Bayesian Inference and Simulation on True Prevalence of the Coronavirus Disease (COVID-19) in China and Its Screening Diagnostic Evaluation

Background: By April 2, 2020, newly emerged 2019 Novel Coronavirus has claimed more than 46 809 death tolls in world, and at least 932 605 people infected. We present inferential dynamic prevalence of COVID-19 in China along with its diagnosis screening and intervention to better inform policy to further reduce the COVID-19 impacts either in or outside China. Methods: We employed Bayesian statistical integrate with RT-PCR and Monte Carlo methods to infer the true prevalence of COVID-19 in China, as well as its uncertainty and variability and associated diagnostic predictive value within 19 randomly selected epidemiology-setting in two distinctive sample space(A-China; B-Hubei), and further conducted sensitivity analysis to evaluate the impact of various factors on inference. Findings: Through the Bayesian modeling, we found that median value of different epidemiology settings in population A-China (Hubei excluded) in 21 Jan, 28 Jan, 08 Feb, 18 Feb , 28 Feb, 07 Mar, 12 Mar, 24 Mar and 29 Mar respectively, were 0·1394 (95% Crl=0·08-0·19), 0·0491 (95 Crl=0·0028-0·07), 0·0226 (95% Crl=0·0006-0·04), 0·0039 (95% Crl=0·00016-0·02), 0·0035 (95% Crl=0·00014-0·01), 0·0029 (95% Crl=0·00013-0·01), 0·0036 (95% Crl=0·00016-0·01), 0·00293 (95% Crl=0·00013-0·01) and 0·00294 (95% Crl=0·00011-0·01), correspondingly the median value of screening false negative (FN) were 0·0284 (95% Crl=0·01-0·04), 0·0091 (95% Crl=0·0005-0·01), 0·004 (95% Crl=0·00012-0·01), 0·00072 (95% Crl=0·000031-0·0029), 0·00063 (95% Crl=0·000026-0·0027), 0·00053 (95% Crl=0·000024-0·00172), 0·00065 (95% Crl=0·000028-0·0027), 0·00053 (95% Crl=0·000022-0·0016), 0·00053 (95% Crl=0·00002-0·0016). In Contrast, in population B-Hubei, the median prevalence after Bayesian modeling in each epidemiology setting in 11 Jan, 20 Jan, 28 Jan, 08 Feb, 18 Feb , 28 Feb, 07 Mar, 12 Mar, 24 Mar and 29 Mar were 0·0521 (95% Crl=0·0046-0·08), 0·3283 (95% Crl=0·24-0·42), 0·1790 (95% Crl=0·12-0·22), 0·2489 (95% Crl=0·2-0·29), 0·2859 (95% Crl=0·23-0·34), 0·2811 (95% Crl=0·23-0·34), 0·1160 (95% CrI= 0·05-0·16), 0·0205 (95% Crl=0·0011-0·06), 0·00346 (95% Crl=0·00016-0·01) and 0·00341 (95% Crl=0·00014-0·01) respectively, correspondingly the median FN is 0·0095 (95% Crl=0·00073-0·0166), 0·0804 (95% Crl=0·05-0·1170), 0·0372 (95% Crl=0·025-0·0492), 0·0556 (95% Crl=0·04-0·0722), 0·0668 (95% Crl=0·05-0·0861), 0·0652 (95% Crl=0·05-0·0860), 0·0223 (95% Crl=0·01-0·04), 0·0036 (95% Crl=0·0002-0·01), 0·00061 (95% Crl=0·000027- 0·0028) and 0·00059 (95% Crl=0·000026-0·0026). The sensitivity analysis revealed that the clinical RT-PCR specificity had the most important and highest impact on the uncertainty and variability inference of true prevalence as well as for positive predictive value (PPV) ,negative predictive value (NPV) and FN, followed by clinical sensitivity, and then the prior distribution. Interpretation: Large sample size designed-experiment of Bayesian inference provided an accurate and intuitive understanding on current status of COVID-19 in China. Dynamic prevalence changes implied that a gradually and significant decreasing trend of infection have been formed along with line of strict intervention and screening, but due to uncertainty and variability of pathogen as well as the wide spectrum hosts and various environmental exposure, more risk-based screening and surveillance is further needed. Given institutively recognition on the lower concentrated distribution of current prevalence of COVID-19 and the existence of asymptomatic infections, current predictive value of PPV is not so sensitive to help in making a right decision for confirmed infection in low risk prevailed region. In such circumstance, two solutions are suggested, first is to seek the second or more confirmatory RT-PCR assays that target a different part of genome to increase statistical assurance for positive confirmation. Secondly, if contradictory results of tests are still yielded, the confirmed case should be based more on patient epidemiology history and clinical assessment, rather than just RT-PCR test. Funding Statement: This research was supported by a commissioned grant from Ministry of Science & Technology, China (P193999098) (2016YFD0501104). Declaration of Interests: The authors declare no competing interests.