Evidence map›Paper›PMID 33984687›Full record

ArticleEpidemics2021

Assessing the nationwide impact of COVID-19 mitigation policies on the transmission rate of SARS-CoV-2 in Brazil.

Daniel C P Jorge, Moreno S Rodrigues, Mateus S Silva, Luciana L Cardim, Nívea B da Silva, Ismael H Silveira, Vivian A F Silva, Felipe A C Pereira, Arthur R de Azevedo, Alan A S Amad and 4 more

Abstract read
In one paragraph

Article in Epidemics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

14 authors.

Daniel C P JorgeInstituto de Fısica, Universidade Federal da Bahia, Salvador, Bahia, Brazil.
Moreno S RodriguesFundação Oswaldo Cruz, Porto Velho, Rondônia, Brazil.
Mateus S SilvaInstituto de Fısica, Universidade Federal da Bahia, Salvador, Bahia, Brazil.
Luciana L CardimCenter for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Bahia, Brazil.
Nívea B da SilvaCenter for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Bahia, Brazil; Instituto de Matemática e Estatística, Universidade Federal da Bahia, Salvador, Bahia, Brazil.
Ismael H SilveiraInstituto de Saúde Coletiva, Universidade Federal da Bahia, Salvador, Bahia, Brazil.
Vivian A F SilvaCenter for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Bahia, Brazil.
Felipe A C PereiraInstituto de Física, Universidade de São Paulo, São Paulo, Brazil.
Arthur R de AzevedoInstituto de Matemática e Estatística, Universidade Federal da Bahia, Salvador, Bahia, Brazil.
Alan A S AmadCollege of Engineering, Swansea University, Swansea, Wales, United Kingdom.
Suani T R PinhoInstituto de Fısica, Universidade Federal da Bahia, Salvador, Bahia, Brazil.
Roberto F S AndradeInstituto de Fısica, Universidade Federal da Bahia, Salvador, Bahia, Brazil; Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Bahia, Brazil.
Pablo I P RamosCenter for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Bahia, Brazil.
Juliane F OliveiraCenter for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Bahia, Brazil; Centre of Mathematics of the University of Porto (CMUP), Department of Mathematics, Porto, Portugal. Electronic address: julianlanzin@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 is now identified in almost all countries in the world, with poorer regions being particularly more disadvantaged to efficiently mitigate the impacts of the pandemic. In the absence of efficient therapeutics or large-scale vaccination, control strategies are currently based on non-pharmaceutical interventions, comprising changes in population behavior and governmental interventions, among which the prohibition of mass gatherings, closure of non-essential establishments, quarantine and movement restrictions. In this work we analyzed the effects of 707 governmental interventions published up to May 22, 2020, and population adherence thereof, on the dynamics of COVID-19 cases across all 27 Brazilian states, with emphasis on state capitals and remaining inland cities. A generalized SEIR (Susceptible, Exposed, Infected and Removed) model with a time-varying transmission rate (TR), that considers transmission by asymptomatic individuals, is presented. We analyze the effect of both the extent of enforced measures across Brazilian states and population movement on the changes in the TR and effective reproduction number. The social mobility reduction index, a measure of population movement, together with the stringency index, adapted to incorporate the degree of restrictions imposed by governmental regulations, were used in conjunction to quantify and compare the effects of varying degrees of policy strictness across Brazilian states. Our results show that population adherence to social distance recommendations plays an important role for the effectiveness of interventions and represents a major challenge to the control of COVID-19 in low- and middle-income countries.

Indexed as

SARS-CoV-2Basic Reproduction NumberBrazilCommunicable Disease ControlCOVID-19HumansModels, TheoreticalPublic PolicyBrazilCOVID-19Mathematical modelingNon-pharmaceutical interventionsPublic policiesTransmission rate

Identifiers

PMID33984687
PMCPMC8106524

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.