Influence of environmental, geographic, socio-demographic, and epidemiological factors on presence of malaria at the community level in two continents

• Published in: Scientific reports Vol. 14; no. 1; pp. 16734 - 18
• Main Authors: Villena, Oswaldo C., Arab, Ali, Lippi, Catherine A., Ryan, Sadie J., Johnson, Leah R.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.07.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/158; 692/699; 704/106; 704/158; 704/172; Africa - epidemiology; Animals; Aquatic insects; Asia - epidemiology; Bioclimatic variables; Cross-Sectional Studies; Demography; Disease transmission; Environment; Epidemiology; Generalized linear models; Geography; Humanities and Social Sciences; Humans; Malaria; Malaria - epidemiology; Malaria - transmission; Malaria survey data; Malaria, Falciparum - epidemiology; Malaria, Falciparum - parasitology; Malaria, Falciparum - transmission; Malaria, Vivax - epidemiology; Malaria, Vivax - parasitology; Malaria, Vivax - transmission; Mosquito Vectors - parasitology; multidisciplinary; Plasmodium falciparum; Plasmodium vivax; Precipitation; Regression analysis; Science; Science (multidisciplinary); Sociodemographics; Socioeconomic Factors; Temperature; Vector-borne diseases; Vectors; Asia; Africa; Vector-borne diseases; Temperature; Malaria; Asia; Bioclimatic variables; Africa; Malaria survey data; Plasmodium falciparum; Plasmodium vivax
• ISSN: 2045-2322; 2045-2322
• 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.