Evaluation of Vaccination Strategies for the metropolitan area of Madrid

  1. David E. Singh
  2. Carmen Olmedo Lucerón
  3. Aurora Limia Sánchez
  4. Miguel Guzman-Merino
  5. Christian Duran
  6. Concepción Delgado-Sanz
  7. Diana Gomez-Barroso
  8. Jesus Carretero
  9. Maria-Cristina Marinescu

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Abstract

Background

This work analyses the impact of different vaccination strategies on the propagation of COVID-19 within the Madrid metropolitan area starting the 27th of December 2020 and ending in the Summer of 2021. The predictions are based on simulation using EpiGraph, an agent-based COVID-19 simulator.

Methods

We briefly summarize the different interconnected models of EpiGraph and then we provide a comprehensive description of the vaccination model. We evaluate different vaccination strategies, and we validate the simulator by comparing the simulation results with real data from the metropolitan area of Madrid during the third wave.

Results

We consider the different COVID-19 propagation scenarios on a social environment consisting of the ten largest cities in the Madrid metropolitan area, with 5 million individuals. The results show that the strategy that fares best is to vaccinate the elderly first with the two doses spaced 56 days apart; this approach reduces the final infection rate and the number of deaths by an additional 6% and 3% with respect to vaccinating the elderly first at the interval between doses recommended by the vaccine producer.

Conclusion

Results show that prioritizing the vaccination of young individuals would significantly increase the number of deaths. On the other hand, spacing out the first and second dose by 56 days would result in a slight reduction in the number of infections and deaths. The reason is the increase in the number of vaccinated individuals at any time during the simulation.

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  1. ScreenIT

    SciScore for 10.1101/2021.11.29.21267009: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    2.1 Simulator overview: EpiGraph consists of several different models that together reproduce the most important aspects of the simulation environment.
    EpiGraph
    suggested: (EpiGRAPH, RRID:SCR_004326)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Some of the limitations of this approach are the absence of age structure and the assumption of a well-mixed population. Covasim [7] includes demographic information about age structure and population size. Different from our work, the contacts are not based on existing patterns; scalability issues are partly sidestepped by dynamic scaling. Vaccines are modelled by adjusting individuals’ susceptibility to infection and probability of developing symptoms after being infected; both of these modifications affect the overall probability of progressing to severe disease and death. However, some features we consider in EpiGraph (like vaccine effectiveness across variants) are not currently implemented in Covasim. Modelling social mixing a crucial factor for obtaining realistic simulations. In [8, 10, 11] different ways for refining the social interactions are considered. In EpiGraph the social mixing modelling is carried out using Facebook and Enron contact networks and individual contact matrices. [12] compares five age-stratified prioritization strategies in terms of cumulative incidence, mortality, and years of life lost. Some limitations have to do with using pre-pandemic contact matrices, not incorporating nonpharmaceutical interventions, and only considering variation in disease severity and risk by age - although contact rates, and thus infection potential, vary greatly not only by occupation and age. Results show, like in our work, that people aged 60 years and older should...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  2. Version published to 10.1101/2021.11.29.21267009v1 on medRxiv