Two waves of COIVD-19 in Brazilian cities and vaccination impact

Lixin Lin1, Boqiang Chen1, Yanji Zhao1

  • 1Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong 999077, China.

Insights

Brazil

Area of Science:

  • Epidemiology
  • Mathematical Modeling

Background:

  • Brazil experienced two severe waves of Coronavirus Disease 2019 (COVID-19), with the second wave exacerbated by the Gamma variant.
  • Key factors like reinfection rates, infection fatality rate (IFR), and vaccination impact remained unclear.
  • Previous reports indicated low levels of confirmed COVID-19 reinfection.

Purpose of the Study:

  • To model COVID-19 dynamics in Brazil, incorporating severe cases, vaccination, and time-varying transmission rates.
  • To assess the impact of vaccination campaigns across different Brazilian cities.
  • To evaluate the role of reinfection in the second wave of COVID-19.

Main Methods:

  • Modified Susceptible-Exposed-Infectious-Recovered (SEIR) model with compartments for severe cases and vaccination.
  • Model fitted to severe acute respiratory infection (SARI) deaths in 20 major Brazilian cities.
  • Comparative analysis of scenarios with and without vaccination; model evaluation with absent reinfection.

Main Results:

  • The model accurately simulated deaths in the 20 studied cities.
  • Vaccination effectiveness varied significantly among different cities.
  • The estimated median infection fatality rate (IFR) was approximately 1.2%.

Conclusions:

  • Vaccination campaign effectiveness differed across Brazilian cities.
  • Reinfection was not a critical factor driving the second wave of COVID-19.
  • High infection fatality rates may be linked to healthcare system strain in numerous cities.
Abstract

Related Concept Videos

Vaccinations01:51

Vaccinations

Overview
45.4K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
738
Prevalence and Incidence01:08

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
865
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
971
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
196
Cross-reactivity00:42

Cross-reactivity

Overview
31.6K