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Modelling epidemic spread in cities using public transportation as a proxy for generalized mobility trends
Omar Malik1,2, Bowen Gong3, Alaa Moussawi4
1Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA. maliko@rpi.edu.
Scientific Reports
|April 17, 2022
Summary
Public transportation data, like subway usage, can improve infectious disease modeling. This approach enhances COVID-19 spread forecasts by reflecting general mobility patterns and infection rates.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- Infectious disease spread is influenced by population mobility.
- Traditional epidemiological models often lack granular mobility data.
- Accurate forecasting requires incorporating real-world movement patterns.
Purpose of the Study:
- To develop and validate a model integrating public transportation data into infectious disease dynamics.
- To assess the utility of New York City subway data for modeling COVID-19 spread.
- To improve the accuracy of infectious disease forecasting using mobility indicators.
Main Methods:
- Utilized the SIR (Susceptible-Infected-Recovered) dynamics framework.
- Incorporated public transportation data as a proxy for overall urban mobility.
- Derived a mobility parameter to estimate the effective infection rate.
- Applied the model to COVID-19 data in New York City during 2020.
Main Results:
- Public transportation data effectively indicates broader urban mobility patterns.
- The enhanced model demonstrated improved forecasting accuracy for COVID-19 spread in NYC.
- The model successfully predicted the two major peaks of COVID-19 cases in NYC in 2020.
- Standard SIR models failed to capture the second peak observed in 2020.
Conclusions:
- Public transportation data is a valuable indicator for infectious disease modeling.
- Integrating mobility data enhances the predictive power of epidemiological models.
- This approach offers a more accurate method for forecasting disease outbreaks in urban environments.
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