Anatomy of the first six months of COVID-19 vaccination campaign in Italy

  1. Nicolò Gozzi
  2. Matteo Chinazzi
  3. Jessica T. Davis
  4. Kunpeng Mu
  5. Ana Pastore y Piontti
  6. Marco Ajelli
  7. Nicola Perra
  8. Alessandro Vespignani

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Abstract

We analyze the effectiveness of the first six months of vaccination campaign against SARS-CoV-2 in Italy by using a computational epidemic model which takes into account demographic, mobility, vaccines data, as well as estimates of the introduction and spreading of the more transmissible Alpha variant. We consider six sub-national regions and study the effect of vaccines in terms of number of averted deaths, infections, and reduction in the Infection Fatality Rate (IFR) with respect to counterfactual scenarios with the actual non-pharmaceuticals interventions but no vaccine administration. Furthermore, we compare the effectiveness in counterfactual scenarios with different vaccines allocation strategies and vaccination rates. Our results show that, as of 2021/07/05, vaccines averted 29, 350 ( IQR : [16, 454–42, 826]) deaths and 4, 256, 332 ( IQR : [1, 675, 564–6, 980, 070]) infections and a new pandemic wave in the country. During the same period, they achieved a −22.2% ( IQR : [−31.4%; −13.9%]) IFR reduction. We show that a campaign that would have strictly prioritized age groups at higher risk of dying from COVID-19, besides frontline workers and the fragile population, would have implied additional benefits both in terms of avoided fatalities and reduction in the IFR. Strategies targeting the most active age groups would have prevented a higher number of infections but would have been associated with more deaths. Finally, we study the effects of different vaccination intake scenarios by rescaling the number of available doses in the time period under study to those administered in other countries of reference. The modeling framework can be applied to other countries to provide a mechanistic characterization of vaccination campaigns worldwide.

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  1. Version published to 10.1371/journal.pcbi.1010146
  2. ScreenIT

    SciScore for 10.1101/2021.11.24.21266820: (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
    We model the variations in contacts induced by non-pharmaceutical interventions at workplaces and in the community settings using data from the COVID-19 Community Mobility Report published by Google [20].
    Google
    suggested: (Google, RRID:SCR_017097)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The present work comes with limitations. First, the compartmental setup used to model disease progression is a relatively simple one compared to other approaches that consider, for example, also the pre-symptomatic and asymptomatic stages of the infection [36, 37]. Nonetheless, it has been previously used in several works in the context of COVID-19 modeling [22, 38–40]. Second, both the vaccination protocol and the effect of vaccines on disease progression are an approximation of reality [41]. For simplicity we considered, besides the wild type, only one additional virus strain, although we acknowledge that the Alpha variant was not the only variant of concern circulating in Italy during the period considered [42]. Beside the importation data from GLEAM, we model each region separately, thus neglecting the coupling between them via different forms of mobility. Finally, in the counterfactual scenarios we considered all individuals willing to receive vaccines. While the current vaccination rates in Italy show a high vaccine acceptance (81.5% of the population 12+ completed the vaccination course and 85.7% received at least one dose as of 2021/10/19 according to official sources [43]), this is an optimistic assumption. In the Supplementary Information we relax this and we study the effect of vaccine hesitancy. We measured the effects of vaccines respect to a baseline that considers the observed contact patterns during the rollout. As mentioned above, the resurgence of infections...

    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.

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  3. Version published to 10.1101/2021.11.24.21266820v1 on medRxiv