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. 2024 Mar 20;24(1):865.
doi: 10.1186/s12889-024-17929-9.

Risk assessment of imported malaria in China: a machine learning perspective

Shuo Yang  1 , Ruo-Yang Li  1 , Shu-Ning Yan  1 , Han-Yin Yang  1 , Zi-You Cao  1 , Li Zhang  1 , Jing-Bo Xue  2   3 , Zhi-Gui Xia  1 , Shang Xia  4   5 , Bin Zheng  6
Affiliations

Risk assessment of imported malaria in China: a machine learning perspective

Shuo Yang et al. BMC Public Health. .

Abstract

Background: Following China's official designation as malaria-free country by WHO, the imported malaria has emerged as a significant determinant impacting the malaria reestablishment within China. The objective of this study is to explore the application prospects of machine learning algorithms in imported malaria risk assessment of China.

Methods: The data of imported malaria cases in China from 2011 to 2019 was provided by China CDC; historical epidemic data of malaria endemic country was obtained from World Malaria Report, and the other data used in this study are open access data. All the data processing and model construction based on R, and map visualization used ArcGIS software.

Results: A total of 27,088 malaria cases imported into China from 85 countries between 2011 and 2019. After data preprocessing and classification, clean dataset has 765 rows (85 * 9) and 11 cols. Six machine learning models was constructed based on the training set, and Random Forest model demonstrated the best performance in model evaluation. According to RF, the highest feature importance were the number of malaria deaths and Indigenous malaria cases. The RF model demonstrated high accuracy in forecasting risk for the year 2019, achieving commendable accuracy rate of 95.3%. This result aligns well with the observed outcomes, indicating the model's reliability in predicting risk levels.

Conclusions: Machine learning algorithms have reliable application prospects in risk assessment of imported malaria in China. This study provides a new methodological reference for the risk assessment and control strategies adjusting of imported malaria in China.

Keywords: China; Imported Malaria; Machine learning; Random Forest; Risk Assessment; Risk mapping; Risk prediction.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Research flowchart
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Fig. 2
Confusion matrix of six models
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Model evaluation indicators
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Feature importance of RF and XGBoost
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Fig. 5
(a) 2019 Risk level of China imported malaria source countries (predicted by RF). (b) 2019 Risk level of China imported malaria source countries (actual situation)
See this image and copyright information in PMC

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References

    1. Cao CL, Guo JG. Challenge and strategy of prevention and control of important parasitic diseases under the Belt and Road Initiative[J] Chin J Schistosomiasis Control. 2018;30(02):111–6. - PubMed
    1. Feng J, Zhang L, Huang F, Yin JH, Tu H, Xia ZG, Zhou SS, Xiao N, Zhou XN. Ready for malaria elimination: zero indigenous case reported in the people’s Republic of China. Malar J. 2018;17(1):315. doi: 10.1186/s12936-018-2444-9. - DOI - PMC - PubMed
    1. World Health Organization., World Malaria Report(2020).
    1. Bitoh T, Fueda K, Ohmae H, et al. Risk analysis of the re-emergence of Plasmodium Vivax malaria in Japan using a stochastic transmission model[J]. Volume 16. Environmental Health & Preventive Medicine; 2011. pp. 171–7. 3. - PMC - PubMed
    1. Zhang T, Jiang J, Lyu X, Xu X, Wang S, Liu Z, Yin J, Li W. Surveillance and response to Imported Malaria during the COVID-19 Epidemic - Anhui Province, China, 2019–2021. China CDC Wkly. 2022;4(28):622–5. doi: 10.46234/ccdcw2022.135. - DOI - PMC - PubMed

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