The burden of arboviruses in the Americas is high and may result in long-term sequelae with infants disabled by Zika virus infection (ZIKV) and arthritis caused by infection with Chikungunya virus ...(CHIKV). We aimed to identify environmental drivers of arbovirus epidemics to predict where the next epidemics will occur and prioritize municipalities for vector control and eventual vaccination. We screened sera and urine samples (n = 10,459) from residents of 48 municipalities in the state of Rio de Janeiro for CHIKV, dengue virus (DENV), and ZIKV by molecular PCR diagnostics. Further, we assessed the spatial pattern of arbovirus incidence at the municipal and neighborhood scales and the timing of epidemics and major rainfall events. Lab-confirmed cases included 1,717 infections with ZIKV (43.8%) and 2,170 with CHIKV (55.4%) and only 29 (<1%) with DENV. ZIKV incidence was greater in neighborhoods with little access to municipal water infrastructure (r = -0.47, p = 1.2x10-8). CHIKV incidence was weakly correlated with urbanization (r = 0.2, p = 0.02). Rains began in October 2015 and were followed one month later by the largest wave of ZIKV epidemic. ZIKV cases markedly declined in February 2016, which coincided with the start of a CHIKV outbreak. Rainfall predicted ZIKV and CHIKV with a lead time of 3 weeks each time. The association between rainfall and epidemics reflects vector ecology as the larval stages of Aedes aegypti require pools of water to develop. The temporal dynamics of ZIKV and CHIKV may be explained by the shorter incubation period of the viruses in the mosquito vector; 2 days for CHIKV versus 10 days for ZIKV.
Abstract
Background
The objective of the present study was to identify drivers of the ZIV epidemic in the state of Rio de Janeiro to predict where the next hotspots will occur and prioritize areas ...for vector control and eventual vaccination once available.
Methods
To assess climatic and socio-economic drivers of arbovirus epidemics, we mapped rainfall, temperature, and sanitation infrastructure in the municipalities where individuals with laboratory confirmed cases of arboviral infection resided using our spatial pattern risk model.
Results
From March 2015 to May 2016, 3,916 participants from 58 municipalities in the state of Rio de Janeiro were tested for dengue, Chikungunya (CHKV), and ZIKV by RT-PCR and enzyme immunoassays. During the same period, 69,256 suspected cases of dengue, CHKV, and ZIKV were reported to the Rio Health Department, including 23,983 of dengue, 44,572 of ZIKV, and 701 of CHKV. Laboratory confirmed cases included 29 cases (0.7%) of dengue, 1,717 of ZIKV (43.8%), and 2,170 of CHKV (55.4%). Rains in Rio began in October 2015 and were followed one month later by the largest wave of the ZIKV epidemic (Figure 1). ZIKV cases markedly declined in February 2016, which coincided with the start of a CHKV outbreak. Rainfall predicted ZIKV and CHKV in Rio with a lead-time of 3 weeks each time. Social and environmental variables predicted the number of cases. The temporal dynamics of ZIKV and CHKV in Rio de Janeiro are explained by the shorter incubation period of the viruses in the mosquito vector; 2 days for CHKV vs 10 days for ZIKV.
Conclusion
The association between rainfall and ZIKV reflects vector ecology, as the larval stages of Aedes aegypti require pools of water to develop. Rainfall in October 2015 would have produced such pools resulting in increased mosquito abundance likely contributing to the ZIKV epidemic in humans the following month. The decrease in ZIKV in February 2016 and the increase in CHKV likely arose due to within-vector competition. The Pan American Health Organization’s ZIKV Strategic Plan states that controlling arboviruses requires mapping their social and environmental drivers. Our findings contribute to such control efforts.
Figure 1.
Lab-confirmed cases of ZIKV and CHKV per week in the state of Rio de Janeiro, March 2015 to May 2016.
Disclosures
All authors: No reported disclosures.
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