Deep learning hybrid model for analyzing and predicting the impact of imported malaria cases from Africa on the rise of Plasmodium falciparum in China before and during the COVID-19 pandemic

Background: Plasmodium falciparum cases are rising in China due to the imported malaria cases from African countries. The main goal of this study is to examine the impact of imported malaria cases in African countries on the rise of P. falciparum cases in China before and during the COVID-19 pandemic. Methods: A generalized regression model was used to investigate the association of time trends between imported malaria cases from 45 African countries and P. falciparum cases in 31 provinces of China from 2012 to 2018 before the COVID-19 pandemic and during the COVID-19 pandemic from October 2020 to May 2021. Based on the analysis, we proposed a statistical and deep learning hybrid approach to model the resurgence of malaria in China using monthly data of P. falciparum from 2004 to 2016. This study builds a hybrid model known as the ARIMA-GRU approach for modeling the P. falciparum cases in all provinces of China and the number of malaria deaths in China before and during the COVID-19 pandemic. Results: The analysis showed an emerging link between the rise of imported malaria cases from Africa and P. falciparum cases in many provinces of China. Many imported malaria cases from Africa were P. falciparum cases. The proposed deep learning model achieved a high prediction accuracy score on the testing dataset of 96%. Conclusion: The study provided an analysis of the reduction of P. falciparum cases and deaths caused by imported P. falciparum cases during the COVID-19 pandemic due to the control measures regarding the limitation of international travel in China. The Chinese government has to prepare the imported malaria control measures after the normalization of international travel, to prevent the resurgence of malaria disease in China.