The Earth Commons Institute, Georgetown University, Washington, DC, 20057, USA.
Department of Mathematics and Statistics, Georgetown University, Washington, DC, 20057, USA.
Sci Rep. 2024 Jul 20;14(1):16734. doi: 10.1038/s41598-024-67452-5.
The interactions of environmental, geographic, socio-demographic, and epidemiological factors in shaping mosquito-borne disease transmission dynamics are complex and changeable, influencing the abundance and distribution of vectors and the pathogens they transmit. In this study, 27 years of cross-sectional malaria survey data (1990-2017) were used to examine the effects of these factors on Plasmodium falciparum and Plasmodium vivax malaria presence at the community level in Africa and Asia. Monthly long-term, open-source data for each factor were compiled and analyzed using generalized linear models and classification and regression trees. Both temperature and precipitation exhibited unimodal relationships with malaria, with a positive effect up to a point after which a negative effect was observed as temperature and precipitation increased. Overall decline in malaria from 2000 to 2012 was well captured by the models, as was the resurgence after that. The models also indicated higher malaria in regions with lower economic and development indicators. Malaria is driven by a combination of environmental, geographic, socioeconomic, and epidemiological factors, and in this study, we demonstrated two approaches to capturing this complexity of drivers within models. Identifying these key drivers, and describing their associations with malaria, provides key information to inform planning and prevention strategies and interventions to reduce malaria burden.
环境、地理、社会人口和流行病学因素相互作用，共同影响蚊媒传染病传播动态，这些因素影响着病媒的数量和分布及其传播的病原体。本研究利用非洲和亚洲 27 年来（1990-2017 年）的疟疾横断面调查数据，研究这些因素对社区层面间日疟原虫和恶性疟原虫疟疾流行的影响。通过广义线性模型和分类回归树，对各因素的月长期开源数据进行了编译和分析。温度和降水均与疟疾呈单峰关系，在达到一定程度后，随着温度和降水的增加，其影响由正转负。模型很好地捕捉到了 2000 年至 2012 年间疟疾的总体下降趋势，以及此后的反弹。模型还表明，经济和发展指标较低的地区疟疾发病率更高。疟疾是由环境、地理、社会经济和流行病学因素共同驱动的，在本研究中，我们展示了两种在模型中捕捉这些驱动因素复杂性的方法。确定这些关键驱动因素，并描述它们与疟疾的关联，为规划和预防策略以及减少疟疾负担的干预措施提供了关键信息。
