Abstract
Background: In the absence of any pharmaceutical interventions, the management of the COVID-19 pandemic is based on public health measures. The present study fosters evidence-based decision making by estimating various “a posteriori probability distributions" from COVID-19 patients. 
 Methods: In this retrospective observational study, 987 RT-PCR positive COVID-19 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. 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 general population's age distribution using the 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 knowledge of disease frequency patterns helps in the optimum allocation of limited resources and manpower. The study provides information to various epidemiological models for further analysis.
Highlights
In the absence of any pharmaceutical interventions, the management of the COVID-19 pandemic is based on public health measures
Full list of author information is available at the end of the article epidemiological models, such as the “mutually exclusive compartments SIR” model (Susceptible, Infectious, or Recovered), used structured age data and social contact matrices to study the progress of the COVID-19 epidemic [3]
The probability of men (P [Male] = 0.62, 95% CI: 0.59-0.65) suffering from COVID-19 was higher than for women (P [Female] = 0.38, 95% CI: 0.35-0.41) (Figure 3 Panel A and Table 2)
Summary
In the absence of any pharmaceutical interventions, the management of the COVID-19 pandemic is based on public health measures. The present study fosters evidence-based decision making by estimating various “a posteriori probability distributions" from COVID-19 patients. In the absence of a vaccine, disease pandemic control includes public health measures such as lockdown and social distancing. Full list of author information is available at the end of the article epidemiological models, such as the “mutually exclusive compartments SIR” model (Susceptible, Infectious, or Recovered), used structured age data and social contact matrices to study the progress of the COVID-19 epidemic [3]. The information can be extracted from this data in the form of ‘a posterior probability distributions”. These distributions generate scientific evidence for further decision making [5]. The lesser frequency of occurrence of COVID-19 in children might be due to their having fewer outdoor activities and less international travel [6]
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