial Pacific, which creates a ripple effect of anticipated weather changes in far-spread regions on Earth. Weather changes associated with the El Niño-Southern Oscillation phenomenon result in rainfall, temperature and environmental anomaly conditions worldwide that directly favor outbreaks of infectious diseases of public health concern.During the last 20 years NASA scientist Dr. Assaf Anyamba and colleagues have been studying interannual climate variability patterns associated with El Niño by monitoring various climate datasets, among them land surface temperature and vegetation data from the Advanced High Resolution Radiometer (AVHRR) on board NOAA POES satelittes, the Moderate Resolution Imaging Spectroradiometer aboard NASA's Terra and Aqua satellites, and Sea Surface Temperature and precipitation anomaly datasets from NASA and the National Oceanic and Atmospheric Administration (NOAA). At the same time, the science team has been collecting, cataloguing and analyzing patterns of disease outbreaks worldwide.Dr. Anyamba and colleagues conducted a scientific study - the first one to comprehensively assess the public health impacts of the major climate event on a global scale - that was published in the journal Nature Scientific Reports, with the title Global Disease Outbreaks Associated with the 2015-2016 El Niño event and is open access available. According to the study, the 2015-2016 El Niño event brought weather conditions that triggered disease outbreaks in ENSO teleconnected regions throughout the world. The visualization showcases a global flat map with monthly Sea Surface Temperature (SST) anomaly data over water, the locations of Global Disease Outbreaks of ten infectious diseases on land, along with a timeline plot of the ENSO Index (Niño 3.4 Index region SST anomaly) for the period 2009-2018 on the bottom. The Nino 3.4 Index region SST with extents (5N-5S, 120W-170W) is the box region, highlighted on the Pacific Ocean.During ENSO events, SST anomalies influence the nature and patterns of rainfall, vegetation and land surface temperatures on the land surface, which in turn influence the disease outbreaks that are mapped on a global scale. The 11 diseases mapped on this visualization are: chinkungunya, cholera, dengue virus, hantavirus, respiratory illness, Rift Valley fever, Ross River virus, St. Louis encephalitis, tularemia and zika virus/During the 2015-2016 El Nino event, which is manifested in the visualization with increased sea surface temperature anomaly (reds in Niño 3.4 Index Region), changes in precipitation, land surface temperatures and vegetation created and facilitated conditions for transmission of diseases, resulting in an uptick in reported cases for plague and hantavirus in Colorado and New Mexico (in 2015), cholera in East Africa’s Tanzania (during 2015 and 2016), and dengue fever in Brazil and Southeast Asia (during 2015), among others.According to the study, El Niño-driven increase in rainfall and milder temperatures over the American Southwest, spurred vegetative growth, providing more food for rodents that carry hantavirus. A resulting rodent population explosion put them in more frequent contact with humans, who contract the potentially fatal disease mostly through rodent fecal or urine contamination. As their rodent hosts proliferated, so did plague-carrying fleas. Regarding dengue outbreaks, the strong El Niño period produced higher than normal land surface temperatures and therefore drier habitats, which drew mosquitoes into populated, urban areas where there are open water storage containers providing ideal habitats for mosquito production. In addition, the higher the normal temperatures increase the maturation time of larvae to adult mosquitos and also induce frequent blood feeding/biting by mosquito vectors resulting in increased number of disease cases. The following 3 data driven visualizations demonstrate the complex relationships between the El Niño event in 2015-2016 and disease outbreaks of dengue in the South East Asia region:Teleconnections in South East Asia for the period of 2015-2016Precipitation Anomaly and Dengue Outbreaks in South East Asia: 2015-2016Land Surface Temperature Anomaly and Dengue Outbreaks in South East Asia Region: 2015-2016The impact of ENSO induced anomalous rainfall is clearly illustrated by outbreak patterns of Rift Valley fever (RVF) in East and South Africa. During ENSO events, Eastern Africa (El Niño) and South Africa (La Niña) receive persistent and above normal rainfall, which floods habitats of RVF mosquito vectors triggering hatching of RVF virus infected eggs. The above-normal rainfall is followed by an increase in vegetation creating appropriate habitats for the mosquito vectors setting the stage for RVF outbreak activity, which in simple terms means an uptick in mosquito populations that cause infections of domestic livestock and human populations. The results is the sea-saw pattern exhibited by the ENSO events drives patterns of disease outbreaks in different regions around the world. To learn more about the relationship between ENSO and Rift Valley fever outbreaks in the region of South Africa, please refer to:ENSO Teleconnections and Rift Valley fever (RVF) OutbreaksPrecipitation Anomaly and Rift Valley fever (RVF) outbreaks in South Africa: 2008-2011Vegetation Index Anomalies and Rift Valley fever (RVF) outbreaks in South Africa region: 2008-2011The strong relationship between ENSO events (i.e El Niño, La Niña) and disease outbreaks underscores the importance of seasonal forecasts. Since disease outbreaks typically manifest 2-3 months after the start of ENSO events, early and regular climate monitoring, paired with the use of monthly and seasonal climate forecasts become significant tools for disease control and prevention. Findings of the scientific study suggest that by monitoring monthly climate datasets, country public health agencies and organizations such as the United Nations' World Health Organization and Food and Agriculture Organizations, can utilize early warning forecasts to undertake preventive measures to minimize the spread of ecologically coupled diseases.Data