Department of Community Medicine, Ayub Medical College, Abbottabad, Pakistan.
Ayub Teaching Hospital, Abbottabad, Pakistan.
J Ayub Med Coll Abbottabad. 2020 Oct-Dec;32(Suppl 1)(4):S686-S690.
The purpose of this study was to estimate the effect of social distancing (days since the imposition of a lock-down) and the number of daily tests conducted per million population on the daily growth rate of COVID-19 cases.
After excluding the first 30 days since the announcement of an index case in a country, relevant data for the next forty days was collected from four countries: Belgium, Italy, South Korea and United Kingdom. Two online databases: Our World in Data and worldometer were used for the collection of data which included the number of new COVID-19 cases and the number of tests conducted on a given day. The acquired figures were transformed into per million population of the given country. The growth rate of daily COVID-19 cases was derived and was used as the regress and in a multiple linear regression with the number of tests per million population per day and the number of days since a lock-down was imposed as the regressors.
It was found that the growth rate of daily COVID-19 cases decreased by .051% when the number of daily tests conducted per million population increased by 1. A .532% decrease in the growth rate of daily COVID-19 cases was observed with each passing day of a lock-down, which essentially represented the most effective form of social distancing. A significant regression was calculated (F (2, 155) = 35.191, p=.014), with an R2 of .054. Neither the daily number of tests conducted per million population nor the number of days of maintaining social distancing (lock-down) was individually significant contributors to the prediction of the growth rate of daily COVID-19 cases (p=.267 and p=.554 respectively).
An extensive and rapid increase in the daily number of testing capacity and maintaining social distancing can decrease the growth rate of daily COVID-19 cases. Depending on the availability of the required resources, timely implementation of these measures can lead to better outcomes for a given population.
本研究旨在估计社交距离（从实施封锁起的天数）和每日每百万人进行的检测数量对 COVID-19 病例日增长率的影响。
排除一个国家宣布首例病例后的头 30 天，从四个国家（比利时、意大利、韩国和英国）收集接下来 40 天的数据：Our World in Data 和 worldometer 这两个在线数据库用于收集数据，包括每日新的 COVID-19 病例数和当日进行的检测次数。获取的数字转换为给定国家的每百万人。推导出每日 COVID-19 病例增长率，并将其作为回归量，在多元线性回归中，每日每百万人进行的检测次数和实施封锁的天数作为回归量。
发现当每日每百万人进行的检测次数增加 1 时，每日 COVID-19 病例增长率下降 0.051%。实施封锁的每一天，COVID-19 病例增长率下降 0.532%，这是最有效的社交距离形式。计算出一个显著的回归（F (2, 155) = 35.191，p=.014），R2 为 0.054。每日每百万人进行的检测次数和维持社交距离（封锁）的天数都不能单独显著预测每日 COVID-19 病例增长率（p=.267 和 p=.554）。
广泛而快速增加每日检测能力和维持社交距离可以降低每日 COVID-19 病例增长率。根据所需资源的可用性，及时实施这些措施可以为特定人群带来更好的结果。
