Multivariable Behavioral Change Modeling of Epidemics in the Presence of Undetected Infections

Music City Center 

Epidemic models are invaluable tools to understand and implement strategies to control the spread of infectious diseases, as well as to inform public health policies and resource allocation. However, current modeling approaches have limitations that reduce their practical utility, such as the exclusion of human behavioral change in response to the epidemic or oversimplifying the disease transmission process. These limitations became particularly evident during the COVID-19 pandemic, underscoring the need for more accurate and informative models. Motivated by these challenges, we develop a novel Bayesian epidemic modeling framework to better capture the complexities of disease spread by incorporating behavioral responses. In particular, our framework makes two contributions: 1) leveraging additional data on hospitalizations and deaths in modeling the disease dynamics and 2) allowing the population behavioral change to be dynamically influenced by multiple data sources (cases and deaths). Given the large presence of asymptomatic infections and lack of testing during the COVID-19 pandemic, we also evaluate incorporating data uncertainty from undetected infections. We thoroughly investigate the properties of the proposed model via simulation and illustrate its utility on COVID-19 data from Montréal and Miami.

SIR

SIHRD

Bayesian

Compartmental model

Infectious disease