DOI: 10.5061/dryad.vdncjsxs2
title: SARS-CoV-2 non-pharmaceutical interventions in Brazilian municipalities

description: Brazil has one of the fastest-growing COVID-19 epidemics worldwide.
 Non-pharmaceutical interventions (NPIs) have been adopted on a municipal
 level, with asynchronous actions taken across 5,568 municipalities and the
 Federal District. This paper addresses this complexity reporting on a
 novel dataset with survey responses from 4,027 mayors, 72.3% of the total
 municipalities in the country. This dataset responds to the urgency to
 track and share findings on fragmented policies to tackle health crises
 like the COVID-19 pandemic. Quantifying NPIs can allow for understanding
 the effectiveness of interventions in reducing transmission. We offer
 temporal details for a range of measures aimed at generating social
 distancing as well as when local governments started to relax those
 measures.
description: Information on local NPI policies related to COVID-19 were
 collected through a phone-based survey conducted directly with mayors,
 with an option to receive a protected password to respond to the
 questionnaire online at a later time or to update previous answers. We
 focused on information that has a direct impact on the mobility of
 residents; that was associated with a specific date of implementation; and
 that, as policies, are of public domain but were not yet tabled
 together.  We collected information on policies
 adapting a classification system that included: (1) adoption of cordon
 sanitaire, (2) prohibition of agglomeration, (3) closure of all but
 essential services, and (4) compulsory use of face covers, (5) reduction
 in public transportation offer and if so, what was the percentage of the
 reduction, and (6) if there was already any easing of the above distancing
 measures. For all questions there was a side column asking when the action
 was adopted, and that field was populated in the format
 DD/MM/YYYY.  In order to collect these data, we started
 a collaboration with the Brazilian Confederation of Municipalities (CNM).
 This cooperation was established through a meeting followed by a written
 agreement signed by the first and last authors of this paper with CNM on
 April 9, 2020. The authors were allowed to describe, deposit and analyse
 the dataset. The public availability of these data also extends to other
 scholars the right to analyse the data. CNM has a call centre and as the
 largest municipal association in Brazil, they possess the email and
 telephone number of all Brazilian elected mayors. The capillarity of that
 organisation makes it an ideal partner for such large-scale data
 collection. The partnership was established because of the need to
 understand the impact of decentralized measures in Brazil and what
 decentralisation causes to the spread of infectious diseases. Upon the
 establishment of this collaboration, CNM designed further questions to the
 questionnaire that are of interest to their monitoring of municipalities,
 such as budgetary information possibly affected by the pandemic. In total,
 the questionnaire had 47 questions; our database has 5 columns related to
 the identification of the municipality and 13 of the 47 questions that
 were part of our collaboration to document NPI policy strategies. The 13
 questions that form this dataset (6 thematic questions with respective 6
 dates of implementation and 1 question pertaining to percentage) were
 discussed through coordinated orientation.
description: Because not all municipal authorities answered to all questions,
 we suggest users to consider additional sources of information to document
 missing policy implementation using preferably official source of
 information such as local decrees. However, as decrees are not always
 available online, secondary sources such as media reports may need to be
 consulted.  As the pandemic
 progresses and as Brazil is a highly affected country, we invite
 researchers to use the data to best understand the pandemic and support
 health policymakers in their efforts.