Evidence map›Paper›PMID 33171068›Full record

ArticleOpen biology2020

Age separation dramatically reduces COVID-19 mortality rate in a computational model of a large population.

Liron Mizrahi, Huda Adwan Shekhidem, Shani Stern

Open access · goldAbstract read
In one paragraph

Article in Open biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.6field-weighted citation impact, top 25% 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

6 citing papers in PubMed, 18 citations in OpenAlex.

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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.

Liron MizrahiSagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa 3498838, Israel.
Huda Adwan ShekhidemSagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa 3498838, Israel.
Shani SternSagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa 3498838, Israel.
University of Haifa · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 pandemic has caused a global lockdown in many countries throughout the world. Faced with a new reality, and until a vaccine or efficient treatment is found, humanity must figure out ways to keep the economy going, on one hand, while keeping the population safe, on the other hand, especially those that are susceptible to this virus. Here, we use a Watts-Strogatz network simulation, with parameters that were drawn from what is already known about the virus, to explore five different scenarios of partial lockdown release in two geographical locations with different age distributions. We find that separating age groups by reducing interactions between them protects the general population and reduces mortality rates. Furthermore, the addition of new connections within the same age group to compensate for the lost connections outside the age group still has a strong beneficial influence and reduces the total death toll by about 62%. While complete isolation from society may be the most protective scenario for the elderly population, it would have an emotional and possibly cognitive impact that might outweigh its benefit. Therefore, we propose creating age-related social recommendations or even restrictions, thereby allowing social connections while still offering strong protection for the older population.

Indexed as

Computer SimulationAdolescentAdultAge FactorsBetacoronavirusChildChild, PreschoolCoronavirus InfectionsCOVID-19HumansInfantInfant, NewbornIsraelItalyMiddle AgedPandemicsage separationcomputational modelcorona virusCOVID-19Erdős–Rényi modelnetwork

Identifiers

PMID33171068
PMCPMC7729024
OpenAlexW3102655401

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.