Evidence map›Paper›PMID 35877667›Full record

ArticlePloS one2022

An epidemic model for SARS-CoV-2 with self-adaptive containment measures.

Sabina Marchetti, Alessandro Borin, Francesco Paolo Conteduca, Giuseppe Ilardi, Giorgio Guzzetta, Piero Poletti, Patrizio Pezzotti, Antonino Bella, Paola Stefanelli, Flavia Riccardo and 3 more

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Sabina MarchettiDirectorate General for Economics, Statistics and Research, Bank of Italy, Rome, Italy.ORCID 0000-0003-4388-8575
Alessandro BorinDirectorate General for Economics, Statistics and Research, Bank of Italy, Rome, Italy.
Francesco Paolo ConteducaDirectorate General for Economics, Statistics and Research, Bank of Italy, Rome, Italy.ORCID 0000-0002-1468-5392
Giuseppe IlardiDirectorate General for Economics, Statistics and Research, Bank of Italy, Rome, Italy.
Giorgio GuzzettaCenter for Health Emergencies, Bruno Kessler Foundation (FBK), Trento, Italy.
Piero PolettiCenter for Health Emergencies, Bruno Kessler Foundation (FBK), Trento, Italy.ORCID 0000-0001-5453-5199
Patrizio PezzottiDepartment of Infectious Diseases, Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy.
Antonino BellaDepartment of Infectious Diseases, Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy.
Paola StefanelliDepartment of Infectious Diseases, Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy.
Flavia RiccardoDepartment of Infectious Diseases, Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy.
Stefano MerlerCenter for Health Emergencies, Bruno Kessler Foundation (FBK), Trento, Italy.
Andrea BrandoliniDirectorate General for Economics, Statistics and Research, Bank of Italy, Rome, Italy.
Silvio BrusaferroItalian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the COVID-19 pandemic, several countries have resorted to self-adaptive mechanisms that tailor non-pharmaceutical interventions to local epidemiological and health care indicators. These mechanisms reinforce the mutual influence between containment measures and the evolution of the epidemic. To account for such interplay, we develop an epidemiological model that embeds an algorithm mimicking the self-adaptive policy mechanism effective in Italy between November 2020 and March 2022. This extension is key to tracking the historical evolution of health outcomes and restrictions in Italy. Focusing on the epidemic wave that started in mid-2021 after the diffusion of Delta, we compare the functioning of alternative mechanisms to show how the policy framework may affect the trade-off between health outcomes and the restrictiveness of mitigation measures. Mechanisms based on the reproduction number are generally highly responsive to early signs of a surging wave but entail severe restrictions. The emerging trade-off varies considerably depending on specific conditions (e.g., vaccination coverage), with less-reactive mechanisms (e.g., those based on occupancy rates) becoming more appealing in favorable contexts.

Indexed as

COVID-19SARS-CoV-2HumansItalyPandemics

Identifiers

PMID35877667
PMCPMC9312378

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.