Source-linked AI summary
Vaccination and SARS-CoV-2 variants: how much containment is still needed? A quantitative assessment
Giulia Giordano, Marta Colaneri, Alessandro Di Filippo, Franco Blanchini, Paolo Bolzern, Giuseppe De Nicolao, Paolo Sacchi, Raffaele Bruno, Patrizio Colaneri
TL;DR
The paper examines how vaccination pace, transmissible variants, and containment measures jointly affect COVID-19 outcomes. It combines SIDARTHE-based epidemic projections with a data-based healthcare-cost model and finds that containment scenarios substantially shape projected deaths and costs during vaccination.
Problem
Population-wide interventions remain important during vaccination because SARS-CoV-2 variants can increase transmission.
Method
The study combines a compartmental infection model with a data-based model that projects cases onto healthcare outcomes and costs across vaccination and containment scenarios.
Results
Under ℛ0 = 1.27, thousand deaths from February 2021 to January 2022 increase from 196 to 330 with slow vaccination, from 174 to 325 with medium vaccination, and from 146 to 319 with fast vaccination.
Takeaways & Limitations
Containment measures should remain in place during vaccination campaigns, while intermittent strategies should begin with closure to reduce deaths and healthcare costs.
Takeaways & Limitations
The quantitative analysis focuses on the Italian case study, although the methodology is described as generally applicable.
Abstract
from arXiv · showhide
Despite the progress in medical care, combined population-wide interventions (such as physical distancing, testing and contact tracing) are still crucial to manage the SARS-CoV-2 pandemic, aggravated by the emergence of new highly transmissible variants. We combine the compartmental SIDARTHE model, predicting the course of COVID-19 infections, with a new data-based model that projects new cases onto casualties and healthcare system costs. Based on the Italian case study, we outline several scenarios: mass vaccination campaigns with different paces, different transmission rates due to new variants, and different enforced countermeasures, including the alternation of opening and closure phases. Our results demonstrate that non-pharmaceutical interventions (NPIs) have a higher impact on the epidemic evolution than vaccination, which advocates for the need to keep containment measures in place throughout the vaccination campaign. We also show that, if intermittent open-close strategies are adopted, deaths and healthcare system costs can be drastically reduced, without any aggravation of socioeconomic losses, as long as one has the foresight to start with a closing phase rather than an opening one.
Methods
The overall model combines a compartmental infection model with a data-based healthcare-cost model. Predicted cases are translated into hospitalisation flows, deaths, occupancy, and costs.
- The model combines the flexibility and insight of compartmental models with the robustness of a black-box healthcare system cost model based on observed data.
- The SIDARTHE-V model predicts the evolution of new positive cases, which feeds a data-based model of hospitalisation flows and healthcare costs.
- Healthcare system costs are quantified through deaths, hospital occupancy, and ICU occupancy.
SIDARTHE-V Compartmental Model
SIDARTHE-V extends the SIDARTHE compartmental model by adding vaccination and representing epidemic progression across nine population clusters. Its dynamics track infection, recovery, death, and immunisation while linking transmission to the susceptible fraction.
- SIDARTHE-V extends SIDARTHE by including vaccination and nine population clusters representing infection stages and immunised, healed, or deceased individuals.
- The model uses nine ordinary differential equations to describe population-fraction dynamics across the compartments.
- Transmission parameters represent infection risk from contacts with infected, diagnosed, ailing, and recognised subjects and can be modified by distancing, hygiene, and protective equipment.
- Diagnosis parameters reflect detection rates for asymptomatic and symptomatic cases and can increase with expanded testing and contact tracing.
- The vaccination function represents the rate at which susceptible people achieve immunity, depending on vaccination rate and vaccine efficacy.
- With positive vaccination while susceptible individuals remain, infection variables converge to zero and only healed, deceased, and vaccinated populations remain at equilibrium.
- Epidemic suppression occurs once the effective reproduction number satisfies ℛt = ℛ0S(t) < 1 from some point onward.
