DOI: 10.5281/zenodo.13286670
title: Dengue, chikungunya, and Zika case notification data from Brazil at the weekly- and municipality-level over 2013-2020, with matched socioeconomic, human connectivity, climate, and meteorological conditions

description: This dataset contains weekly aggregated, non-identifiable case notification data for chikungunya (CHIKV), dengue (DENV), and Zika (ZIKV) virus cases reported in Brazil between 2013 and 2020 at the municipality-level. The original non-aggregated data came from the national information system for notifiable diseases (http://portalsinan.saude.gov.br, SINAN: Sistema de Informação de Agravos de Notificação). In addition to the infectious disease data, the files include matched socioeconomic, climate, centrality metrics, and population-weighted meteorological data for each week and municipality. The methods for data extraction, collation, and matching are described below and in the paper “Spatiotemporal relationships between extreme weather events and arbovirus transmission across Brazil”. The code to accompany this dataset is available at “https://github.com/mrc-ide/Brazil-arbovirus-meteo.” 
description: Infectious disease case notification data. 

Daily reported cases of CHIKV, ZIKV and DENV provided by the Ministry of Health of Brazil between 2015-2020, 2016-2020 and 2013-2020, respectively, were acquired through the national information system for notifiable diseases (SINAN: Sistema de Informação de Agravos de Notificação, which is publicly available upon request). The data were aggregated to weekly time-steps at the municipality level, where the municipality recorded varies per file (see key below). We imputed zeroes for the days in between the first and last date of reported cases for each municipality. To capture local transmission and exclude the municipalities with imported cases but no active transmission, we only included municipalities which reported >=5 cases in each year or >20 cases in any one individual year.

Key for how the municipality was assigned to a case:

1)  _resid files. The municipality assigned to a case was the municipality of residence for all data entries.

2)  _report files. The municipality assigned to a case was the municipality where a case was reported or hospitalized.

3)  _exp_resid files. The municipality assigned to a case was the municipality of suspected infection for 64%, 58%, and 66% of the data entries in the CHIKV, DENV and ZIKV datasets respectively which reported this information, and for the remaining data entries we used the municipality of residence. 

4)  _exp_report files. The municipality assigned to a case was the municipality of suspected infection for 64%, 58%, and 66% of the data entries in the CHIKV, DENV and ZIKV datasets respectively which reported this information, and for the remaining data entries we used the municipality where a case was reported or hospitalized.

Meteorological and MIR data. 

Global hourly climate data for total precipitation, 2m dewpoint temperature, and 2m temperature variables between 01-01-2013 to 31-12-2020, were downloaded from the ERA5-Land reanalysis dataset provided by Copernicus Climate Change Service (C3S) (ERA5-Land hourly data from 1950 to present), as grid points at 0.1 x 0.1 degrees resolution  [1]. Relative humidity (RH) was calculated using the dewpoint (d) and temperature (T) following:RH=100*(exp⁡(d*17.625)/(d+243.04))/(exp⁡(T*17.625)/(T+243.04)) .

The pixel level meteorological data were population weighted to better capture the climate and environmental conditions that the average person in each municipality experiences, which discounts the conditions where there is no population and weights populous locations more compared to less populated areas (see details below).

Mid-infrared (MIR) data collected every eight days between 2013 and 2020, were sourced from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instruments MOD13A2v061 and MYD13A2v061 [2,3]. Vegetated surfaces, agricultural land and urban settlements differentially reflect MIR. The MIR data were scaled to give a value between 0 and 1. The MIR data were population-weighted and averaged for each municipality of Brazil per week (see details below).

Population data.

Annual global population data were extracted from LandScanTM datasets as pixels at approximately 1/120 degrees for 2013 to 2019 [4–11]. The 2020 data were unavailable, and we assumed 2020 values were the same as 2019. The pixel level data were extracted and aggregated over the Brazilian municipalities [12].

Population-weighting the meteorological and MIR data.

A shapefile of Brazil at administration level 2 (units separated by municipality boundaries) [12] was rasterised to the same resolution as the population rasters (1/120 degree cells). Where borders crossed within a cell, we assumed the administration unit that used the largest area of the cell. We assumed that meteorological and MIR point values were constant for all the population cells that were nearest to that point. Then we produced a list of 1/120 resolution pixels, each with associated administration unit, population, meteorological, and MIR variables. The weighted average of the meteorological and MIR data were calculated using the population field across the pixels for each administration unit; and we then summarised the data for each administration unit (averaged for the temperature, humidity, and MIR data and summed for the precipitation data). 

Calculating meteorological indices.

