Título: A Poisson-multinomial spatial model for the number of cases of vector-borne diseases in Rio de Janeiro, Brazil
Palestrante: Alexandra M. Schmidt (McGill University)
A palestra ocorrerá remotamente, via Google Meets. Segue o link para o acesso a sala: meet.google.com/ruv-ruxx-ehg . A sala será aberta sempre 10 minutos antes do início de cada sessão.
Resumo: Dengue-fever, zika, and chikungunya are arboviral infection diseases transmitted by two vectors: Aedes aegypti and Aedes albopictus. During April 2016, the city of Rio de Janeiro experienced the peak of the first joint epidemic of the three diseases. As these diseases are transmitted by the same vectors, and the notified cases are either confirmed by laboratory exam or clinical-epidemiological criteria we propose a model that allows for uncertainty in the allocation of the number of cases per disease per borough. We propose a Poisson model for the total number of cases of arboviral infection diseases and, conditioned on the total number of cases, we assume a multinomial model for the number of cases of the three diseases.
We discuss different parametrizations of the log-relative risk of the total number of cases and the parameters of the multinomial distribution. We have available the number of cases across the n = 160 boroughs of the city, the percentage of green area of the borough, a social-economic index and the population density. Inference is performed under the Bayesian framework. Our analysis suggests that as the percentage of green area increases the relative risk for the total number of cases decreases. The odds of a borough having chikungunya instead of dengue decreases as the social index increases, whereas the odds of having zika instead of chikungunya increases with the social index. The odds ratio of zika or chikungunya with respect to dengue fever is not affected by the percentage of the green area of the borough. This is joint work with Laís P. Freitas, Marília S. Carvalho, and Oswaldo Cruz (Oswaldo Cruz Foundation, Brazil).