A spatial predictive model for malaria resurgence in central Greece integrating entomological, environmental and social data

Malaria constitutes an important cause of human mortality. After 2009 Greece experienced a resurgence of malaria. Here, we develop a model-based 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

Towards a semi-automatic early warning system for vector-borne diseases

The emergence and spread of vector-borne diseases (VBDs) is a function of biotic, abiotic and socio-economic drivers of disease while their economic and societal burden depends upon a number of time-varying factors. This work is concerned with the development of an early warning system that can act as a predictive tool for public health preparedness and response. We employ a host-vector model that combines entomological (mosquito data), social (immigration rate, demographic data), environmental (temperature) and geographical data (risk areas). The output consists of appropriate maps depicting suitable risk measures such as the basic reproduction number, R0, and the probability of getting infected by the disease. These tools consist of the backbone of a semi-automatic early warning system tool which can potentially aid the monitoring and control of VBDs in different settings. In addition, it can be used for optimizing the cost-effectiveness of distinct control measures and the integration of open geospatial and climatological data. The R code used to generate the risk indicators and the corresponding spatial maps along with the data is made available

Total numbers of malaria confirmed cases in Greece (period: 2009–2015).

<p>Total numbers of malaria confirmed cases in Greece (period: 2009–2015).</p

Spatial distribution of risk in malaria resurgence in central Greece as indicated by the median <i>R</i>₀ estimates (Year 2013).

<p>Spatial distribution of risk in malaria resurgence in central Greece as indicated by the median <i>R</i>₀ estimates (Year 2013).</p

Indicative examples of calculation and utilization of average temperature data with IDW interpolation method.

<p>(a) Average temperatures (July 2012) b) Average temperatures (August 2012).</p