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Environmental Drivers of Monkeypox Transmission in the Democratic Republic of the Congo

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Abstract

Monkeypox (MPX) is an emergent severe zoonotic disease resembling that of smallpox. To date, most cases of human MPX have been reported in the Democratic Republic of the Congo (DRC). While the number of cases has increased steadily in the DRC over the last 30 years, the environmental risk factors that drive the spatiotemporal dynamics of MPX transmission remain poorly understood. This study aimed to investigate the spatiotemporal associations between environmental risk factors and annual MPX incidence in the DRC. All MPX cases reported weekly at the health zone level over a 16-year period (2000–2015) were analyzed. A Bayesian hierarchical generalized linear mixed model was conducted to identify the spatiotemporal associations between annual MPX incidence and three types of environmental risk factors illustrating environment as a system resulting from physical, social and cultural interactions Primary forest (IRR 1.034 [1.029–1.040]), economic well-being (IRR 1.038 [1.031–1.047]), and temperature (IRR 1.143 [1.028–1.261]) were positively associated with annual MPX incidence. Our study shows that physical environmental risk factors alone cannot explain the emergence of MPX outbreaks in the DRC. Economic level and cultural practices participate from environment as a whole and thus, must be considered to understand exposure to MPX risk Future studies should examine the impact of these factors in greater detail.

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Figure 1

Source: The shapefile of the provinces was obtained from the free, open, collaborative platform Common Geographical Reference (https://www.rgc.cd/). The map was created using the free software QGIS 2.18.9®.

Figure 2

Source: The figure was created using the free software R® 3.3.2). Altitude: mean altitude of health zone; Deforestation: proportion of health zone area affected by deforestation; DensRivers: average river density in health zone as a proxy for alternative feeding behavior (i.e., fish consumption); DensRoads: average road density in health zone; DRC: Democratic Republic of the Congo; EarlyBfire: early bush fires or fires that occurred in the first half of the year as a proxy for agricultural activities; IEW: index of economic well-being as a proxy for socioeconomic status (poverty level); LateBfire: late bush fires or fires that occurred in the second half of the year as a proxy for agricultural activities; NumNurses: number of nurses per 10,000 inhabitants in health zone; NumPhysicians: number of physicians per 100,000 inhabitants in health zone; PercentRivers: proportion of health zone area intersected by rivers as a proxy for alternative feeding behavior (i.e., fish consumption); PopDensity: population of health zone divided by its surface area; Precipitation: mean annual precipitation in health zone; Primary forest: Proportion of health zone area covered by primary forest; Temperature: mean annual temperature in health zone.

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Acknowledgements

We thank all the members of the Service of the Ecology and Control of Infectious Diseases of the University of Kinshasa in DRC and all the members of the Clinical Methodology Center of CHU Besançon in France for helping to draft this article. We also thank the Direction of Disease Control and the National Institute for Biomedical Research for making the data available for this study.

Funding

The project was funded by the PICS CNRS France 2015 (Grant Number 263320).

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Authors and Affiliations

  1. Département des Sciences de Base, Service d’Écologie et Contrôle des Maladies Infectieuses, Faculté de Médecine, Université de Kinshasa, Quartier Lemba, BP 834 KIN XI, Kinshasa, Democratic Republic of the Congo

    Bien-Aimé Mandja, Didier Bompangue & Jean-Jacques Muyembe

  2. Laboratoire Chrono-Environnement, UMR 6249 CNRS, Université de Bourgogne Franche-Comté, Besançon, France

    Bien-Aimé Mandja, Didier Bompangue & Frédéric Mauny

  3. UMR 912 SESSTIM, INSERM, IRD, U2, Université d’Aix Marseille, Strasbourg, France

    Pascal Handschumacher

  4. Department of Microbiology and Immunology, Division of Biomedical Graduate Research Organization, Georgetown University School of Medicine, 4000 Reservoir Road, Washington, DC, 20057, USA

    Jean-Paul Gonzalez

  5. Institut National de Recherche Biomédicale, Gombe, Kinshasa, Democratic Republic of the Congo

    Jean-Jacques Muyembe

  6. Laboratoire de Biostatistique et Informatique Médicale, Faculté de Médecine, Laboratoire ICube UMR CNRS 7357, Université de Strasbourg, Strasbourg, France

    Erik-André Sauleau

  7. Centre Hospitalier Universitaire de Besançon, uMETh Inserm CIC 1431, Besançon, France

    Frédéric Mauny

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  1. Bien-Aimé Mandja
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  2. Pascal Handschumacher
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Correspondence to Bien-Aimé Mandja.

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Supplementary Information

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Supplementary Material Figure 1

Spatial intercept (β1 + ϕ) in posterior median for the spatiotemporal Bayesian hierarchical model of annual MPX incidence, DRC, 2000-2015. (TIFF) (Source: The shapefile of the 515 HZs was obtained from a free, open, collaborative platform (common geographical reference of DRC) (https://www.rgc.cd/). The map was created using the free software R® 3.3.2. (TIF 31 KB)

Supplementary Material Figure 2

Temporal slopes (α + δ) in posterior median for the spatio-temporal Bayesian hierarchical model of annual MPX incidence, DRC, 2000-2015. (TIFF) (Source: The shapefile of 515 Hz was obtained from a free, open, collaborative platform (Common geographical reference of DRC) (https://www.rgc.cd/). The map was created using the free software R® 3.3.2. (TIF 29 KB)

Supplementary Material Table 1

Posterior median parameter estimates and their 95% confidence intervals for the spatiotemporal Bayesian hierarchical model of annual MPX incidence, DRC, 2000–2015 (DOCX 24 KB)

Supplementary Material Dataset 1

Data file (XLSX 1607 KB)

Supplementary Material Text 1

STROBE Checklist (DOCX 53 KB)

Supplementary Material Text 2

R code (DOCX 20 KB)

Supplementary Material Text 3

Bayesian model description (DOCX 25 KB)

Supplementary Material Text 4

Description of spatial intercept (β1 + ϕ) and temporal slopes (α + δ) in posterior median for the spatiotemporal Bayesian hierarchical model of annual MPX incidence, DRC, 2000–2015 (DOCX 19 KB)

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Mandja, BA., Handschumacher, P., Bompangue, D. et al. Environmental Drivers of Monkeypox Transmission in the Democratic Republic of the Congo. EcoHealth 19, 354–364 (2022). https://doi.org/10.1007/s10393-022-01610-x

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