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    "title": "Precipitation Anomaly and Rift Valley fever (RVF) outbreaks in South Africa: 2008-2011",
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    "release_date": "2020-02-27T00:00:00-05:00",
    "update_date": "2025-02-02T22:45:20.294104-05:00",
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        "Visualizations by": [
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                "name": "Helen-Nicole Kostis",
                "employer": "USRA"
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        "Scientific consulting by": [
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            "description": "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. <br><br>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 <a href=\"https://svs.gsfc.nasa.gov/4784\">ENSO Teleconnections and Rift Valley fever (RVF) Outbreaks</a>.<br><br>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 <a href=\" https://svs.gsfc.nasa.gov/4785\">Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (4K version)</a>). 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.<br><br>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.\r<br><br>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 <i>South Africa region</i> 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: <br><ol><br><li>Number of Rift Valley fever (RVF) outbreak locations in the South Africa region with extents (15E-33E, 26S-35S).</li><li>Precipitation Anomaly over land in the South Africa region with extents (15E-33E, 26S-35S).</li></ol><br>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.\r<br>\r<br>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 <a href=\"https://svs.gsfc.nasa.gov/4782\">Vegetation Index Anomalies and Rift Valley fever (RVF) outbreaks in South Africa region: 2008-2011</a>)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. \r<br>\r<br>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).\r<br>\r<br>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 <b> Nature </b>Scientific Reports with the title <a href=\"https://www.nature.com/articles/s41598-018-38034-z\"><i>Global Disease Outbreaks Associated with the 2015-2016 El Niño event</i></a>  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.\r<br><hr>\r<br><b>Data Sources:</b>\r<br><ul>\r<br><li><b>Disease Outbreak</b> data were collected from the <a href=\"https://promedmail.org/\">Program for Monitoring Emerging Diseases (ProMED)</a> and from the <a href=\"https://www.oie.int/wahis_2/public/wahid.php/Diseaseinformation/WI/index/newlang/en\">World Organisation for Animal Health/Organisation mondiale de la santé animale (OIE).</a></li><br><li><b>Rainfall/Precipitation anomaly </b>data:  African Rainfall Climatology (ARC) dataset from the National Oceanic and Atmospheric Administration (NOAA) – Climate Prediction Center (CPC) <a href=\"https://www.cpc.ncep.noaa.gov/products/international/africa/africa.shtml\">archives</a>. The dataset is available over Africa at 0.1° x 0.1° spatial resolution from 1983 to present. <i>Reference: </i> 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, <a href=”https://doi.org/10.1175/JAMC-D-16-0111.1”> 10.1175/JAMC-D-16-0111.1</a>\r<br></li><br><li><b>ENSO events (El Nino and La Nina) </b>are identified by using SST ENSO index (Niño 3.4) data obtained from the NOAA National Center for Climate Prediction <a href=\"http://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices\">online archives</a>. 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).</li></ul><hr><br><br><i>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_NS</i><br><br><hr><br><br>The rest of this webpage offers additional versions, colorbar information, frames and layers associated with the development of this data-driven visualization.",
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                        "alt_text": "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).",
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                        "alt_text": "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).",
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                        "alt_text": "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).",
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                        "alt_text": "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.",
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                        "alt_text": "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.",
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                        "filename": "PrecipRVF_SAfricaWithDates_3840x2160_p30.webm",
