A spatial predictive model for Malaria resurgence in central Greece integrating entomological, environmental and Social data

TL;DR

This study develops a spatial predictive model integrating various data sources to identify potential malaria hotspots in Central Greece, aiding targeted control efforts and informing policy decisions.

Contribution

The paper introduces a novel framework combining entomological, environmental, and social data to predict malaria resurgence risk in Greece.

Findings

Malaria transmission risk in Greece may be substantial.

Certain districts like seaside, lakeside, and rice fields are potential hotspots.

Maps of R0 can effectively guide control policies.

Abstract

Malaria constitutes an important cause of human mortality. After 2009 Greece experienced a resurgence of malaria. Here, we develop a modelbased framework that integrates entomological, geographical, social and environmental evidence in order to guide the mosquito control efforts and apply this framework to data from an entomological survey study conducted in Central Greece. Our results indicate that malaria transmission risk in Greece is potentially substantial. In addition, specific districts such as seaside, lakeside and rice field regions appear to represent potential malaria hotspots in Central Greece. We found that appropriate maps depicting the basic reproduction number, R0 , are useful tools for informing policy makers on the risk of malaria resurgence and can serve as a guide to inform recommendations regarding control measures.