Fit of the SIDARTHE-V Model for the COVID-19 Epidemic in Italy
The fitted SIDARTHE-V model reproduces Italy’s epidemic evolution and projects healthcare outcomes under vaccination, variant transmission, and changing restrictions. The scenarios compare vaccination speeds with constant, open-close, and close-open containment profiles.
- The model is fitted to Italian official data and reproduces the second infection wave before feeding a healthcare-cost model.
- After the March 2020 lockdown, Italy’s effective reproduction number fell below 1, while eased restrictions and school reopening preceded renewed infection growth.
- The second wave declined more slowly than the first, indicating that later containment measures were milder.
- The scenarios vary transmission, vaccination profiles, and restrictions, including high-transmission, open-close, close-open, and eradication conditions.
- Adaptive vaccination schedules increase healthcare system costs relative to piecewise-constant vaccination functions across all ℛ0 profiles.
- Under ℛ0 = 1.27, thousand deaths from February 2021 to January 2022 increase from 196 to 330 with slow vaccination, from 174 to 325 with medium vaccination, and from 146 to 319 with fast vaccination.
Data-Driven Model of Healthcare System Costs
The paper builds data-based dynamic models that map reported cases to deaths and healthcare occupancies, then adjusts lethality over time to represent vaccination. These models use Italian second-wave data and reproduce observed deaths, hospital occupancy, and ICU occupancy well.
- Deaths model: The data-based dynamic model takes new cases as input and predicts daily deaths as output.Deaths are modeled as depending on cases reported on preceding days through delay weights.
- Vaccination adjustment: The age-specific fatality profile was rescaled to an overall unvaccinated lethality of CFR0 = 0.0272 using Italian health and population data.The rescaling addressed first-wave underreporting, while vaccination order followed the reverse of age.
- Healthcare outcomes: As vaccination protects older population segments, the same number of new cases produces progressively fewer deaths.Hospital and ICU occupancy models apply the same vaccination-related modulation under the assumption that severity reduction parallels lethality reduction.
- Healthcare outcomes: The three data-based dynamic models provide a very good fit to deaths, hospital occupancy, and ICU occupancy.Hospital and ICU occupancies were modeled analogously to deaths.
Competing interests
The authors declare no competing interests. The supplied passages also describe the model, scenarios, and reported policy findings.
- The authors declare no competing interests.
- The integrated model combines the SIDARTHE-V epidemiological model with a data-based model of casualties and healthcare system costs.
- 64% of the population is vaccinated within one year in the medium-speed scenario.
- The three dynamic input-output models have FIT ratios of 83.16%, 84.67%, and 87.53%.
SIDARTHE-V
The section organizes vaccination scenarios by speed and by open-close conditions, including adaptive vaccination. It tracks epidemic and healthcare outcomes over time, including deaths, hospital occupancy, and ICU occupancy.
- Open-close scenarios: The analysis includes an open-close comparison of slow versus fast vaccination.The scenario label explicitly contrasts SLOW vs FAST VACCIN. under OPEN-CLOSE.
- Age and fatality measures: The figures also track time-varying CFR and population or cumulative population by age.Labels include time varying CFR and plots relating cumulative population or population to age.
- Vaccination scenarios: Vaccination scenarios are distinguished as slow, medium, or fast, with daily vaccination rates and cumulative vaccinated population tracked.The displayed labels include 0.5 daily vaccination rate (% population) and 100 cumulative % vaccinated for each speed.
- Adaptive vaccination: Adaptive vaccination is examined under a medium-speed scenario.The section contains repeated labels for ADAPTIVE VACCINATION: MEDIUM.
- Outcomes: Reported epidemic and healthcare outcomes include daily deaths, total hospital occupancy, total ICU occupancy, and moving-average bed occupancy.Hospital and ICU occupancy are labeled for Feb21-Jan22, while beds and deaths are shown as 7 day mov. avg.