Environmental, socioeconomic, and sociocultural drivers of monkeypox transmission in the Democratic Republic of the Congo: a One Health perspective
Background: Monkeypox (mpox) is an emerging zoonotic disease that has persistently impacted public health in endemic regions of West and Central Africa for over half a century. The Democratic Republic of the Congo (DRC) remains one of the countries most affected. Understanding the risk factors for d...
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| Hauptverfasser: | , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
07 February 2025
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| In: |
Infectious diseases of poverty
Year: 2025, Jahrgang: 14, Pages: 1-13 |
| ISSN: | 2049-9957 |
| DOI: | 10.1186/s40249-025-01278-9 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s40249-025-01278-9
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| Verfasserangaben: | Guangyu Lu, Zeyin Chong, Enyu Xu, Ce Na, Kaixuan Liu, Liying Chai, Pengpeng Xia, Kai Yang, Guoqiang Zhu, Jinkou Zhao, Olaf Müller |
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| 245 | 1 | 0 | |a Environmental, socioeconomic, and sociocultural drivers of monkeypox transmission in the Democratic Republic of the Congo |b a One Health perspective |c Guangyu Lu, Zeyin Chong, Enyu Xu, Ce Na, Kaixuan Liu, Liying Chai, Pengpeng Xia, Kai Yang, Guoqiang Zhu, Jinkou Zhao, Olaf Müller |
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| 520 | |a Background: Monkeypox (mpox) is an emerging zoonotic disease that has persistently impacted public health in endemic regions of West and Central Africa for over half a century. The Democratic Republic of the Congo (DRC) remains one of the countries most affected. Understanding the risk factors for disease transmission from a One Health perspective is of great importance in the risk assessment, prevention, and control of zoonotic diseases. Therefore, this study aimed to investigate the risk factors for human mpox transmission at the human–animal–environment interface in the DRC. Methods: Epidemiological, environmental, socioeconomic, and sociocultural data from the DRC from 2000 to 2015 were obtained from publicly available dataset. Using these data, we applied negative binomial regression model, least absolute shrinkage and selection operator regression model, and principal component analysis (PCA) to identify key environmental, socioeconomic, and sociocultural factors contributing to mpox transmission. Moreover, a grey prediction model GM (1, n) was constructed to predict the epidemic trend of mpox post-2015 and validated using suspected mpox case data in the DRC from 2016 to 2021, sourced from the United States Centers for Disease Control and Prevention. Results: Between 2000 and 2021, a total of 43,628 suspected mpox cases were reported in the DRC, with a peak of 6216 cases in 2020. From 2016 to 2021, suspected cases accounted for over half (24,379/43,628, 55.9%) of the total reported during the 2000–2021 period. The proportion of primary forest [incidence rate ratio (IRR): 1.023, 95% confidence interval (CI): 1.018–1.027], index of economic well-being (IRR: 1.046, 95% CI: 1.039–1.052), and mean annual precipitation (IRR 1.040, 95% CI: 1.031–1.049) were positively associated with mpox incidence. PCA identified five principal components, explaining 69% of the variance in the environmental, socioeconomic, and sociocultural variables. The first component was characterized by socioeconomic factors. The GM (1, n) model, based on the proportion of primary forest, index of economic well-being, and mean annual precipitation, predicted the epidemic trend (revealed relative error: 2.69). Conclusions: Both socioeconomic and environmental factors play important roles in mpox transmission. Our study further highlighted the importance of considering the interconnectedness among humans, animals, and the environment, and treating these factors as a whole to explain the transmission and emergence of mpox outbreaks in the DRC according to the One Health concept. | ||
| 650 | 4 | |a Democratic Republic of the Congo | |
| 650 | 4 | |a Grey prediction model | |
| 650 | 4 | |a Human monkeypox | |
| 650 | 4 | |a Mpox | |
| 650 | 4 | |a One Health | |
| 650 | 4 | |a Risk analysis | |
| 700 | 1 | |a Chong, Zeyin |e VerfasserIn |4 aut | |
| 700 | 1 | |a Xu, Enyu |e VerfasserIn |4 aut | |
| 700 | 1 | |a Na, Ce |e VerfasserIn |4 aut | |
| 700 | 1 | |a Liu, Kaixuan |e VerfasserIn |4 aut | |
| 700 | 1 | |a Chai, Liying |e VerfasserIn |4 aut | |
| 700 | 1 | |a Xia, Pengpeng |e VerfasserIn |4 aut | |
| 700 | 1 | |a Yang, Kai |e VerfasserIn |4 aut | |
| 700 | 1 | |a Zhu, Guoqiang |e VerfasserIn |4 aut | |
| 700 | 1 | |a Zhao, Jinkou |e VerfasserIn |4 aut | |
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