{
    "count": 4,
    "next": null,
    "previous": null,
    "results": [
        {
            "id": 40348,
            "url": "https://svs.gsfc.nasa.gov/gallery/esddatafor-societal-benefits/",
            "result_type": "Gallery",
            "release_date": "2018-04-24T00:00:00-04:00",
            "title": "ESD data for Societal Benefit",
            "description": "No description available.",
            "hits": 225
        },
        {
            "id": 12603,
            "url": "https://svs.gsfc.nasa.gov/12603/",
            "result_type": "Produced Video",
            "release_date": "2017-09-13T11:00:00-04:00",
            "title": "Predicting Malaria Outbreaks With NASA Satellites",
            "description": "In the Amazon Rainforest, few animals are as dangerous to humans as mosquitos that transmit malaria. The tropical disease can bring on severe fever, headaches and chills and is particularly severe for children and the elderly and can cause complications for pregnant women. In rainforest-covered Peru the number of malaria cases has spiked such that, in the past five years, it has had on average the second highest rate in the South American continent. In 2014 and 2015 there were 65,000 reported cases in the country.Containing malaria outbreaks is challenging because it is difficult to figure out where people are contracting the disease. As a result, resources such as insecticide-treated bed nets and indoor sprays are often deployed to areas where few people are getting infected, allowing the outbreak to grow.To tackle this problem, university researchers have turned to data from NASA’s fleet of Earth-observing satellites, which are able to track the types of human and environmental events that typically precede an outbreak. With funding from NASA’s Applied Sciences Program, they are working in partnership with the Peruvian government to develop a system that uses satellite and other data to help forecast outbreaks at the household level months in advance and prevent outbreaks.Additional imagery from: Christopher B. Plunkett FortJames GathanyFábio Medeiros da Costa || ",
            "hits": 48
        },
        {
            "id": 4581,
            "url": "https://svs.gsfc.nasa.gov/4581/",
            "result_type": "Visualization",
            "release_date": "2017-07-24T00:00:00-04:00",
            "title": "Using Satellite and Ground-based Data to Develop Malaria Risk Maps",
            "description": "Malaria is a major problem in the Amazon where malaria mosquitoes tend to prefer wet, hot areas with more standing water. Seasonal occupational movement along rivers and in forested areas increases transmission and concentrates malaria in specific regions. The objective of Malaria Project, an ongoing study led by William Pan and Ben Zaitchik, is to develop a detection and early warning system for malaria risk in the Amazon. Using data from NASA satellites and a Land Data Assimilation System (LDAS), the scientists hope that their research can help health officials pinpoint where to deploy resources and what resources to deploy during a disease outbreak.  By incorporating NASA data such as precipitation, soil moisture, air temperature, and humidity into their new system, scientists are better able to predict where malaria-spreading mosquitoes are breeding. These climate factors in conjunction with a population density and human movement model will help scientists better understand where and when people are at high risk for malaria. The malaria warning system will predict outbreaks and simulate response to help a country's health care system to more strategically determine where to deploy their resources.  Visualizations focus on Peru, one of the central areas of malaria transmission in the Amazon.  Four LDAS data sets -- precipitation, soil moisture, air temperature, and humidity are illustrated below. Combined with public health data, the animations show how these factors may affect the outbreak and evolvement of the disease. || ",
            "hits": 28
        },
        {
            "id": 4044,
            "url": "https://svs.gsfc.nasa.gov/4044/",
            "result_type": "Visualization",
            "release_date": "2013-02-27T00:00:00-05:00",
            "title": "The Distributed Water Balance of the Nile Basin",
            "description": "This visualization shows how satellite data and NASA models are being applied to study the hydrology of the Nile basin. The Tropical Rainfall Measurement Mission (TRMM) Multisensor Precipitation Analysis (TMPA) provides three-hourly estimates of rainfall rate across much of the globe. Here we see the seasonal cycle of monthly precipitation derived from TMPA for Africa, including the Nile Basin. The annual migration of the Intertropical Convergence Zone (ITCZ) from the Nile Equatorial Lakes region around Lake Victoria, source of the White Nile, northward into Sudan and the highlands of Ethiopia, headwaters of the Blue Nile, and back is evident in the seasonal cycle in precipitation. This precipitation cycle drives flow through the Nile River system. The Nile basin, however, is intensely evaporative, and the majority of the water that falls as rain leaves the basin as evaporation rather than river flow—either from the humid headwaters regions or from large reservoirs and irrigation developments in Egypt and Sudan. The Atmosphere Land Exchange Inverse (ALEXI) evapotranspiration product, developed by USDA scientists, uses satellite data to map daily evapotranspiration across the entire Nile basin, providing unprecedented information on water consumption. The balance of rainfall and evapotranspiration can be seen in seasonal patterns of soil moisture, as simulated by the NASA Nile Land Data Assimilation System (LDAS), which merges satellite information with a physically-based land surface model to simulate variability in soil moisture—a critical variable for rainfed agriculture and natural ecosystems. Finally, the twin satellites of the Gravity Recovery and Climate Experiment (GRACE) can be used to monitor variability in total water storage, including surface water, soil moisture, and groundwater. The annual cycle in GRACE estimates of water storage anomalies clearly shows the seasonal movement of water storage due to precipitation patterns and the movement of surface waters from headwaters regions into the wetlands of South Sudan and the reservoirs of the lower Nile basin.The Nile is the longest river in the world and its basin is shared by 11 countries. Reliable, spatially distributed estimates of hydrologic storage and fluxes can provide critical information for water managers contending with multiple resource demands, a variable and changing climate, and the risk of damaging floods and droughts. NASA observations and modeling systems offer unique capabilities to meet these information needs. || ",
            "hits": 85
        }
    ]
}