Complexity Science Institute, Qingdao University, Qingdao, China.
PLoS One. 2023 Dec 6;18(12):e0287702. doi: 10.1371/journal.pone.0287702. eCollection 2023.
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.
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.
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%.
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.
由于从非洲国家输入的疟疾病例，中国的恶性疟原虫病例正在增加。本研究的主要目的是在 COVID-19 大流行之前和期间，检查从非洲 45 个国家输入的疟疾病例对中国恶性疟原虫病例增加的影响。
使用广义回归模型调查了从非洲 45 个国家输入的疟疾病例与 2012 年至 2018 年 COVID-19 大流行之前和期间中国 31 个省份的恶性疟原虫病例之间的时间趋势的关联。在此分析的基础上，我们提出了一种统计和深度学习混合方法，使用 2004 年至 2016 年恶性疟原虫的每月数据，对中国疟疾的复发进行建模。本研究构建了一种混合模型，称为 ARIMA-GRU 方法，用于对 COVID-19 大流行之前和期间中国所有省份的恶性疟原虫病例和中国疟疾死亡人数进行建模。
分析表明，非洲输入疟疾病例的增加与中国许多省份恶性疟原虫病例之间存在新的联系。许多来自非洲的输入性疟疾病例是恶性疟原虫病例。所提出的深度学习模型在测试数据集上达到了 96%的高预测准确率。
由于中国限制国际旅行的控制措施，COVID-19 大流行期间输入性恶性疟原虫病例导致的恶性疟原虫病例和死亡人数有所减少。中国政府必须在国际旅行正常化后做好输入性疟疾控制措施的准备，以防止中国疟疾疫情的复发。
