Evidence map›Paper›PMID 33585377›Full record

ArticleFrontiers in public health2020

COVID-19 Dynamics: A Heterogeneous Model.

Andrey Gerasimov, Georgy Lebedev, Mikhail Lebedev, Irina Semenycheva

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 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
0.9field-weighted citation impact, top 28% 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, 12 citations in OpenAlex.

  1. Article
  2. Medical Genetics, Genomics and Bioinformatics-2022.International journal of molecular sciences · 2023
    Article
  3. Article
  4. The role of super-spreaders in modeling of SARS-CoV-2.Infectious Disease Modelling · 2022
    Article
  5. Article
  6. COVID-19 Dynamics: A Heterogeneous Model.Frontiers in public health · 2020
    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

4 authors at 1 institution in 1 country.

Andrey GerasimovDepartment of Information and Internet Technology, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
Georgy LebedevDepartment of Information and Internet Technology, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
Mikhail LebedevDepartment of Information and Internet Technology, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
Irina SemenychevaDepartment of Information and Internet Technology, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
Sechenov University · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mathematical model reported here describes the dynamics of the ongoing coronavirus disease 2019 (COVID-19) epidemic, which is different in many aspects from the previous severe acute respiratory syndrome (SARS) epidemic. We developed this model when the COVID-19 epidemic was at its early phase. We reasoned that, with our model, the effects of different measures could be assessed for infection control. Unlike the homogeneous models, our model accounts for human population heterogeneity, where subpopulations (e.g., age groups) have different infection risks. The heterogeneous model estimates several characteristics of the epidemic more accurately compared to the homogeneous models. According to our analysis, the total number of infections and their peak number are lower compared to the assessment with the homogeneous models. Furthermore, the early-stage infection increase is little changed when population heterogeneity is considered, whereas the late-stage infection decrease slows. The model predicts that the anti-epidemic measures, like the ones undertaken in China and the rest of the world, decrease the basic reproductive number but do not result in the development of a sufficient collective immunity, which poses a risk of a second wave. More recent developments confirmed our conclusion that the epidemic has a high likelihood to restart after the quarantine measures are lifted.

Indexed as

Models, TheoreticalBasic Reproduction NumberChinaCOVID-19EpidemicsGlobal HealthHumansSARS-CoV-2Severe Acute Respiratory Syndromeantiepidemic measuresCOVID 19dynamical modelepidemicpopulation immunityquarantine

Identifiers

PMID33585377
PMCPMC7874228
OpenAlexW3120300730

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.