Evidence map›Paper›PMID 41127794›Full record

ArticleRoyal Society open science2025

Models for SARS-CoV-2 health policies: social distancing amid vaccination and virus variants.

Yi Zhang, Sanjiv Kapoor

Abstract read
In one paragraph

Article in Royal Society open science, 2025. 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

2 authors.

Yi ZhangIllinois Institute of Technology, Chicago, IL, USA.
Sanjiv KapoorDepartment of Computer Science, Illinois Institute of Technology, Chicago, IL, USA.ORCID https://orcid.org/0000-0001-5637-9919

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Policy decisions during the SARS-CoV-2 pandemic were important to contain the spread of the virus, but complicated due to virus variants and the varying impact of societal restrictions. In this paper, we report results from a model that utilized population behaviour to predict the impact of SARS-CoV-2 transmission in India over a number of months from June 2021 to March 2022. The model utilizes deterministic population compartments, incorporating a dynamic transmission factor dependent on the population's behaviour as a function of reported confirmed cases. The model also incorporates the state of vaccination and virus variants as part of the transmission dynamics. The model projections, used for formulating advice towards pre-emptive policy actions by pivotal government organizations involved in developing a national public health strategy, culminated in early warning projections for the Omicron variant. The projections of cumulative cases during the Omicron wave had a mean absolute percentage error of 18.1% when measured 15 days after the start of the projection on 1 December 2021.

Indexed as

CoV-2epidemiological modelshealthmodelspoliciesSARS

Identifiers

PMID41127794
PMCPMC12539966

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.