Evidence map›Paper›PMID 33141823›Full record

ArticlePloS one2020

Effect of mitigation measures on the spreading of COVID-19 in hard-hit states in the U.S.

Ka-Ming Tam, Nicholas Walker, Juana Moreno

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 20% of its field
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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

  1. Pooled it
  2. Minimizing Societal Cost of Epidemics: Optimal Catastrophe Insurance Compensation Allocation.Risk analysis : an official publication of the Society for Risk Analysis · 2026
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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

3 authors at 1 institution in 1 country.

Ka-Ming TamDepartment of Physics & Astronomy, Louisiana State University, Baton Rouge, Louisiana, United States of America.ORCID 0000-0002-2273-000X
Nicholas WalkerDepartment of Physics & Astronomy, Louisiana State University, Baton Rouge, Louisiana, United States of America.
Juana MorenoDepartment of Physics & Astronomy, Louisiana State University, Baton Rouge, Louisiana, United States of America.
Louisiana State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

State government-mandated social distancing measures have helped to slow the growth of the COVID-19 pandemic in the United States. Many of the current predictive models of the development of COVID-19, especially after mitigation efforts, partially rely on extrapolations from data collected in other countries. Since most states enacted stay-at-home orders towards the end of March, the resulting effects of social distancing should be reflected in the death and infection counts by the end of April. Using the data available through April 25th, we investigate the change in the infection rate due to the mitigation efforts and project death and infection counts through September 2020 for some of the most heavily impacted states: New York, New Jersey, Michigan, Massachusetts, Illinois, and Louisiana. We find that with the current mitigation efforts, five of those six states have reduced their base reproduction number to a value less than one, stopping the exponential growth of the pandemic. We also project different scenarios after the mitigation is relaxed.

Indexed as

PandemicsBasic Reproduction NumberBetacoronavirusCoronavirus InfectionsCOVID-19HumansPneumonia, ViralPsychological DistanceSARS-CoV-2United States

Identifiers

PMID33141823
PMCPMC7608876
OpenAlexW3021110394

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.