The Effect of Population Size for Pathogen Transmission on Prediction of COVID-19 Pandemic Spread

Abstract: Extreme public health interventions play a critical role in mitigating the local and global prevalence and pandemic potential of COVID-19. Here, we use population size for pathogen transmission to measure the intensity of public health interventions, which is a key characteristic variable for nowcasting and forecasting of the epidemic. By formulating a hidden Markov dynamic system and using nonlinear filtering theory, we have developed a stochastic epidemic dynamic model under public health interventions. The model parameters and states are estimated in time from internationally available public data by combining an unscented filter and an interacting multiple model filter. Moreover, we consider the computability of the population size and provide its selection criterion. We estimate the mean of the basic reproductive number of China and the rest of the globe except China (GEC) to be 2.46 (95% CI: 2.41-2.51) and 3.64 (95% CI: (3.55-3.72), respectively. We infer that the number of latent infections of GEC is about 7.47*10^5 (95% CI: 7.32*10^5-7.62*10^5) as of April 2, 2020. We predict that the peak of infections in hospitals of GEC may reach 3.00*10^6 on the present trajectory, i.e., if the population size for pathogen transmission and epidemic parameters remains unchanged. If the control intensity is strengthened, e.g., 50% reduction or 75% reduction of the population size for pathogen transmission, the peak would decline to 1.84*10^6, 1.27*10^6, respectively.