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Inference of effective reproduction number dynamics from wastewater data in small populations

Abstract Number:

3356 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Isaac Goldstein (1), Volodymyr Minin (2)

Institutions:

(1) University of California, Irvine, N/A, (2) University of California-Irvine, N/A

Co-Author:

Volodymyr Minin  
University of California-Irvine

First Author:

Isaac Goldstein  
University of California, Irvine

Presenting Author:

Isaac Goldstein  
University of California, Irvine

Abstract Text:

The effective reproduction number is an important descriptor of an infectious disease epidemic. In small populations, ideally we would estimate the effective reproduction number using a Markov Jump Process (MJP) model of the spread of infectious disease, but in practice this is computationally challenging. We propose a computationally tractable approximation to an MJP which tracks only latent and infectious individuals, the EI model, an MJP where the time-varying immigration rate into the E compartment is equal to the product of the proportion of susceptibles in the population and the transmission rate. We use an analogue of the central limit theorem for MJPs to approximate transition densities as normal, which makes Bayesian computation tractable. Using simulated pathogen RNA concentrations collected from wastewater data, we demonstrate the advantages of our stochastic model against deterministic counterparts for the purpose of estimating effective reproduction number dynamics. We apply our new model to estimating the effective reproduction number of SARS-CoV-2 in several college campus communities.

Keywords:

Bayesian Statistics|Infectious Disease Statistics|Stochastic Processes| Nowcasting| Epidemic Modeling| Infectious Disease Surveillance

Sponsors:

Biometrics Section

Tracks:

Longitudinal/Correlated Data

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