Prediction of the COVID-19 outbreak based on a realistic stochastic model

Yuan Zhang; Chong You; Zhenghao Cai; Jiarui Sun; Wenjie Hu; Xiao-Hua Zhou

which perfectly satisfies (2.16) while at the same time provides no information on whether the actual values of E 1 (t) andẼ 1 (t) are close to each other. To summarize, when the size of epidemic outbreak is NOT comparable to N , the stochastic model possesses intrinsic randomness, and may not be well approximated by a deterministic ODE in any non-degenerate sense. The parameters and initial values of the proposed stochastic model can be estimated based on a simplified model in Figure 2 . Here the stage D is removed as fatality rate is extremely low in all the selected regions. The populations of IH(t), R H (t) can be observed directly from the collected data, that is, the number of existing confirmed cases and reported recoveries at time t respectively, while, the remaining states are latent, namely not observable. Among the latent states, the initial value S(0) can be approximated by 11 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.