Evidence map›Paper›PMID 34426816›Full record

ArticlemedRxiv : the preprint server for health sciences2021

SARS-CoV-2 epidemic after social and economic reopening in three US states reveals shifts in age structure and clinical characteristics.

Nathan Wikle, Thu Nguyen-Anh Tran, Bethany Gentilesco, Scott M Leighow, Joseph Albert, Emily R Strong, Karel Břinda, Haider Inam, Fuhan Yang, Sajid Hossain and 6 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Nathan WikleCenter for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA.
Thu Nguyen-Anh TranCenter for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA.
Bethany GentilescoDepartment of Medicine, Brown University, Providence, RI.
Scott M LeighowCenter for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA.
Joseph AlbertDepartment of Physics, Pennsylvania State University, University Park, PA.
Emily R StrongCenter for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA.
Karel BřindaCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
Haider InamCenter for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA.
Fuhan YangCenter for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA.
Sajid HossainYale School of Medicine, Yale University, New Haven, CT.
Philip ChanDepartment of Medicine, Brown University, Providence, RI.
William P HanageCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
Maria MessickRhode Island Office of the Governor and Rhode Island Department of Health, Providence, RI.
Justin R PritchardCenter for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA.
Ephraim M HanksCenter for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA.
Maciej F BoniCenter for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA.

Funding

Understanding and controlling antibiotic resistanceU54GM088558 · NIGMS · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIPSITCH, MARC · 2009 to 2018
$26.4M
Evolutionary Tradeoffs in Antibiotic ResistanceR35GM133700 · NIGMS · HARVARD MEDICAL SCHOOL · PI BAYM, MICHAEL · 2019 to 2023
$2.4M
NIAID NIH HHS HHSN272201400007CNIGMS NIH HHS R35 GM133700NIGMS NIH HHS U54 GM088558
6 · The paper itself

Abstract

In the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission. One important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices. A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases. Here, we analyze age-structured case, hospitalization, and death time series from three states - Rhode Island, Massachusetts, and Pennsylvania - that had successful re-openings in May 2020 without summer waves of infection. Using a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mobility was broken in May as these states partially re-opened. We estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.0% (RI), 72.1% (MA), and 75.5% (PA); in Rhode Island, when accounting for cases caught through general-population screening programs, the reporting rate estimate is 94.5%. We show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Attack rate estimates through August 31 2020 are 6.4% (95% CI: 5.8% ‒ 7.3%) of the total population infected for Rhode Island, 5.7% (95% CI: 5.0% ‒ 6.8%) in Massachusetts, and 3.7% (95% CI: 3.1% ‒ 4.5%) in Pennsylvania, with some validation available through published seroprevalence studies. Infection fatality rates (IFR) estimates for the spring epidemic are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations, especially the most vulnerable of the ≥80 age group.

Identifiers

PMID34426816
PMCPMC8382133

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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.