The Influence of Climate Variables on Malaria Incidence in Vanuatu

被引:0
|
作者
Sorenson, Jade [1 ,2 ]
Watkins, Andrew B. [3 ]
Kuleshov, Yuriy [2 ,4 ]
机构
[1] Monash Univ, Sci Adv Global Challenges Program, Melbourne 3800, Australia
[2] Bur Meteorol, Sci & Innovat Grp, Climate Risk & Early Warning Syst CREWS, Melbourne 3008, Australia
[3] Australian Climate Serv, Melbourne 3116, Australia
[4] Royal Melbourne Inst Technol RMIT Univ, Sch Sci, Melbourne 3000, Australia
关键词
climate variability; vector-borne diseases; malaria; temperature; precipitation; Vanuatu; ANOPHELES-FARAUTI-S.S; PLASMODIUM-FALCIPARUM; TAFEA PROVINCE; GENE FLOW; EPIDEMIOLOGY; TRANSMISSION; ELIMINATION; ISLANDS; VIVAX;
D O I
10.3390/cli13020022
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Malaria, a climate-sensitive mosquito-borne disease, is widespread in tropical and subtropical regions, and its elimination is a global health priority. Malaria is endemic to Vanuatu, where elimination campaigns have been implemented with varied success. In this study, climate variables were assessed for their correlation with national malaria cases from 2014 to 2023 and used to develop a proof-of-concept model for estimating malaria incidence in Vanuatu. Maximum, minimum, and median temperatures; diurnal temperature variation; median temperature during the 18:00-21:00 mosquito biting period (VUT); median humidity; and precipitation (total and anomaly) were evaluated as predictors at different time lags. It was found that maximum temperature had the strongest correlation with malaria cases and produced the best-performing linear regression model, where malaria cases increased by approximately 43 cases for every degree (degrees C) increase in monthly maximum temperature. This aligns with similar findings from climate-malaria studies in the Southwest Pacific, where temperature tends to stimulate the development of both Anopheles farauti and Plasmodium vivax, increasing transmission probability. A Bayesian model using maximum temperature and total precipitation at a two-month time lag was more effective in predicting malaria incidence than using maximum temperature or precipitation alone. A Bayesian approach was preferred due to its flexibility with varied data types and prior information about malaria dynamics. This model for predicting malaria incidence in Vanuatu can be adapted to smaller regions or other malaria-affected areas, supporting malaria early warning and preparedness for climate-related health challenges.
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页数:19
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