ABSTRACT
The global impact of COVID-19 has challenged health systems across the world. This situation highlighted the need to develop policies based on scientific evidence to prepare the health systems and mitigate the pandemic. In this scenario, governments were urged to predict the impact of the measures they were implementing, how they related to the population's behavior, and the capacity of health systems to respond to the pandemic. The overarching aim of this research was to develop a customizable and open-source tool to predict the impact of the expansion of COVID-19 on the level of preparedness of the health systems of different Latin American and the Caribbean countries, with two main objectives. Firstly, to estimate the transmission dynamics of COVID-19 and the preparedness and response capacity of health systems in those countries, based on different scenarios and public policies implemented to control, mitigate, or suppress the spread of the epidemic. Secondly, to facilitate policy makers' decisions by allowing the model to adjust its parameters according to the specific pandemic trajectory and policy context. How many infections and deaths are estimated per day?;When are the peaks of cases and deaths expected, according to the different scenarios?;Which occupancy rate will ICU services have along the epidemiological curve?;When is the optimal time increase restrictions in order to prevent saturation of ICU beds?, are some of the key questions that the model can respond, and is publicly accessible through the following link: http://shinyapps.iecs.org.ar/modelo-covid19/. This open-access and open code tool is based on a SEIR model (Susceptible, Exposed, Infected and Recovered). Using a deterministic epidemiological model, it allows to frame potential scenarios for long periods, providing valuable information on the dynamics of transmission and how it could impact on health systems through multiple customized configurations adapted to specific characteristics of each country.
ABSTRACT
Objective. Our study analyzes the cost-effectiveness of the COVID-19 vaccination campaign in Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, and Peru. Methods. Using a previously published SVEIR model, we analyzed the impact of a vaccination campaign (2021) from a national healthcare perspective. The primary outcomes were quality adjusted life years (QALYs) lost and total costs. Other outcomes included COVID-19 cases, hospitalizations, deaths, and life years. We applied a discount rate of 3% for health outcomes. We modeled a realistic vaccination campaign in each country (the country-specific campaign implemented). Additionally, we assessed a standard campaign (similar, “typical“ for all countries), and an optimized campaign (similar in all countries with higher but plausible population coverage). One-way deterministic sensitivity analyses were performed. Findings. Vaccination was health improving as well as cost-saving in almost all countries and scenarios. Our analysis shows that vaccination in this group of countries prevented 573,141 deaths (508,826 standard; 685,442 optimized) and gained 5.07 million QALYs (4.53 standard; 6.03 optimized). Despite the incremental costs of vaccination campaigns, they had a total net cost saving to the health system of US$16.29 billion (US$16.47 standard; US$18.58 optimized). The realistic vaccination campaign in Chile was the only scenario, which was not cost saving, but it was still highly cost-effective with an ICER of US$22 per QALY gained. Main findings were robust in the sensitivity analyses. Interpretation. The COVID-19 vaccination campaign in seven Latin American and Caribbean countries -that comprise nearly 80% of the region- was beneficial for population health and was also cost-saving or highly cost-effective.
Subject(s)
COVID-19ABSTRACT
The global impact of COVID-19 has challenged health systems across the world. This situation highlighted the need to develop policies based on scientific evidence to prepare the health systems and mitigate the pandemic. In this scenario, governments were urged to predict the impact of the measures they were implementing, how they related to the population's behavior, and the capacity of health systems to respond to the pandemic. The overarching aim of this research was to develop a customizable and open-source tool to predict the impact of the expansion of COVID-19 on the level of preparedness of the health systems of different Latin American and the Caribbean countries, with two main objectives. Firstly, to estimate the transmission dynamics of COVID-19 and the preparedness and response capacity of health systems in those countries, based on different scenarios and public policies implemented to control, mitigate, or suppress the spread of the epidemic. Secondly, to facilitate policy makers' decisions by allowing the model to adjust its parameters according to the specific pandemic trajectory and policy context. How many infections and deaths are estimated per day?; When are the peaks of cases and deaths expected, according to the different scenarios?; Which occupancy rate will ICU services have along the epidemiological curve?; When is the optimal time increase restrictions in order to prevent saturation of ICU beds?, are some of the key questions that the model can respond, and is publicly accessible through the following link: http://shinyapps.iecs.org.ar/modelo-covid19/. This open-access and open code tool is based on a SEIR model (Susceptible, Exposed, Infected and Recovered). Using a deterministic epidemiological model, it allows to frame potential scenarios for long periods, providing valuable information on the dynamics of transmission and how it could impact on health systems through multiple customized configurations adapted to specific characteristics of each country.