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

Music City Center 

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.