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The research also identifies the countries most vulnerable to outbreaks \u2013 and the least equipped to detect and contain them.<\/p>\n
What this disease risk map reveals<\/p>\n
Using machine learning and satellite data, researchers mapped<\/a> epidemic-prone diseases across nearly every country on Earth.<\/p>\n The work was led by Angela Fanelli, a veterinary epidemiologist at the European Commission\u2019s Joint Research Centre (JRC<\/a>). <\/p>\n Her research focuses on how human-driven environmental change shapes epidemic risk and strains countries\u2019 abilities to respond effectively to crises.<\/p>\n The model showed that 6.3 percent of global land area falls into the high-risk category, with another 3 percent classified as very high risk.<\/p>\n Roughly 20 percent of people live in medium risk areas, while 3 percent inhabit zones of high or very high-risk.<\/p>\n Zoonotic threats and disease risk<\/p>\n Most of the diseases in the map are zoonotic \u2013 diseases that move from animals to people through infection and contact.<\/p>\n One overview<\/a> estimated that around three quarters of emerging infections in humans, worldwide, start in other animals.<\/p>\n When people expand settlements into forests or wildlife markets, that crowding raises spillover, the moment that a virus<\/a> crosses into humans.<\/p>\n All of the WHO priority diseases in this map sit on a short list of carefully monitored dangerous zoonotic threats.<\/p>\n Climate patterns and outbreaks<\/p>\n Warming air and water change where animals, insects, and viruses can thrive \u2013 which reshapes the basic geography of infectious disease.<\/p>\n The new modeling suggests that higher temperatures, heavier rainfall, and deeper droughts all push the outbreak risk upward.<\/p>\n Recent research<\/a> links climate change with altered migration routes, breeding cycles, and habitats that bring wildlife closer to people.<\/p>\n Longer warm seasons let disease-carrying mosquitoes and ticks survive in new places, pushing previously tropical infections into higher latitudes.<\/p>\n Human land use stacks the deck<\/p>\n Clearing forests for farms, roads, and mines moves people into closer contact with wildlife that can host new viruses and pathogens.<\/p>\n Packing many people and animals into dense settlements and industrial farms boosts opportunities for viruses<\/a> to jump and spread quickly.<\/p>\n In addition, losing biodiversity, the variety of plant and animal life in an ecosystem, can sometimes favor species that carry dangerous pathogens.<\/p>\n In the new work, population density emerged as the single strongest driver of outbreak risk, outweighing any individual environmental factor.<\/p>\n To move from raw hazard to real-world danger, the researchers built an epidemic risk index that combines outbreak likelihood with response capacity.<\/p>\n Countries like Papua New Guinea and the Republic of Congo sit on top, facing intense risk but limited health infrastructure.<\/p>\n This index does not assign blame. It reveals where clinics, laboratories, and trained staff may be overwhelmed by a fast-moving outbreak.<\/p>\n In contrast, many high-income countries show low outbreak risk yet strong capacity, making them potential hubs for laboratory support and vaccine manufacturing.<\/p>\n What high disease risk means <\/p>\n Even if you live far from a highlighted hotspot, the timing and location of outbreaks shape travel patterns, trade, and supply chains.<\/p>\n Modern air travel can move an infected person between continents in hours, but the strength of health systems determines what happens next.<\/p>\n Large populations live in places where the model shows moderate risk, meaning local health services could still be strained during a severe outbreak.<\/p>\n Preparedness does not only relate to the number of hospital beds available locally. It also includes early disease detection, clear communication, and reliable access to vaccines.<\/p>\n Using predictive models before crises hit<\/p>\n Computer models that learn from past outbreaks can flag places where dangerous viruses<\/a> are likely to appear before doctors see cases.<\/p>\n In this study, algorithms combined satellite imagery, climate records, land use maps, and past infections to estimate where future outbreaks might begin.<\/p>\n Another study<\/a> ranked virus families by pandemic potential to help governments decide which vaccines to develop in advance more efficiently.<\/p>\n Tools like these cannot tell us exactly which virus will emerge, but they narrow the possibilities and guide targeted surveillance and stockpiles.<\/p>\n Preparing for Disease X<\/p>\n The WHO<\/a> keeps its short priority list under constant revision, adding unknown threats under the placeholder name Disease X, to encourage flexible preparedness.<\/p>\n In Europe, the Health Emergency Preparedness and Response Authority focuses on climate sensitive threats like Ebola, Zika, and Crimean Congo hemorrhagic fever.<\/p>\n Global cooperation on surveillance data, vaccine platforms, and rapid financing means that a cluster in one country can spark protective action elsewhere.<\/p>\n This map that no one wants to see works as a warning and planning tool. It shows where support must arrive before outbreaks become established.<\/p>\n The research was published in the journal Science Advances<\/a>.<\/p>\n \u2014\u2013<\/p>\n Like what you read? Subscribe to our newsletter<\/a> for engaging articles, exclusive content, and the latest updates.<\/p>\n Check us out on EarthSnap<\/a>, a free app brought to you by Eric Ralls<\/a> and Earth.com.<\/p>\n \u2014\u2013<\/p>\n","protected":false},"excerpt":{"rendered":"New global modeling shows that about 9.3% of the world\u2019s land area is highly vulnerable to the risk…\n","protected":false},"author":2,"featured_media":608028,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[34],"tags":[97],"class_list":{"0":"post-608027","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-health","8":"tag-health"},"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/608027","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/comments?post=608027"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/608027\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media\/608028"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media?parent=608027"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/categories?post=608027"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/tags?post=608027"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"Risk](\"https:\/\/www.newsbeep.com\/us\/wp-content\/uploads\/2026\/04\/who-global-disease-outbreak-risk-map_ScienceAdvances_1s.webp.webp\")
<\/a>Risk of the WHO priority diseases, bias adjusted. The map displays the risk levels across different regions, with white areas indicating insufficient data for one or more predictor layers. Credit: Science Advances. Click image to enlarge.Who is ready and who is not<\/p>\n