Explorer la répartition spatiotemporelle des piqûres de tiques sur les humains en France : la température moyenne comme indicateur du risque acarologique
Among vector-borne diseases, tick-borne diseases (including Lyme borreliosis) are those whose incidence is constantly on the rise. Europe and North America are particularly affected in temperate regions. As there is a seasonal pattern to tick bites, with more reports in summer and fewer in winter, w...
| Main Authors: | , , |
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| Format: | Article |
| Language: | deu |
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Unité Mixte de Recherche 8504 Géographie-cités
2024-03-01
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| Series: | Cybergeo |
| Subjects: | |
| Online Access: | https://journals.openedition.org/cybergeo/40927 |
| _version_ | 1797222836207616000 |
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| author | Khaldoune Hilami Vincent Godard Camille Noûs |
| author_facet | Khaldoune Hilami Vincent Godard Camille Noûs |
| author_sort | Khaldoune Hilami |
| collection | DOAJ |
| description | Among vector-borne diseases, tick-borne diseases (including Lyme borreliosis) are those whose incidence is constantly on the rise. Europe and North America are particularly affected in temperate regions. As there is a seasonal pattern to tick bites, with more reports in summer and fewer in winter, we wondered whether there were any climatic markers that could be used to identify the start of the tick-borne risk season, so that the public could be informed. Thanks to INRAe's participatory science program, CiTIQUE, 22,000 geolocated cases in France of tick bites on humans were collected, between July 2017 and April 2020. These reports were matched with the weather conditions (from the Météo-France and Dark Sky databases) prevailing at the location and date of each reported bite. Of all the variables analyzed, average daily temperature was the best indicator. Measured at the time of the reports, it is significantly higher by almost 6°C than that of the control dataset (measured at randomly distributed points across mainland France). This temperature difference allows us to go beyond the mere activity of ticks in search of a host (the hazard) and to integrate the human factor - when present and sufficiently "accessible" (dress, behavior) – so that the interaction transforms the hazard into a risk. This indicator is the most easily manipulated of those studied in this article, in the context of public authorities "informing" citizens who frequent nature areas. |
| first_indexed | 2024-04-24T13:27:39Z |
| format | Article |
| id | doaj.art-a115e35f6fd742b68c1ee64d15d09732 |
| institution | Directory Open Access Journal |
| issn | 1278-3366 |
| language | deu |
| last_indexed | 2024-04-24T13:27:39Z |
| publishDate | 2024-03-01 |
| publisher | Unité Mixte de Recherche 8504 Géographie-cités |
| record_format | Article |
| series | Cybergeo |
| spelling | doaj.art-a115e35f6fd742b68c1ee64d15d097322024-04-04T09:34:13ZdeuUnité Mixte de Recherche 8504 Géographie-citésCybergeo1278-33662024-03-0110.4000/cybergeo.40927Explorer la répartition spatiotemporelle des piqûres de tiques sur les humains en France : la température moyenne comme indicateur du risque acarologiqueKhaldoune HilamiVincent GodardCamille NoûsAmong vector-borne diseases, tick-borne diseases (including Lyme borreliosis) are those whose incidence is constantly on the rise. Europe and North America are particularly affected in temperate regions. As there is a seasonal pattern to tick bites, with more reports in summer and fewer in winter, we wondered whether there were any climatic markers that could be used to identify the start of the tick-borne risk season, so that the public could be informed. Thanks to INRAe's participatory science program, CiTIQUE, 22,000 geolocated cases in France of tick bites on humans were collected, between July 2017 and April 2020. These reports were matched with the weather conditions (from the Météo-France and Dark Sky databases) prevailing at the location and date of each reported bite. Of all the variables analyzed, average daily temperature was the best indicator. Measured at the time of the reports, it is significantly higher by almost 6°C than that of the control dataset (measured at randomly distributed points across mainland France). This temperature difference allows us to go beyond the mere activity of ticks in search of a host (the hazard) and to integrate the human factor - when present and sufficiently "accessible" (dress, behavior) – so that the interaction transforms the hazard into a risk. This indicator is the most easily manipulated of those studied in this article, in the context of public authorities "informing" citizens who frequent nature areas.https://journals.openedition.org/cybergeo/40927environmentclimateparticipatory democracyhealth riskdata analysis |
| spellingShingle | Khaldoune Hilami Vincent Godard Camille Noûs Explorer la répartition spatiotemporelle des piqûres de tiques sur les humains en France : la température moyenne comme indicateur du risque acarologique Cybergeo environment climate participatory democracy health risk data analysis |
| title | Explorer la répartition spatiotemporelle des piqûres de tiques sur les humains en France : la température moyenne comme indicateur du risque acarologique |
| title_full | Explorer la répartition spatiotemporelle des piqûres de tiques sur les humains en France : la température moyenne comme indicateur du risque acarologique |
| title_fullStr | Explorer la répartition spatiotemporelle des piqûres de tiques sur les humains en France : la température moyenne comme indicateur du risque acarologique |
| title_full_unstemmed | Explorer la répartition spatiotemporelle des piqûres de tiques sur les humains en France : la température moyenne comme indicateur du risque acarologique |
| title_short | Explorer la répartition spatiotemporelle des piqûres de tiques sur les humains en France : la température moyenne comme indicateur du risque acarologique |
| title_sort | explorer la repartition spatiotemporelle des piqures de tiques sur les humains en france la temperature moyenne comme indicateur du risque acarologique |
| topic | environment climate participatory democracy health risk data analysis |
| url | https://journals.openedition.org/cybergeo/40927 |
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