Progress on the basic reproduction number of SARS-CoV-2

Abstract

Coronavirus disease 2019 (COVID-19) is an emerging infectious diseases caused by a new coronavirus named SARS-CoV-2. In the epidemiology of infectious diseases, the basic reproduction number (R0) is a central quantitative parameter that can be used to measure the transmission potential of infectious diseases. R0 represents the average number of new infections generated by an infectious person in a totally naïve population. If R0>1, the number infected is likely to increase. If R0<1, transmission is likely to die out. In this study, we present a review on the estimation of basic reproduction number of SARS-CoV-2, in order to provide a scientific basis for better understanding the dynamic characteristics of SARS-CoV-2 transmission. R0 is usually estimated with various types of complex mathematical models. It can be calculated and estimated by differential equations in the propagation dynamics model (such as SIR model and SEIR model), or estimated using maximum likelihood method, stochastic model, etc. Modeled R0 values are dependent on model structures and assumptions. With the rapid spread of the global COVID-19 epidemic, there is an increasing evidence on R0 of SARS-CoV-2. However, the results of R0 estimation varied a lot in different studies, due to different model assumptions, parameters settings, and data used in the models. The estimated median R0 is about 3.15 (95%CI: 2.26-6.20) in studies published by peer-reviewed journals, 3.01 (95%CI: 1.99-5.44) in preprinted platform without peer-reviewed and online reports, and 2.55 (95%CI: 1.61-3.55) in studies published in Chinese language, and 3.10 (95%CI: 2.09-6.05) in studies published in English language. With the implementation of comprehensive intervention measures, R0 showed a downward trend. In the basic propagation dynamics model, R0 might be estimated with biases, due to the limitations of its basic assumptions (such as fixed population). Regardless of the methods used, characteristics of the virus should be considered fully during estimation. To better estimate R0, some researchers improved the basic propagation dynamics model and considered the assumption of unfixed population, population mobility, infectivity of the virus during incubation period, and quarantine measures, to make it more in line with the characteristics of SARS-CoV-2. When adopting other methods such as exponential growth model and stochastic model to estimate R0, the estimated results is relatively stable because that it is not restricted by assumptions of the propagation dynamics model. However, the estimation are more susceptible to the data used in the model (such as distribution of the data) because that the inherent characteristics of the spread of infectious diseases are not considered. Thus, it is particularly critical to use appropriate data distribution and parameters during estimation.