Sources:Sea Surface Temperature (SST) data: The SST known as the NOAA OI.v2 SST monthly fields are derived by a linear interpolation of the weekly optimum interpolation (OI) version 2 fields to daily fields then averaging the daily values over a month. The analysis uses in situ and satellite SST's plus SST's simulated by sea-ice cover. Before the analysis is computed, the satellite data is adjusted for biases using the method of Reynolds (1988) and Reynolds and Marsico (1993). The SST dataset is available hereDisease Outbreak data were collected from the Program for Monitoring Emerging Diseases (ProMED), the Pan-American Health Organization (PAHO) online country reports, weekly summaries of disease outbreaks reported by the Department of Defense Armed Forces Health Surveillance Branch and from the World Organisation for Animal Health/Organisation mondiale de la santé animale (OIE).SST ENSO index (Niño 3.4) for the period 2009-2018 is obtained from the NOAA National Center for Climate Prediction online archives. The warm (El Niño) and cold (La Niña) periods of ENSO events were determined using the Oceanic Niño Index (ONI) threshold of +/- 0.5°C based on centered 30-year base periods updated every 5 years. The ONI is a 3-month running mean of Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4) SST anomalies in the Niño 3.4 region (5 N-5 S, 120W-170W).Supported with funding from the Defense Threat Reduction Agency's (DTRA) Joint Science and Technology Office for Chemical and Biological Defense (JSTO-CBD) Biosurveillance Ecosystem (BSVE) Program (HDTRA1-16-C-0045) and the Defense Health Agency-Armed Forces Health Surveillance Branch (AFHSB) Global Emerging Infections Surveillance and Response System (GEIS) under Project # P0072_19_NS.This rest of this webpage provides frames, movies, colorbar information and layers associated with the development of this data visualization. Related pages

This visualization shows the variability in global sea surface temperature anomalies, the associated ENSO index timeline and locations of infectious disease outbreaks over the global land surface. This visualization shows the variability in global sea surface temperature anomalies, the associated ENSO index timeline and locations of infectious disease outbreaks over the global land surface. This group of frames and movies does not include the label Strong El Niño Period in the ENSO index timeline for the period 2015-2016. This visualization captures monthly Sea Surface Temperature (SST) anomalies around the world from 2009-2018, along with global disease outbreaks. This version does not include the timeline of the Niño 3.4 index Frames of monthly Sea Surface Temperature Anomalies and locations of Global Disease Outbreaks for the period of 2009-2018. Frames are provided with alpha channel. Still image of latitude axes with equator line, provided with transparency. This still image can be overlaid on top of the Sea Surface Temperature (SST) Anomaly with Disease frames and it is provided to develop a composite. Still image of Niño 3.4 region with transparency. The coordinates of the Niño 3.4 region are: 5N-5S, 120W-170W. This still image can be overlaid on top of the Sea Surface Temperature (SST) Anomaly with Diseases frames and it is provided to develop a composite. Still image of the global diseases panel and their associated symbols (provided with transparency). The 11 infectious diseases visualized are: Chinkungunya, Cholera, Dengue virusl, Hantavirus, Plague, Respiratory Illness, Rift Valley fever, Ross River virus, St. Louis Encephalitis, Tularemia and Zika virus. This set of frames provides the corresponding ENSO 3.4 plot timeline for the period of 2009-2018 and it includes the label "Strong El Niño Period" in the highlighted 2015-2016 area. The frames contain transparency so that they can be overlaid as a layer. This set of frames provides the corresponding ENSO 3.4 plot timeline for the period of 2009-2018. The frames contain transparency so that they can be overlaid as a layer. This set of frames provides the corresponding Dates Layer for the period of 2009-2018. Frames are provided with transparency. Sea Surface Temperature (SST) Anomaly colorbar The El Niño-Southern Oscillation (ENSO) phenomenon is an irregularly recurring climate pattern characterized by warmer (El Niño) and colder (La Niña) than usual ocean temperatures in the equatorial Pacific, which creates a ripple effect of anticipated weather changes in far-spread regions on Earth. Weather changes associated with the El Niño-Southern Oscillation phenomenon result in rainfall, temperature and environmental anomaly conditions worldwide that directly favor outbreaks of infectious diseases of public health concern.During the last 20 years NASA scientist Dr. Assaf Anyamba and colleagues have been studying interannual climate variability patterns associated with El Niño by monitoring various climate datasets, among them land surface temperature and vegetation data from the Advanced High Resolution Radiometer (AVHRR) on board NOAA POES satelittes, the Moderate Resolution Imaging Spectroradiometer aboard NASA's Terra and Aqua satellites, and Sea Surface Temperature and precipitation anomaly datasets from NASA and the National Oceanic and Atmospheric Administration (NOAA). At the same time, the science team has been collecting, cataloguing and analyzing patterns of disease outbreaks worldwide.Dr. Anyamba and colleagues conducted a scientific study - the first one to comprehensively assess the public health impacts of the major climate event on a global scale - that was published in the journal Nature Scientific Reports, with the title Global Disease Outbreaks Associated with the 2015-2016 El Niño event and is open access available. According to this study, the 2015-2016 El Niño event brought weather conditions that triggered disease outbreaks in ENSO teleconnected regions throughout the world. The visualization showcases a global flat map with monthly Sea Surface Temperature (SST) anomaly data on the water, the locations of Global Disease Outbreaks of eleven infectious diseases on land, along with a timeline plot