Moving-average based index to timely evaluate the current epidemic situation after COVID-19 outbreak

Yun-ting He; Hao He; Jing Zhai; Xiao-jin Wang; Bing-shun Wang

The copyright holder for this preprint . https: //doi.org/10.1101 //doi.org/10. /2020 reported that Severe Acute Respiratory Syndrome-associated Coronavirus (SARS-CoV) had an R0 value of approximately 2.2 to 3.6 (Lipsitch et al. 2003) . The transmission capacity of COVID-19 appeared to be similar or higher compared to SARS-CoV. As a continuous unconventional public health event that can lead to economic recession and health damage, it is of great importance to timely estimate the epidemic stage of the outbreak and determine whether it is possible to return to normal work and life. Faced with such a sudden outbreak of new infectious diseases, we have no history to learn from and no evidence to count on. The traditional epidemic prediction models, including SEIR, are highly sensitive to the pre-specified epidemiological parameters, and the prediction results may be highly inconsistent due to different assumptions and data sources. The reliability of the SEIR model can be affected by many factors that are difficult to estimate, including seasonality of coronavirus, inter-city mobility, transmission of travel behavior, and the number of asymptomatic infections. The model may underestimate the scale of the outbreak (Wu et al. 2020) . Traditional models prove to be most reliable only under ideal conditions.