Mosquito capture and temperature data. Article Swipe
Sk Shahid Nadim (22756500)
,
Francesco Menegale (22756503)
,
Mattia Manica (2552488)
,
Alexander R. Kaye (22756506)
,
Georgios Balatsos (13208038)
,
Marina Bisia (8872451)
,
Verena Pichler (1936114)
,
Piero Poletti (130294)
,
Stefano Merler (42326)
,
Alessandra Della Torre (8032817)
,
Robin N. Thompson (2591524)
,
Antonios Michaelakis (743834)
,
Giorgio Guzzetta (100661)
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pntd.0013785.s002
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pntd.0013785.s002
The stable presence of the Aedes albopictus mosquito in Europe has set the stage for the emergence of tropical arboviral outbreaks (such as dengue and chikungunya), following the importation of infection by international travelers. Here, we leverage Ae.albopictus capture data collected weekly in Chania, Greece, in 2017 and 2018, to calibrate a model for assessing the potential epidemiological risks of mosquito-borne outbreaks such as dengue, chikungunya, and Zika. We estimated a peak density of female mosquitoes of 459 (95% Credible Interval, CrI: 424–508) per hectare in 2017 and 757 (95% CrI: 728–785) in 2018. The peak reproduction numbers occurred in early September and exceeded the epidemic threshold of 1 in 20–26% of the municipality area for dengue and in 40–70% for chikungunya (depending on the year). In contrast, we found a negligible risk of Zika transmission. We assessed the quantitative risks of outbreaks for both dengue and chikungunya, using two alternative measures, the Instantaneous Epidemic Risk (IER), and the Threshold Epidemic Risk (TER). We assessed quantitative differences in the two metrics and their determinants, showing that the IER tends to underestimate the risk of onward transmission early in the summer and to overestimate it in the second half of the season. This study identifies non-negligible risks of arboviral outbreaks in a country that, to date, has not recorded autochthonous transmission. It also underscores the importance of considering and adjusting for potential biases in traditional measures of epidemic risk.
Related Topics
Concepts
Outbreak
Dengue fever
Chikungunya
Basic reproduction number
Geography
Aedes
Transmission (telecommunications)
Risk assessment
Attack rate
Epidemiology
Arbovirus
Disease transmission
Aedes aegypti
Environmental health
Mosquito control
Demography
Malaria
Environmental science
Epizootic
Ecology
Population
Metadata
- Type
- dataset
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7110983698
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7110983698Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pntd.0013785.s002Digital Object Identifier
- Title
-
Mosquito capture and temperature data.Work title
- Type
-
datasetOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
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2025-12-01Full publication date if available
- Authors
-
Sk Shahid Nadim (22756500), Francesco Menegale (22756503), Mattia Manica (2552488), Alexander R. Kaye (22756506), Georgios Balatsos (13208038), Marina Bisia (8872451), Verena Pichler (1936114), Piero Poletti (130294), Stefano Merler (42326), Alessandra Della Torre (8032817), Robin N. Thompson (2591524), Antonios Michaelakis (743834), Giorgio Guzzetta (100661)List of authors in order
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- Concepts
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Outbreak, Dengue fever, Chikungunya, Basic reproduction number, Geography, Aedes, Transmission (telecommunications), Risk assessment, Attack rate, Epidemiology, Arbovirus, Disease transmission, Aedes aegypti, Environmental health, Mosquito control, Demography, Malaria, Environmental science, Epizootic, Ecology, PopulationTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.occurred | 99 |
| abstract_inverted_index.presence | 3 |
| abstract_inverted_index.recorded | 218 |
| abstract_inverted_index.tropical | 19 |
| abstract_inverted_index.Interval, | 81 |
| abstract_inverted_index.September | 102 |
| abstract_inverted_index.Threshold | 160 |
| abstract_inverted_index.adjusting | 229 |
| abstract_inverted_index.arboviral | 20, 208 |
| abstract_inverted_index.assessing | 55 |
| abstract_inverted_index.calibrate | 51 |
| abstract_inverted_index.collected | 41 |
| abstract_inverted_index.contrast, | 128 |
| abstract_inverted_index.emergence | 17 |
| abstract_inverted_index.estimated | 70 |
| abstract_inverted_index.following | 27 |
| abstract_inverted_index.infection | 31 |
| abstract_inverted_index.measures, | 152 |
| abstract_inverted_index.outbreaks | 21, 62, 143, 209 |
| abstract_inverted_index.potential | 57, 231 |
| abstract_inverted_index.threshold | 107 |
| abstract_inverted_index.(depending | 123 |
| abstract_inverted_index.424–508) | 83 |
| abstract_inverted_index.728–785) | 92 |
| abstract_inverted_index.identifies | 204 |
| abstract_inverted_index.importance | 225 |
| abstract_inverted_index.mosquitoes | 76 |
| abstract_inverted_index.negligible | 132 |
| abstract_inverted_index.travelers. | 34 |
| abstract_inverted_index.alternative | 151 |
| abstract_inverted_index.chikungunya | 122 |
| abstract_inverted_index.considering | 227 |
| abstract_inverted_index.differences | 167 |
| abstract_inverted_index.importation | 29 |
| abstract_inverted_index.traditional | 234 |
| abstract_inverted_index.underscores | 223 |
| abstract_inverted_index.chikungunya, | 66, 148 |
| abstract_inverted_index.municipality | 114 |
| abstract_inverted_index.overestimate | 193 |
| abstract_inverted_index.quantitative | 140, 166 |
| abstract_inverted_index.reproduction | 97 |
| abstract_inverted_index.transmission | 186 |
| abstract_inverted_index.Instantaneous | 154 |
| abstract_inverted_index.autochthonous | 219 |
| abstract_inverted_index.chikungunya), | 26 |
| abstract_inverted_index.determinants, | 174 |
| abstract_inverted_index.international | 33 |
| abstract_inverted_index.transmission. | 136, 220 |
| abstract_inverted_index.underestimate | 181 |
| abstract_inverted_index.albopictus</i> | 7 |
| abstract_inverted_index.mosquito-borne | 61 |
| abstract_inverted_index.non-negligible | 205 |
| abstract_inverted_index.epidemiological | 58 |
| abstract_inverted_index.<i>Ae.albopictus</i> | 38 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 13 |
| citation_normalized_percentile |