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                        "alt_text": "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.",
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            "description": "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.",
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                    "instance": {
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                        "alt_text": "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.",
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                        "media_type": "Frames",
                        "alt_text": "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.",
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            "description": "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.",
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                },
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                },
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                        "media_type": "Frames",
                        "alt_text": "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.",
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                        "alt_text": "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.",
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                        "alt_text": "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.",
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                        "alt_text": "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.",
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                        "alt_text": "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.",
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            "page_type": "Visualization",
            "title": "Vegetation Index Anomalies and Rift Valley fever (RVF) outbreaks in South Africa region: 2008-2011",
            "description": "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). || NDVI_RVF_SAfrica_Composite_3840x2160_2657_print.jpg (1024x576) [102.7 KB] || NDVI_RVF_SAfrica_Composite_3840x2160_2657_searchweb.png (320x180) [57.8 KB] || NDVI_RVF_SAfrica_Composite_3840x2160_2657_thm.png (80x40) [5.0 KB] || NDVI_RVF_SAfrica_Composite_1920x1080p30.mp4 (1920x1080) [35.6 MB] || NDVI_RVF_SAfrica_Composite_1920x1080p30.webm (1920x1080) [7.1 MB] || Composite (3840x2160) [0 Item(s)] || Composite (3840x2160) [0 Item(s)] || NDVI_RVF_SAfrica_Composite_3840x2160_p30.mp4 (3840x2160) [72.6 MB] || NDVI_RVF_SAfrica_Composite_3840x2160_2657.tif (3840x2160) [31.6 MB] || ",
            "release_date": "2020-03-04T00:00:00-05:00",
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                "filename": "NDVI_RVF_SAfrica_Composite_3840x2160_2657_print.jpg",
                "media_type": "Image",
                "alt_text": "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).",
                "width": 1024,
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        {
            "id": 4724,
            "url": "https://svs.gsfc.nasa.gov/4724/",
            "page_type": "Visualization",
            "title": "Vegetation index anomalies and Rift Valley fever (RVF) outbreaks in Africa and Middle East during 2000-2018",
            "description": "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. || Africa_NDVIRVF_2000_2018_3840x2160_2430_print.jpg (1024x576) [78.8 KB] || Africa_NDVIRVF_2000_2018_3840x2160_2430_searchweb.png (320x180) [48.8 KB] || Africa_NDVIRVF_2000_2018_3840x2160_2430_thm.png (80x40) [4.4 KB] || Africa_NDVIRVFComposite_2000_2018_3840x2160_1080p30.mp4 (1920x1080) [88.7 MB] || Africa_NDVIRVFComposite_2000_2018_3840x2160_1080p30.webm (1920x1080) [25.5 MB] || Africa_NDVIRVF_2000_2018_Composite (3840x2160) [0 Item(s)] || Africa_NDVIRVF_2000_2018_3840x2160_2430.tif (3840x2160) [6.0 MB] || Africa_NDVIRVFComposite_2000_2018_3840x2160_p30.mp4 (3840x2160) [283.2 MB] || ",
            "release_date": "2020-02-21T00:00:00-05:00",
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                "filename": "Africa_NDVIRVF_2000_2018_3840x2160_2430_print.jpg",
                "media_type": "Image",
                "alt_text": "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.",
                "width": 1024,
                "height": 576,
                "pixels": 589824
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            "url": "https://svs.gsfc.nasa.gov/4747/",
            "page_type": "Visualization",
