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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. [ABSTRACT FROM AUTHOR]

Background: Qualified malaria diagnosis competency has contributed to the great achievement of malaria elimination in China. After eliminating malaria, it is still critical to the prevention of re-establishment of malaria transmission in China. This study was aimed to assess the malaria detection competency at national and provincial levels in China at the beginning of malaria post-elimination phase. Methods: In the present study, different competency assessment activities on the laboratory malaria diagnosis were carried out for national and provincial malaria diagnostic laboratories based on the WHO scoring schedules, including malaria microscopy or nucleic acid amplification tests (NAAT), at the beginning of malaria post-elimination phase (2021–2022) in China. Results: A total of 60 slides for malaria microscopy and 10 specimen for NAAT were included into the WHO External Quality Assessments of malaria parasite qualitative detection and species identification, and the scoring rate was 96.6% (microscopy: 171/177) and 85.0% (NAAT: 17/20), respectively. Moreover, 124 samples were included into the national NAAT quality assessment, and an accuracy of 87.9% (109/124) was found without significance among reference laboratories and non-reference laboratories. Conclusions: The findings suggest that there is still a need for sustained strengthening of malaria detection competency, particularly in the areas of parasite counting and detection of low-density parasitemia, to ensure prompt detection of the sources of infection and accurate identification of Plasmodium species, and contribute to case management and focus disposal, thereby effectively preventing the malaria re-establishment. [ABSTRACT FROM AUTHOR]