A probabilistic estimation of the basic reproduction number: A case of control strategy of pneumonia

摘要

Deterministic models have been used in the past to understand the epidemiology of infectious diseases, most importantly to estimate the basic reproduction number, R_o by using disease parameters. However, the approach overlooks variation on the disease parameter(s) which are function of R_o and can introduce random effect on R_o. In this paper, we estimate the R_o as a random variable by first developing and analyzing a deterministic model for transmission patterns of pneumonia, and then compute the probability distribution of R_o using Monte Carlo Markov Chain (MCMC) simulation approach. A detailed analysis of the simulated transmission data, leads to probability distribution of R_o as opposed to a single value in the convectional deterministic modeling approach. Results indicate that there is sufficient information generated when uncertainty is considered in the computation of R_o and can be used to describe the effect of parameter change in deterministic models.