Author: Peter X Song; Lili Wang; Yiwang Zhou; Jie He; Bin Zhu; Fei Wang; Lu Tang; Marisa Eisenberg
Title: An epidemiological forecast model and software assessing interventions on COVID-19 epidemic in China Document date: 2020_3_3
ID: m9icky9z_3
Snippet: As shown in Figure 1 , these observed time series are emitted from the underlying latent dynamics of COVID-19 infection characterized by the latent Markov process θ t . It is easy to see that the expected proportions in both models (1) and (2) are equal to the prevalence of infection and the probability of removal at time t, namely EpY I t |θ t q " θ I t and EpY R t |θ t q " θ R t . See Appendix B. Moreover, the latent population prevalence .....
Document: As shown in Figure 1 , these observed time series are emitted from the underlying latent dynamics of COVID-19 infection characterized by the latent Markov process θ t . It is easy to see that the expected proportions in both models (1) and (2) are equal to the prevalence of infection and the probability of removal at time t, namely EpY I t |θ t q " θ I t and EpY R t |θ t q " θ R t . See Appendix B. Moreover, the latent population prevalence θ t " pθ S t , θ I t , θ R t q J is a three-dimensional Markov process, in which θ S t is the probability of a person being susceptible or at risk at time t, θ I t is the probability of a person being infected at time t, and θ R t is the probability of a person being removed from the infectious system (either recovered or dead) at time t. Obviously, θ S t`θ I t`θ R t " 1. We assume that this 3-dimensional prevalence process θ t is governed by the following Markov model:
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