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

• Published in: Tropical medicine & international health 2024-05, Vol.29 (5), p.414-423
• Main Authors: Leandro, André de Souza, Pires-Vieira, Lara Helena, Lopes, Renata Defante, Rivas, Açucena Veleh, Amaral, Caroline, Silva, Isaac, Maciel-de-Freitas, Rafael, Chiba de Castro, Wagner A
• Format: Article
• Language: English
• Subjects: Aedes aegypti; Aquatic insects; Cities; Culicidae; Dengue fever; Disease control; Disease transmission; Early warning systems; Entomology; Health risks; Health surveillance; Mosquitoes; Outbreaks; Pest outbreaks; Public health; Spatial analysis; Surveillance; Traps; Vector-borne diseases; Vectors
• ISSN: 1360-2276; 1365-3156
• DOI: 10.1111/tmi.13985

Arboviruses, 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. We created Sentinel Geographic Units (SGUs), smaller areas of 1 km 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). Entomological 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. The 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.