of the ENSO Index (Niño 3.4 Index region SST anomaly) for the period 2009-2018 on the bottom. The Nino 3.4 Index region SST with extents (5N-5S, 120W-170W) is the box region, highlighted on the Pacific Ocean.During ENSO events, SST anomalies influence the nature and patterns of rainfall, vegetation and land surface temperatures on the land surface, which in turn influence the disease outbreaks that are mapped on a global scale. The 10 diseases mapped on this visualization are: chinkungunya, cholera, dengue virus, hantavirus, respiratory illness, Rift Valley fever, Ross River virus, St. Louis encephalitis, tularemia and zika virus. During the 2015-2016 El Nino event, which is manifested in the visualization with increased sea surface temperature anomaly (reds in Niño 3.4 Index Region), changes in precipitation, land surface temperatures and vegetation created and facilitated conditions for transmission of diseases, resulting in an uptick in reported cases for plague and hantavirus in Colorado and New Mexico (in 2015), cholera in East Africa’s Tanzania (during 2015 and 2016), and dengue fever in Brazil and Southeast Asia (during 2015), among others.According to the study, El Niño-driven increase in rainfall and milder temperatures over the American Southwest, spurred vegetative growth, providing more food for rodents that carry hantavirus. A resulting rodent population explosion put them in more frequent contact with humans, who contract the potentially fatal disease mostly through rodent fecal or urine contamination. As their rodent hosts proliferated, so did plague-carrying fleas. Regarding dengue outbreaks, the strong El Niño period produced higher than normal land surface temperatures and therefore drier habitats, which drew mosquitoes into populated, urban areas where there are open water storage containers providing ideal habitats for mosquito production. In addition, the higher the normal temperatures increase the maturation time of larvae to adult mosquitos and also induce frequent blood feeding/biting by mosquito vectors resulting in increased number of disease cases. The following 3 data driven visualizations demonstrate the complex relationships between the El Niño event in 2015-2016 and disease outbreaks of dengue in the South East Asia region:Teleconnections in South East Asia for the period of 2015-2016Precipitation Anomaly and Dengue Outbreaks in South East Asia: 2015-2016Land Surface Temperature Anomaly and Dengue Outbreaks in South East Asia Region: 2015-2016The impact of ENSO induced anomalous rainfall is clearly illustrated by outbreak patterns of Rift Valley fever (RVF) in East and South Africa. During ENSO events, Eastern Africa (El Niño) and South Africa (La Niña) receive persistent and above normal rainfall, which floods habitats of RVF mosquito vectors triggering hatching of RVF virus infected eggs. The above-normal rainfall is followed by an increase in vegetation creating appropriate habitats for the mosquito vectors setting the stage for RVF outbreak activity, which in simple terms means an uptick in mosquito populations that cause infections of domestic livestock and human populations. The results is the sea-saw pattern exhibited by the ENSO events drives patterns of disease outbreaks in different regions around the world. To learn more about the relationship between ENSO and Rift Valley fever outbreaks in the region of South Africa, please refer to:ENSO Teleconnections and Rift Valley fever (RVF) OutbreaksPrecipitation Anomaly and Rift Valley fever (RVF) outbreaks in South Africa: 2008-2011Vegetation Index Anomalies and Rift Valley fever (RVF) outbreaks in South Africa region: 2008-2011The strong relationship between ENSO events (i.e El Niño, La Niña) and disease outbreaks underscores the importance of seasonal forecasts. Since disease outbreaks typically manifest 2-3 months after the start of ENSO events, early and regular climate monitoring, paired with the use of monthly and seasonal climate forecasts become significant tools for disease control and prevention. Findings of the scientific study suggest that by monitoring monthly climate datasets, country public health agencies and organizations such as the United Nations' World Health Organization and Food and Agriculture Organizations, can utilize early warning forecasts to undertake preventive measures to minimize the spread of ecologically coupled diseases.Data Sources:Sea Surface Temperature (SST) data: The SST known as the NOAA OI.v2 SST monthly fields are derived by a linear interpolation of the weekly optimum interpolation (OI) version 2 fields to daily fields then averaging the daily values over a month. The analysis uses in situ and satellite SST's plus SST's simulated by sea-ice cover. Before the analysis is computed, the satellite data is adjusted for biases using the method of Reynolds (1988) and Reynolds and Marsico (1993). The SST dataset is available hereDisease Outbreak data were collected from the Program for Monitoring Emerging Diseases (ProMED), the Pan-American Health Organization (PAHO) online country reports, weekly summaries of disease outbreaks reported by the Department of Defense Armed Forces Health Surveillance Branch and from the World Organisation for Animal Health/Organisation mondiale de la santé animale (OIE).SST ENSO index (Niño 3.4) for the period 2009-2018 is obtained from the NOAA National Center for Climate Prediction on-line archives. The warm (El Niño) and cold (La Niña) periods of ENSO events were determined using the Oceanic Niño Index (ONI) threshold of +/- 0.5°C based on centered 30-year base periods updated every 5 years. The ONI is a 3-month running mean of Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4) SST anomalies in the Niño 3.4 region (5 N-5 S, 120W-170W).Supported with funding from the Defense Threat Reduction Agency's (DTRA) Joint Science and Technology Office for Chemical and Biological Defense (JSTO-CBD) Biosurveillance Ecosystem (BSVE) Program (HDTRA1-16-C-0045) and the Defense Health Agency-Armed Forces Health Surveillance Branch (AFHSB) Global Emerging Infections Surveillance and Response System (GEIS) under Project # P0072_19_NS.This webpage provides frames, movies, colorbar information and layers associated with the development of this data visualization. Related pages