            "title": "Vegetation index anomalies and Rift Valley fever (RVF) outbreaks in South Africa during 2009-2011",
            "description": "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. || SAfrica_NDVIRVFwDates_3840x2160_1263_print.jpg (1024x576) [86.2 KB] || SAfrica_NDVIRVFwDates_3840x2160_1263_searchweb.png (320x180) [56.0 KB] || SAfrica_NDVIRVFwDates_3840x2160_1263_thm.png (80x40) [4.5 KB] || SAfrica_NDVIRVFComposite_1080p30.mp4 (1920x1080) [31.6 MB] || SAfrica_NDVIRVFComposite_1080p30.webm (1920x1080) [7.0 MB] || Composite (3840x2160) [0 Item(s)] || SAfrica_NDVIRVFwDates_3840x2160_1263.tif (3840x2160) [7.6 MB] || SAfrica_NDVIRVFComposite_3840x2160_p30.mp4 (3840x2160) [96.4 MB] || ",
            "release_date": "2020-02-21T00:00:00-05:00",
            "update_date": "2025-02-02T00:12:35.273066-05:00",
            "main_image": {
                "id": 392107,
                "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004700/a004747/SAfrica_NDVIRVFwDates_3840x2160_1263_print.jpg",
                "filename": "SAfrica_NDVIRVFwDates_3840x2160_1263_print.jpg",
                "media_type": "Image",
                "alt_text": "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.",
                "width": 1024,
                "height": 576,
                "pixels": 589824
            }
        },
        {
            "id": 4784,
            "url": "https://svs.gsfc.nasa.gov/4784/",
            "page_type": "Visualization",
            "title": "ENSO Teleconnections and Rift Valley fever (RVF) Outbreaks",
            "description": "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. || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_2960_print.jpg (1024x576) [107.8 KB] || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_3525_searchweb.png (320x180) [63.0 KB] || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_3525_thm.png (80x40) [6.5 KB] || ENSO_Teleconnections (1920x1080) [0 Item(s)] || SST_Precip_NDVI_Dashboard_2008_2011_1920x1080_p30.mp4 (1920x1080) [22.7 MB] || ENSO_Teleconnections (3840x2160) [0 Item(s)] || ENSO_Teleconnections (3840x2160) [0 Item(s)] || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_p30.mp4 (3840x2160) [56.0 MB] || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_p30.webm (3840x2160) [10.2 MB] || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_2960.tif (3840x2160) [3.4 MB] || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_3525.tif (3840x2160) [3.4 MB] || ",
            "release_date": "2020-02-21T00:00:00-05:00",
            "update_date": "2025-02-02T00:13:13.644499-05:00",
            "main_image": {
                "id": 386811,
                "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004700/a004784/ENSO_TeleconnectionsRVF_2008_2011_3840x2160_3525_searchweb.png",
                "filename": "ENSO_TeleconnectionsRVF_2008_2011_3840x2160_3525_searchweb.png",
                "media_type": "Image",
                "alt_text": "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.",
                "width": 320,
                "height": 180,
                "pixels": 57600
            }
        },
        {
            "id": 4785,
            "url": "https://svs.gsfc.nasa.gov/4785/",
            "page_type": "Visualization",
            "title": "Sea Surface Temperature Anomalies and Patterns of Global Disease Outbreaks: 2009-2018 (4K version)",
            "description": "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. || ",
            "release_date": "2020-01-09T00:00:00-05:00",
            "update_date": "2025-02-02T00:13:15.129941-05:00",
            "main_image": {
                "id": 388258,
                "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004700/a004785/CompositeWLabel_2009_2018_3840x2160_30fps_0852_print.jpg",
                "filename": "CompositeWLabel_2009_2018_3840x2160_30fps_0852_print.jpg",
                "media_type": "Image",
                "alt_text": "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.",
                "width": 1024,
                "height": 576,
                "pixels": 589824
            }
        },
        {
            "id": 4781,
            "url": "https://svs.gsfc.nasa.gov/4781/",
            "page_type": "Visualization",
            "title": "Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (updated)",