We aggregated the hourly data to calculate population-weighted weekly average mean, maximum, and minimum relative humidity (RH_mean, RH_max, RH_min), average mean, maximum, and minimum temperature (Tmean, Tmax, Tmin), absolute maximum of maximum relative humidity (RH_max_max), absolute minimum of minimum relative humidity (RH_min_min), absolute maximum of maximum temperature* (Tmax_max), absolute minimum of minimum temperature* (Tmin_min), and average temperature range* (TR). We also calculated the number of rain days where daily cumulative precipitation exceeded 1mm (raindays), the number of heavy rain days (precipitation >10mm)* (raindays_heavy), the number of very heavy rain days (precipitation >20mm)* (raindays_heavy+), average precipitation (mean of rain on days where precipitation >1mm) (Rf_mean), cumulative precipitation (Rf_cum), and the maximum number of preceding consecutive rain days (precipitation >1mm)* and dry days (precipitation <1mm)* (raindays_cons and drydays_cons) across the timeseries. Variables denoted with an asterisk (*) are core measures of extreme climate as selected by the Joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices [13]. For each week t, the variables were calculated at time lags of 0 (t), 1 (t-1), 2 (t-2) and 3 (t-3) weeks, and over the cumulative time periods t to t-1, t to t-2 and t to t-3. 

The meteorological data were aggregated to the monthly level and used to calculate the Standardized Precipitation Evapotranspiration Index (SPEI), a measure of long-term wetness or dryness/drought across different time-scales (1-, 3-, 6- and 12-months) using the SPEI R package [14].  For each month ( and municipality (, we calculated the reference evapotranspiration () using the Hargreaves equation, which uses the latitude and the average maximum and minimum temperature. The water surplus or deficit () is calculated as the difference between cumulative monthly precipitation and the  The  values are aggregated across different time-scales (1-, 3-, 6- and 12-months) and used to estimate the SPEI. We assumed that the SPEI values were constant across the weeks in a given month.

ENSO data. 

El Niño Southern Oscillation (ENSO) is a periodic cycle in sea surface temperatures (SST) in the Pacific Ocean which determines whether El Niño or La Niña events are occurring and impacts the atmosphere and global weather conditions. Monthly SST anomalies in the east-central equatorial Pacific region 3.4 (Niño 3.4, 5N-5S, 170-120W), which are defined as departures from the long-term average monthly temperature between 1991 to 2020, were obtained from the National Oceanic and Atmospheric Administration [15] for January 2013 to December 2020.

Extreme weather events data. 

Disaster events for Brazil between 2013 and 2020 were obtained from the International Disaster Database [16]. Disaster events are classified as those where 10+ people were killed, 100+ people are affected, there is a call for international assistance or a declaration of a state of emergency. Disaster events which led to flooding (e.g., storms, landslides, and the dam breaks in Minas Gerais in 2015 and 2019) were recorded. The raw disaster data were given at either the municipality level or the state level (in which case we assumed that every municipality in the state experienced the disaster event). Categorical variables for each week in 2013 to 2020 were created which described if there had been a flooding event during the previous 1, 2, and 3 weeks.

Socioeconomic data.

Data for the median household income, illiteracy rate (percentage of people over 15 years old who cannot read or write), and sanitation (percentage of people in houses with adequate sanitation) per municipality, were collated from the most recent census conducted in 2010 [17].

Human connectivity: centrality of the municipalities.

Mobility data were collected, under a research agreement, by a private Brazilian company (formerly called InLoco, now Incognia - https://www.incognia.com/pt/politicas/covid) that gathered anonymous geolocation of users of mobile apps that used the company’s software development kit (SDK). Original anonymous data were provided as discussed in [18], considering origin-destination latitude-longitude pairs of Brazilian users, here called “journeys”. Only journeys with a change in position of at least 0.01 degrees latitude/longitude in periods greater than 1 hour were gathered in the dataset. Data were summed to consider journeys between Brazilian municipalities on a monthly timescale for the period of February 2019 to December 2019. This period of 2019 gathers mobility data of tens of millions of app users and was considered representative of all years in our analysis.

We calculated the centrality of each municipality within the mobility network per month, and we assumed that the centralities were constant over the weeks in each month. The weeks in January was set as NA because the mobility data were not available for this month. To calculate measures of network centrality the mobility data matrices were converted to graphs using the igraph package [19,20], where each municipality was represented as a node in a directional network with edges weighted by the number of journeys. The degree, betweenness, and alpha centrality [21,22] of each node were calculated using functions from the igraph package, and we used inward directional movement to calculate the centrality indices for each node in the network. We scaled the alpha centrality scores as: (centrality – 1)*1000.

Table of variable definitions.






