Space-time clusters of dengue, chikungunya, and Zika cases in the city of Rio de Janeiro
Brazil is a dengue-endemic country where all four dengue virus serotypes circulate and cause seasonal epidemics. Recently, chikungunya and Zika viruses were also introduced. In Rio de Janeiro city, the three diseases co-circulated for the first time in 2015-2016, resulting in what is known as the ‘triple epidemic’. In this study, we identify space-time clusters of dengue, chikungunya, and Zika, to understand the dynamics and interaction between these simultaneously circulating arboviruses in a densely populated and heterogeneous city.
We conducted a spatio-temporal analysis of weekly notified cases of the three diseases in Rio de Janeiro city (July 2015 – January 2017), georeferenced by 160 neighbourhoods, using Kulldorff’s scan statistic with discrete Poisson probability models. There were 26549, 13662, and 35905 notified cases of dengue, chikungunya, and Zika, respectively. The 17 dengue clusters and 15 Zika clusters were spread all over the city, while the 14 chikungunya clusters were more concentrated in the North and Downtown areas. Zika clusters persisted over a longer period of time. The multivariate scan statistic – used to analyse the three diseases simultaneously – detected 17 clusters, nine of which included all three diseases.
This is the first study exploring space-time clustering of dengue, chikungunya, and Zika in an intraurban area. In general, the clusters did not coincide in time and space. This is probably the result of the competition between viruses for host resources, and of vector-control attitudes promoted by previous arbovirus outbreaks. The main affected area – the North region – is characterised by a combination of high population density and low human development index, highlighting the importance of targeting interventions in this area. Spatio-temporal scan statistics have the potential to direct interventions to high-risk locations in a timely manner and should be considered as part of the municipal surveillance routine as a tool to optimize prevention strategies.