Next Article in Journal
Distinct African Swine Fever Virus Shedding in Wild Boar Infected with Virulent and Attenuated Isolates
Next Article in Special Issue
Current State of the First COVID-19 Vaccines
Previous Article in Journal
Virus-Like Particle Based Vaccines Elicit Neutralizing Antibodies against the HIV-1 Fusion Peptide
Previous Article in Special Issue
Is a COVID-19 Vaccine Likely to Make Things Worse?
Open AccessArticle

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

1
Istituto Nazionale di Geofisica e Vulcanologia, 00143 Rome, Italy
2
UOC Sistemi Informativi Azienda Zero—Regione del Veneto, 35131 Padua, Italy
3
Department of Molecular Medicine, University of Padua, 35121 Padua, Italy
4
Istituto per le Applicazioni del Calcolo Mauro Picone, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy
5
Mathematics Department “Guido Castelnuovo”, Sapienza University of Rome, 00185 Rome, Italy
6
Department of Mathematics and Statistics, University of Troms∅, N-9037 Troms∅, Norway
*
Author to whom correspondence should be addressed.
Vaccines 2020, 8(4), 766; https://doi.org/10.3390/vaccines8040766
Received: 9 November 2020 / Revised: 28 November 2020 / Accepted: 13 December 2020 / Published: 15 December 2020
(This article belongs to the Special Issue Vaccines: Uptake and Equity in Times of the COVID-19 Pandemic)
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. View Full-Text
Keywords: SARS-CoV-2; statistical analysis; vaccines; pandemic preparedness SARS-CoV-2; statistical analysis; vaccines; pandemic preparedness
Show Figures

Figure 1

MDPI and ACS Style

Spassiani, I.; Gubian, L.; Palù, G.; Sebastiani, G. Vaccination Criteria Based on Factors Influencing COVID-19 Diffusion and Mortality. Vaccines 2020, 8, 766. https://doi.org/10.3390/vaccines8040766

AMA Style

Spassiani I, Gubian L, Palù G, Sebastiani G. Vaccination Criteria Based on Factors Influencing COVID-19 Diffusion and Mortality. Vaccines. 2020; 8(4):766. https://doi.org/10.3390/vaccines8040766

Chicago/Turabian Style

Spassiani, Ilaria; Gubian, Lorenzo; Palù, Giorgio; Sebastiani, Giovanni. 2020. "Vaccination Criteria Based on Factors Influencing COVID-19 Diffusion and Mortality" Vaccines 8, no. 4: 766. https://doi.org/10.3390/vaccines8040766

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop