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COVID-19 optimal vaccination policies: A modeling study on efficacy, natural and vaccine-induced immunity responses

Home / Resources / COVID-19 optimal vaccination policies: A modeling study on efficacy, natural and vaccine-induced immunity responses
June 1, 2021 by Immunise - a PI India Initiative Resources

The goal of the study is to compare different vaccine profiles in order to compute optimal COVID-19 vaccination policies and to assess the impact of hypothetical reinfection and immunisation responses. According to the study simulations, the optimal strategy design is influenced by vaccine profile and natural immunity period. Similarly, the researchers believe that both natural and vaccine-induced immunity will be critical in lowering COVID-19 disease mortality and prevalence levels.

  • URL
 https://www.sciencedirect.com/science/article/pii/S0025556421000596?via%3Dihub
  • Date of publication
 4 May 2021, Mathematical Biosciences
  • Category :
  • Vaccine Efficacy
  • Vaccine-induced immunity 
  • Study Design
 Mathematical Model- classical Kermack–McKendrick model.
  • Country of Study
 Mexico
  • Level of Evidence
 Economic study / Decision analyses: Systematic review of Level-I studies.
  • Aim/ Question studied
 The main objective is the formulation of optimal schedules for vaccine administration that ensure:

1.To cover a target population fraction in a fixed time horizon.

2.To minimize COVID-19 burden quantified in  Disability-Adjusted Life Years (DALYs).

3. To preserve hospitalization occupancy below a required bound.
  • Study done / What did they do?
 The goal of the study is to compare different vaccine profiles in order to compute optimal COVID-19 vaccination policies and to assess the impact of hypothetical reinfection and immunisation responses. According to the study simulations, the optimal strategy design is influenced by vaccine profile and natural immunity period. Similarly, the researchers believe that both natural and vaccine-induced immunity will be critical in lowering COVID-19 disease mortality and prevalence levels.
  • Results
  • In SCN-1: They observe CP (counter vaccine policy) and OVP reduce the symptomatic prevalence and cumulative deaths.
  • SCN-2: 50% coverage at a time horizon of 1 year, displays an improvement from 1.5 to 3 times regarding the counterfactual scenariofor the cumulative deaths at time horizon. Also, there is a notorious im-provement in the hospital occupancy.
  • SCN-3: There has been a significant reduction in the prevalence and total number of deaths associated with the uncontrolled outbreak. This decrease corresponds to the vaccine reproductive number values associated with each vaccine-induced immunity.
  • SCN-4:After a year of natural immunity, the burden of COVID-19 drops to around 120 DALYs.
  • Conclusion
  • The researchers correlate vaccination rate with available vaccine stock and health system response—which is constrained by logistics, management, politics, and other issues associated with each vaccine development. As a result, their research contribution can assist decision-makers in developing vaccination schedules for homogeneous populations.
  • Summary
  • This study emphasises the significance of knowing the vaccine profile as well as the natural immunity period when developing an optimal vaccination strategy. The simulation is run against various vaccine-induced and natural immunisation profiles, allowing the effects of these variables to be studied and optimal dose administration schedules to be developed. Furthermore, the optimal solution meets modelling constraints in order to achieve the desired vaccination coverage while avoiding healthcare system overload. The researchers optimised vaccination strategies by reducing the COVID-19 burden, which was measured in DALYs. They use an optimal control approach to answer WHO-SAGE vaccination modelling questions.

 

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