This repository includes the codes used for the study: "Spatio-temporal modelling of the first Chikungunya epidemic in an intra-urban setting: the role of socioeconomic status, environment and temperature", by Laís Picinini Freitas, Alexandra M. Schmidt, William Cossich, Oswaldo Gonçalves Cruz, and Marilia Sá Carvalho.
Publication at PLOS NTDs: https://doi.org/10.1371/journal.pntd.0009537
Three key elements are the drivers of Aedes-borne disease: mosquito infestation, virus circulating, and susceptible human population. However, information on these aspects are not easily available in low- and middle-income countries. We analysed data on factors that influence one or more of those elements to study the first chikungunya epidemic in Rio de Janeiro city in 2016. Using spatio-temporal models, under the Bayesian framework, we estimated the association of those factors with chikungunya notified cases by neighbourhood and week. To estimate the minimum temperature effect in a non-linear fashion, we used a transfer function considering an instantaneous effect and propagation of a proportion of such effect to future times. The sociodevelopment index and the proportion of green areas were included in the model with time-varying coefficients, allowing us to explore how their associations change throughout the epidemic. There were 13627 chikungunya cases in the study period. The sociodevelopment index presented the strongest association, inversely related with the risk of cases. Such association was more pronounced in the first weeks, indicating that socioeconomically vulnerable neighbourhoods were affected first and hardest by the epidemic. The proportion of green areas effect was null for most weeks. The temperature was directly associated with the risk of chikungunya for most neighbourhoods, with different decaying patterns. The temperature effect persisted longer where the epidemic was concentrated. In such locations, interventions should be designed to be continuous and to work in the long term. We observed that the role of the covariates change over time. Therefore, time-varying coefficients should be widely incorporated when modelling Aedes-borne diseases. Our model contributed to the understanding of the spatio-temporal dynamics of an urban Aedes-borne disease introduction in a tropical metropolitan city.