ArticleBMC public health2024
Risk assessment of imported malaria in China: a machine learning perspective.
Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Mapping of the Global Importation Risk of Malaria to China - Global, 2024.China CDC weekly · 2026Article
- Spatiotemporal Dynamics ofChina CDC weekly · 2026Article
- Epidemiological Characteristics, Risk Factors, and Countermeasures of Imported Malaria - China, 2017-2024.China CDC weekly · 2026Article
- Digital Health Center App for Community and National Malaria Surveillance in Cambodia: Implementation Case Study.JMIR public health and surveillance · 2026Article
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- GIS-based neural network framework for zoonotic cutaneous leishmaniasis risk mapping in Western Iran.Environmental monitoring and assessment · 2026Article
- Epidemiological and entomological analysis of imported malaria in Pemba Island, Zanzibar: a study from 2020-2022.Malaria journal · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundFollowing 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.
methodsThe 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.
resultsA 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.
conclusionsMachine 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.
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