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*Aedes aegypti*’s image by E. A. Goeldi (1905).
Dengue, chikungunya, and Zika, also known as “arboviral diseases”, are transmitted to humans by the bite of infected Aedes mosquitoes, mainly Aedes aegypti. The presence, activity, density and vector competence of these mosquitoes depend on many factors that vary in space and time, such as climate, land use, and sociodemographic conditions. Therefore, these factors may also be associated with the spatio-temporal distribution of dengue, chikungunya, and Zika. To study these dynamics, I apply methods of spatial analysis and spatial and spatio-temporal statistical models under the Bayesian framework.
How are dengue, Zika, and chikungunya distributed within a city when they are circulating simultaneously? Rio, one of the most famous touristic destinations in Brazil, experienced a triple epidemic of these diseases between 2015 and 2016.
In Freitas et al. (2019), we used the weekly counts of reported cases by neighborhood to identify high-risk spatio-temporal clusters of each disease in separate and simultaneously using SaTScan. Interestingly, we observed that, when analysed in separate, clusters of each disease were not usually detected in the same neighborhood at the same time. However, using multivariate scan statistics we identified simultaneous clusters of the three diseases, mostly in neighborhoods with high population density and socioeconomic vulnerabilities.
Start date of dengue, Zika, and chikungunya high-risk clusters in the city of Rio de Janeiro, Brazil, 2015-2016. Freitas et al. (2019)
Then, we fitted a space-time model to study the intra-urban factors associated with the distribution of an emerging arboviral disease (Freitas et al. 2021). For this work, we used the data of the first ever chikungunya epidemic in Rio and Stan. We estimated the association with temperature using a transfer function, including an immediate effect and a memory effect that propagates in time. We observed that the temperature positive association with chikungunya cases persisted for longer in areas where the epidemic was concentrated. We also found that poorer neighborhoods were affected first and harder by the epidemic.
[Chikungunya estimated relative risk by neighborhood and total number of cases by week, Rio de Janeiro city, Brazil, 2016. Freitas et al. (2021)](https://s3-us-west-2.amazonaws.com/secure.notion-static.com/e6c5b825-e00a-4af3-9adb-9a2b2d24936c/S2_video.mp4)
Chikungunya estimated relative risk by neighborhood and total number of cases by week, Rio de Janeiro city, Brazil, 2016. Freitas et al. (2021)
Finally, in Schmidt et al. (2022), we proposed a Poisson-multinomial spatial model for simultaneous outbreaks and applied it to study the triple epidemic of dengue, Zika, and chikungunya in Rio. The proposed model allows us to infer about the relative risk of the total cases of Aedes-borne diseases, investigate the association of covariates with the relative risk of the total and with the odds of presence of one disease in comparison to the other, and to estimate the probability of presence of each disease in each neighborhood.
Soon.
Soon.