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 Jr; Ester C Sabino; Nuno R Faria; LSHTM Centre for Mathematical Modelling of Infectious Disease COVID-19 working group; Oliver J Brady

doi:10.1016/j.lana.2021.100119

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

Lancet Reg Health Am. 2022 Jan:5:None. doi: 10.1016/j.lana.2021.100119.

Authors

Paul Mee^{1

2

3}, Neal Alexander^{1

3}, Philippe Mayaud⁴, Felipe de Jesus Colón González^{1

2}, Sam Abbott^{1

2}, Andreza Aruska de Souza Santos⁵, André Luís Acosta⁶, Kris V Parag⁷, Rafael H M Pereira⁸, Carlos A Prete Jr⁹, Ester C Sabino¹⁰, Nuno R Faria^{7

10

11}; LSHTM Centre for Mathematical Modelling of Infectious Disease COVID-19 working group; Oliver J Brady^{1

2}

Affiliations

¹ Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
² Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
³ International Statistics and Epidemiology Group, London School of Hygiene and Tropical Medicine, London, United Kingdom.
⁴ Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
⁵ Oxford School of Global and Area Studies, University of Oxford, Oxford, United Kingdom.
⁶ Instituto de Biociências, Universidade de São Paulo, São Paulo, Brazil.
⁷ MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, United Kingdom.
⁸ Instituto de Pesquisa Econômica Aplicada (Ipea), Brasilia, Brazil.
⁹ Department of Electronic Systems Engineering, University of São Paulo, São Paulo, Brazil.
¹⁰ Departamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, Brazil.
¹¹ Department of Zoology, University of Oxford, Oxford, United Kingdom.

Abstract

Background: Brazil 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.

Methods: We 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 (R_t ). We show how such platforms can perform real-time regression analyses to identify factors associated with the rate of initial spread and early reproduction number. We also use survival methods to predict the likelihood of occurrence of a new peak of COVID-19 incidence.

Findings: After 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 R_t ) were largely driven by geographic location and the date of local onset.

Interpretation: This 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.

Funding: This 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).

Keywords: App; Brazil; COVID-19; Outbreak; Real-time; Regression; Reproduction number; Standardisation; Visualiation.

Abstract

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