10.5061/DRYAD.83NJ1
Siraj, Amir S.
University of Notre Dame
Rodriguez-Barraquer, I.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public
Health, 615 N. Wolfe Street, Baltimore, USA
Barker, Christopher M.
Department of Pathology, Microbiology and Immunology, University of
California, 5329 Vet Med 3A, Davis, USA
Tejedor-Garavito, Natalia
University of Southampton
Harding, Dennis
Bellevue Hospital Center
Lorton, Christopher
Bellevue Hospital Center
Lukacevic, Dejan
Bellevue Hospital Center
Oates, Gene
Bellevue Hospital Center
Espana, Guido
University of Notre Dame
Kraemer, Moritz U. G.
University of Oxford
Manore, Carrie
Los Alamos National Laboratory
Johansson, Michael A.
Centers for Disease Control and Prevention
Tatem, Andrew J.
University of Southampton
Reiner, Robert C.
Institute for Health Metrics and Evaluation
Perkins, T. Alex
University of Notre Dame
Data from: Spatiotemporal incidence of Zika and associated environmental
drivers for the 2015-2016 epidemic in Colombia
Dryad
dataset
2019
epidemic data
arbovirus
Zika
demographics
National Science Foundation
https://ror.org/021nxhr62
NSF DEB1641130
2019-04-04T00:00:00Z
2019-04-04T00:00:00Z
en
https://doi.org/10.1038/sdata.2018.73
3259076355 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Despite a long history of mosquito-borne virus epidemics in the Americas,
the impact of the Zika virus (ZIKV) epidemic of 2015-2016 was unexpected.
The need for scientifically informed decision-making is driving research
to understand the emergence and spread of ZIKV. To support that research,
we assembled a data set of key covariates for modeling ZIKV transmission
dynamics in Colombia, where ZIKV transmission was widespread and the
government made incidence data publically available. On a weekly basis
between January 1, 2014 and October 1, 2016 at three administrative
levels, we collated spatiotemporal Zika incidence data, nine environmental
variables, and demographic data into a single downloadable database. These
new datasets and those we identified, processed, and assembled at
comparable spatial and temporal resolutions will save future researchers
considerable time and effort in performing these data processing steps,
enabling them to focus instead on extracting epidemiological insights from
this important data set. Similar approaches could prove useful for filling
data gaps to enable epidemiological analyses of future disease emergence
events.
Weekly mean temperature at 2.5 arc-minutesRaster brick of weekly mean
temperature calculated as the average of the daily mean temperature (a
total of 143 weeks between Jan 5, 2014 and Oct 1, 2016), in GRI format, at
a resolution of 2.5 arc-minutes. Layer names indicate the date each week
starts on. For example, the layer named tmean_wk151025 has mean
temperature for the week that starts on October 25,
2015.mean_temperature.zipWeekly minimum temperature at 2.5
arc-minutesRaster brick of weekly minimum temperature calculated as the
average of the daily minimum temperature (a total of 143 weeks between Jan
5, 2014 and Oct 1, 2016), in GRI format, at a resolution of 2.5
arc-minutes. Layer names indicate the date each week starts on. For
example, the layer named tmin_wk151025 has minimum temperature for the
week that starts on October 25, 2015.min_temperature.zipWeekly maximum
temperature at 2.5 arc-minutesRaster brick of weekly maximum temperature
calculated as the average of the daily maximum temperature (a total of 143
weeks between Jan 5, 2014 and Oct 1, 2016), in GRI format, at a resolution
of 2.5 arc-minutes. Layer names indicate the date each week starts on. For
example, the layer named tmax_wk151025 has maximum temperature for the
week that starts on October 25, 2015.max_temperature.zipWeekly relative
humidity at 2.5 arc-minutesRaster brick of weekly average relative
humidity calculated as the average of the daily mean relative humidity (a
total of 143 weeks between Jan 5, 2014 and Oct 1, 2016), in GRI format, at
a resolution of 2.5 arc-minutes. Layer names indicate the date each week
starts on. For example, the layer named rh_wk151025 has average relative
humidity for the week that starts on October 25,
2015.rel_humidity.zipWeekly MODIS Terra NDVI at 2.5 arc-minutesRaster
brick of weekly average NDVI from NASA's Terra satellite and MODIS
sensor (a total of 143 weeks between Jan 5, 2014 and Oct 1, 2016), in GRI
format, at a resolution of 2.5 arc-minutes. Layer names indicate the date
each week starts on. For example, the layer named
ndvi_modis_terra_wk151025 has the NDVI values for the week that starts on
October 25, 2015.ndvi_modis_terra.zipWeekly MODIS Aqua NDVI at 2.5
arc-minutesRaster brick of weekly average NDVI from NASA's Aqua
satellite and MODIS sensor (a total of 143 weeks between Jan 5, 2014 and
Oct 1, 2016), in GRI format, at a resolution of 2.5 arc-minutes. Layer
names indicate the date each week starts on. For example, the layer named
ndvi_modis_aqua_wk151025 has average NDVI for the week starting on October
