A quantitative method to project the probability of the end of an epidemic: Application to the COVID-19 outbreak in Wuhan, 2020. (21st July 2022) - Yuan, Baoyin
Highlights: Countries pursuing 'zero COVID' rely on strong restrictions or even city lockdown. Optimally balancing restrictions's length and confidence that the outbreak is ending is important. The proportion of cases who miss the intervention restrictions and thus are capable of causing secondary transmission is estimated. An iterative method is designed to quantify the daily end-of-outbreak confidence since the last reported case. The projection of the end-of-outbreak day relies on the size of reproduction number and the acceptable risk threshold assuming the invariant distribution of serial interval. Abstract: The end-of-outbreak declaration is an important part of epidemic control, marking the relaxation or cancellation of prevention and control measures. We propose a probability model to retrospectively quantify the confidence of giving the end-of-outbreak declaration during the COVID-19 epidemic in early 2020 in Wuhan. By using the linear spline, we firstly estimates the time-varying proportion of cases who miss the nonpharmaceutical interventions (NPIs) among all reported cases. Assuming the reproduction numbers being 1.5, 2.0, 3.0, 4.0, 5.0 and 6.0, the respective probability of the end of the COVID-19 outbreak with time after the last reported case can be iteratively computed. Consequently, the varying reproduction numbers produce slightly different increasing patterns of NPI effectiveness, and the end-of-outbreak declarations with 95% confidence are projected consistently earlier than the day when the lockdown was actually lifted. The reason for the timing discrepancy is discussed as well.