Epidemiology and Outcome Research, Real World Solutions, Scientific Services, IQVIA, Novus Tower, 4th Floor, Plot No. 18, Sector 18, Gurgaon, 122015, Haryana, India.
Public Health. 2020 Aug;185:199-201. doi: 10.1016/j.puhe.2020.06.047. Epub 2020 Jul 1.
The effective reproduction number (R) is a more practical epidemiological parameter than basic reproduction number (R) for characterization of infectious disease epidemics as it takes into account presence of immune individuals in the population which R does not. Periodic assessment of R can inform public health strategies during long-standing epidemics such as the current coronavirus disease 2019 (COVID-19) situation. This is especially relevant for large and resource-poor countries such as India, which may require differential intervention strategies in different regions. However, the complexity of the calculation involved often proves to be a barrier for calculation of R. This communication proposes a simpler data collection and analytical method - involving a combination approach instead of full-fledged primary data collection - to estimate R for public health decision-making.
Literature review.
Data from available sources (time series data of new cases at population level) can be combined with some primary data (time interval between infection of index and secondary cases in family clusters) that can be collected with little resources. These data can then be fed into an approximation-based method (Wallinga and Lipsitch) for R calculation at the state/regional levels. The calculations can be repeated every fortnight using newly available data.
The value of R, estimated using the proposed method, from subsequent periods can be used for assessing the status of the epidemic and values from subsequent periods can be compared for decision-making regarding implementation/modification of control measures.
The approximate R may be a little inaccurate but can still prove useful for rough estimation of epidemic evolution and for comparison between different periods, as the extent of error in R values across different periods is likely to be similar. Thus, the approximate R may not only be used to estimate the epidemic change in smaller geographies such as states/regions but also used for making appropriate changes to public health measures for managing a pandemic such as COVID-19.
有效繁殖数（R）比基本繁殖数（R）更能实际描述传染病的流行情况，因为它考虑了人群中存在免疫个体的情况，而 R 则没有。定期评估 R 可以为长期流行期间的公共卫生策略提供信息，例如当前的 2019 年冠状病毒病（COVID-19）情况。这对于印度等大型和资源匮乏的国家尤为重要，印度可能需要在不同地区采取不同的干预策略。然而，计算的复杂性往往成为计算 R 的障碍。本通讯提出了一种更简单的数据收集和分析方法-涉及组合方法而不是全面的原始数据收集-以估算 R 以进行公共卫生决策。
文献综述。
可以将来自现有来源（人群中新发病例的时间序列数据）的数据与一些可以用少量资源收集的原始数据（家庭群集中感染指数和二级病例之间的时间间隔）相结合。然后，可以将这些数据输入到基于近似的方法（Wallinga 和 Lipsitch）中，以计算州/地区级别的 R。可以使用新可用的数据每两周重复计算。
使用建议的方法估算的 R 的后续时期的值可用于评估流行状况，并且可以比较后续时期的值以做出有关实施/修改控制措施的决策。
近似 R 可能有点不准确，但仍可用于粗略估计流行情况的演变，并在不同时期之间进行比较，因为不同时期 R 值的误差程度可能相似。因此，近似 R 不仅可用于估计较小地理区域（如州/地区）的疫情变化，还可用于对管理大流行（如 COVID-19）的公共卫生措施进行适当调整。
