Evidence map›Paper›PMID 33330874›Full record

ArticlemedRxiv : the preprint server for health sciences2020

Characterizing superspreading of SARS-CoV-2 : from mechanism to measurement.

Zachary Susswein, Shweta Bansal

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Zachary SussweinDepartment of Biology, Georgetown University, Washington, D.C. 20057, USA.
Shweta BansalDepartment of Biology, Georgetown University, Washington, D.C. 20057, USA.

Funding

Vaccine hesitancy and erosion of herd immunity: harnessing big data to forecast disease re-emergenceR01GM123007 · NIGMS · GEORGETOWN UNIVERSITY · PI BANSAL, SHWETA · 2017 to 2021
$2.1M
NIGMS NIH HHS R01 GM123007
6 · The paper itself

Abstract

Superspreading is a ubiquitous feature of SARS-CoV-2 transmission dynamics, with a few primary infectors leading to a large proportion of secondary infections. Despite the superspreading events observed in previous coronavirus outbreaks, the mechanisms behind the phenomenon are still poorly understood. Here, we show that superspreading is largely driven by heterogeneity in contact behavior rather than heterogeneity in susceptibility or infectivity caused by biological factors. We find that highly heterogeneous contact behavior is required to produce the extreme superspreading estimated from recent COVID-19 outbreaks. However, we show that superspreading estimates are noisy and subject to biases in data collection and public health capacity, potentially leading to an overestimation of superspreading. These results suggest that superspreading for COVID-19 is substantial, but less than previously estimated. Our findings highlight the complexity inherent to quantitative measurement of epidemic dynamics and the necessity of robust theory to guide public health intervention.

Identifiers

PMID33330874
PMCPMC7743081

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.