Evidence map›Paper›PMID 32425631›Full record

ArticleInternational journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases2020

A dynamic modeling tool for estimating healthcare demand from the COVID19 epidemic and evaluating population-wide interventions.

Gabriel Rainisch, Eduardo A Undurraga, Gerardo Chowell

Abstract read
In one paragraph

Article in International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

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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.

Gabriel RainischDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA. Electronic address: grainisch1@student.gsu.edu.
Eduardo A UndurragaEscuela de Gobierno, Pontificia Universidad Católica de Chile, RM, Chile; Millennium Initiative for Collaborative Research in Bacterial Resistance (MICROB-R), RM, Chile.
Gerardo ChowellDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesPublic health officials need tools to assist in anticipating the healthcare resources required to confront the SARS-COV-2 pandemic. We constructed a modeling tool to aid active public health officials to estimate healthcare demand from the pandemic in their jurisdictions and to evaluate the potential impact of population-wide social-distancing interventions.

methodsThe tool uses an SEIR compartmental model to project the pandemic's local spread. Users input case counts, healthcare resources, and select intervention strategies to evaluate. Outputs include the number of infections and deaths with and without intervention, and the demand for hospital and critical care beds and ventilators relative to existing capacity. We illustrate the tool using data from three regions of Chile.

resultsOur scenarios indicate a surge in COVID-19 patients could overwhelm Chilean hospitals by June, peaking in July or August at six to 50 times the current supply of beds and ventilators. A lockdown strategy or combination of case isolation, home quarantine, social distancing of individuals >70 years, and telework interventions may keep treatment demand below capacity.

conclusionsAggressive interventions can avert substantial morbidity and mortality from COVID-19. Our tool permits rapid evaluation of locally-applicable policy scenarios and updating of results as new data become available.

Indexed as

BetacoronavirusCoronavirus InfectionsCOVID-19Delivery of Health CareHealth ResourcesHumansPandemicsPneumonia, ViralSARS-CoV-2Ventilators, MechanicalCapacityCOVIDHospitalInterventionModelSocial distancing

Identifiers

PMID32425631
PMCPMC7229979

What OpenQuestion holds

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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.