            "description": "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. || CompositeWLabel_2009_2018_1920x108060fps_1705_print.jpg (1024x576) [135.9 KB] || CompositeWLabel_2009_2018_1920x108060fps_1705_searchweb.png (320x180) [82.6 KB] || CompositeWLabel_2009_2018_1920x108060fps_1705_thm.png (80x40) [7.1 KB] || Composite_StrongElNino (1920x1080) [0 Item(s)] || Composite_StrongElNino (1920x1080) [0 Item(s)] || CompositeWLabel_2009_2018_1920x1080_p30.mp4 (1920x1080) [22.1 MB] || CompositeWLabel_2009_2018_1920x108060fps_1705.tif (1920x1080) [1.3 MB] || CompositeWLabel_2009_2018_1920x1080_p30.webm (1920x1080) [4.6 MB] || CompositeWLabel_2009_2018_1920x1080_p30.mp4.hwshow [205 bytes] || ",
            "release_date": "2020-01-06T00:00:00-05:00",
            "update_date": "2025-02-02T00:13:06.818408-05:00",
            "main_image": {
                "id": 388349,
                "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004700/a004781/CompositeWLabel_2009_2018_1920x108060fps_1705_print.jpg",
                "filename": "CompositeWLabel_2009_2018_1920x108060fps_1705_print.jpg",
                "media_type": "Image",
                "alt_text": "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. ",
                "width": 1024,
                "height": 576,
                "pixels": 589824
            }
        },
        {
            "id": 4765,
            "url": "https://svs.gsfc.nasa.gov/4765/",
            "page_type": "Visualization",
            "title": "Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018",
            "description": "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. || SSTENSO_Diseases_Comp_2009_2018_1920x1080_0769_print.jpg (1024x576) [130.6 KB] || SSTENSO_Diseases_Comp_2009_2018_1920x1080_0769_searchweb.png (320x180) [79.7 KB] || SSTENSO_Diseases_Comp_2009_2018_1920x1080_0769_thm.png (80x40) [7.0 KB] || Composite (1920x1080) [0 Item(s)] || Composite (1920x1080) [0 Item(s)] || SSTENSO_Diseases_Comp_2009_2018_1920x1080_p30.mp4 (1920x1080) [23.0 MB] || SSTENSO_Diseases_Comp_2009_2018_1920x1080_0769.tif (1920x1080) [1.3 MB] || SSTENSO_Diseases_Comp_2009_2018_1920x1080_p30.webm (1920x1080) [4.7 MB] || SSTENSO_Diseases_Comp_2009_2018_1920x1080_p30.mp4.hwshow [211 bytes] || ",
            "release_date": "2019-12-10T00:00:00-05:00",
            "update_date": "2025-02-02T00:12:46.498564-05:00",
            "main_image": {
                "id": 388873,
                "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004700/a004765/SSTENSO_Diseases_Comp_2009_2018_1920x1080_0769_print.jpg",
                "filename": "SSTENSO_Diseases_Comp_2009_2018_1920x1080_0769_print.jpg",
                "media_type": "Image",
                "alt_text": "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.",
                "width": 1024,
                "height": 576,
                "pixels": 589824
            }
        },
        {
            "id": 4693,
            "url": "https://svs.gsfc.nasa.gov/4693/",
            "page_type": "Visualization",
            "title": "Precipitation Anomaly and Dengue Outbreaks in South East Asia: 2015-2016",
            "description": "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 precipitation anomaly in Southeast Asia and dengue outbreaks. Drier than normal habitats drew mosquitoes into populated, urban areas containing the open water needed for laying eggs. As the air warmed, mosquitoes also grew hungrier and reached sexual maturity more quickly, resulting in an increase in mosquito bites. || SEAsia_PrecipDengueComposite_1920x1080_1211_print.jpg (1024x576) [75.8 KB] || SEAsia_PrecipDengueComposite_1920x1080_1211_searchweb.png (320x180) [52.9 KB] || SEAsia_PrecipDengueComposite_1920x1080_1211_thm.png (80x40) [5.4 KB] || SEAsia_PrecipDengueComposite_1920x1080_p30.webm (1920x1080) [6.4 MB] || SEAsia_PrecipDengue_Composite (1920x1080) [0 Item(s)] || SEAsia_PrecipDengueComposite_1920x1080_p30.mp4 (1920x1080) [14.8 MB] || SEAsia_PrecipDengueComposite_1920x1080_1211.tif (1920x1080) [1.5 MB] || SEAsia_PrecipDengueComposite (3840x2160) [0 Item(s)] || ",
            "release_date": "2019-02-28T09:00:00-05:00",
            "update_date": "2025-02-02T00:11:30.105895-05:00",
            "main_image": {
                "id": 397408,
                "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004600/a004693/SEAsia_PrecipDengueComposite_1920x1080_1211_print.jpg",
                "filename": "SEAsia_PrecipDengueComposite_1920x1080_1211_print.jpg",
                "media_type": "Image",
                "alt_text": "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 precipitation anomaly in Southeast Asia and dengue outbreaks. Drier than normal habitats drew mosquitoes into populated, urban areas containing the open water needed for laying eggs. As the air warmed, mosquitoes also grew hungrier and reached sexual maturity more quickly, resulting in an increase in mosquito bites.",
                "width": 1024,