Variable



Definition





ENSO_anom



El Niño and the Southern Oscillation sea surface temperature anomalies





SPEI1, SPEI3, SPEI6, SPEI12



Standardised Precipitation Evapotranspiration Index, a measure of long-term wetness or dryness across different time-scales (1-, 3-, 6- and 12-months)





RH_mean, RH_mean_1, RH_mean_2, RH_mean_3, RH_mean_01, RH_mean_02, RH_mean_03



Average daily mean humidity calculated over the 0, 1, 2 and 3 weeks before, and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





RH_min, RH_min_1, RH_min_2, RH_min_3, RH_min_01, RH_min_02, RH_min_03



Average daily minimum humidity calculated over the 0, 1, 2 and 3 weeks before, and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





RH_max, RH_max_1, RH_max_2, RH_max_3, RH_max_01, RH_max_02, RH_max_03



Average daily maximum humidity calculated over the 0, 1, 2 and 3 weeks before, and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





RH min_min, RH_min_min_1, RH min_min_2, RH_min_min_3, RH_min_min_01, RH min_min_02, RH min_min_03



Absolute minimum of the daily humidity calculated over the 0, 1, 2 and 3 weeks before, and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





RH_max_max, RH_max_max_1, RH_max_max_2, RH_max_max_3, RH_max_max_01, RH_max_max_02, RH_max_max_03



Absolute maximum of the daily humidity calculated over the 0, 1, 2 and 3 weeks before, and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





Tmean, Tmean_1, Tmean_2, Tmean_3, Tmean_01, Tmean_02, Tmean_03



Average daily mean temperature calculated over the 0, 1, 2 and 3 weeks before, and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





Tmin, Tmin_1, Tmin_2, Tmin_3, Tmin_01, Tmin_02, Tmin_03



Average daily minimum temperature calculated over the 0, 1, 2 and 3 weeks before, and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





Tmax, Tmax_1, Tmax_2, Tmax_3, Tmax_01, Tmax_02, Tmax_03



Average daily maximum temperature calculated over the 0, 1, 2 and 3 weeks before, and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





Tmin_min, Tmin_min_1, Tmin_min_2, Tmin_min_3, Tmin_min_01, Tmin_min_02, Tmin_min_03



Absolute minimum of the daily temperature calculated over the 0, 1, 2 and 3 weeks before, and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





Tmax_max, Tmax_max_1, Tmax_max_2, Tmax_max_3, Tmax_max_01, Tmax_max_02, Tmax_max_03



Absolute maximum of the daily temperature calculated over the 0, 1, 2 and 3 weeks before, and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





TR, TR_1, TR_2, TR_3, TR_01, TR_02, TR_03



Average daily temperature range calculated over the 0, 1, 2 and 3 weeks before, and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





Rf_mean, Rf_mean_1, Rf_mean_2, Rf_mean_3, Rf_mean_01, Rf_mean_02, Rf_mean_03



Mean of rain on days where precipitation >1mm calculated over the 0, 1, 2 and 3 weeks before, and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





Rf_cum, Rf_cum_1, Rf_cum_2, Rf_cum_3, Rf_cum_01, Rf_cum_02, Rf_cum_03



Cumulative rainfall calculated over the 0, 1, 2 and 3 weeks before, and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





Raindays, raindays_1, raindays_2, raindays_3, rain_days_01, rain_days_02, rain_days_03



The number of days in the time period where the precipitation > 1mm, where the time periods are 0, 1, 2 and 3 weeks before and 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





Raindays_heavy,raindays_heavy_1, raindays_heavy_2, raindays_heavy_3, raindays_heavy_01, raindays_heavy_02, raindays_heavy_03



The number of days in the time period where the precipitation > 10mm, where the time periods are 0, 1, 2 and 3 weeks before and 0 to 1, 0 to 2 and 0 to 3 weeks before





Raindays_heavy+,raindays_heavy+_1, raindays_heavy+_2, raindays_heavy+_3, raindays_heavy+_01, raindays_heavy+_02, raindays_heavy+_03



The number of days in the time period where the precipitation > 20mm, where the time periods are 0, 1, 2 and 3 weeks before and 0 to 1, 0 to 2 and 0 to 3 weeks before





Drydays_cons, drydays_cons_1, drydays_cons_2, drydays_cons_3 



The maximum number of consecutive dry days (precipitation <1mm) up to a given week, calculated over the previous 0, 1, 2 and 3 weeks





Raindays_cons, raindays_cons_1, raindays_cons_2, raindays_cons_3



The maximum number of consecutive rain days (precipitation >1mm) up to a given week, calculated over the previous 0, 1, 2 and 3 weeks





