Vaccination Criteria Based on Factors Influencing COVID-19 Diffusion and Mortality

Abstract

SARS-CoV-2 is highly contagious, rapidly turned into a pandemic, and is causing a relevant number of critical to severe life-threatening COVID-19 patients. However, robust statistical studies of a large cohort of patients, potentially useful to implement a vaccination campaign, are rare. We analyzed public data of about 19,000 patients for the period 28 February to 15 May 2020 by several mathematical methods. Precisely, we describe the COVID-19 evolution of a number of variables that include age, gender, patient's care location, and comorbidities. It prompts consideration of special preventive and therapeutic measures for subjects more prone to developing life-threatening conditions while affording quantitative parameters for predicting the effects of an outburst of the pandemic on public health structures and facilities adopted in response. We propose a mathematical way to use these results as a powerful tool to face the pandemic and implement a mass vaccination campaign. This is done by means of priority criteria based on the influence of the considered variables on the probability of both death and infection.

Keywords: SARS-CoV-2; pandemic preparedness; statistical analysis; vaccines.

Figure 1

Schematic representation of the work.

Figure 2

Age distributions of COVID-19 patients relative to the Veneto region health system (Italy), in the period 28 February–15 May 2020. (a–c) respectively refer to: whole tested patients, positive, and dead ones. The number of patients is also reported. The age range was discretized in equally spaced subintervals of 1 year. Means and standard deviations obtained are respectively: 53.4 and 20.8 for all the patients, 60.3 and 22.0 for the positive ones, and 83.6 and 10.1 for the positive, dead patients.

Figure 3

Distribution of dead patients conditioned to the test positivity. To obtain the figure, we took for each age the ratio between the kernel density estimation in Figure 2c by that in Figure 2b, and then we normalized it. The age range was discretized in equally spaced subintervals of 1 year.

Figure 4

Time between first symptoms and first positive test of COVID-19 patients in Veneto, in the time interval 24 February–15 May 2020. The temporal range was discretized in equally spaced subintervals of 1 day. (a–c) refers to the positive tested whole population, females and males, respectively. The number of patients is also reported. Means and standard deviations obtained are respectively: 7.89 and 7.28 for all the patients, 7.84 and 7.5 for the females, and 7.93 and 7.06 for the males.

Figure 5

(a): Probability of death as a function of the number of comorbidities (see also Table S8 of the Supplementary Materials). The continuous line represents the best fit given by the extended logistic function. (b): the same as (a) but relative to being admitted or not to ICU (see Tables S9 and S10 of the Supplementary Materials). For the patients admitted to ICU, the best fit is given by a horizontal straight line. Instead, the best fit for the death probability of patients not being admitted to ICU is obtained with an extended logistic function (continuous curve in the panel).