El Niño is an irregularly recurring climate pattern characterized by warmer than usual ocean temperatures in the equatorial Pacific, which creates a ripple effect of anticipated weather changes in far-spread regions. This visualization captures monthly Sea Surface Temperature (SST) anomalies around the world from 2009-2018, along with locations of global disease outbreaks and a corresponding timeline showcasing the Niño 3.4 Index. The Niño 3.4 Index represents average equatorial sea surface temperatures in the Pacific Ocean from about the International Date Line to the coast of South America. Highlighted in the timeline are the above average El Niño years, in which sea surface temperature anomalies peaked during 2015-2016. This visualization captures monthly Sea Surface Temperature (SST) anomalies around the world from 2009-2018, along with locations of global disease outbreaks. This set of frames visualizes monthly Sea Surface Temperature Anomalies and locations of Global Disease Outbreaks for the period of 2009-2018. Frames are provided with alpha channel. This set of frames provides the corresponding ENSO 3.4 plot timeline for the period of 2009-2018. Frames are provided with transparency. This set of frames provides the corresponding Dates Layer for the period of 2009-2018. Frames are provided with transparency. Still image of the global diseases panel and their associated symbols (provided with transparency). The diseases visualized are: Chinkungunya, Cholera, Dengue virusl, Hantavirus, Plague, Respiratory Illness, Rift Valley fever, Ross River virus, St. Louis Encephalitis and Tularemia. Still image of latitude axes with equator line, provided with transparency. This still image can be overlaid on top of the Sea Surface Temperature (SST) Anomaly with Disease frames and it is provided to develop a composite. Still image of the Niño 3.4 region with transparency. The coordinates of the Niño 3.4 region are: 5N-5S, 120W-170W. This still image can be overlaid on top of the Sea Surface Temperature (SST) Anomaly with Diseases frames and it is provided to develop a composite. Sea Surface Temperature (SST) Anomaly colorbar The El Niño-Southern Oscillation (ENSO) phenomenon is an irregularly recurring climate pattern characterized by warmer (El Niño) and colder (La Niña) than usual ocean temperatures in the equatorial Pacific, which creates a ripple effect of anticipated weather changes in far-spread regions of Earth. Weather changes associated with the El Niño-Southern Oscillation phenomenon result in rainfall, temperature and environmental anomaly conditions worldwide that directly favor outbreaks of infectious diseases of public health concern.During the last 20 years NASA scientist Dr. Assaf Anyamba and colleagues have been studying interannual climate variability patterns associated with El Niño by monitoring various climate datasets, among them land surface temperature and vegetation data from the Advanced High Resolution Radiometer (AVHRR) on board NOAA POES satelittes, the Moderate Resolution Imaging Spectroradiometer aboard NASA's Terra and Aqua satellites, and Sea Surface Temperature and precipitation anomaly datasets from NASA and the National Oceanic and Atmospheric Administration (NOAA). At the same time, the science team has been collecting, cataloguing and analyzing patterns of disease outbreaks worldwide.Dr. Anyamba and colleagues conducted a scientific study - the first one to comprehensively assess the public health impacts of the major climate event on a global scale - that was published in the journal Nature Scientific Reports, with the title Global Disease Outbreaks Associated with the 2015-2016 El Niño event and is open access available. According to this study, the 2015-2016 El Niño event brought weather conditions that triggered disease outbreaks in ENSO teleconnected regions throughout the world. The visualization showcases a global flat map with monthly Sea Surface Temperature (SST) anomaly data on the water, the locations of Global Disease Outbreaks of ten infectious diseases on land, along with a timeline plot of the ENSO Index (Niño 3.4 Index region SST anomaly) for the period 2009-2018 on the bottom. The Nino 3.4 Index region SST with extents (5N-5S, 120W-170W) is the box region, highlighted on the Pacific Ocean.During ENSO events, SST anomalies influence the nature and patterns of rainfall, vegetation and land surface temperatures on the land surface, which in turn influence the disease outbreaks that are mapped on a global scale. The 10 diseases mapped on this visualization are: chinkungunya, cholera, dengue virus, hantavirus, respiratory illness, Rift Valley fever, Ross River virus, St. Louis encephalitis, and tularemia. During the 2015-2016 El Nino event, which is manifested in the visualization with increased sea surface temperature anomaly (reds in Niño 3.4 Index Region), changes in precipitation, land surface temperatures and vegetation created and facilitated conditions for transmission of diseases, resulting in an uptick in reported cases for plague and hantavirus in Colorado and New Mexico (in 2015), cholera in East Africa’s Tanzania (during 2015 and 2016), and dengue fever in Brazil and Southeast Asia (during 2015), among others.According to the study, El Niño-driven increase in rainfall and milder temperatures over the American Southwest, spurred vegetative growth, providing more food for rodents that carry hantavirus. A resulting rodent population explosion put them in more frequent contact with humans, who contract the potentially fatal disease mostly through rodent fecal or urine contamination. As their rodent hosts proliferated, so did plague-carrying fleas. Regarding dengue outbreaks, the strong El Niño period produced higher than normal land surface temperatures and therefore drier habitats, which drew mosquitoes into populated, urban areas where there are open water storage containers providing ideal habitats for mosquito production. In addition, the higher the