Evidence map›Paper›PMID 35098203›Full record

ArticleLancet regional health. Americas2022

Tracking the emergence of disparities in the subnational spread of COVID-19 in Brazil using an online application for real-time data visualisation: A longitudinal analysis.

Paul Mee, Neal Alexander, Philippe Mayaud, Felipe de Jesus Colón González, Sam Abbott, Andreza Aruska de Souza Santos, André Luís Acosta, Kris V Parag, Rafael H M Pereira, Carlos A Prete and 4 more

Abstract read
In one paragraph

Article in Lancet regional health. Americas, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

14 authors.

Paul MeeFaculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Neal AlexanderFaculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Philippe MayaudFaculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Felipe de Jesus Colón GonzálezFaculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Sam AbbottFaculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Andreza Aruska de Souza SantosOxford School of Global and Area Studies, University of Oxford, Oxford, United Kingdom.
André Luís AcostaInstituto de Biociências, Universidade de São Paulo, São Paulo, Brazil.
Kris V ParagMRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, United Kingdom.
Rafael H M PereiraInstituto de Pesquisa Econômica Aplicada (Ipea), Brasilia, Brazil.
Carlos A PreteDepartment of Electronic Systems Engineering, University of São Paulo, São Paulo, Brazil.
Ester C SabinoDepartamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, Brazil.
Nuno R FariaMRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, United Kingdom.
LSHTM Centre for Mathematical Modelling of Infectious Disease COVID-19 working group
Oliver J BradyFaculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.

Funding

Medical Research Council MC_PC_19012Medical Research Council MR/R010161/1Medical Research Council MR/R015600/1Medical Research Council MR/S019510/1Medical Research Council MR/V031112/1Wellcome Trust
6 · The paper itself

Abstract

backgroundBrazil is one of the countries worst affected by the COVID-19 pandemic with over 20 million cases and 557,000 deaths reported by August 2021. Comparison of real-time local COVID-19 data between areas is essential for understanding transmission, measuring the effects of interventions, and predicting the course of the epidemic, but are often challenging due to different population sizes and structures.

methodsWe describe the development of a new app for the real-time visualisation of COVID-19 data in Brazil at the municipality level. In the CLIC-Brazil app, daily updates of case and death data are downloaded, age standardised and used to estimate the effective reproduction number (

findingsAfter an initial introduction in São Paulo and Rio de Janeiro states in early March 2020, the epidemic spread to northern states and then to highly populated coastal regions and the Central-West. Municipalities with higher metrics of social development experienced earlier arrival of COVID-19 (decrease of 11·1 days [95% CI:8.9,13.2] in the time to arrival for each 10% increase in the social development index). Differences in the initial epidemic intensity (mean

interpretationThis study demonstrates that platforms that monitor, standardise and analyse the epidemiological data at a local level can give useful real-time insights into outbreak dynamics that can be used to better adapt responses to the current and future pandemics.

fundingThis project was supported by a Medical Research Council UK (MRC-UK) -São Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0).

Indexed as

AppBrazilCOVID-19OutbreakReal-timeRegressionReproduction numberStandardisationVisualiation

Identifiers

PMID35098203
PMCPMC8782271

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.