Spatial modelling of the infestation indices of Aedes aegypti: an innovative strategy for vector control actions in developing countries

Abstract

Background: Larval indices such as the house index (HI), Breteau index (BI) and container index (CI) are widely used to interpret arbovirus vector density in surveillance programmes. However, the use of such data as an alarm signal is rarely considered consciously when planning programmes. The present study aims to investigate the spatial distribution pattern of the infestation of Aedes aegypti, considering the data available in the Ae. aegypti Infestation Index Rapid Survey (LIRAa) for the city of Campina Grande, Paraíba State in Brazil. Methods:The global and local Moranʼs indices were used in spatial analysis to measure the effects of spatial dependencies between neighbourhoods, using secondary data related to HI and BI gathered from surveillance service.Results: Our analysis shows that there is a predominance of high rates of mosquito infestation, placing Campina Grande at a near-constant risk of arbovirus outbreaks and epidemics. A highly significant Moranʼs index value (P < 0.001) was observed, indicating a positive spatial dependency between the neighbourhoods in Campina Grande. Using the Moran mapping and LISA mapping, the autocorrelation patterns of Ae. aegypti infestation rates among neighbourhoods have revealed hotpots that should be considered a priority to preventive actions of the entomological surveillance services. Predominance of high infestation rates and clearer relationships of these between neighbourhoods were observed between the months of May and July, the period with the highest rainfall in the city. Conclusions:This analysis is an innovative strategy capable of providing detailed information on infestation locations to the relevant public health authorities, which will enable a more efficient allocation of resources, particularly for arbovirus prevention.

Larval Indices of Vector Mosquitoes as Predictors of Dengue Epidemics: An Approach to Manage Dengue Outbreaks Based on Entomological Parameters in the Districts of Colombo and Kandy, Sri Lanka

“…Interestingly, the Aedes vector density is clearly influenced by most of these factors such as meteorological parameters, land use, meteorological factors, and practices among the local communities, etc. [ 17 , 48 , 52 ]. The contrasting variations in environmental conditions (both meteorological and land use) practices among the local communities and degree of urbanization among the studied MOH areas could be the reason behind the spatial discriminations in both vector densities and thereby threshold levels [ 17 , 52 – 54 ].…”

“…In light of this, thresholds defined in this study would be helpful for adopting and directing vector control activities, with optimum utilization of limited human and financial resources to control dengue epidemics [48,52]. Therefore, VCE in relevant study areas are recommended to apply the above-developed thresholds in epidemic prediction and management, while evaluating the practical feasibility of the thresholds, subjecting to further modifications and adjustments.…”

Identification of high-risk and low-risk clusters and estimation of the relative risk of acute lymphoblastic leukemia in provinces of Iran during 2006–2014 period: A geo-epidemiological study

“…Futhermore, values near zero suggests that the spatial distribution of the phenomenon under study is random. [ 15 ] Finally, Kuldorff's purely spatial scan statistic was used to identify the exact location of high-risk and low-risk clusters of ALL disease in Iran.…”

MEWAR: Development of a Cross-Platform Mobile Application and Web Dashboard System for Real-Time Mosquito Surveillance in Northeast Brazil

“…It is considered the industrial center of Northeast Brazil and the main technology center in South America. The estimated population in 2021 is 413,830, making it the second-most populous city of Paraíba ( 15 ). Since 1995, the state of Pernambuco has been endemic for mosquito-borne disease (dengue virus).…”

“…The co-circulating of these viruses poses a significant challenge to health authorities ( 16 ). Climatic conditions (temperature, humidity, rainfall level, etc.,) in Northeast Brazil, including, Recife and Campina Grande, are ideal for mosquito living, making them suitable for mosquitoes abundance, thus, optimal sites for our study objective [i.e., mosquito surveillance; ( 14 , 15 , 17 )].…”