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

ABSTRACT

BackgroundBrazil is one of the countries worst affected by the COVID-19 pandemic with over 20 million cases and 557,000 deaths reported. 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 reproduction number (Rt). 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. FindingsAfter an initial introduction in Sao 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{middle dot}1 days [95% CI13{middle dot}2,8{middle dot}9] in the time to arrival for each 10% increase in the social development index). Differences in the initial epidemic intensity (mean Rt) were largely driven by geographic location and the date of local onset. 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) -Sao Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0)