Amy Wesolowski
Johns Hopkins University, MD·Public Health & Epidemiology
Infectious diseases travel with people. Where and when outbreaks spread depends on how people move, yet scientists still do not know the best way to include human movement in disease models.
- Fields
Public Health & Epidemiology
Mathematics & Statistics
Computer Science
- Topics
human mobility
infectious disease modeling
mathematical epidemiology
- Grants
- 1 known grant, through Mar 2027
What they work on
- Collect and standardize many human mobility datasets gathered by different methods, and statistically describe how movement varies at individual, household, community and larger scales.
- Develop several candidate mobility models and incorporate them into commonly used infectious disease transmission models.
- Test which mobility models best reproduce observed contact patterns and pathogen genetic sequences from outbreaks of dengue, Zika, Ebola and COVID-19, using simulations and historic outbreak data.
- Identify conditions under which generalized mobility models or data from other locations give misleading predictions of disease spread.
More about the research
Many models used for influenza, COVID-19, malaria and other diseases already include travel, but different approaches have rarely been compared or checked against real outbreaks. Mobility data are also scarce, so patterns from one place or time are often applied elsewhere, which may work for some pathogens and fail badly for others. Each pathogen's life cycle, transmission route and age pattern of infection may make different kinds of movement matter.
Why it matters
Health agencies use disease models to plan outbreak responses and judge whether interventions are working. Knowing which human mobility data and models give reliable answers for which diseases could make forecasts for outbreaks such as dengue, Zika, Ebola and COVID-19 more trustworthy. It could also help responders avoid decisions based on misleading mobility assumptions.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Olivia Feldman
University of Tennessee-Knoxville, TN·Mathematics & Statistics·Early-career
Develop improved, tractable likelihood models that capture the correlated, complex structure of epidemic data, for more accurate uncertainty estimates in mechanistic disease models.
Alun Lloyd
North Carolina State University, NC·Mathematics & Statistics
Measure how residents of Ibagué, Colombia differ in adopting mosquito-prevention behaviors around the home, such as water container management, and identify the cues that drive them, using observational and experimental social science methods.
Fuqing Wu
University of Texas Health Science Center at Houston, TX·Public Health & Epidemiology
Build models that connect how virus levels change inside one infected person with how the virus passes between people, so that SARS-CoV-2 levels in wastewater can be read as measures of community transmission.
The school
Johns Hopkins University: 100 labs on PI Pages, $4.1B a year on research, 622 research doctorates a year.
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