{"homeCamera":{"north":10,"east":100,"south":10,"west":-50},"corsDomains":["api.planet.com","terria-wrapper-dot-fao-maps-review.appspot.com"],"catalog":[{"description":"**Rift Valley Fever Early Warning Decision Support Tool (RVF DST)**\n\n![](https://data.apps.fao.org/catalog/dataset/c9e5e226-4863-4c29-93c9-e41bbcefec3b/resource/30d55710-b5fd-411e-b9a8-858912b62483/download/key_data_image_800x400.png)\n\nRift Valley Fever (RVF) is a climate-sensitive, vector-borne zoonotic disease that affects animals and humans, with significant impacts on livelihoods and health. Currently confined to Africa and parts of the Near East, it has the potential to expand globally. Spread by mosquitoes, particularly Aedes and Culex species, and through infected animal movement, RVF causes high mortality in young animals and abortion in livestock. Human cases are usually mild but can become severe. The virus circulates sub-clinically in endemic areas and outbreaks are triggered by abnormal rainfall and flooding. Mosquitoes breed in floodwater, amplifying the virus, and eggs can survive dry periods for years, making eradication difficult. Climate change, including phenomena like El Ni\u00f1o, increases the frequency of RVF outbreaks. Early warning systems are vital for proactive measures to prevent outbreaks.  \n\nTo enable national authorities to implement measures preventing outbreaks, the Food and Agriculture Organization of the United Nations (FAO) has developed the web-based RVF Early Warning Decision Support Tool. The tool forecasts RVF hotspots by providing monthly and 8-day vector amplification maps for Africa at 250m resolution, along with historical RVF outbreak data and ecological indicators (risk factors) to interpret the risk of occurrence and spread. It facilitates data sharing, integrates user data and expert knowledge for risk assessment, and provides alerts and recommendations to at-risk countries to prevent outbreaks. \n\nThe RVF DST includes a dynamic prediction model calibrated by FAO that builds upon the work by Anyamba et al. (2009) and utilizes rainfall and vegetation anomalies such as the Normalized Difference Vegetation Index (NDVI) as a proxy for ecological dynamics to map areas at potential risk of RVF vector amplification in Africa. In this approach, climate data are utilized to identify and map regions experiencing persistent, heavy, above-average rainfall and vegetation anomalies over the past three consecutive months. The results are then analyzed and assessed in relation to El Ni\u00f1o, past, present and forecasted precipitation anomalies, along with other ecological indicators and compared with historical data, as identified by the stakeholders. Within these hotspots, an estimate of domestic animals and humans at potential risk of RVF virus exposure is provided. \n\nThis section includes key, essential data for RVF risk monitoring and assessment. \n\n\n**Useful Links**\n\n\u25b6 [Rift Valley fever action framework (FAO Animal Production and Health Guidelines, April 2022)](https://www.fao.org/fileadmin/user_upload/remesa/library/1_cb8653en.pdf)\n\n\u25b6 [Driving preparedness and anticipatory actions through innovation: A web-based Rift Valley fever Early Warning \nDecision Support Tool (September 2021)](https://www.fao.org/3/cb5875en/cb5875en.pdf)\n\n\u25b6 [Real-time monitoring and forecasting of Rift Valley fever in Africa (FAO FCC Information Sheet 2019)](https://www.fao.org/3/ca5511en/ca5511en.pdf)\n\n\u25b6 [Rift Valley fever surveillance (FAO Manual 2018)](https://www.fao.org/3/I8475EN/i8475en.pdf)\n\n\u25b6 [Recognizing Rift Valley fever (FAO Manual 2003)](https://www.fao.org/3/y4611e/y4611e00.htm)\n\n\u25b6 [Preparation of Rift Valley fever contingency plans (FAO Manual 2002)](https://www.fao.org/3/Y4140E/Y4140E00.htm)\n\n\u25b6 [Anyamba, et al. 2009. Prediction of a Rift Valley fever outbreak. Proceedings of the National Academy of Sciences 106(3): 955-959](https://www.pnas.org/content/pnas/106/3/955.full.pdf)\n\n\u25b6 [What you need to know about Rift Valley Fever](https://youtu.be/OBAkrTZMtqY?si=zkxwmj1IO0E9DgcL)\n\n\u25b6 [Introduction to Rift Valley Fever open access](https://eufmdlearning.works/enrol/index.php?id=295)\n\n\u25b6 Pittiglio, et al. 2024. Real-time disease risk monitoring and forecasting for early action. 12th International Conference on Agro-Geoinformatics, Novi Sad, Serbia, 2024, pp. 1-5, DOI: 10.1109/Agro-Geoinformatics262780.2024.10661081\n\n\u25b6 RVF alerts\n\n \n  \n  \n\n  \n\n\n\n\n\n\n \n\n[//]: # (Comment)\n\n\n\n**Metadata Link**: [https://data.apps.fao.org/catalog/dataset/rvf-master-config](https://data.apps.fao.org/catalog//dataset/rvf-master-config) \n\n\n revised: 2025-05-06T11:29:17.988544\n","tags":"platform rift valley fever ","items":[{"url":"https://data.apps.fao.org/catalog/terriajs/item/dbb1a67c-ed2a-4fde-ab15-05d93a5d0615.json","type":"remote-config-group","name":"Potential risk of RVF","isOpen":false},{"url":"https://data.apps.fao.org/catalog/terriajs/item/ec29c83d-a4fd-4732-92ac-bba0afed14fa.json","type":"remote-config-group","name":"Suitability for RVF Vector Amplification","isOpen":true},{"url":"https://data.apps.fao.org/catalog/terriajs/item/4afc433d-e308-41b3-be91-7a7ca5fdb5b8.json","type":"remote-config-group","name":"Rift Valley Fever Events","isOpen":false},{"url":"https://data.apps.fao.org/catalog/terriajs/item/8596769a-7cb8-4f9d-8146-6899bebcab18.json","type":"remote-config-group","name":"Environment","isOpen":false},{"url":"https://data.apps.fao.org/catalog/terriajs/item/21dafd84-07a2-4ac6-bc15-eb19cfe07aa7.json","type":"remote-config-group","name":"Exposure","isOpen":false},{"url":"https://data.apps.fao.org/catalog/terriajs/item/590ec53e-74ca-4930-9afb-081d1678e4b7.json","type":"remote-config-group","name":"Infrastructure","isOpen":false}],"preserveOrder":true,"type":"group","order":1,"id":"c9e5e226-4863-4c29-93c9-e41bbcefec3b_d230a710-9706-4041-9ab1-f5d473b54c84_31470760-27b5-42c8-a26c-5e6a8f53c681","name":"Key Data"},{"description":"**Rift Valley Fever Early Warning Decision Support Tool (RVF DST)**\n\n![](https://data.apps.fao.org/catalog/dataset/c9e5e226-4863-4c29-93c9-e41bbcefec3b/resource/483d0bde-35a9-4b95-946c-778abd88580a/download/supp_data_image_800x500.png)\n\nThis section includes supplementary data for advanced Rift Valley Fever risk monitoring and assessment.