A multi-region discrete time chain binomial model for infectious disease transmission

Conventional mathematical models of infectious diseases often overlook the spatial spread of the disease, focusing only on local transmission. However, spatial propagation of various diseases has been observed mainly due to the movement of infectious individuals from one geographical region to another. In this work, we propose a multiregion discrete-time chain binomial framework to model the dependencies between the multiple infection time series from neighboring regions. It is assumed that infection counts in each region at various time points are governed not only by local transmissions but also by interactions of individuals between spatial units. The effects of intervention strategies such as vaccination campaigns used in disease control and sociodemographic factors such as live births and population density were taken into account while modeling multiple infection time series. For estimating these multiregion chain-binomial models, an appropriate likelihood function maximization approach is proposed. A predictive likelihood based method is used to generate short- and long term forecasts of disease counts in connected spatial units. In-depth analyses of two real world datasets and simulation studies--incorporating seasonal patterns, asynchronous outbreaks across connected regions, and varying intervention strategies--are used to motivate the proposed modeling framework and demonstrate its ability to reproduce realistic outbreak dynamics.