De Carvalho Eduardo Atem, De Carvalho Rogerio Atem
Center for Science and Technology Universidade Estadual do Norte Fluminense Campos Brazil.
Innovation Hub Instituto Federal Fluminense Campos Brazil.
JMIRx Med. 2021 Mar 18;2(1):e22617. doi: 10.2196/22617. eCollection 2021 Jan-Mar.
Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that drive its local transmission cycles to make better decisions regarding prevention and control measures. Different modeling approaches have been proposed in an attempt to predict the behavior of these local cycles.
This paper presents a framework to characterize the different variables that drive the local, or epidemic, cycles of the COVID-19 pandemic, in order to provide a set of relatively simple, yet efficient, statistical tools to be used by local health authorities to support decision making.
Virtually closed cycles were compared to cycles in progress from different locations that present similar patterns in the figures that describe them. With the aim to compare populations of different sizes at different periods of time and locations, the cycles were normalized, allowing an analysis based on the core behavior of the numerical series. A model for the reproduction number was derived from the experimental data, and its performance was presented, including the effect of subnotification (ie, underreporting). A variation of the logistic model was used together with an innovative inventory model to calculate the actual number of infected persons, analyze the incubation period, and determine the actual onset of local epidemic cycles.
The similarities among cycles were demonstrated. A pattern between the cycles studied, which took on a triangular shape, was identified and used to make predictions about the duration of future cycles. Analyses on effective reproduction number (R) and subnotification effects for Germany, Italy, and Sweden were presented to show the performance of the framework introduced here. After comparing data from the three countries, it was possible to determine the probable dates of the actual onset of the epidemic cycles for each country, the typical duration of the incubation period for the disease, and the total number of infected persons during each cycle. In general terms, a probable average incubation time of 5 days was found, and the method used here was able to estimate the end of the cycles up to 34 days in advance, while demonstrating that the impact of the subnotification level (ie, error) on the effective reproduction number was <5%.
It was demonstrated that, with relatively simple mathematical tools, it is possible to obtain a reliable understanding of the behavior of COVID-19 local epidemic cycles, by introducing an integrated framework for identifying cycle patterns and calculating the variables that drive it, namely: the R, the subnotification effects on estimations, the most probable actual cycles start dates, the total number of infected, and the most likely incubation period for SARS-CoV-2.
自新冠疫情开始以来，研究人员和卫生当局一直在努力确定驱动其本地传播周期的不同参数，以便在预防和控制措施方面做出更好的决策。人们提出了不同的建模方法来预测这些本地周期的行为。
本文提出了一个框架，用于描述驱动新冠疫情本地或流行周期的不同变量，以便提供一套相对简单但高效的统计工具，供地方卫生当局用于支持决策。
将虚拟封闭周期与来自不同地点的正在进行的周期进行比较，这些周期在描述它们的图表中呈现出相似的模式。为了在不同时间和地点比较不同规模的人群，对周期进行了归一化处理，从而能够基于数值序列的核心行为进行分析。从实验数据中推导出繁殖数模型，并展示了其性能，包括漏报（即报告不足）的影响。使用逻辑模型的一个变体与一个创新的库存模型一起计算实际感染人数、分析潜伏期并确定本地疫情周期的实际开始时间。
证明了各周期之间的相似性。确定了所研究周期之间呈现三角形的一种模式，并用于预测未来周期的持续时间。展示了对德国、意大利和瑞典的有效繁殖数（R）和漏报影响的分析，以说明此处介绍的框架的性能。在比较这三个国家的数据后，能够确定每个国家疫情周期实际开始的可能日期、该疾病潜伏期的典型持续时间以及每个周期内的感染总人数。一般来说，发现可能的平均潜伏期为5天，此处使用的方法能够提前34天估计周期的结束时间，同时表明漏报水平（即误差）对有效繁殖数的影响小于5%。
结果表明，通过引入一个用于识别周期模式并计算驱动该模式的变量（即R、估计中的漏报影响、最可能实际周期开始日期、感染总人数以及SARS-CoV-2最可能的潜伏期）的综合框架，使用相对简单的数学工具就有可能可靠地了解新冠本地疫情周期的行为。
