Evidence map›Paper›PMID 37754616›Full record

ArticleInternational journal of environmental research and public health2023

Analysis of the Effectiveness of Public Health Measures on COVID-19 Transmission.

Thiago Christiano Silva, Leandro Anghinoni, Cassia Pereira das Chagas, Liang Zhao, Benjamin Miranda Tabak

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Public Telemedicine Policy in Brazilian Unified Health System: An Impact Analysis.International journal of environmental research and public health · 2024
    Article
  2. Article
  3. Review
  4. Article
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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

5 authors.

Thiago Christiano SilvaUniversidade Católica de Brasília, Brasilia 71966-700, Brazil.ORCID 0000-0003-2631-4393
Leandro AnghinoniDepartment of Computing and Mathematics, Faculty of Philosophy, Sciences, and Literatures in Ribeirão Preto, Universidade de São Paulo, São Paulo 14040-901, Brazil.
Cassia Pereira das ChagasUniversidade Católica de Brasília, Brasilia 71966-700, Brazil.
Liang ZhaoDepartment of Computing and Mathematics, Faculty of Philosophy, Sciences, and Literatures in Ribeirão Preto, Universidade de São Paulo, São Paulo 14040-901, Brazil.
Benjamin Miranda TabakFGV/EPPG Escola de Políticas Públicas e Governo, Fundação Getúlio Vargas (School of Public Policy and Government, Getulio Vargas Foundation), Brasilia 70830-020, Brazil.

Funding

Coordenação de Aperfeicoamento de Pessoal de Nível Superior 2018.1.1702.59.8Fundação de Apoio a Pesquisa do Distrito Federal (FAP-DF) 777National Council for Scientific and Technological Development 302703/2022-5National Council for Scientific and Technological Development 305485/2022-9São Paulo State Research Foundation (FAPESP) 2013/07375-0São Paulo State Research Foundation (FAPESP) 2015/50122-0
6 · The paper itself

Abstract

In this study, we investigate the COVID-19 epidemics in Brazilian cities, using early-time approximations of the SIR model in networks and combining the VAR (vector autoregressive) model with machine learning techniques. Different from other works, the underlying network was constructed by inputting real-world data on local COVID-19 cases reported by Brazilian cities into a regularized VAR model. This model estimates directional COVID-19 transmission channels (connections or links between nodes) of each pair of cities (vertices or nodes) using spectral network analysis. Despite the simple epidemiological model, our predictions align well with the real COVID-19 dynamics across Brazilian municipalities, using data only up until May 2020. Given the rising number of infectious people in Brazil-a possible indicator of a second wave-these early-time approximations could be valuable in gauging the magnitude of the next contagion peak. We further examine the effect of public health policies, including social isolation and mask usage, by creating counterfactual scenarios to quantify the human impact of these public health measures in reducing peak COVID-19 cases. We discover that the effectiveness of social isolation and mask usage varies significantly across cities. We hope our study will support the development of future public health measures.

Indexed as

COVID-19Public HealthBrazilCitiesHumansMachine LearningMasksSARS-CoV-2Social Isolationartificial intelligenceCOVID-19networkSIRVAR

Identifiers

PMID37754616
PMCPMC10531329

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.