Effective immunization scenarios by vaccination against COVID-19 obtained through Bayesian analysis

Abstract

The COVID-19 pandemic caused a health crisis on an unprecedented scale, due to the unstoppable spread of the disease through the global integration provided by the current strongly globalized society. Isolation policies recommended by WHO impacted countries economies, making it necessary to implement a range of economic, health, social and economic security policies, such as: financial aid packages for the population and companies, investment of resources for the structuring of hospital beds for the treatment of patients, implementation of population testing programs, among others. The impact of the pandemic proved to be quite significant especially for the so-called "developing countries", due to the difficulties in establishing the aforementioned range of security policies as well as mass testing of the population in order to reliably map the disease spreading. The implementation of those measure were limited not only by economic reasons, but also by political impediments. In the present study, a Bayesian analysis of the immunization rate against COVID-19 through vaccination, evaluating the immunization scenarios lasting or temporary. The logistics required for the vaccination campaign in Brazil were assessed by considering uncertainties in determining the daily rate of immunization, as well as the effectiveness of the vaccine made available for the population. In this intent, an epidemiological model was used compartmentalized, calibrated against historical data using the CATMIP stochastic algorithm. The obtained results indicate that the daily vaccination rate is the most important parameter of a successful immunization program, given an effectiveness rate equal or superior to 50%. However, a massive immunization rate needs to be conjugated with a responsible relaxing of the social distancing measures after the vaccination program start, under the risk of compromising any positive result in terms of the number of contaminated and deceased individuals.