Markov switching zero-inflated space-time multinomial models for comparing multiple infectious diseases

Abstract: Despite multivariate spatio-temporal counts often containing many zeroes, zero-inflated multinomial models for space-time data have not been considered. We are interested in comparing the transmission dynamics of several co-circulating infectious diseases across space and time where some can be absent for long periods. We first assume there is a baseline disease that is well-established and always present in the region. The other diseases switch between periods of presence and absence in each area through a series of coupled Markov chains, which account for long periods of disease absence, disease interactions and disease spread from neighboring areas. Since we are mainly interested in comparing the diseases, we assume the cases of the present diseases in an area jointly follow an autoregressive multinomial model. We use the multinomial model to investigate whether there are associations between certain factors, such as temperature, and differences in the transmission intensity of the diseases. Inference is performed using efficient Bayesian Markov chain Monte Carlo methods based on jointly sampling all presence indicators. We apply the model to spatio-temporal counts of dengue, Zika and chikungunya cases in Rio de Janeiro, during the first triple epidemic.