InForMID
Tufts Initiative for the Forecasting and Modeling of Infectious Diseases
Tufts Initiative for the Forecasting and Modeling of Infectious Diseases

Ryan Simpson | MS, PhD ’22

CV | Email | LinkedIn | Google Scholar | ResearchGate | ORCID

Education
• PhD Candidate, 2022, Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University
• MS, 2019, Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University
• BA, 2017, Global Affairs: International Development, Yale University
• BA, 2017, Environmental Engineering: Urban Agriculture, Yale University

Research Interests
• Designing analytical workflows to inform data’s end-to-end usage from extraction to visualizations; transforming data into actionable recommendations
• Creating and evaluating data quality, usability, and completeness metrics – assessing their effect on statistical models/forecasts
• Applying time series methods to describe, explain, and predict periodic and aperiodic infectious disease trends and outbreak signatures

Research Highlights

Zhou B, Liang S, Monahan KM, Singh GM, Simpson RB, Reedy J, Zhang J, DeVane A, Cruz MS, Marshak A, Mozaffarian D, Wang D, Semenova I, Roura IM, Prozorovscaia D, Naumova EN. Food and nutrition systems dashboards: A systematic review. Adv Nutr. (2022) https://doi.org/10.1093/advances/nmac022

Simpson RB, Kulinkina AV, Naumova EN. Investigating seasonal patterns in enteric infections: a systematic review of time series methods. Epidemiol Infect 150, e50, 1-25 (2022) https://doi.org/10.1017/S0950268822000243

Simpson RB, Zhou B, Alarcon Falconi TM, Naumova EN. An analecta of visualizations for foodborne illness trends and seasonality. Sci Data 7, 346 (2020) https://doi.org/10.1038/s41597-020-00677-x

Simpson RB, Zhou B, Naumova EN. Seasonal synchronization of foodborne outbreaks in the United States, 1996–2017. Sci Rep 10, 17500 (2020) https://doi.org/10.1038/s41598-020-74435-9

Simpson RB, Alarcon Falconi TM, Venkat A, Chui KHH, Navidad J, Naumov YN, Gorski J, Bhattacharyya S, Naumova EN. Incorporating calendar effects to predict influenza seasonality in Milwaukee, Wisconsin. Epidemiol Infect 147, E268 (2019) https://doi.org/10.1017/S0950268819001511

Simpson RB, Lauren BN, Schipper KH, McCann JC, Tarnas MC, Naumova EN. Critical periods, critical time points and day-of-the-week effects in COVID-19 surveillance data: an example in Middlesex County, Massachusetts, USA. Int J Environ Res Public Health 19, 1321 (2022) https://doi.org/10.3390/ijerph19031321

Zhang Y, Simpson RB, Sallade LE, Sanchez E, Monahan KM, Naumova EN. Evaluating completeness of foodborne outbreak reporting in the United States, 1998-2019. Int J Environ Res Public Health 19, 2898 (2022) https://doi.org/10.3390/ijerph19052898

Simpson RB, Gottlieb J, Zhou B, Hartwick MA, Naumova EN. Completeness of open access FluNet influenza surveillance data for Pan-America in 2005-2019. Sci Rep 11, 795 (2021) https://doi.org/10.1038/s41598-020-80842-9

Simpson RB, Babool S, Tarnas MC, Kaminski PM, Hartwick MA, Naumova EN. Signatures of cholera outbreak during the Yemeni Civil War, 2016-2019. Int J Environ Res Public Health 19, 378 (2022) https://doi.org/10.3390/ijerph19010378

Simpson RB, Babool S, Tarnas MC, Kaminski PM, Hartwick MA, Naumova EN. Dynamic mapping of cholera spread and conflict severity during the Yemeni Civil War, 2016-2019. J Public Health Policy. Accepted.

Sanchez E, Gelfand AR, Perkins MD, Tarnas MC, Simpson RB, McGee JA, Naumova EN. Providing food and nutrition services during the COVID-19 surge at the Javits New York Medical Station. Int. J. Environ. Res. Public Health 18, 7430 (2021) https://doi.org/10.3390/ijerph18147430

Taylor S, Korpusik M, Das S, Gilhooly C, Simpson RB, Glass J, Roberts S. Use of Natural Spoken Language with automated mapping of self-reported food intake to food composition data for low-burden real-time dietary assessment: Method comparison study. J Med Internet Res 23, e26988 (2021) https://doi.org/10.2196/26988