Evidence map›Paper›PMID 32587997›Full record

ArticlemedRxiv : the preprint server for health sciences2020

Estimating the Cumulative Incidence of COVID-19 in the United States Using Four Complementary Approaches.

Fred S Lu, Andre T Nguyen, Nicholas B Link, Jessica T Davis, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Marc Lipsitch, Mauricio Santillana

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Fred S LuComputational Health Informatics Program, Boston Children's Hospital, Boston, MA.
Andre T NguyenComputational Health Informatics Program, Boston Children's Hospital, Boston, MA.
Nicholas B LinkComputational Health Informatics Program, Boston Children's Hospital, Boston, MA.
Jessica T DavisLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA.
Matteo ChinazziLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA.
Xinyue XiongLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA.
Alessandro VespignaniLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA.
Marc LipsitchDepartment of Epidemiology, Harvard T.H. Chan School of Public Health.
Mauricio SantillanaComputational Health Informatics Program, Boston Children's Hospital, Boston, MA.

Funding

Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease ActivityR01GM130668 · NIGMS · NORTHEASTERN UNIVERSITY · PI SANTILLANA, MAURICIO · 2018 to 2022
$1.9M
NIGMS NIH HHS R01 GM130668
6 · The paper itself

Abstract

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the useful-ness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.2 to 4.9 million, with possibly as many as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 10.3 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.

Identifiers

PMID32587997
PMCPMC7310656

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.