normal temperatures increase the maturation time of larvae to adult mosquitos and also induce frequent blood feeding/biting by mosquito vectors resulting in increased number of disease cases. The following 3 data driven visualizations demonstrate the complex relationships between the El Niño event in 2015-2016 and disease outbreaks of dengue in the South East Asia region:Teleconnections in South East Asia for the period of 2015-2016Precipitation Anomaly and Dengue Outbreaks in South East Asia: 2015-2016Land Surface Temperature Anomaly and Dengue Outbreaks in South East Asia Region: 2015-2016The effects of ENSO induced anomalous rainfall are clearly illustrated by outbreak patterns of Rift Valley fever (RVF) in East and South Africa. During ENSO events, Eastern Africa (El Niño) and South Africa (La Niña) receive persistent and above normal rainfall, which floods habitats of RVF mosquito vectors triggering hatching of RVF virus infected eggs. The above-normal rainfall is followed by an increase in vegetation creating appropriate habitats for the mosquito vectors setting the stage for RVF outbreak activity, which in simple terms means an uptick in mosquito populations that cause infections of domestic livestock and human populations. The results is the sea-saw pattern exhibited by the ENSO events drives patterns of disease outbreaks in different regions around the world. To learn more about the relationship between ENSO and Rift Valley fever outbreaks in the region of South Africa, please refer to:ENSO Teleconnections and Rift Valley fever (RVF) OutbreaksPrecipitation Anomaly and Rift Valley fever (RVF) outbreaks in South Africa: 2008-2011Vegetation Index Anomalies and Rift Valley fever (RVF) outbreaks in South Africa region: 2008-2011The strong relationship between ENSO events (i.e El Niño, La Niña) and disease outbreaks underscores the importance of seasonal forecasts. Since disease outbreaks typically manifest 2-3 months after the start of El Niño events, early and regular climate monitoring, paired with the use of monthly and seasonal climate forecasts become significant tools for disease control and prevention. Findings of the scientific study suggest that by monitoring monthly climate datasets, country public health agencies and organizations such as the United Nations' World Health Organization and Food and Agriculture Organizations, can utilize early warning forecasts to undertake preventive measures to minimize the spread of ecologically coupled diseases.Data Sources:Sea Surface Temperature (SST) data: The SST known as the NOAA OI.v2 SST monthly fields are derived by a linear interpolation of the weekly optimum interpolation (OI) version 2 fields to daily fields then averaging the daily values over a month. The analysis uses in situ and satellite SST's plus SST's simulated by sea-ice cover. Before the analysis is computed, the satellite data is adjusted for biases using the method of Reynolds (1988) and Reynolds and Marsico (1993). The SST dataset is available here.Disease Outbreak data were collected from the Program for Monitoring Emerging Diseases (ProMED), the Pan-American Health Organization (PAHO) online country reports, weekly summaries of disease outbreaks reported by the Department of Defense Armed Forces Health Surveillance Branch and from the World Organisation for Animal Health/Organisation mondiale de la santé animale (OIE).SST ENSO index (Niño 3.4) for the period 2009-2018 is obtained from the NOAA National Center for Climate Prediction online archives. The warm (El Niño) and cold (La Niña) periods of ENSO events were determined using the Oceanic Niño Index (ONI) threshold of +/- 0.5°C based on centered 30-year base periods updated every 5 years. The ONI is a 3-month running mean of Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4) SST anomalies in the Niño 3.4 region (5 N-5 S, 120W-170W).This visualization was created to support the AGU 2019 conference presentation, titled El Niño-Southern Oscillation Teleconnections and Global Patterns of Disease Outbreaks (December 11 2019, Moscone Conference Center, San Francisco, CA)Supported with funding from the Defense Threat Reduction Agency's (DTRA) Joint Science and Technology Office for Chemical and Biological Defense (JSTO-CBD) Biosurveillance Ecosystem (BSVE) Program (HDTRA1-16-C-0045) and the Defense Health Agency-Armed Forces Health Surveillance Branch (AFHSB) Global Emerging Infections Surveillance and Response System (GEIS) under Project # P0072_19_NS.Below, you can find frames, alternate movies, colorbar information and layers associated with the development of this data-driven visualization. Related pages

This visualization captures Sea Surface Temperature (SST) anomalies around the world from 1982 to 2017, along with a corresponding timeplot graph focusing on the Niño 3.4 SST Index region (5N-5S, 120W-170W), which represents average equatorial sea surface temperatures in the Pacific Ocean from about the International Date Line to the coast of South America. Highlighted in the timeline are the El Niño years, in which sea surface temperature anomalies peaked: 1982-1983, 1997-1998, and 2015-2016. Monthly Sea Surface Temperature Anomaly data visualized for the period 1982-2017. Frames contain alpha channel. Still Image of ENSO 3.4 box area with transparency. The coordinates of the Nino 3.4 region are: 5N-5S, 120W-170W. This still image can be overlaid on top of the flat map monthly Sea Surface Temperature Anomaly data visualization. Still image of ENSO 3.4 Area with alpha channel. The Nino 3.4 region coordinates are: 5N-5S, 120W-170W. This still image can be overlaid on top of the flat map monthly Sea Surface Temperature Anomaly data visualization. Monthly Sea Surface Temperature Anomaly visualization for the period of 1982-2017. This set of frames is provided with alpha channel. This set of frames provides the dates layer for the Sea Surface Temperature Anomaly visuals for the period 1982-2017. Frames are provided with alpha channel. Sea Surface Temperature Anomaly colorbar. El Niño-Southern Oscillation (ENSO) phenomenon is an irregularly recurring climate pattern characterized by