The Consistent Transmission Laws Under the Consistent Intervention Policy

COVID-19 has been spreading globally from 2019 to 2022, prompting many countries to establish consistent, timely, and strict intervention policies during this period. However, the existing analysis of central epidemiological parameters (CPPs) falls short in exploring the consistency of transmission laws implied by these policies. To resolve this limitation, we propose a consistent transmission law inference framework. This framework first provides a coarse-to-fine detection method to extract outbreaks and devises an autonomous inference sliding window (ASW) algorithm to estimate the infected-case-dependent CPPs of these outbreaks. Then, we reveal consistent transmission laws through a differential analysis of the estimated CPPs. Focusing on the transmission of COVID-19 in China, a typical country with a consistent intervention policy, our model reveals remarkably consistent outbreak laws. In short, the policy controls the effective growth rate almost to zero (to a 1E-03 scale) within three days since the outbreak started. Specifically: 1) the policy was very effective at the beginning of the outbreak, leading to the exposure rate hitting the bottom on one day between the second and eleventh days and then keeping it at a plateau; 2) under large-scale nucleic acid testing and contact tracking, the incubation rate decreased and reached a plateau mainly on the third day; and 3) the recovery rate fluctuated extremely little on a 1E-03 scale, showing no significant breakthrough in COVID-19 treatment that helped the policy work. Our method can be readily adapted to other countries and SEIR epidemic.