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        {
            "id": 4782,
            "url": "https://svs.gsfc.nasa.gov/4782/",
            "result_type": "Visualization",
            "release_date": "2020-03-04T00:00:00-05:00",
            "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] || ",
            "hits": 38
        },
        {
            "id": 4783,
            "url": "https://svs.gsfc.nasa.gov/4783/",
            "result_type": "Visualization",
            "release_date": "2020-02-27T00:00:00-05:00",
            "title": "Precipitation Anomaly and Rift Valley fever (RVF) outbreaks in South Africa: 2008-2011",
            "description": "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). || PrecipRVF_SAfrica_Composite_3840x2160_3422_print.jpg (1024x576) [97.8 KB] || PrecipRVF_SAfrica_Composite_3840x2160_3422_searchweb.png (320x180) [57.6 KB] || PrecipRVF_SAfrica_Composite_3840x2160_3422_thm.png (80x40) [5.2 KB] || PrecipRVF_SAfrica_Composite_1920x1080p30.mp4 (1920x1080) [31.5 MB] || Composite (3840x2160) [0 Item(s)] || Composite (3840x2160) [0 Item(s)] || PrecipRVF_SAfrica_Composite_3840x2160_p30.mp4 (3840x2160) [68.2 MB] || PrecipRVF_SAfrica_Composite_3840x2160_3422.tif (3840x2160) [4.0 MB] || PrecipRVF_SAfrica_Composite_3840x2160_p30.webm (3840x2160) [14.1 MB] || ",
            "hits": 26
        },
        {
            "id": 4724,
            "url": "https://svs.gsfc.nasa.gov/4724/",
            "result_type": "Visualization",
            "release_date": "2020-02-21T00:00:00-05:00",
            "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] || ",
            "hits": 37
        },
        {
            "id": 4747,
            "url": "https://svs.gsfc.nasa.gov/4747/",
            "result_type": "Visualization",
            "release_date": "2020-02-21T00:00:00-05:00",
            "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] || ",
            "hits": 35
        },
        {
            "id": 4784,
            "url": "https://svs.gsfc.nasa.gov/4784/",
            "result_type": "Visualization",
            "release_date": "2020-02-21T00:00:00-05:00",
            "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] || ",
            "hits": 32
        },
        {
            "id": 4785,
            "url": "https://svs.gsfc.nasa.gov/4785/",
            "result_type": "Visualization",
            "release_date": "2020-01-09T00:00:00-05:00",
            "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. || ",
            "hits": 69
        },
        {
            "id": 4781,
            "url": "https://svs.gsfc.nasa.gov/4781/",
            "result_type": "Visualization",
            "release_date": "2020-01-06T00:00:00-05:00",
            "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] || ",
            "hits": 83
        },
        {
            "id": 4765,
            "url": "https://svs.gsfc.nasa.gov/4765/",
            "result_type": "Visualization",
            "release_date": "2019-12-10T00:00:00-05:00",
            "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] || ",
            "hits": 106
        },
        {
            "id": 4693,
            "url": "https://svs.gsfc.nasa.gov/4693/",
            "result_type": "Visualization",
            "release_date": "2019-02-28T09:00:00-05:00",
            "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)] || ",
            "hits": 33
        },
        {
            "id": 4695,
            "url": "https://svs.gsfc.nasa.gov/4695/",
            "result_type": "Visualization",
            "release_date": "2019-02-28T09:00:00-05:00",
            "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)] || ",
            "hits": 351
        },
        {
            "id": 4696,
            "url": "https://svs.gsfc.nasa.gov/4696/",
            "result_type": "Visualization",
            "release_date": "2019-02-28T09:00:00-05:00",
            "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)] || ",
            "hits": 25
        },
        {
            "id": 4697,
            "url": "https://svs.gsfc.nasa.gov/4697/",
            "result_type": "Visualization",
            "release_date": "2019-02-28T09:00:00-05:00",
            "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] || ",
            "hits": 80
        },
        {
            "id": 3905,
            "url": "https://svs.gsfc.nasa.gov/3905/",
            "result_type": "Visualization",
            "release_date": "2012-04-13T09:00:00-04:00",
            "title": "Mapping Diseases",
            "description": "The print-resolution still images were created for the February 2012 issue of The Scientist (print and online). In an article in the same issue, NASA scientist Assaf Anyamba explains how he can predict diseases with remote-sensing data.The data used are: 1. NDVI is an index that quantifies the photosynthetic capacity of vegetation. It is derived from visible and near-infrared reflectance measurements made by Advanced Very High Resolution Radiometer (AVHRR) sensors onboard NOAA's polar orbiting satellites (in this case NOAA-17). Taken as time series measurements, NDVI indicates the response of vegetation to seasonal and interannual variations in climate.2. SST data are a blend of direct observations from ships, buoys, satellite imagery also from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) instruments, and SSTs simulated by sea-ice cover. The monthly optimum interpolated fields were derived by a linear interpolation of the weekly fields to daily fields, and then averaging daily values over a month.All anomaly fields (as shown here) are derived by subtracting the monthly values from the respective long-term monthly means. || ",
            "hits": 29
        },
        {
            "id": 3807,
            "url": "https://svs.gsfc.nasa.gov/3807/",
            "result_type": "Visualization",
            "release_date": "2011-08-31T00:00:00-04:00",
            "title": "Predicting Disease Outbreaks from Space",
            "description": "These visualizations were created for the May 18, 2012 Library of Congress Talk Predictiding Disease Outbreaks from Space. In this talk NASA scientist Assaf Anyamba, will present how using remote-sensing data we can see links among weather, diseases and famine.An early warning system more than a decade in development successfully predicted the 2006-2007 outbreak of the deadly Rift Valley Fever (RVF) in East Africa and subsequent outbreaks in Sudan (2007) and South Africa (2008-2011). RVF is a deadly hemorrhagic disease transmitted by mosquitoes that infects livestock and human populations episodically. An international team of research scientists, public-health professionals, agricultural specialists and military personnel had worked for a decade to successfully predict when and where an outbreak of RVF would occur. || ",
            "hits": 23
        }
    ]
}