Entomo-Virological Aedes aegypti Surveillance Applied for Prediction of Dengue Transmission: A Spatio-Temporal Modeling Study

André de Souza Leandro, Mario J C Ayala, Renata Defante Lopes, Caroline Amaral Martins, Rafael Maciel-de-Freitas, Daniel A M Villela, André de Souza Leandro, Mario J C Ayala, Renata Defante Lopes, Caroline Amaral Martins, Rafael Maciel-de-Freitas, Daniel A M Villela

Abstract

Currently, DENV transmitted primarily by Aedes aegypti affects approximately one in three people annually. The spatio-temporal heterogeneity of vector infestation and the intensity of arbovirus transmission require surveillance capable of predicting an outbreak. In this work, we used data from 4 years of reported dengue cases and entomological indicators of adult Aedes collected from approximately 3500 traps installed in the city of Foz do Iguaçu, Brazil, to evaluate the spatial and temporal association between vector infestation and the occurrence of dengue cases. Entomological (TPI, ADI and MII) and entomo-virological (EVI) indexes were generated with the goal to provide local health managers with a transmission risk stratification that allows targeting areas for vector control activities. We observed a dynamic pattern in the evaluation; however, it was a low spatio-temporal correlation of Ae. aegypti and incidence of dengue. Independent temporal and spatial effects capture a significant portion of the signal given by human arbovirus cases. The entomo-virological index (EVI) significantly signaled risk in a few areas, whereas entomological indexes were not effective in providing dengue risk alert. Investigating the variation of biotic and abiotic factors between areas with and without correlation should provide more information about the local epidemiology of dengue.

Keywords: Zika; arbovirus; chikungunya; dengue; disease transmission; entomological surveillance; epidemiology; vector control; vectorial capacity.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Dengue cases per week in Foz do Iguaçu from 2017 to 2021.
Figure 2
Figure 2
Temporal random effect (A), and posterior probability of dengue risk from spatial random effect (B).
Figure 3
Figure 3
Posterior probability of dengue risk associated to infestation indexes (MII, ADI and TPI), and positive arboviral test (EVI) without time lag.
Figure 4
Figure 4
Posterior probability of dengue risk associated to infestation indexes (MII, ADI and TPI), and positive arboviral test (EVI) with one-week lag.
Figure 5
Figure 5
Posterior probability of dengue risk associated to infestation indexes (MII, ADI and TPI), and positive arboviral test (EVI) with two-week lag.
Figure 6
Figure 6
Posterior probability of dengue risk associated to infestation indexes (MII, ADI and TPI), and positive arboviral test (EVI) with three-week lag.
Figure 7
Figure 7
Posterior probability of dengue risk associated to infestation indexes (MII, ADI and TPI), and positive arboviral test (EVI) with four-week lag.

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