warmer (El Niño) and colder (La Niña) than usual ocean temperatures in the equatorial Pacific Ocean from about the International Date Line to the coast of South America. ENSO (El Niño, La Niña) events create a ripple effect of anticipated weather changes in far-spread regions on Earth. Weather changes associated with the ENSO phenomenon result in rainfall, temperature, vegetation and environmental anomaly conditions worldwide that directly favor outbreaks of infectious diseases of public health concern typically 2-3 months after.During the last 20 years NASA scientist Dr. Assaf Anyamba and colleagues have been studying interannual climate variability patterns associated with ENSO events by monitoring the Niño 3.4 Index region over the Pacific Ocean (5N-5S, 120W-170W), along with rainfall, land surface temperature and vegetation anomaly data from NASA and NOAA.Dr. Anyamba and colleagues conducted a scientific study - the first one to comprehensively assess the public health impacts of the major climate event on a global scale - that was open access published in the journal Nature Scientific Reports, with the title Global Disease Outbreaks Associated with the 2015-2016 El Niño event. According to the study, the 2015-2016 El Niño event brought weather conditions that triggered infectious disease outbreaks in teleconnected regions around the world. For example, plague and hantavirus in Colorado and New Mexico (in 2015), cholera in East Africa’s Tanzania (during 2015 and 2016), and dengue fever in Brazil and Southeast Asia (during 2015) among others.The data visualization featured on this page provides a historical timeline of El Niño events for the period of 1982-2017 and illustrates the relationship between ENSO events (El Niño, La Niña) and the increase (red hues)/decrease (blue hues) of average Sea Surface Temperature (SST) Anomaly data over the Niño 3.4 Index region. The visualization showcases a global flat map with monthly Sea Surface Temperature (SST) anomaly data over water, along with a timeline plot of the ENSO Index (Niño 3.4 Index region SST anomaly) for the same period. The Nino 3.4 Index region SST with extents (5N-5S, 120W-170W) is the box region highlighted over the Pacific Ocean. Highlighted in the timeline are the strong El Niño years, in which Sea Surface Temperature (SST) anomalies peaked: 1982-1983, 1997-1998, and 2015-2016.To learn more about the relationship between ENSO events and infectious disease outbreaks, please see the following data visualizations:Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (4K version)Teleconnections in South East Asia for the period of 2015-2016Precipitation Anomaly and Dengue Outbreaks in South East Asia: 2015-2016Land Surface Temperature Anomaly and Dengue Outbreaks in South East Asia Region: 2015-2016ENSO Teleconnections and Rift Valley fever (RVF) OutbreaksPrecipitation Anomaly and Rift Valley fever (RVF) outbreaks in South Africa: 2008-2011Vegetation Index Anomalies and Rift Valley fever (RVF) outbreaks in South Africa region: 2008-2011The strong relationship between ENSO events and disease outbreaks underscores the importance of seasonal forecasts. Since disease outbreaks typically manifest 2-3 months after the start of El Niño and La Niña events, early and regular climate monitoring, paired with the use of monthly and seasonal climate forecasts become significant tools for disease control and prevention. Findings of the scientific study by Dr. Assaf Anyamba and colleagues, suggests that by monitoring monthly climate datasets, country public health agencies and organizations such as the United Nations' World Health Organization and Food and Agriculture Organizations, can utilize early warning forecasts to undertake preventive measures to minimize the spread of ecologically coupled diseases.Data Sources:Sea Surface Temperature Anomaly data (global, monthly time series: 1982-2017)Global monthly SST data known as Optimum Interpolation (OI) SST version 2 dataset produced by NOAA can be accessed from https://www.ncdc.noaa.gov/oisstNiño 3.4 Sea Surface Temperature (SST) ENSO index (monthly time series: 1982-2017) is obtained from the NOAA National Center for Climate Prediction online archives. The warm (El Niño) and cold (La Niña) periods of ENSO events were determined using the Oceanic Niño Index (ONI) threshold of +/- 0.5°C based on centered 30-year base periods updated every 5 years. The ONI is a 3-month running mean of Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4) SST anomalies in the Niño 3.4 region (5N-5S, 120W-170W).Supported with funding from the Defense Threat Reduction Agency's (DTRA) Joint Science and Technology Office for Chemical and Biological Defense (JSTO-CBD) Biosurveillance Ecosystem (BSVE) Program (HDTRA1-16-C-0045) and the Defense Health Agency-Armed Forces Health Surveillance Branch (AFHSB) Global Emerging Infections Surveillance and Response System (GEIS) under Project # P0072_19_NS.The rest of this webpage offers frames, layers and colorbar information associated with the development of this data-driven visualization. Related pages

The 2015-2016 El Niño event brought changes to weather conditions across the globe that triggered regional disease outbreaks, including mosquito-borne dengue fever in Southeast Asia. This visualization with corresponding timeplot graph reveals the relationship between land surface temperature anomaly in Southeast Asia and dengue outbreaks. Higher than normal land surface temperatures results in an increase of dengue reported locations. Land Surface Temperature Anomaly Colorbar Related pages

