Evidence map›Paper›PMID 32834624›Full record

ArticleChaos, solitons, and fractals2020

COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions.

Petrônio C L Silva, Paulo V C Batista, Hélder S Lima, Marcos A Alves, Frederico G Guimarães, Rodrigo C P Silva

Open access · greenAbstract read
In one paragraph

Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers.

0numbers the graph read from it
0cells of the map it votes in
113citing papers in PubMed
11.7field-weighted citation impact, top 1% 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

113 citing papers in PubMed, 406 citations in OpenAlex.

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53 more citing papers are in PubMed but not listed here.

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

6 authors at 3 institutions in 1 country.

Petrônio C L SilvaGrupo de Pesquisa em Ciência de Dados e Inteligência Computacional - {ci∂ic}, Brazil.
Paulo V C BatistaGrupo de Pesquisa em Ciência de Dados e Inteligência Computacional - {ci∂ic}, Brazil.
Hélder S LimaInstituto Federal do Norte de Minas Gerais (IFNMG), Brazil.
Marcos A AlvesMachine Intelligence and Data Science (MINDS) Laboratory, Federal University of Minas Gerais, Brazil.
Frederico G GuimarãesMachine Intelligence and Data Science (MINDS) Laboratory, Federal University of Minas Gerais, Brazil.
Rodrigo C P SilvaMachine Intelligence and Data Science (MINDS) Laboratory, Federal University of Minas Gerais, Brazil.
Universidade Federal de Minas Gerais · BRInstituto Federal de Educação Ciência e Tecnologia do Norte de Minas Gerais · BRUniversidade Federal de Ouro Preto · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.

Indexed as

Agent-based simulationCOVID-19Epidemic modelsSEIR

Identifiers

PMID32834624
PMCPMC7340090
OpenAlexW3039119913

What OpenQuestion holds

Textmetadata
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.