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Towards a Semi-Automatic Early Warning System for Vector-Borne Diseases

Author

Listed:
  • Panagiotis Pergantas

    (Bioapplications Ltd., 30 Ioannou Perganta Str., 32100 Levadia, Greece)

  • Nikos E. Papanikolaou

    (Laboratory of Agricultural Zoology and Entomology, Department of Crop Science, Agricultural University of Athens, 75 Iera Odos Str., 11855 Athens, Greece
    Directorate of Plant Produce Protection, Greek Ministry of Rural Development and Food, 150 Sygrou Ave., 17671 Athens, Greece)

  • Chrisovalantis Malesios

    (Department of Agricultural Economics and Rural Development, Agricultural University of Athens, 75 Iera Odos Str., 11855 Athens, Greece)

  • Andreas Tsatsaris

    (Department of Surveying and Geoinformatics Engineering, University of West Attica, 28 Ag. Spiridonos Str., 12243 Egaleo, Athens, Greece)

  • Marios Kondakis

    (Department of Statistics, Athens University of Economics and Business, 76 Patision Str., 10434 Athens, Greece)

  • Iokasti Perganta

    (Bioapplications Ltd., 30 Ioannou Perganta Str., 32100 Levadia, Greece)

  • Yiannis Tselentis

    (Regional Public Health Laboratory, Faculty of Medicine, University of Crete, 13 Andrea Kalokerinou Str., 71500 Giofirakia, Greece)

  • Nikos Demiris

    (Department of Statistics, Athens University of Economics and Business, 76 Patision Str., 10434 Athens, Greece)

Abstract

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, R 0 , 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.

Suggested Citation

  • Panagiotis Pergantas & Nikos E. Papanikolaou & Chrisovalantis Malesios & Andreas Tsatsaris & Marios Kondakis & Iokasti Perganta & Yiannis Tselentis & Nikos Demiris, 2021. "Towards a Semi-Automatic Early Warning System for Vector-Borne Diseases," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:1823-:d:498748
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    References listed on IDEAS

    as
    1. Jan C. Semenza, 2015. "Prototype Early Warning Systems for Vector-Borne Diseases in Europe," IJERPH, MDPI, vol. 12(6), pages 1-19, June.
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