Spatial and Temporal Variation in Vector-borne Disease Risk; Influence of Land Cover, Irrigation, and Multiple Vector Species on West Nile Virus Transmission
Notice bibliographique
Résumé
Vector-borne pathogens, such as Malaria, Dengue, West Nile and Zika virus, infect hundreds of millions of people each year and lead to widespread human morbidity and mortality, with enormous spatial and temporal variation in disease risk. The recent emergence of West Nile virus (WNV) into the developed world offers a unique opportunity to better understand ecological drivers that contribute to variation in disease risk, as a step toward more effective disease management through targeted interventions. In Chapter 1, we examined correlations between rice cultivation and WNV human disease incidence in rice-growing regions within the United States (US). We found WNV human disease incidence increased with the fraction of each county under rice cultivation in California, but not in the southern US. We show that this is likely due to regional differences in the mosquitoes transmitting WNV. These results illustrate how cultivation of particularly water-demanding agricultural crops can increase mosquito-borne disease risk and how spatial variation in vector ecology can alter the relationship between land cover and disease. In Chapter 2, we examined the effect of irrigation, climate and land cover on mosquito abundance and WNV human disease cases across California. Irrigation made up nearly a third of total water inputs to the region, with irrigation exceeding precipitation in some dry regions. Irrigation reduced seasonal variability in mosquito abundance by more than 40%, and increased abundance by more than an order of magnitude. In addition, irrigation increased human WNV cases and explained 33% of variation in WNV cases among California counties. These results suggest that irrigation can increase and decouple mosquito populations from natural precipitation variability, resulting in sustained and increased disease risk. In Chapter 3, we quantified the risk of WNV transmission to humans from 6 Culex mosquito species by integrating mosquito abundance, infection prevalence, vector competence, and blood feeding patterns, and examined correlations between risk indices and human disease cases. Human WNV cases were strongly correlated with the density of infectious vectors feeding on humans. However, different mosquito vector species contributed to transmission in different land use types and within seasons and across years. Culex tarsalis was more abundant in agricultural areas, whereas Culex pipiens and Culex quinquefasciatus were abundant in developed and agricultural areas, and Culex erythrothorax was abundant in wetland areas. As a result, WNV risk did not change substantially along either agricultural or urbanization land use gradients because the diversity of vectors maintained high disease risk across a range of habitats. These results show how a diversity of vectors can maintain an ecosystem disservice - vector borne disease – by their differential response to environmental disturbance.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».