{"id":79197,"date":"2026-09-16T13:20:52","date_gmt":"2026-09-16T20:20:52","guid":{"rendered":"https:\/\/in.nau.edu\/news\/?p=79197"},"modified":"2026-09-21T16:51:54","modified_gmt":"2026-09-21T23:51:54","slug":"fox-pandemic-research","status":"publish","type":"post","link":"https:\/\/in.nau.edu\/news\/fox-pandemic-research\/","title":{"rendered":"How NAU is developing better disease forecasting"},"content":{"rendered":"

Pandemics are unpredictable. An\u00a0NAU\u00a0epidemiologist is part of an international effort to make\u00a0the diseases, or at least their\u00a0dynamics,\u00a0a little less so.<\/span>\u00a0<\/span><\/p>\n

Spencer Fox<\/span><\/b>, an\u00a0assistant\u00a0professor in the\u00a0<\/span>School of Informatics, Computing, and Cyber Systems<\/span><\/a>, uses\u00a0mathematical and statistical models to\u00a0predict the behavior of illnesses like COVID-19, influenza,\u00a0Ebola\u00a0and Zika. For\u00a0more than\u00a0a decade he has been forecasting influenza dynamics in the United States.\u00a0This year, he expanded his work\u00a0internationally, spending time in South America\u00a0and India\u00a0training public health officials\u00a0to better\u00a0anticipate\u00a0seasonal epidemics and emerging threats.<\/span>\u00a0<\/span><\/p>\n

\"Participants<\/a>
Participants at a training in India discuss disease forecasting and modeling.<\/em><\/figcaption><\/figure>\n

Real-time forecasting\u00a0can be a\u00a0game\u00a0changer\u00a0in public health,\u00a0just as it is in weather.\u00a0Predictions\u00a0give\u00a0cities and regions\u00a0advance warning of increases in\u00a0infections and hospitalizations, giving officials precious time to prepare a response.\u00a0Even so, infectious disease forecasting is a young field, and researchers often struggle to accurately predict the size and timing of seasonal\u00a0diseases like\u00a0influenza.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cIf we can\u2019t reliably forecast\u00a0an\u00a0influenza\u00a0epidemic\u00a0that we see every year, how can we be ready to forecast the\u00a0course of the\u00a0next pandemic?\u201d Fox said.\u00a0<\/span>\u00a0<\/span><\/p>\n

To improve\u00a0forecasts, Fox is testing his models\u00a0in places\u00a0where forecasting has not historically happened\u2014from individual communities in the U.S. to countries\u00a0south of the equator.\u00a0The goal is to give public health officials\u00a0virus\u00a0forecasts that\u00a0can\u00a0save and improve lives\u00a0in their own communities.\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n

Forecasting\u00a0closer to home<\/span><\/b>\u00a0<\/span><\/h3>\n

One thing Fox and his colleagues saw during the COVID-19 pandemic was that national and state-level forecasts weren\u2019t specific enough. Public health officials needed local forecasts to make informed decisions. Fox saw this firsthand while working with the City of Austin, where a model developed by his team helped officials better anticipate the city\u2019s healthcare needs.<\/span><\/p>\n

\u201cLike the\u00a0weather,\u00a0disease patterns vary significantly from place to place,\u201d Fox said.\u00a0\u201cPublic health decisions are made locally,\u00a0so the forecasts need to be local too.\u201d<\/span>\u00a0<\/span><\/p>\n

In response, the CDC created the Center for Forecasting and Outbreak Analytics and later launched\u00a0<\/span>InsightNet<\/span><\/a>, a network of academic institutions and state, territorial, local, and tribal public health (STLT) agencies working together to improve and scale analytic tools such as forecasts. Fox is part of\u00a0<\/span>epiENGAGE<\/span><\/a>, a member of Insight Net, who are producing US metropolitan region forecasts tailored to the communities decision makers serve.<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

Building forecasting capacity around the world<\/span><\/b>\u00a0<\/span><\/h3>\n

Until recently, only a few countries had the resources to produce real-ime disease forecasts. Fox has been working to expand that capability by turning forecasting tools developed by his team in the U.S. into accessible tools that can be used elsewhere.\u00a0<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

\"People<\/a>
At the training at Organizaci\u00f3n Panamericana de la Salud in Chile, participants discuss disease forecasting and learn how to do it.<\/em><\/figcaption><\/figure>\n

