喀麦隆COVID-19死亡风险分析

S. W. Youdom, Henri E. Z. Tonnang
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The COVID-19 data collected in Cameroon during the period of March 6 to July 30, 2020 were used in the analysis.\n\nResults\nCOVID-19 epidemic showed several peaks. The reproductive number was 3.08 between May 18 and May 31; 2.75 between June 1 and June 25, and 2.84 between June 16 and June 24. The number of infected individuals ranged from 17632 to 26424 (June 1 to June 15), and 28100 to 36628 (June 16 to June 24). The month of January 2021 was estimated as the last epidemic peak. Under the assumption that a recovered person will get infected again with probability 0.15, 50000 iterations of the Markov chain (10 and 3- state) demonstrated that the death state was the most probable state. The estimated lethality rate was 0.44, 95%CI=0.10%-0.79%. Mean lethality rate assuming ii) was 0.10. 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摘要

喀麦隆正在与新型冠状病毒(COVID-19)大流行作斗争。虽然已经实施了若干控制措施,但该流行病仍在继续发展。本文分析了喀麦隆大流行的演变,并试图提供有关COVID-19在该国人口中的演变的见解。方法采用易感感染-恢复-死亡(SIRD)样模型结合离散时间相关马尔可夫链预测COVID-19分布并评估死亡风险。在10状态和3状态马尔可夫链中检验了两个主要假设:i)康复的人可能再次感染;Ii)该人将继续康复。分析使用了2020年3月6日至7月30日在喀麦隆收集的COVID-19数据。结果新型冠状病毒肺炎疫情出现多个高峰。5月18日~ 5月31日繁殖数为3.08;6月1日至25日2.75英镑,6月16日至24日2.84英镑。感染人数为17632 ~ 26424人(6月1日~ 6月15日),28100 ~ 36628人(6月16日~ 6月24日)。据估计,2021年1月是最后一个疫情高峰。假设一个康复的人再次感染的概率为0.15,50000次马尔可夫链(10和3状态)的迭代证明死亡状态是最可能的状态。估计致死率为0.44,95%CI=0.10% ~ 0.79%。假设ii)的平均死亡率为0.10。根据报告数据计算的转移概率显示,2020年7月和8月期间活跃病例数量显著增加,到2020年9月的平均致死率为3%。结论多种数据分析方法是喀麦隆管理和控制COVID-19的基础步骤。由于公共卫生措施落实不力,COVID-19的传播速度正在迅速增长。在疫情蔓延期间,对导致covid -19相关死亡的主要因素进行评估,可为该国公共卫生系统提供减轻疾病负担的战略。模型输出显示了该疾病的威胁性及其后果。考虑示范产出并采取具体行动可加强喀麦隆现行公共卫生干预战略的执行。在2020/2021历年及下一历年学习机构(学校和大学)开学前后,应加强严格实施佩戴口罩和保持社交距离等预防措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Risk of Death due to COVID-19 in Cameroon
Background Cameroon is battling against the novel coronavirus (COVID-19) pandemic. Although several control measures have been implemented, the epidemic continues to progress. This paper analyses the evolution of the pandemic in Cameroon and attempts to provide insight on the evolution of COVID-19 within the country’s population. Methods A susceptible-infected-recovered-dead (SIRD)-like model coupled with a discrete time-dependent Markov chain was applied to predict COVID-19 distribution and assess the risk of death. Two main assumptions were examined in a 10-state and 3-state Markov chain: i) a recovered person can get infected again; ii) the person will remain recovered. The COVID-19 data collected in Cameroon during the period of March 6 to July 30, 2020 were used in the analysis. Results COVID-19 epidemic showed several peaks. The reproductive number was 3.08 between May 18 and May 31; 2.75 between June 1 and June 25, and 2.84 between June 16 and June 24. The number of infected individuals ranged from 17632 to 26424 (June 1 to June 15), and 28100 to 36628 (June 16 to June 24). The month of January 2021 was estimated as the last epidemic peak. Under the assumption that a recovered person will get infected again with probability 0.15, 50000 iterations of the Markov chain (10 and 3- state) demonstrated that the death state was the most probable state. The estimated lethality rate was 0.44, 95%CI=0.10%-0.79%. Mean lethality rate assuming ii) was 0.10. Computation of transition probabilities from reported data revealed a significant increase in the number of active cases throughout July and August, 2020, with a mean lethality rate of 3% by September 2020. Conclusion Multiple approaches to data analysis is a fundamental step for managing and controlling COVID-19 in Cameroon. The rate of transmission of COVID-19 is growing fast because of insufficient implementation of public health measures. While the epidemic is spreading, assessment of major factors that contribute to COVID-19-associated mortality may provide the country’s public health system with strategies to reduce the burden of the disease. The model outputs present the threatening nature of the disease and its consequences. Considering the model outputs and taking concrete actions may enhance the implementation of current public health intervention strategies in Cameroon. Strict application of preventive measures, such as wearing masks and social distancing, could be reinforced before and after the opening of learning institutions (schools and universities) in the 2020/2021 calendar year and next.
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