The effective reproduction number (R) which signifies the number of secondary cases infected by one infectious individual, is an important measure of the spread of an infectious disease. Due to the dynamics of COVID-19 where many infected people are not showing symptoms or showing mild symptoms, and where different countries are employing different testing strategies, it is quite difficult to calculate the R, while the pandemic is still widespread. This paper presents a probabilistic methodology to evaluate the effective reproduction number by considering only the daily death statistics of a given country. The methodology utilizes a linearly constrained Quadratic Programming scheme to estimate the daily new infection cases from the daily death statistics, based on the probability distribution of delays associated with symptom onset and to reporting a death. The proposed methodology is validated in-silico by simulating an infectious disease through a Susceptible-Infectious-Recovered (SIR) model. The results suggest that with a reasonable estimate of distribution of delay to death from the onset of symptoms, the model can provide accurate estimates of R. The proposed method is then used to estimate the R values for two countries.

有效繁殖数（R）表示被一个传染性个体感染的继发病例数，是衡量传染病传播的重要指标。由于COVID-19的动态，许多感染者没有出现症状或出现轻度症状，并且不同国家采用不同的检测策略，因此很难计算出R，而大流行仍很普遍。本文提出了一种概率方法，通过仅考虑给定国家的每日死亡统计数据来评估有效繁殖数量。该方法利用线性约束二次规划方案，根据与症状发作相关的延迟的概率分布并报告死亡，从每日死亡统计数据中估计每天的新感染病例。拟议的方法已通过“可疑传染病恢复”（SIR）模型模拟​​传染病进行了计算机模拟验证。结果表明，通过合理的估计从症状发作到死亡延迟的分布，该模型可以提供R的准确估计。。然后将所提出的方法用于估算两个国家的R值。