COVID-19 Vaccination and Healthcare Demand

Cost-based COVID-19 vaccination and willingness to pay: A post-pandemic review

The primary objective of this paper is to serve as a valuable resource for policymakers who are confronted with the evolving landscape of the coronavirus disease 2019 (COVID-19), considering both free and cost-based vaccination approaches. The potential consequences of shifting from free to cost-based vaccination are explored, encompassing its impact on global vaccine equity and prioritization, economic well-being, healthcare systems and delivery, public health policies, and vaccine distribution strategies. Examining past studies on willingness to pay for the initial COVID-19 vaccine dose and booster shots provides insights into how individuals value COVID-19 vaccinations and underscores the significance of addressing issues related to affordability. If COVID-19 vaccinations incur expenses, using effective communication strategies that emphasize the importance of vaccination and personal health benefits can increase willingness to pay. Making COVID-19 vaccines accessible through public health programs or health insurance can help alleviate financial barriers and increase vaccination rates.

Linking Spontaneous Behavioral Changes to Disease Transmission Dynamics: Behavior Change Includes Periodic Oscillation

Behavior change significantly influences the transmission of diseases during outbreaks. To incorporate spontaneous preventive measures, we propose a model that integrates behavior change with disease transmission. The model represents behavior change through an imitation process, wherein players exclusively adopt the behavior associated with higher payoff. We find that relying solely on spontaneous behavior change is insufficient for eradicating the disease. The dynamics of behavior change are contingent on the basic reproduction number R a corresponding to the scenario where all players adopt non-pharmaceutical interventions (NPIs). WhenR a < 1 , partial adherence to NPIs remains consistently feasible. We can ensure that the disease stays at a low level or maintains minor fluctuations around a lower value by increasing sensitivity to perceived infection. In cases where oscillations occur, a further reduction in the maximum prevalence of infection over a cycle can be achieved by increasing the rate of behavior change. WhenR a > 1 , almost all players consistently adopt NPIs if they are highly sensitive to perceived infection. Further consideration of saturated recovery leads to saddle-node homoclinic and Bogdanov-Takens bifurcations, emphasizing the adverse impact of limited medical resources on controlling the scale of infection. Finally, we parameterize our model with COVID-19 data and Tokyo subway ridership, enabling us to illustrate the disease spread co-evolving with behavior change dynamics. We further demonstrate that an increase in sensitivity to perceived infection can accelerate the peak time and reduce the peak size of infection prevalence in the initial wave.