Abstract
Background: In the absence of a vaccine for coronavirus disease-19, the management of the current pandemic revolves around public health measures such as social distancing, lockdown, and contact tracing. A number of epidemiological models are used in decision making and for generating research intelligence. The models require information regarding the structures of social contact between different ages and genders. The present study fosters evidence-based decision making by estimating various a posteriori probability distributions from data of COVID-19 patients. Patients and Methods: In this retrospective observational study, 987 real-time RT-PCR SARS CoV-2 positive patients from SMS Medical College, Jaipur, India were enrolled after approval of the institutional ethics committee. The data regarding age, gender, and outcome were collected from case sheets. The univariate and bivariate distributions of COVID-19 cases with respect to age, gender, and outcome were estimated. The age distribution of COVID-19 cases was compared with the age distribution of general population using goodness of fit c2 test. The independence of attributes in bivariate distributions was evaluated using the chi square test for independence.Results: The age group ‘25-29’ has shown highest probability of COVID-19 cases (P[25-29] = 0.14, 95% CI: 0.12- 0.16). The men (P[Male] = 0.62, 95%CI: 0.59-0.65) were dominant sufferers. The most common outcome was recovery (P[Recovered] = 0.79, 95%CI: 0.76-0.81) followed by admitted cases (P[Active]= 0.13, 95%CI: 0.11-0.15) and death (P[Death] = 0.08, 95%CI: 0.06-0.10). The age distribution of COVID-19 cases differs significantly from the age distribution of the general population (c2 = 399.04, p < 0.001). The bivariate distribution of COVID-19 across age and outcome was not independent (c2 =106.21, df = 32, p < 0.001).Conclusion: The age, gender, and outcome distributions helps in evaluating disease dynamics and the social structure of the community. The knowledge of patterns of disease frequency helps in optimum allocation of limited resources and manpower. The study provides information for various epidemiological models, to decide the duration of lockdown.
Evidence based decision making and COVID-19: what a posteriori probability distributions speak.