The 2015-2016 strong El Niño event brought changes to weather conditions across the globe that triggered regional infectious disease outbreaks, including mosquito-borne dengue fever in South East Asia. This visualization with corresponding multi-plot graph shows how Sea Surface Temperature anomalies in the equatorial Pacific Ocean (left), resulted in anomalous drought conditions (center) and increase in land surface temperatures (right) in South East Asia. During the 2015-2016 El Niño event, the South East Asia region received below than normal precipitation resulting in drier and warner than normal conditions, which increased the populations of mosquito vectors in urban areas, where there are open water storage containers providing ideal habitats for mosquito production. In addition, the higher than normal temperature on land shortens the maturation time of larvae to adult mosquitos and induces frequent blood feeding/biting of humans by mosquito vectors resulting in the amplification of dengue disease outbreaks over the South East Asia region. This visualization is similar to the one above, except the timeplot graph is unveiled for the entire period 2015-2016. The 2015-2016 strong El Niño event brought changes to weather conditions across the globe that triggered regional infectious disease outbreaks, including mosquito-borne dengue fever in South East Asia. This visualization with corresponding multi-plot graph shows how Sea Surface Temperature anomalies in the equatorial Pacific Ocean (left), resulted in anomalous drought conditions (center) and increase in land surface temperatures (right) in South East Asia. During the 2015-2016 El Niño event, the South East Asia region received below than normal precipitation resulting in drier and warner than normal conditions, which increased the populations of mosquito vectors in urban areas, where there are open water storage containers providing ideal habitats for mosquito production. In addition, the higher than normal temperature on land shortens the maturation time of larvae to adult mosquitos and induces frequent blood feeding/biting of humans by mosquito vectors resulting in the amplification of dengue disease outbreaks over the South East Asia region. This set of frames provides the Sea Surface Temperature Anomaly layer for the period 2014-2016, with alpha channel. This set of frames provides the dates layer for the Sea Surface Temperature Anomaly visuals. Colorbar for sea surface temperature anomaly. Monthly preicipitation anomaly in the Asia Region for the period of 2014-2016. This set of frames are provided with alpha channel. Colorbar for precipitation anomaly. Monthly Land Surface Temperature Anomaly data in the Asia region during 2014-2016. This set of frames is provided with alpha channel. This set of frames provides the dates layer for the Land Surface Temperature Anomaly visuals. Frames are provided with alpha channel. Colorbar for land surface temperature anomaly. El Niño-Southern Oscillation (ENSO) is an irregularly recurring climate pattern characterized by warmer (El Niño) and colder (La Niña) than usual ocean temperatures in the equatorial eastern Pacific, which creates a ripple effect of anticipated weather changes in far-spread regions on our planet. Weather changes associated with the ENSO phenomenon result in climate anomalies related to each other, such as rainfall (increase or lack of thereof) and land surface temperature anomaly conditions that trigger outbreaks of infectious diseases of public health concern in different regions around the world. These distant weather effects are called teleconnections. Therefore, the effects of ENSO are called ENSO teleconnections, highlighting that warmer or colder than usual ocean temperatures in the equatorial Pacific Ocean with extents (5N-5S, 120W-170W) affect areas far from the source typically 2-3 months after.During the last 20 years NASA scientist Dr. Assaf Anyamba and colleagues have been tracking ENSO events (please see: Niño 3.4 Index and Sea Surface Temperature Anomaly Timeline: 1982-2017) and studying associated teleconnections by monitoring various climate datasets, among them Sea Surface Temperature, Precipitation and Land Surface Anomaly datasets from NASA and National Oceanic and Atmospheric Administration (NOAA). At the same time, the science team has been collecting, cataloguing and analyzing patterns and sources of infectious disease outbreaks worldwide. Dr. Anyamba and colleagues conducted a scientific study - the first one to comprehensively assess the public health impacts of the major climate event on a global scale - that was open access published in the journal Nature Scientific Reports, with the title Global Disease Outbreaks Associated with the 2015-2016 El Niño event. According to the study, the 2015-2016 El Niño event brought weather conditions that triggered infectious disease outbreaks in ENSO teleconnected regions around the world, such as plague and hantavirus in Colorado and New Mexico (in 2015), cholera in East Africa’s Tanzania (during 2015- 2016), and dengue fever in Brazil and Southeast Asia (during 2015) among others. These outbreaks have been visualized with data in web entry: Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (4K version)).The data visualization featured on this page with corresponding multiplot graph illustrates the relationship between the 2015-2016 El Niño event and the amplification of dengue outbreaks over the region of South East Asia for the same period. The visualization comprises of two parts:Top:On the top part we can see three separate representations of Earth (spheres) with three distinct datasets. On the left sphere Sea Surface Temperature (SST) anomaly data are mapped over water and our planet is rotated so that we can observe changes of temperature (increase/red hues, decrease/blue hues) over the equatorial Pacific Ocean. The strong ENSO (El Niño) event during May 2015-May 2016 is manifested in the visualization as increased temperature over water (red hues) in the equatorial Pacific Ocean, where the Nino 3.4 Index SST region (5N-5S, 120W-170W) is located. In the middle sphere, our planet is rotated so that we can see Precipitation anomaly data (dry/brown to wet/teal) over land in the South East Asia region. On the right sphere, Land Surface Temperature (LST) anomaly data (low/blue to high/red) are mapped on land.The three distinct representations of each dataset are accompanied right below each sphere with the corresponding colorbar information (for example, Sea Surface Temperature colorbar, Precipitation Anomaly colorbar and Land Surface Temperature Anomaly colorbar)Bottom:On the bottom, a synchronized multiplot of Precipitation