Human West Nile Meningo-Encephalitis in a Highly Endemic Country: A Complex Epidemiological Analysis on Biotic and Abiotic Risk Factors

Mircea Coroian, Mina Petrić, Adriana Pistol, Anca Sirbu, Cristian Domșa and Andrei Daniel Mihalca
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Mircea Coroian: Department of Parasitology and Parasitic Diseases, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, 400372 Cluj-Napoca, Romania
Mina Petrić: Department of Physics, Faculty of Sciences, Ghent University, B-9000 Ghent, Belgium
Adriana Pistol: National Centre for Communicable Diseases Surveillance and Control, National Institute of Public Health, 050463 Bucharest, Romania
Anca Sirbu: National Centre for Communicable Diseases Surveillance and Control, National Institute of Public Health, 050463 Bucharest, Romania
Cristian Domșa: Department of Parasitology and Parasitic Diseases, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, 400372 Cluj-Napoca, Romania
Andrei Daniel Mihalca: Department of Parasitology and Parasitic Diseases, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, 400372 Cluj-Napoca, Romania

IJERPH, 2020, vol. 17, issue 21, 1-15

Abstract: West Nile virus (WNV) is one of the most prevalent mosquito-borne viruses. Although the infection in humans is mostly asymptomatic, 15–20% of cases show flu-like symptoms with fever. In 1% of infections, humans develop severe nervous symptoms and even die, a condition known as West Nile neuroinvasive disease (WNND). The aim of our study was to analyze the influence of abiotic and biotic factors with the human WNND cases during the period 2015–2019. A database containing all the localities in Romania was developed. Abiotic and biotic predictors were included for each locality: geographic variables, climatic data, and biotic factors. Spatial distribution of the WNND infections was analyzed using directional distribution (DD). The Spearman’s rank correlation coefficient was employed to assess the strength of association between the WNND infections and predictors. A model was generated using the random forest ensemble learning method. A total number of 535 human WNND cases were confirmed in 308 localities. The DD showed a south-eastern geographical distribution. Weak correlation was observed between the number of human WNND cases for each year and the predictors. The highest predicted probability was around urbanized patches in the south and southeast. Increased surveillance and control measures of vectors in risk areas should be implemented and educational campaigns should be made available for the general public in order to raise awareness of the disease and inform the population about prophylactic measures.

Keywords: abiotic; biotic; mosquito; modelling; predictors; West Nile virus; WNND (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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