The work\u00a0started\u00a0with a pilot program in Paraguay, where Fox and his collaborators found that models developed for U.S. influenza could be adapted to produce real-time forecasts in the Southern Hemisphere.\u00a0This year,\u00a0he\u00a0spent the summer traveling between Arizona and a few South American countries\u00a0and India\u00a0to train public health officials on forecasting respiratory diseases.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cA model that works here in the U.S may not work as well elsewhere,\u201d Fox said. \u201cTesting these tools across different countries and disease patterns pushes our models in new directions and ultimately helps us build better forecasts everywhere.\u201d<\/span>\u00a0<\/span><\/p>\n

Expanding forecasting capacity abroad also improves the broader picture of how respiratory diseases are spreading around the world. Because flu seasons occur at different times in the Northern and Southern hemispheres, forecasts from one can provide helpful clues as the other prepares for its own flu season.<\/span>\u00a0<\/span><\/p>\n

Read about Fox\u2019s work in Chile.<\/span><\/a>\u00a0<\/span><\/p>\n

Preparing for the next pandemic<\/span><\/b>\u00a0<\/span><\/h3>\n

For Fox, the local and global forecasting efforts are part of the same larger goal: improving how we respond to seasonal epidemics while building the systems and\u00a0expertise\u00a0needed for the next pandemic.<\/span>\u00a0<\/span><\/p>\n

\u201cEvery flu season gives us\u00a0an\u00a0opportunity to test\u00a0our\u00a0models, learn where they fail and make them better,\u201d Fox said. \u201cThat helps us respond better now, but it also helps us build the muscle we\u2019ll need when the next pandemic arrives.\u201d<\/span>\u00a0<\/span><\/p>\n

His lab is now working to bring those lessons together by integrating data from different diseases, locations,\u00a0scales\u00a0and sources\u00a0into a more unified forecasting system.<\/span>\u00a0<\/span><\/p>\n

\u201cThe goal I have for my lab over the next five years is to synthesize all of this data into a single forecasting framework that can make\u00a0accurate\u00a0predictions from the local to the global scale,\u201d Fox said. \u201cWe\u2019re using machine learning and artificial intelligence approaches to help connect those different scales\u00a0and\u00a0learn from data collected around the world.\u201d<\/span>\u00a0<\/span><\/p>\n

\"Northern<\/a><\/p>\n

 <\/p>\n

Heidi Toth | NAU Communications
\n(928) 523-8737 | heidi.toth@nau.edu<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Pandemics are unpredictable. An\u00a0NAU\u00a0epidemiologist is part of an international effort to make\u00a0the diseases, or at least their\u00a0dynamics,\u00a0a little less so.\u00a0 Spencer Fox, an\u00a0assistant\u00a0professor in the\u00a0School of Informatics, Computing, and Cyber Systems, uses\u00a0mathematical and statistical models to\u00a0predict the behavior of illnesses like COVID-19, influenza,\u00a0Ebola\u00a0and Zika. For\u00a0more than\u00a0a decade he has been forecasting influenza dynamics in the…<\/a><\/p>\n","protected":false},"author":2797,"featured_media":79198,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[11],"tags":[],"class_list":["post-79197","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-academics"],"acf":[],"_links":{"self":[{"href":"https:\/\/in.nau.edu\/news\/wp-json\/wp\/v2\/posts\/79197","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/in.nau.edu\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/in.nau.edu\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/in.nau.edu\/news\/wp-json\/wp\/v2\/users\/2797"}],"replies":[{"embeddable":true,"href":"https:\/\/in.nau.edu\/news\/wp-json\/wp\/v2\/comments?post=79197"}],"version-history":[{"count":3,"href":"https:\/\/in.nau.edu\/news\/wp-json\/wp\/v2\/posts\/79197\/revisions"}],"predecessor-version":[{"id":79416,"href":"https:\/\/in.nau.edu\/news\/wp-json\/wp\/v2\/posts\/79197\/revisions\/79416"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/in.nau.edu\/news\/wp-json\/wp\/v2\/media\/79198"}],"wp:attachment":[{"href":"https:\/\/in.nau.edu\/news\/wp-json\/wp\/v2\/media?parent=79197"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/in.nau.edu\/news\/wp-json\/wp\/v2\/categories?post=79197"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/in.nau.edu\/news\/wp-json\/wp\/v2\/tags?post=79197"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}