Applying Remotely Sensed Environmental Information to Model Mosquito Populations

Abstract

Vector borne diseases have been related to various environmental parameters and environmental changes like climate change, which impact their propagation in time and space. Remote sensing data have been used widely for monitoring environmental conditions and changes. We hypothesized that changes in various environmental parameters may be reflected in changes in mosquito population size, thus impacting the temporal and spatial patterns of vector diseases. The aim of this study is to analyze the effect of environmental variables on mosquito populations using the remotely sensed Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) obtained from Landsat 8, along with other factors, such as altitude and water covered areas surrounding the examined locations. Therefore, a Multilayer Perceptron (MLP) Artificial Neural Network (ANN) model was developed and tested for its ability to predict mosquito populations. The model was applied in NE Greece using mosquito population data from 17 locations where mosquito traps were placed from June to October 2019. All performance metrics indicated a high predictive ability of the model. LST was proved to be the factor with the highest relative importance in the prediction of mosquito populations, whereas the developed model can predict mosquito populations 13 days ahead to allow a substantial window for appropriate control measures.

Advancing vector-borne disease prediction through functional classifier integration: A novel approach for enhanced modeling

“…In the context of vector-borne diseases, the MLP model processes input data related to environmental conditions, demographic factors, and historical disease occurrences through the input layer. The hidden layers, characterized by nodes employing weighted connections and activation functions, enable the network to discern intricate patterns and non-linear relationships within the data (Javaid et al 2023;Kofidou et al 2021). The output layer provides predictions or classifications, such as the likelihood of vectorborne disease occurrence in a specific region or population.…”

Assessment of environmental and spatial factors influencing the establishment of Anopheles gambiae larval habitats in the malaria endemic province of Woleu-Ntem, northern Gabon

“…At the national level, this technology has enabled the identification of regions that are conducive to the formation of larval habitats, including wetlands, mangroves, swamps, rice fields and temporary water bodies [17,18]. In addition, remote sensing tools are being used to monitor environmental changes that may influence the distribution of mosquito larval habitats [19][20][21][22], thereby facilitating the efficient allocation of resources by health authorities in their vector control efforts. Despite its recognized role in the fight against malaria, this approach remains under-utilized by Central African countries.…”