Predicting the risk of malaria importation into Jiangsu Province, China: a modeling study - Kaixuan Liu; Yuanyuan Cao; Enyu Xu; Zeyin Chong; Liying Chai; Yi Wang; Yuhui Xu; Yin Wang; Jun Zhang; Olaf Müller; Jun Cao; Guoding Zhu; et al. (BMC, 2024)
Abstract Background The World Health Organization certified China malaria-free in 2021. Consequently, preventing the risk of malaria re-introduction caused by imported malaria has now become a major challenge. This study aims to characterize the dynamics and predict the risk of malaria importation in Jiangsu Province, where the number of imported malaria cases ranks among the highest in China. Methods The annual number of cases with imported malaria in Jiangsu Province, the annual number of travelers from sub-Saharan Africa (SSA) to Jiangsu Province (both Chinese and international travelers), and the annual number of Chinese migrant workers from Jiangsu Province who stayed abroad between 2013 and 2020 were assessed. The spatio-temporal dynamics of malaria importation was characterized with ArcGIS 10.8. A negative binomial model was applied to model malaria importation to Jiangsu Province, China. Results A total of 2,221 of imported malaria cases were reported from January 1, 2013, until December 31, 2020. Imported malaria cases into China were mainly from SSA (98%) and P. falciparum (78%), the most common species. A seasonal pattern was observed, with the most cases occurring from December to February. The negative binomial model, which incorporates the number of Chinese migrant workers from Jiangsu Province who stayed abroad as an independent variable, demonstrated better performance (AIC: 96.495, BIC: 94.230) compared to the model based solely on travelers from SSA to Jiangsu Province. The model indicated an estimated 139% increase in imported cases for a 10% increase in Chinese migrant workers from Jiangsu Province who stayed abroad. Conclusion In conclusion, our study underscores the importance of incorporating data on Chinese migrant workers who have stayed abroad when predicting malaria importation risks. By integrating both international travel patterns and migrant worker data, our findings offer a more robust framework for assessing and managing malaria risk in Jiangsu Province. This approach provides valuable insights for public health officials, enabling more effective resource allocation and targeted interventions to prevent the re-introduction of malaria and improve overall disease management.