Evidence map›Paper›PMID 33166336›Full record

ArticlePloS one2020

Projected impact of COVID-19 mitigation strategies on hospital services in the Mexico City Metropolitan Area.

Zachary Fowler, Ellie Moeller, Lina Roa, Isaac Deneb Castañeda-Alcántara, Tarsicio Uribe-Leitz, John G Meara, Arturo Cervantes-Trejo

Abstract 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 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Observational
  6. Article
  7. Article
  8. COVID-19: Current knowledge in clinical features, immunological responses, and vaccine development.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2021
    Review
  9. Article
  10. 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

7 authors.

Zachary FowlerProgram in Global Surgery and Social Change, Harvard Medical School, Boston, Massachusetts, United States of America.ORCID 0000-0002-4777-413X
Ellie MoellerProgram in Global Surgery and Social Change, Harvard Medical School, Boston, Massachusetts, United States of America.
Lina RoaProgram in Global Surgery and Social Change, Harvard Medical School, Boston, Massachusetts, United States of America.ORCID 0000-0001-6552-4452
Isaac Deneb Castañeda-AlcántaraAnahuac Institute of Public Health, Faculty of Health Sciences, Anahuac University Mexico, Huixquilucan, State of Mexico, Mexico.
Tarsicio Uribe-LeitzProgram in Global Surgery and Social Change, Harvard Medical School, Boston, Massachusetts, United States of America.
John G MearaProgram in Global Surgery and Social Change, Harvard Medical School, Boston, Massachusetts, United States of America.ORCID 0000-0003-4369-3209
Arturo Cervantes-TrejoAnahuac Institute of Public Health, Faculty of Health Sciences, Anahuac University Mexico, Huixquilucan, State of Mexico, Mexico.ORCID 0000-0002-7107-9217

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evidence-based models may assist Mexican government officials and health authorities in determining the safest plans to respond to the coronavirus disease 2019 (COVID-19) pandemic in the most-affected region of the country, the Mexico City Metropolitan Area. This study aims to present the potential impacts of COVID-19 in this region and to model possible benefits of mitigation efforts. The COVID-19 Hospital Impact Model for Epidemics was used to estimate the probable evolution of COVID-19 in three scenarios: (i) no social distancing, (ii) social distancing in place at 50% effectiveness, and (iii) social distancing in place at 60% effectiveness. Projections of the number of inpatient hospitalizations, intensive care unit admissions, and patients requiring ventilators were made for each scenario. Using the model described, it was predicted that peak case volume at 0% mitigation was to occur on April 30, 2020 at 11,553,566 infected individuals. Peak case volume at 50% mitigation was predicted to occur on June 1, 2020 with 5,970,093 infected individuals and on June 21, 2020 for 60% mitigation with 4,128,574 infected individuals. Occupancy rates in hospitals during peak periods at 0%, 50%, and 60% mitigation would be 875.9%, 322.8%, and 203.5%, respectively, when all inpatient beds are included. Under these scenarios, peak daily hospital admissions would be 40,438, 13,820, and 8,650. Additionally, 60% mitigation would result in a decrease in peak intensive care beds from 94,706 to 23,116 beds and a decrease in peak ventilator need from 67,889 to 17,087 units. Mitigating the spread of COVID-19 through social distancing could have a dramatic impact on reducing the number of infected people and minimize hospital overcrowding. These evidence-based models may enable careful resource utilization and encourage targeted public health responses.

Indexed as

Delivery of Health CareHealth PolicyBetacoronavirusCoronavirus InfectionsCOVID-19HospitalizationHumansMexicoModels, TheoreticalPandemicsPneumonia, ViralSARS-CoV-2

Identifiers

PMID33166336
PMCPMC7652345

What OpenQuestion holds

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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.