                "height": 576,
                "pixels": 589824
            }
        },
        {
            "id": 4695,
            "url": "https://svs.gsfc.nasa.gov/4695/",
            "page_type": "Visualization",
            "title": "Niño 3.4 Index and Sea Surface Temperature Anomaly Timeline: 1982-2017",
            "description": "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. || NINO3.4SST_FlatMapComposite_1920x1080_00932_print.jpg (1024x576) [104.9 KB] || NINO3.4SST_FlatMapComposite_1920x1080_00932_searchweb.png (320x180) [72.1 KB] || NINO3.4SST_FlatMapComposite_1920x1080_00932_thm.png (80x40) [6.8 KB] || SST_Nino3.4Index_1982_2017_Composite (1920x1080) [0 Item(s)] || NINO3.4SST_FlatMapComposite_1920x1080_p30.mp4 (1920x1080) [57.2 MB] || NINO3.4SST_FlatMapComposite_1920x1080_00932.tif (1920x1080) [1.4 MB] || NINO3.4SST_FlatMapComposite_1920x1080_p30.webm (1920x1080) [9.3 MB] || SSTNino3.4Index_1982_2017_Composite (3840x2160) [0 Item(s)] || ",
            "release_date": "2019-02-28T09:00:00-05:00",
            "update_date": "2025-02-02T22:39:44.671922-05:00",
            "main_image": {
                "id": 398258,
                "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004600/a004695/NINO3.4SST_FlatMapComposite_1920x1080_00932_print.jpg",
                "filename": "NINO3.4SST_FlatMapComposite_1920x1080_00932_print.jpg",
                "media_type": "Image",
                "alt_text": "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.",
                "width": 1024,
                "height": 576,
                "pixels": 589824
            }
        },
        {
            "id": 4696,
            "url": "https://svs.gsfc.nasa.gov/4696/",
            "page_type": "Visualization",
            "title": "Land Surface Temperature Anomaly and Dengue Outbreaks in South East Asia Region: 2015-2016",
            "description": "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. || SEAsia_LSTDiseases_1920x1080_1730_print.jpg (1024x576) [85.1 KB] || SEAsia_LSTDiseases_1920x1080_1730_searchweb.png (320x180) [54.4 KB] || SEAsia_LSTDiseases_1920x1080_1730_thm.png (80x40) [5.3 KB] || SEAsia_LSTDengue_Composite (1920x1080) [0 Item(s)] || SEAsia_LSTDiseases_1920x1080_p30.mp4 (1920x1080) [33.8 MB] || SEAsia_LSTDiseases_1920x1080_1730.tif (1920x1080) [1.7 MB] || SEAsia_LSTDiseases_1920x1080_p30.webm (1920x1080) [6.2 MB] || SEAsia_LSTDengue_Composite (3840x2160) [0 Item(s)] || ",
            "release_date": "2019-02-28T09:00:00-05:00",
            "update_date": "2025-02-02T00:11:33.263475-05:00",
            "main_image": {
                "id": 397244,
                "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004600/a004696/SEAsia_LSTDiseases_1920x1080_1730_print.jpg",
                "filename": "SEAsia_LSTDiseases_1920x1080_1730_print.jpg",
                "media_type": "Image",
                "alt_text": "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.",
                "width": 1024,
                "height": 576,
                "pixels": 589824
            }
        },
        {
            "id": 4697,
            "url": "https://svs.gsfc.nasa.gov/4697/",
            "page_type": "Visualization",
            "title": "ENSO teleconnections in South East Asia for the period of 2015-2016",
            "description": "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. || SST_LST_Precip_2014_2016_Comp_print.jpg (1024x576) [82.9 KB] || SST_LST_Precip_2014_2016_Comp_searchweb.png (320x180) [51.5 KB] || SST_LST_Precip_2014_2016_Comp_thm.png (80x40) [6.0 KB] || SST_Precip_LST_Plot_Composite (1920x1080) [0 Item(s)] || SST_LST_Precip_2014_2016_Comp_1080p30.mp4 (1920x1080) [9.7 MB] || SST_LST_Precip_2014_2016_Comp.tif (1920x1080) [1.1 MB] || SST_LST_Precip_2014_2016_Comp_1080p30.webm (1920x1080) [4.2 MB] || TeleconnectionsSEAsia (3840x2160) [0 Item(s)] || SST_LST_Precip_2014_2016_Comp_1080p30.mp4.hwshow [203 bytes] || ",
            "release_date": "2019-02-28T09:00:00-05:00",
            "update_date": "2025-02-02T00:11:35.977656-05:00",
            "main_image": {
                "id": 398276,
                "url": "https://svs.gsfc.nasa.gov/vis/a000000/a004600/a004697/SST_LST_Precip_2014_2016_Comp_print.jpg",
                "filename": "SST_LST_Precip_2014_2016_Comp_print.jpg",
                "media_type": "Image",
                "alt_text": "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.",
                "width": 1024,
                "height": 576,
                "pixels": 589824
            }
        }
    ],
    "sources": [],
    "products": [],
    "newer_versions": [],
    "older_versions": [],
    "alternate_versions": []
}