Evidence map›Paper›PMID 35301385›Full record

ArticleScientific reports2022

Post-lockdown changes of age-specific susceptibility and its correlation with adherence to social distancing measures.

Max S Y Lau, Carol Liu, Aaron J Siegler, Patrick S Sullivan, Lance A Waller, Kayoko Shioda, Benjamin A Lopman

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Droplet Micro-Sensor and Detection of Respiratory Droplet Transmission.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Article
  6. 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

7 authors.

Max S Y LauDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, USA. msy.lau@emory.edu.
Carol LiuDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, USA.
Aaron J SieglerDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, USA.
Patrick S SullivanDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, USA.
Lance A WallerDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, USA.
Kayoko ShiodaDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, USA.
Benjamin A LopmanDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, USA.

Funding

Population-based survey of SARS-CoV-2 infection and immune responseR01AI143875 · NIAID · EMORY UNIVERSITY · PI SIEGLER, AARON JULIUS, SULLIVAN, PATRICK SEAN · 2019 to 2023
$9.2M
Preparing for the SARS-CoV2 vaccine: Modeling of transmission pathways, viral evolution and vaccination strategiesR01GM124280 · NIGMS · EMORY UNIVERSITY · PI LOPMAN, BENJAMIN A · 2018 to 2022
$2.2M
NCEZID CDC HHS U01 CK000572NIAID NIH HHS R01 AI143875NIGMS NIH HHS R01 GM124280NIH HHS 3R01AI143875-02S1NIH HHS R01AI143875-02S1
6 · The paper itself

Abstract

Social distancing measures are effective in reducing overall community transmission but much remains unknown about how they have impacted finer-scale dynamics. In particular, much is unknown about how changes of contact patterns and other behaviors including adherence to social distancing, induced by these measures, may have impacted finer-scale transmission dynamics among different age groups. In this paper, we build a stochastic age-specific transmission model to systematically characterize the degree and variation of age-specific transmission dynamics, before and after lifting the lockdown in Georgia, USA. We perform Bayesian (missing-)data-augmentation model inference, leveraging reported age-specific case, seroprevalence and mortality data. We estimate that overall population-level transmissibility was reduced to 41.2% with 95% CI [39%, 43.8%] of the pre-lockdown level in about a week of the announcement of the shelter-in-place order. Although it subsequently increased after the lockdown was lifted, it only bounced back to 62% [58%, 67.2%] of the pre-lockdown level after about a month. We also find that during the lockdown susceptibility to infection increases with age. Specifically, relative to the oldest age group (> 65+), susceptibility for the youngest age group (0-17 years) is 0.13 [0.09, 0.18], and it increases to 0.53 [0.49, 0.59] for 18-44 and 0.75 [0.68, 0.82] for 45-64. More importantly, our results reveal clear changes of age-specific susceptibility (defined as average risk of getting infected during an infectious contact incorporating age-dependent behavioral factors) after the lockdown was lifted, with a trend largely consistent with reported age-specific adherence levels to social distancing and preventive measures. Specifically, the older groups (> 45) (with the highest levels of adherence) appear to have the most significant reductions of susceptibility (e.g., post-lockdown susceptibility reduced to 31.6% [29.3%, 34%] of the estimate before lifting the lockdown for the 6+ group). Finally, we find heterogeneity in case reporting among different age groups, with the lowest rate occurring among the 0-17 group (9.7% [6.4%, 19%]). Our results provide a more fundamental understanding of the impacts of stringent lockdown measures, and finer evidence that other social distancing and preventive measures may be effective in reducing SARS-CoV-2 transmission. These results may be exploited to guide more effective implementations of these measures in many current settings (with low vaccination rate globally and emerging variants) and in future potential outbreaks of novel pathogens.

Indexed as

COVID-19Physical DistancingAdolescentAge FactorsBayes TheoremChildChild, PreschoolCommunicable Disease ControlHumansInfantInfant, NewbornSARS-CoV-2Seroepidemiologic Studies

Identifiers

PMID35301385
PMCPMC8929451

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.