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Evaluating the Impact of Vaccine Hesitancy on the Allocation of Vital Resources During COVID-19 Pandemic

Hieu Bui, Sandra Eksioglu, Ruben Proano

TL;DR

This work proposes improvements to the susceptible-exposed-infectious-recovered (SEIR) model for COVID-19 by incorporating the influence of vaccination, VH, and resource availability on the disease dynamics, and demonstrates that reducing VH improves health outcomes.

Abstract

The COVID-19 pandemic highlighted significant challenges in the allocation of vital healthcare resources. Existing epidemiological models, specifically compartmental models, aimed to predict the spread of the COVID-19 virus and its impact on the population, but they overlooked the influence of \ac{VH} on disease dynamics, including the expected number of hospitalizations and fatalities. We propose improvements to the \ac{SEIR} model for COVID-19 by incorporating the influence of vaccination, \ac{VH}, and resource availability on the disease dynamics. We collect publicly available data and perform data analysis to capture \ac{VH} dynamic changes over time and develop scenario paths for \ac{VH}. We simulate the proposed compartmental model for each \ac{VH} path to explain the impacts of public attitudes toward vaccination, the impacts of healthcare resources on patient outcomes, and the timing of vaccination rollout on the progression and severity of the epidemic. Our analysis demonstrates that reducing \ac{VH} improves health outcomes, reinforcing the importance of addressing \ac{VH} to curb the spread of infectious diseases. Our results show that adequate levels of critical healthcare resources are crucial for minimizing fatalities and also highlight the life-saving impact of timely and effective vaccination programs.

Evaluating the Impact of Vaccine Hesitancy on the Allocation of Vital Resources During COVID-19 Pandemic

TL;DR

This work proposes improvements to the susceptible-exposed-infectious-recovered (SEIR) model for COVID-19 by incorporating the influence of vaccination, VH, and resource availability on the disease dynamics, and demonstrates that reducing VH improves health outcomes.

Abstract

The COVID-19 pandemic highlighted significant challenges in the allocation of vital healthcare resources. Existing epidemiological models, specifically compartmental models, aimed to predict the spread of the COVID-19 virus and its impact on the population, but they overlooked the influence of \ac{VH} on disease dynamics, including the expected number of hospitalizations and fatalities. We propose improvements to the \ac{SEIR} model for COVID-19 by incorporating the influence of vaccination, \ac{VH}, and resource availability on the disease dynamics. We collect publicly available data and perform data analysis to capture \ac{VH} dynamic changes over time and develop scenario paths for \ac{VH}. We simulate the proposed compartmental model for each \ac{VH} path to explain the impacts of public attitudes toward vaccination, the impacts of healthcare resources on patient outcomes, and the timing of vaccination rollout on the progression and severity of the epidemic. Our analysis demonstrates that reducing \ac{VH} improves health outcomes, reinforcing the importance of addressing \ac{VH} to curb the spread of infectious diseases. Our results show that adequate levels of critical healthcare resources are crucial for minimizing fatalities and also highlight the life-saving impact of timely and effective vaccination programs.
Paper Structure (6 sections, 1 equation, 5 figures, 1 table)

This paper contains 6 sections, 1 equation, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. COVID-19-Specific Compartmental Model
  4. Modeling of Uncertainties in Vaccine Hesitancy
  5. Numerical Analysis and Results
  6. Conclusions and Future Research Directions

Figures (5)

  • Figure 1: (a) SVEIHR: the proposed COVID-19 compartmental model. (b) and (c) illustrate the impact of healthcare capacity on the number of deaths and hospitalizations for a population of size 1.2M.
  • Figure 2: Vaccine hesitancy and vaccination uptake trends of Arkansas and New York counties.
  • Figure 3: (a)-(d) Distribution of the monthly rate of change of vaccine hesitancy, (e) Example of VH scenario paths over four months.
  • Figure 4: Evaluating the impact of different scenario paths illustrated in Figure \ref{['fig:roc']}(e) on the outcomes of SVEIHR model. The optimistic, pessimistic, and baseline scenario paths are highlighted.
  • Figure 5: (a) Expected number of fatalities relative to the number of beds and ventilators available. (b) Changes in weekly VH over time for the baseline scenario path. (c) Expected number of fatalities over time.