Abstract Number:
3159
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Alexander Kagan (1), Jonathan Terhorst (2)
Institutions:
(1) N/A, N/A, (2) University of Michigan, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
The Susceptible-Infected-Removed (SIR) model stands as one of the most famous mathematical models describing the propagation of viruses within a population. A notable drawback of the SIR model is its unrealistic assumption that an infected individual transmits the virus to any susceptible person with equal probability. For instance, the transmission probability of COVID-19 is likely higher among individuals within a specific city or state than between different states. In response to this limitation, we present the Community SIR (CSIR) model, which addresses this issue by allowing heterogeneous probabilities of transmission within pre-specified groups of people. To facilitate parameter estimation for the CSIR model, we propose a computationally efficient approach through the linearization of the resulting ODE system, as well as a more expensive but more accurate likelihood optimization approach. Application of the CSIR model to COVID-19 case data across US counties reveals spatial correlations between neighboring counties. This integration of community structure results in a higher accuracy of COVID-19 spread forecasting compared to existing SIR-type models.
Keywords:
SIR model|COVID-19|Networks | | |
Sponsors:
Section on Statistics in Epidemiology
Tracks:
Disease Prediction
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