Alexander Travis
Cornell University, NY·Public Health & Epidemiology
Weather and seasons strongly shape infectious disease. Rainfall drives malaria risk for billions of people.
- Fields
Public Health & Epidemiology
Microbiology & Immunology
Ecology & Evolution
- Topics
infectious disease
epidemiology
predictive modeling
- Grants
- 2 known grants, through Aug 2027
What they work on
- Test whether community-engaged research that combines weather, land-use, human and animal health, behavior and pathogen genomic data can produce predictive epidemiological models that forecast infectious disease outbreaks.
- Test whether weather extremes and land-use change stress wildlife, increasing viral shedding and contact with humans and so driving spillover of paramyxoviruses, coronaviruses and filoviruses.
- Build a community-based early warning system for weather-sensitive mosquito-borne viruses and tick-borne pathogens such as Anaplasma, Rickettsia and Coxiella, using remote sensing and genomic analysis of selection hotspots.
- Run pilot and Rapid Response Fund grants favoring early-stage investigators, plus a cross-disciplinary training program for graduate students, postdocs and junior faculty in predictive infectious disease research.
More about the research
Extreme weather is increasing viral spillover from animals to humans and the spread of diseases carried by mosquitoes and ticks. These are known as vector-borne diseases, or VBD. The Center for Transformative Infectious Disease Research (CTIDR) at Cornell, led by this PI, aims to shift public health from reacting to outbreaks toward predicting and preventing them. Its central hypothesis is that research done together with communities can feed predictive epidemiological models, meaning statistical models of when and where disease will appear. That research would combine data on human, wildlife and vector behavior, weather, land use, human and animal health, and the genomes of vectors and pathogens. The models could then guide the design and testing of preventive interventions.
Why it matters
Extreme weather is raising the risk of new viral spillovers and the spread of tick- and mosquito-borne diseases. Models that combine social, biological and weather data could warn when and where outbreaks are likely. Communities and public health agencies could then act before disease spreads. The focus is on vulnerable rural communities most at risk.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Alistair Hayden
Cornell University, NY·Public Health & Epidemiology
Test whether the living-evidence model of systematic review — continuously updated rather than one-time literature synthesis — makes infectious disease research products more applicable to real decisions.
Vanessa Rivera-Amill
Ponce Health Sciences University, PR·Public Health & Epidemiology
Strengthen surveillance and research capacity for acute febrile illnesses in Puerto Rico to improve detection of dengue, chikungunya, Zika and Oropouche, by combining clinical, community-based and laboratory data through the existing SEDSS and COPA platforms.
Patrick Stephens
Oklahoma State University, OK·Ecology & Evolution
Identify environmental factors (weather, air quality) and socioeconomic factors (physician numbers, vaccine willingness) that explain why Oklahoma counties and metro areas had more or fewer COVID-19 cases than their population size predicted.
The school
Cornell University: 37 labs on PI Pages, $1.6B a year on research, 537 research doctorates a year.
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