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. 2024 Jul 20;14(1):16734.
doi: 10.1038/s41598-024-67452-5.

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

Oswaldo C Villena  1 , Ali Arab  2 , Catherine A Lippi  3   4 , Sadie J Ryan  3   4   5 , Leah R Johnson  6   7   8
Affiliations

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

Oswaldo C Villena et al. Sci Rep. .

Abstract

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.

Keywords: Plasmodium falciparum; Plasmodium vivax; Africa; Asia; Bioclimatic variables; Malaria; Malaria survey data; Temperature; Vector-borne diseases.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Marginal predictions based on particular environmental and bioclimatic predictors for P. falciparum in Africa. (A) Temperature 1st quarter prior to the start of the survey study, (B) Precipitation 1st quarter prior to the start of the survey study, (C) Elevation, (D) NDVI, (E) Isothermality, (F) Precipitation of the wettest quarter, and (G) Precipitation of the driest quarter.
Figure 2
Figure 2
Marginal predictions based on socio-demographic and epidemiological predictors for P. falciparum in Africa. (A) Year at which the survey study started, (B) Gross domestic product per capita, (C) Population density, (D) Human development index, and (E) Basic reproductive number (R0) 1st quarter prior to the start of the survey study.
Figure 3
Figure 3
Marginal predictions based on particular environmental and bioclimatic predictors for P. falciparum in Asia. (A) Temperature 1st quarter prior to the start of the survey study, (B) Precipitation 1st quarter prior to the start of the survey study, (C) Elevation, (D) NDVI, (E) Isothermality, (F) Precipitation of the wettest quarter, and (G) Precipitation of the driest quarter.
Figure 4
Figure 4
Marginal predictions based on socio-demographic and epidemiological predictors for P. falciparum in Asia. (A) Year at which the survey study started, (B) Gross domestic product per capita, (C) Population density, (D) Human development index, and (E) Basic reproductive number (R0) 1st quarter prior to the start of the survey study.
Figure 5
Figure 5
Marginal predictions based on particular environmental and bioclimatic predictors (A) Temperature 1st quarter prior to the start of the survey study, (B) Precipitation 1st quarter prior to the start of the survey study, (C) Elevation, (D) NDVI, (E) Isothermality, (F) Precipitation of the wettest quarter, and (G) Precipitation of the driest quarter for P. vivax in Asia.
Figure 6
Figure 6
Marginal predictions based on socio-demographic and epidemiological predictors (A) Year at which the survey study started, (B) Gross domestic product per capita, (C) Population density, (D) Human development index, and (E) Basic reproductive number (R0) 1st quarter prior to the start of the survey study for P. vivax in Asia.
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