Sandie Arsène Brunelle, Tejiokem Mathurin Cyrille, Faye Cheikh Mbacké, Hamadou Achta, Abah Aristide Abah, Mbah Serge Sadeuh, Tagnouokam-Ngoupo Paul Alain, Njouom Richard, Eyangoh Sara, Abanda Ngu Karl, Diarra Maryam, Ben Miled Slimane, Tchuente Maurice, Tchatchueng-Mbougua Jules Brice
African Population and Health Research Center, West Africa Regional Office, Dakar, Senegal.
Centre Pasteur du Cameroon, membre du Réseau International des Instituts Pasteur, Cameroon.
Infect Dis Model. 2023 Mar;8(1):228-239. doi: 10.1016/j.idm.2023.02.001. Epub 2023 Feb 8.
Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.
对喀麦隆来说,控制新冠疫情仍是一项挑战,全球许多其他国家亦是如此。喀麦隆卫生当局报告的确诊病例数基于观测数据,而这些数据不具有全国代表性。从该国报告首例病例到现在,疫情的实际规模仍不清楚。本研究旨在基于观测到的分类数据集,估计并模拟喀麦隆2020年3月5日至2021年5月31日期间新冠新增感染病例数的实际趋势。我们使用了一个大型分类数据集,并前瞻性地应用多水平回归和事后分层模型来估计喀麦隆2020年3月5日至2021年5月31日期间的新冠病例趋势。随后,使用季节性自回归积分滑动平均(SARIMA)模型进行预测。根据前瞻性多水平回归和事后分层模型的结果,估计喀麦隆在2020年3月5日至2021年5月31日期间共有约7450935例(30%)新冠病例。总体而言,喀麦隆在此期间报告的新冠感染病例数低估了估计的实际病例数约94倍。预测表明,在2021年5月31日之后的两年里,疫情将出现连续两波爆发。如果不采取行动,未来可能会出现多波疫情。为避免这种可能对全球健康构成威胁的情况,公共卫生当局应有效监测民众对预防措施的遵守情况,并实施提高民众疫苗接种覆盖率的策略。