Income



Median household income





Sanitation



Percentage of people in houses with adequate sanitation





Illiteracy



Percentage of people over 15 years old who cannot read or write





MIR



Population-weighted Mid-Infrared Index





Degree



Degree centrality – the total number of connected municipalities to the municipality of interest based on human mobility network analysis





Between



Betweenness centrality - a measure of how each municipality influences the flow of movement through the human mobility network





Alpha



Alpha centrality - a measure of the relative influence of a municipality in the human mobility network, based on the weighted number of connected municipalities





Flood, flood_1, flood_2, flood_3, flood_01, flood_02, flood_03



Binary yes/no variable describing if a disaster event causing flooding occurred in the weeks 0, 1, 2 and 3 weeks before and over the 0 to 1, 0 to 2 and 0 to 3 weeks before inclusively





References.

1.     Muñoz Sabater J. ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 2019. doi:DOI: 10.24381/cds.e2161bac2.     Didan K. MODIS/Aqua Vegetation Indices 16-Day L3 Global 1km SIN Grid V061., distributed by NASA EOSDIS Land Processes Distributed Active Archive Center,. 2021. doi:https://doi.org/10.5067/MODIS/MYD13A2.0613.     Didan K. MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V061., distributed by NASA EOSDIS Land Processes Distributed Active Archive Center,. 2021. doi:https://doi.org/10.5067/MODIS/MOD13A2.0614.     Bright, E., Rose, A., Urban, M. LandScan Global 2013 [Data set]. Oak Ridge National Laboratory. 2014. doi:https://doi.org/10.48690/15242085.     Bright E, Rose A, Urban M. LandScan Global 2014 [Data set]. Oak Ridge National Laboratory. 2015. doi:https://doi.org/10.48690/15242096.     Bright E, Rose A, Urban M. LandScan Global 2015 [Data set]. Oak Ridge National Laboratory. 2016. doi:https://doi.org/10.48690/15242107.     Bright E, Rose A, Urban M, McKee J. LandScan Global 2016 [Data set]. Oak Ridge National Laboratory. 2017. doi:https://doi.org/10.48690/15242118.     Rose A, McKee J, Urban M, Bright E. LandScan Global 2017 [Data set]. Oak Ridge National Laboratory. 2018. doi:https://doi.org/10.48690/15242129.     Rose A, McKee J, Urban M, Bright E, Sims K. LandScan Global 2018 [Data set]. Oak Ridge National Laboratory. 2019. doi:https://doi.org/10.48690/152421310.     Rose A, McKee J, Sims K, Bright E, Reith A, Urban M. LandScan Global 2019 [Data set]. Oak Ridge National Laboratory. 2020. doi:https://doi.org/10.48690/152421411.     Rose A, McKee J, Sims K, Bright E, Reith A, Urban M. LandScan Global 2020 [Data set]. Oak Ridge National Laboratory. 2021. doi:https://doi.org/10.48690/152337812.     Pereira, R, Goncalves, C. geobr: Download Official Spatial Data Sets of Brazil. 2023. 13.     World Meteorological Organization. Guidelines on Analysis of extremes in a changing climate in support of informed decisions for adaptation. Climate Data and Monitoring. 2009. 14.     Beguería, S,Vicente-Serrano, SM. SPEI: Calculation of the Standardized Precipitation-Evapotranspiration Index. R package version 1.8.0. 2023. 15.     NOAA. East Central Tropical Pacific SST (5N-5S)(170-120W). 16.     EM-DAT, CRED / UCLouvain, Brussels, Belgium. Available: https://www.emdat.be/database17.     2010 Population Census | IBGE. 2010 [cited 20 Apr 2024]. Available: https://www.ibge.gov.br/en/statistics/social/labor/18391-2010-population-census.html?edicao=19720&t=resultados18.     Peixoto PS, Marcondes D, Peixoto C, Oliva SM. Modeling future spread of infections via mobile geolocation data and population dynamics. An application to COVID-19 in Brazil. PLoS One. 2020;15: e0235732. doi:10.1371/JOURNAL.PONE.023573219.     Csárdi, G, Nepusz, T, Traag, V, Horvát, Sz, Zanini, F, Noom, D, et al. igraph: Network Analysis and Visualization in R. 2023. doi:10.5281/zenodo.768260920.     Csardi, G, Nepusz T. The igraph software package for complex network research. InterJournal. 2006;Complex Sy. 21.     Bonacich P, Lloyd P. Eigenvector-like measures of centrality for asymmetric relations. Social Networks. 2001;23: 191–201. 22.     Newman MEJ. Networks: an introduction. Oxford University Press; 2010.