Background: Since the novel coronavirus emerged in late 2019, the scientific and public health community around the world have sought to better understand, surveil, treat, and prevent the disease, COVID-19. In sub-Saharan Africa (SSA), many countries responded aggressively and decisively with lockdown measures and border closures. Such actions may have helped prevent large outbreaks throughout much of the region, though there is substantial variation in caseloads and mortality between nations. Additionally, the health system infrastructure remains a concern throughout much of SSA, and the lockdown measures threaten to increase poverty and food insecurity for the subcontinent’s poorest residents. The lack of sufficient testing, asymptomatic infections, and poor reporting practices in many countries limit our understanding of the virus’s impact, creating a need for better and more accurate surveillance metrics that account for underreporting and data contamination. Objective: The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with new and decomposable surveillance metrics of COVID-19 that overcome data limitations and contamination inherent in public health surveillance systems. In addition to prevalence of observed daily and cumulative testing, testing positivity rates, morbidity, and mortality, we derived COVID-19 transmission in terms of speed, acceleration or deceleration, change in acceleration or deceleration (jerk), and 7-day transmission rate persistence, which explains where and how rapidly COVID-19 is transmitting and quantifies shifts in the rate of acceleration or deceleration to inform policies to mitigate and prevent COVID-19 and food insecurity in SSA. Methods: We extracted 60 days of COVID-19 data from public health registries and employed an empirical difference equation to measure daily case numbers in 47 sub-Saharan countries as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Results: Kenya, Ghana, Nigeria, Ethiopia, and South Africa have the most observed cases of COVID-19, and the Seychelles, Eritrea, Mauritius, Comoros, and Burundi have the fewest. In contrast, the speed, acceleration, jerk, and 7-day persistence indicate rates of COVID-19 transmissions differ from observed cases. In September 2020, Cape Verde, Namibia, Eswatini, and South Africa had the highest speed of COVID-19 transmissions at 13.1, 7.1, 3.6, and 3 infections per 100,0000, respectively; Zimbabwe had an acceleration rate of transmission, while Zambia had the largest rate of deceleration this week compared to last week, referred to as a jerk. Finally, the 7-day persistence rate indicates the number of cases on September 15, 2020, which are a function of new infections from September 8, 2020, decreased in South Africa from 216.7 to 173.2 and Ethiopia from 136.7 to 106.3 per 100,000. The statistical approach was validated based on the regression results; they determined recent changes in the pattern of infection, and during the weeks of September 1-8 and September 9-15, there were substantial country differences in the evolution of the SSA pandemic. This change represents a decrease in the transmission model R value for that week and is consistent with a de-escalation in the pandemic for the sub-Saharan African continent in general. Conclusions: Standard surveillance metrics such as daily observed new COVID-19 cases or deaths are necessary but insufficient to mitigate and prevent COVID-19 transmission. Public health leaders also need to know where COVID-19 transmission rates are accelerating or decelerating, whether those rates increase or decrease over short time frames because the pandemic can quickly escalate, and how many cases today are a function of new infections 7 days ago. Even though SSA is home to some of the poorest countries in the world, development and population size are not necessarily predictive of COVID-19 transmission, meaning higher income countries like the United States can learn from African countries on how best to implement mitigation and prevention efforts.

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中文翻译：

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撒哈拉以南非洲的SARS-CoV-2监视系统：持续存在和传递信息政策的模型研究

背景：自从新型冠状病毒于2019年底问世以来，世界各地的科学和公共卫生界都在寻求更好地了解，监测，治疗和预防COVID-19疾病。在撒哈拉以南非洲（SSA），许多国家采取了果断的措施，采取了封锁措施和关闭边界。尽管各国之间的病案数量和死亡率存在很大差异，但此类行动可能有助于防止该地区大部分地区的大规模暴发。此外，整个撒哈拉以南非洲地区，卫生系统的基础设施仍然是一个令人担忧的问题，锁定措施有可能加剧该次大陆最贫困居民的贫困和粮食不安全状况。在许多国家/地区，缺乏足够的测试，无症状感染以及不良的举报做法，限制了我们对病毒影响的了解，这就需要更好，更准确的监视指标，以应对报告不足和数据污染的问题。目的：本研究的目的是通过用新的和可分解的COVID-19监测指标补充标准化指标来改善传染病监测，以克服公共卫生监测系统固有的数据限制和污染。除了观察到的日常和累积测试的普遍性，测试阳性率，发病率和死亡率外，我们还从速度，加速或减速，加速或减速（急动）变化和7天传输率方面推导了COVID-19传输坚持不懈 它解释了COVID-19在何处以及以多快的速度传播，并量化了加速或减速速率的变化，从而为缓解和预防SAV中的COVID-19和粮食不安全的政策提供了依据。方法：我们从公共卫生登记处提取了60天的COVID-19数据，并采用经验差异方程来衡量47个撒哈拉以南国家的每日病例数，这是先前病例数，检测水平和每周轮班的函数基于动态面板模型的变量，该模型是使用R中的Arellano-Bond估计量通过广义矩量法进行估计的。结果：肯尼亚，加纳，尼日利亚，埃塞俄比亚和南非的观察到的COVID-19病例最多，塞舌尔，厄立特里亚，毛里求斯，科摩罗和布隆迪最少。相比之下，速度，加速度，冲击 和7天的持续时间表明，COVID-19传播率与观察到的病例不同。2020年9月，佛得角，纳米比亚，埃斯瓦蒂尼和南非的COVID-19传播速度最高，分别为每100,0000次感染13.1、7.1、3.6和3次感染。津巴布韦的变速箱加速，而本周与上周相比，赞比亚的变速箱最大。最后，这7天的持续感染率表明了2020年9月15日的病例数，这是从2020年9月8日开始的新感染病例，南非的发病率从100,000例的216.7例降至173.2例，埃塞俄比亚的发病率从136.7例降至106.3例。基于回归结果验证了统计方法；他们确定了感染模式的最新变化，在9月1日至8日和9月9日至15日的几周中，SSA大流行的演变存在明显的国家差异。这种变化表示该周的传播模型R值下降，并且与整个撒哈拉以南非洲大陆的大流行趋势的降低相一致。结论：标准的监测指标是必要的，例如每天观察到的新的COVID-19病例或死亡，但不足以减轻和预防COVID-19的传播。公共卫生领导者还需要知道COVID-19传播率在哪里加速或下降，这些率是否在短时间内增加或减少，因为大流行可以迅速升级，以及今天有多少病例是7天前新感染的原因。即使SSA居住在世界上一些最贫穷的国家中，

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