Optimising the surveillance of Aedes aegypti in Brazil by selecting smaller representative areas within an endemic city

Author:

Leandro André de Souza12ORCID,Pires‐Vieira Lara Helena3ORCID,Lopes Renata Defante14ORCID,Rivas Açucena Veleh5ORCID,Amaral Caroline1ORCID,Silva Isaac1ORCID,Maciel‐de‐Freitas Rafael26ORCID,Chiba de Castro Wagner A.4ORCID

Affiliation:

1. Centro de Controle de Zoonoses de Foz do Iguaçu Secretaria Municipal de Saúde Foz do Iguaçu Paraná Brazil

2. Laboratório de Mosquitos Transmissores de Hematozoários Instituto Oswaldo Cruz, Fiocruz Rio de Janeiro Brazil

3. Department of Ecology University of Brasilia Distrito Federal Brazil

4. Universidade Federal da Integração Latino‐Americana, Instituto Latino‐Americano de Ciências da Vida e da Natureza Foz do Iguaçu Paraná Brazil

5. Laboratory of Clinical Analysis at Hospital Ministro Costa Cavalcanti Itaiguapy Foundation Foz do Iguaçu Paraná Brazil

6. Bernhard Nocht Institute for Tropical Medicine Hamburg Germany

Abstract

AbstractObjectivesArboviruses, such as dengue (DENV), zika (ZIKV), and chikungunya (CHIKV), constitute a growing urban public health threat. Focusing on Aedes aegypti mosquitoes, their primary vectors, is crucial for mitigation. While traditional immature‐stage mosquito surveillance has limitations, capturing adult mosquitoes through traps yields more accurate data on disease transmission. However, deploying traps presents logistical and financial challenges, demonstrating effective temporal predictions but lacking spatial accuracy. Our goal is to identify smaller representative areas within cities to enhance the early warning system for DENV outbreaks.MethodsWe created Sentinel Geographic Units (SGUs), smaller areas of 1 km2 within each stratum, larger areas, with the aim of aligning the Trap Positivity Index (TPI) and Adult Density Index (ADI) with their respective strata. We conducted a two‐step evaluation of SGUs. First, we examined the equivalence of TPI and ADI between SGUs and strata from January 2017 to July 2022. Second, we assessed the ability of SGU's TPI and ADI to predict DENV outbreaks in comparison to Foz do Iguaçu's Early‐Warning System, which forecasts outbreaks up to 4 weeks ahead. Spatial and temporal analyses were carried out, including data interpolation and model selection based on Akaike information criteria (AIC).ResultsEntomological indicators produced in small SGUs can effectively replace larger sentinel areas to access dengue outbreaks. Based on historical data, the best predictive capability is achieved 2 weeks after infestation verification. Implementing the SGU strategy with more frequent sampling can provide more precise space–time estimates and enhance dengue control.ConclusionsThe implementation of SGUs offers an efficient way to monitor mosquito populations, reducing the need for extensive resources. This approach has the potential to improve dengue transmission management and enhance the public health response in endemic cities.

Publisher

Wiley

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