Modeling the spatial risk of malaria through probability distribution of Anopheles maculipennis s.l. and imported cases
AbstractMalaria remains one of the most important infectious diseases globally due to its high incidence and mortality rates. The influx of infected cases from endemic to non-endemic malaria regions like Europe has resulted in a public health concern over sporadic local outbreaks. This is facilitated by the continued presence of competent Anopheles vectors in non-endemic countries.We modeled the potential distribution of the main malaria vector across Spain using the ensemble of eight modeling techniques based on environmental parameters and the Anopheles maculipennis s.l. presence/absence data collected from 2000 to 2020. We then combined this map with the number of imported malaria cases in each municipality to detect the geographic hot spots with a higher risk of local malaria transmission.The malaria vector occurred preferentially in irrigated lands characterized by warm climate conditions and moderate annual precipitation. Some areas surrounding irrigated lands in northern Spain (e.g., Zaragoza, Logroño), mainland areas (e.g., Madrid, Toledo) and in the South (e.g., Huelva), presented a significant likelihood of A. maculipennis s.l. occurrence, with a large overlap with the presence of imported cases of malaria.While the risk of malaria re-emergence in Spain is low, it is not evenly distributed throughout the country. The four recorded local cases of mosquito-borne transmission occurred in areas with a high overlap of imported cases and mosquito presence. Integrating mosquito distribution with human incidence cases provides an effective tool for the quantification of large-scale geographic variation in transmission risk and pinpointing priority areas for targeted surveillance and prevention.
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E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
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Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Emerging microbes & infections - (2024) vom: 15. Apr., Seite 2343911 |
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Englisch |
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Journal Article |
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| Anmerkungen: |
Date Revised 15.04.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1080/22221751.2024.2343911 |
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| PPN (Katalog-ID): |
NLM37108265X |
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| 100 | 1 | |a Taheri, Shirin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Modeling the spatial risk of malaria through probability distribution of Anopheles maculipennis s.l. and imported cases |
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| 520 | |a AbstractMalaria remains one of the most important infectious diseases globally due to its high incidence and mortality rates. The influx of infected cases from endemic to non-endemic malaria regions like Europe has resulted in a public health concern over sporadic local outbreaks. This is facilitated by the continued presence of competent Anopheles vectors in non-endemic countries.We modeled the potential distribution of the main malaria vector across Spain using the ensemble of eight modeling techniques based on environmental parameters and the Anopheles maculipennis s.l. presence/absence data collected from 2000 to 2020. We then combined this map with the number of imported malaria cases in each municipality to detect the geographic hot spots with a higher risk of local malaria transmission.The malaria vector occurred preferentially in irrigated lands characterized by warm climate conditions and moderate annual precipitation. Some areas surrounding irrigated lands in northern Spain (e.g., Zaragoza, Logroño), mainland areas (e.g., Madrid, Toledo) and in the South (e.g., Huelva), presented a significant likelihood of A. maculipennis s.l. occurrence, with a large overlap with the presence of imported cases of malaria.While the risk of malaria re-emergence in Spain is low, it is not evenly distributed throughout the country. The four recorded local cases of mosquito-borne transmission occurred in areas with a high overlap of imported cases and mosquito presence. Integrating mosquito distribution with human incidence cases provides an effective tool for the quantification of large-scale geographic variation in transmission risk and pinpointing priority areas for targeted surveillance and prevention | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a spatial epidemiology | |
| 650 | 4 | |a species distribution modeling | |
| 650 | 4 | |a vector-borne diseases | |
| 700 | 1 | |a González, Mikel Alexander |e verfasserin |4 aut | |
| 700 | 1 | |a Ruiz-López, María José |e verfasserin |4 aut | |
| 700 | 1 | |a Magallanes, Sergio |e verfasserin |4 aut | |
| 700 | 1 | |a Delacour-Estrella, Sarah |e verfasserin |4 aut | |
| 700 | 1 | |a Lucientes, Javier |e verfasserin |4 aut | |
| 700 | 1 | |a Bueno-Marí, Rubén |e verfasserin |4 aut | |
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| 700 | 1 | |a Bravo-Barriga, Daniel |e verfasserin |4 aut | |
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