25, 2015.ndvi_modis_aqua.zipWeekly precipitation at 2.5 arc-minutesRaster
brick of weekly total precipitation calculated as the total of the daily
precipitation (a total of 143 weeks between Jan 5, 2014 and Oct 1, 2016),
in GRI format, at a resolution of 2.5 arc-minutes. Layer names indicate
the date each week starts on. For example, the layer named precip_wk151025
has precipitation for the week that starts on October 25,
2015.precipitation.zipAedes aegypti population at 2.5 arc-minutesRaster
brick of ratio of Aedes aegypti population to human population at each
week of the year (a total of 52 weeks), in GRI format, at a resolution of
2.5 arc-minutes.aegypti_population.zipGridded population in 2015 at 3
arc-secondsColombia population in 2015. The file is in BIL format, at a
resolution of 3 arc-seconds.wpop_ppp_v2b_col_2015_0_05m.zipGridded births
in 2015 at 3 arc-secondsColombia births in 2015. The file is in BIL
format, at a resolution of 3
arc-seconds.wpop_births_col_2015_0_05m.zipGridded urban population in 2015
at 15 arc-secondsColombia urban population in 2015 obtained by multiplying
WorldPop gridded population by urban extent binary raster file. the file
in BIL format at a resolution of 15
arc-seconds.urban_pop_col_0_25m.zipGridded travel time at 30
arc-secondsColombia travel time (in minutes) to the nearest city of 50,000
or more population in year 2000. The file is in BIL format, at a
resolution of 30 arc-seconds.travel_time_50k_col_0_5m.zipGridded gross
cell product at 2.5 arc-minutesPer capita gross cell product in 2005 $US
for Colombia cropped to match all other raster outputs. The file is in BIL
format, at a resolution of 2.5 arc-minutes (resampled from the original 60
arc-minutes raster file).gecon_col_pcppp_2005_2_5m.zipWeekly Zika
casesWeekly Zika cases at municipality, department and national levels (in
.csv format).weekly_zika_cases.zipWeekly covariates aggregated at
municipality levelTime series of weekly covariates aggregated at
municipality level. This .zip file contains eight tables (in .csv format)
of time series for aegypti population, maximum temperature, mean
temperature, minimum temperature, NDVI MODIS Aqua, NDIV MODIS Terra,
precipitation and relative humidity.spatial_aggregates_municip.zipWeekly
covariates aggregated at department levelTime series of weekly covariates
aggregated at department level. This .zip file contains eight tables (in
.csv format) of time series for aegypti population, maximum temperature,
mean temperature, minimum temperature, NDVI MODIS Aqua, NDIV MODIS Terra,
precipitation and relative humidity.spatial_aggregates_dept.zipWeekly
covariates aggregated at national levelTime series of weekly covariates
aggregated at national level. This .zip file contains eight tables (in
.csv format) of time series for aegypti population, maximum temperature,
mean temperature, minimum temperature, NDVI MODIS Aqua, NDIV MODIS Terra,
precipitation and relative humidity.spatial_aggregates_national.zipWeekly
weighted covariates aggregated at municipality levelTime series of weekly
covariates, weighted by population, aggregated at municipality level. This
.zip file contains eight tables (in .csv format) of time series for
aegypti population, maximum temperature, mean temperature, minimum
temperature, NDVI MODIS Aqua, NDIV MODIS Terra, precipitation and relative
humidity.weighted_spatial_aggregates_municip.zipWeekly weighted covariates
aggregated at department levelTime series of weekly covariates, weighted
by population, aggregated at department level. This .zip file contains
eight tables (in .csv format) of time series for aegypti population,
maximum temperature, mean temperature, minimum temperature, NDVI MODIS
Aqua, NDIV MODIS Terra, precipitation and relative
humidity.weighted_spatial_aggregates_dept.zipWeekly weighted covariates
aggregated at national levelTime series of weekly covariates, weighted by
population, aggregated at national level. This .zip file contains eight
tables (in .csv format) of time series for aegypti population, maximum
temperature, mean temperature, minimum temperature, NDVI MODIS Aqua, NDIV
MODIS Terra, precipitation and relative
humidity.weighted_spatial_aggregates_national.zipFixed time covariates
aggregated at all levelsFixed time covariates aggregated at municipality,
department and national levels (in .csv format). This .zip file contains
three tables (in .csv format), one for each level, with data on
population, births, urban population, mean gross cell product and mean
travel time.spatial_aggregate_non_timeseries.zipSpatial time series movies
ZIKV cases and environmental drivers.Spatial time series movies ZIKV cases
and environmental drivers at weekly time step and municipality level (in
.MP4 format). This file includes nine movies: weekly number of cases,
cumulative number of cases, average NDVI (Aqua), average NDVI (Terra),
total precipitation, relative humidity, minimum temperature, mean
temperature and maximum temperature.spatial_timeseries_movies.zip
Colombia