Evidence map›Paper›PMID 31895933›Full record

ArticlePLoS neglected tropical diseases2020

A quantitative comparison of West Nile virus incidence from 2013 to 2018 in Emilia-Romagna, Italy.

Giovanni Marini, Mattia Calzolari, Paola Angelini, Romeo Bellini, Silvia Bellini, Luca Bolzoni, Deborah Torri, Francesco Defilippo, Ilaria Dorigatti, Birgit Nikolay and 3 more

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Giovanni MariniDepartment of Biodiversity and Molecular Ecology, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige (TN), Italy.ORCID 0000-0001-9721-7211
Mattia CalzolariLaboratory of Entomology, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "B. Ubertini", Reggio Emilia, Italy.
Paola AngeliniPublic Health Service, Emilia-Romagna Region, Bologna, Italy.
Romeo BelliniDept. Medical & Veterinary Entomology, Centro Agricoltura Ambiente "G. Nicoli", Crevalcore, Italy.
Silvia BelliniEpidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "B. Ubertini", Bologna, Italy.
Luca BolzoniRisk Analysis and Genomic Epidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "B. Ubertini", Parma, Italy.ORCID 0000-0001-8523-4460
Deborah TorriLaboratory of Entomology, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "B. Ubertini", Reggio Emilia, Italy.
Francesco DefilippoLaboratory of Entomology, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "B. Ubertini", Reggio Emilia, Italy.
Ilaria DorigattiMRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.
Birgit NikolayMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.
Andrea PuglieseDepartment of Mathematics, University of Trento, Trento, Italy.
Roberto RosàDepartment of Biodiversity and Molecular Ecology, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige (TN), Italy.
Marco TambaEpidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "B. Ubertini", Bologna, Italy.

Funding

Medical Research Council MR/R015600/1
6 · The paper itself

Abstract

backgroundWest Nile virus (WNV) transmission was much greater in 2018 than in previous seasons in Europe. Focusing on Emilia-Romagna region (northern Italy), we analyzed detailed entomological and epidemiological data collected in 2013-2018 to quantitatively assess environmental drivers of transmission and explore hypotheses to better understand why the 2018 epidemiological season was substantially different than the previous seasons. In particular, in 2018 WNV was detected at least two weeks before the observed circulation in 2013-2017 and in a larger number of mosquito pools. Transmission resulted in 100 neuroinvasive human cases in the region, more than the total number of cases recorded between 2013 and 2017. METHODOLOGY: We used temperature-driven mathematical models calibrated through a Bayesian approach to simulate mosquito population dynamics and WNV infection rates in the avian population. We then estimated the human transmission risk as the probability, for a person living in the study area, of being bitten by an infectious mosquito in a given week. Finally, we translated such risk into reported WNV human infections. PRINCIPAL

findingsThe estimated prevalence of WNV in the mosquito and avian populations were significantly higher in 2018 with respect to 2013-2017 seasons, especially in the eastern part of the region. Furthermore, peak avian prevalence was estimated to have occurred earlier, corresponding to a steeper decline towards the end of summer. The high mosquito prevalence resulted in a much greater predicted risk for human transmission in 2018, which was estimated to be up to eight times higher than previous seasons. We hypothesized, on the basis of our modelling results, that such greater WNV circulation might be partially explained by exceptionally high spring temperatures, which have likely helped to amplify WNV transmission at the beginning of the 2018 season.

Indexed as

TemperatureAnimalsBirdsCulexFemaleHumansIncidenceInsect Bites and StingsItalyModels, TheoreticalMosquito VectorsSeasonsWest Nile FeverWest Nile virus

Identifiers

PMID31895933
PMCPMC6939904

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.