\n\n**Useful Links**\n\n\u25b6 [Rift Valley fever action framework (FAO Animal Production and Health Guidelines, April 2022)](https://www.fao.org/fileadmin/user_upload/remesa/library/1_cb8653en.pdf)\n\n\u25b6 [Driving preparedness and anticipatory actions through innovation: A web-based Rift Valley fever Early Warning \nDecision Support Tool (September 2021)](https://www.fao.org/3/cb5875en/cb5875en.pdf)\n\n\u25b6 [Real-time monitoring and forecasting of Rift Valley fever in Africa (FAO FCC Information Sheet 2019)](https://www.fao.org/3/ca5511en/ca5511en.pdf)\n\n\u25b6 [Rift Valley fever surveillance (FAO Manual 2018)](https://www.fao.org/3/I8475EN/i8475en.pdf)\n\n\u25b6 [Recognizing Rift Valley fever (FAO Manual 2003)](https://www.fao.org/3/y4611e/y4611e00.htm)\n\n\u25b6 [Preparation of Rift Valley fever contingency plans (FAO Manual 2002)](https://www.fao.org/3/Y4140E/Y4140E00.htm)\n\n\u25b6 [Anyamba, et al. 2009. Prediction of a Rift Valley fever outbreak. Proceedings of the National Academy of Sciences 106(3): 955-959](https://www.pnas.org/content/pnas/106/3/955.full.pdf)\n\n\u25b6 [What you need to know about Rift Valley Fever](https://youtu.be/OBAkrTZMtqY?si=zkxwmj1IO0E9DgcL)\n\n\u25b6 [Introduction to Rift Valley Fever open access](https://eufmdlearning.works/enrol/index.php?id=295)\n\n\u25b6 Pittiglio, et al. 2024. Real-time disease risk monitoring and forecasting for early action. 12th International Conference on Agro-Geoinformatics, Novi Sad, Serbia, 2024, pp. 1-5, DOI: 10.1109/Agro-Geoinformatics262780.2024.10661081\n\n\u25b6 RVF alerts\n\n \n  \n  \n\n  \n\n\n\n\n\n\n \n\n[//]: # (Comment)\n\n\n\n**Metadata Link**: [https://data.apps.fao.org/catalog/dataset/rvf-master-config](https://data.apps.fao.org/catalog//dataset/rvf-master-config) \n\n\n revised: 2025-05-06T11:29:17.988544\n","tags":"platform rift valley fever","items":[{"url":"https://data.apps.fao.org/catalog/terriajs/item/c97bf2d2-0f11-4e60-ab53-3ab8b6b8f6a5.json","type":"remote-config-group","name":"Environment","isOpen":false},{"url":"https://data.apps.fao.org/catalog/terriajs/item/d9ab6966-d318-4f01-8367-f6c1e9903718.json","type":"remote-config-group","name":"Exposure","isOpen":false},{"url":"https://data.apps.fao.org/catalog/terriajs/item/8ff9c5fa-ab88-4ab1-9572-925c706c8b52.json","type":"remote-config-group","name":"Infrastructure","isOpen":false},{"url":"https://data.apps.fao.org/catalog/terriajs/item/f8f36a05-cead-49ed-8f65-04e307743176.json","type":"remote-config-group","name":"Boundaries","isOpen":true},{"url":"https://data.apps.fao.org/catalog/terriajs/item/665f8911-0af1-49af-bbb6-77fa59deb92e.json","type":"remote-config-group","name":"Backgrounds and Foregrounds","isOpen":false},{"url":"https://data.apps.fao.org/catalog/terriajs/item/26ce446c-c0a5-467d-abd8-f272f4191471.json","type":"remote-config-group","name":"Auxillary","isOpen":false}],"preserveOrder":true,"type":"group","order":2,"id":"c9e5e226-4863-4c29-93c9-e41bbcefec3b_fa165829-913c-4c8d-97a1-9637e3d615d0_277fe13b-e32e-4143-a7e8-fbc01d293418","name":"Supplementary Data"},{"description":"\n\n\n \n  \n  \n\n  \n\n\n\n\n\n\n \n\n[//]: # (Comment)\n\n\n\n**Metadata Link**: [https://data.apps.fao.org/catalog/dataset/rvf-master-config](https://data.apps.fao.org/catalog//dataset/rvf-master-config) \n\n\n revised: 2025-06-17T18:33:30.722859\n","tags":"platform rift valley fever ","items":[{"layers":"rvf_dst_aoi_updates","lineColor":"rgb(0,0,0)","featureInfoTemplate":"<div>\n<a class=\"tjs-_buttons__btn tjs-_buttons__btn-primary tjs-_buttons__btn--small\" style=\"width:auto; 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