Anomaly (mm) and Temperature Anomaly (Co ) for the same period, tracks and visualizes indicators from three sources represented in the top part of the visualization. The three indicators are:Monthly Sea Surface Temperature (SST) Anomaly data (°C) for the Niño 3.4 Index region over the equatorial Pacific with extents (5N-5S, 120W-170W). Represented in the multiplot as the orange area graph. Monthly Precipitation Anomaly data (mm) for the South East (SE) Asia Region (Myanmar, Vietnam, Laos, Thailand, Cambodia, Malaysia, Singapore, Indonesia). Represented in the multiplot as the grey area graph.Monthly Land Surface Temperature (LST) Anomaly data (°C) for the South East (SE) Asia Region (Myanmar, Vietnam, Laos, Thailand, Cambodia, Malaysia, Singapore, Indonesia). Represented in the multiplot as the yellow area graph.The multiplot references both temperature-related anomaly datasets: Sea Surface Temperature (SST) Anomaly and Land Surface Temperature (LST) Anomaly data to the right axis of the multiplot. Temperature Anomaly values are referenced to the left axis of the multiplot. The overall design of this data visualization was chosen in an effort to make visible the relationships between the three datasets and their indicators concurrently. As time progresses, labels and visual cues in the multiplot guide the viewer about the occurrence and duration of the El Niño event, its different phases (Moderate, Strong, Very Strong) and the Dengue Amplification Period.To explain a bit further the weather patterns and teleconnections, lets take a closer look at the sequence of events and their timelines. The El Niño event (May 2015-May 2016) is manifested over the equatorial Pacific Ocean, as increased temperature (left sphere, red hues) on the top part of the visualization and with the orange area graph on the bottom. During the same period, the South East Asia region receives below than normal precipitation (middle sphere, brown hues) resulting in drier than usual conditions, which in turn caused an anomalous increase in land surface temperature (left sphere, red hues). The dry and hot conditions in the South East Asia region were conducive for the upsurge in populations of mosquito vectors in urban areas, where there are open water storage containers providing ideal habitats for mosquito production. In addition, the higher than normal temperature on land shortens the maturation time of larvae to adult mosquitos and induces frequent blood feeding/biting of humans by mosquito vectors, resulting in the Dengue Amplification Period (July 2015-March 2016) over the South East Asia region. Dengue fever is a painful, debilitating disease and is transmitted between people by mosquito vectors. It is a predominantly tropical disease affecting approximately 400 million people annually in many areas of the global tropics including South America and South East Asia. Dengue epidemics worldwide occur in urban areas where there is a coincidence of large numbers of dengue vectors (Aedes aegypti) and people with no immunity to one of the virus types.The impact of precipitation and land surface temperature anomalies on the dengue outbreaks over the South East Asia region have been visualized with data on the following two web entries: Precipitation Anomaly and Dengue Outbreaks in South East Asia: 2015-2016Land Surface Temperature Anomaly and Dengue Outbreaks in South East Asia Region: 2015-2016The strong relationship between ENSO events and disease outbreaks underscores the importance of seasonal forecasts. Since disease outbreaks typically manifest 2-3 months after the start of El Niño and La Nina events, early and regular climate monitoring, paired with the use of monthly and seasonal climate forecasts become significant tools for disease control and prevention. Findings of the scientific study by Dr. Assaf Anyamba and colleagues, suggests that by monitoring monthly climate datasets, country public health agencies such as CDC and international organizations such as the United Nations' World Health Organization and Food and Agriculture Organizations, can utilize early warning forecasts to undertake preventive measures to minimize the spread of ecologically coupled diseases.Data SourcesSea Surface Temperature Anomaly (global, monthly time series: 1982-2017)Global monthly SST data known as Optimum Interpolation (OI) SST version 2 dataset produced by NOAA can be accessed from: https://www.ncdc.noaa.gov/oisstPrecipitation Anomaly data (global and subset of SE Asia region, monthly time series: 2002-2017) Global Precipitation Climatology Project (GPCP) Global 1° Monitoring Product, available atftp://ftp-anon.dwd.de/pub/data/gpcc/html/monitoring_ download.htmlLand Surface Temperature (LST) Anomaly data (global, monthly time series: 2002-2017) Global monthly 0.05° LST MOD11C3 data is available at: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11c3Dengue outbreak data are georeferenced as sourced from global disease occurrences at the Program for Monitoring Emerging Diseases (ProMED). Dengue outbreak reports defined the Dengue Amplification Period. Niño 3.4 Sea Surface Temperature (SST) ENSO index for the period 2015-2016 is obtained from the NOAA National Center for Climate Prediction online archives.The warm (El Niño) and cold (La Niña) periods of ENSO events were determined using the Oceanic Niño Index (ONI) threshold of +/- 0.5Co based on centered 30-year base periods updated every 5 years. The ONI is a 3-month running mean of Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4) SST anomalies in the Niño 3.4 region (5 N-5 S, 120W-170W).Supported with funding from the Defense Threat Reduction Agency's (DTRA) Joint Science and Technology Office for Chemical and Biological Defense (JSTO-CBD) Biosurveillance Ecosystem (BSVE) Program (HDTRA1-16-C-0045) and the Defense Health Agency-Armed Forces Health Surveillance Branch (AFHSB) Global Emerging Infections Surveillance and Response System (GEIS) under Project # P0072_19_NS.The rest of this webpage offers additional versions, frames, layers and colorbar information associated with the development of this data-driven visualization. Related pages