Data and code for "Characterizing US contact patterns relevant to respiratory transmission from a pandemic to baseline: Analysis of a large cross-sectional survey"

This repository provides the data and source code for the following study: Juliana C Taube, Zachary Susswein, Vittoria Colizza, Shweta Bansal. "Characterizing US contact patterns relevant to respiratory transmission from a pandemic to baseline: Analysis of a large cross-sectional survey." https://doi.org/10.1101/2024.04.26.24306450

Estimates (estimates/)

Pandemic and baseline estimates of contact at the county-week scale are provided. Estimates are also provided dis-aggregated by age, gender, race/ethnicity, and setting of contact. The contact setting file has fewer columns so that it is small enough to upload here.

Columns of interest:

• contact_fit has modeled pandemic estimates of mean contact
• non_hh_contacts has observed pandemic mean contact
• scale_baseline has inferred baseline estimates of mean contact

Data (data/)

Reference files that may be required to run the code, including fips and state crosswalk files, urban/rural classifications, etc. are in data/input/. Files to reproduce supplementary figures are not provided here but can be found using references in the article.

• Average_Household_Size_and_Population_Density_-_County.csv contains population density for each fips code (from https://covid19.census.gov/datasets/USCensus::average-household-size-and-population-density-county/explore?location=4.945434%2C0.315550%2C1.99&showTable=true)
• census_regions.csv delineates which states are in which census regions (from https://github.com/cphalpert/census-regions/blob/master/us%20census%20bureau%20regions%20and%20divisions.csv)
• co-est2021-alldata.csv contains population estimates for each county (from https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2020-2021/CO-EST2021-ALLDATA.pdf)
• COVID_county_vacc_data_dataverse.csv contains county level vaccination coverage (from https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BFRIKI)
• covs_updated_for_inla_6_10.csv contains county and state policy data from the COVID-AMP Project and the CDC
• HHS_regions.csv contains information on what HHS region each county is in
• NCHSURCodes2013.xlsx contains urban-rural classifications for each fips code (from https://www.cdc.gov/nchs/data_access/urban_rural.htm)
• nyt-us-counties-rolling-avg-2020.csv, nyt-us-counties-rolling-avg-2021.csv, nyt-us-national-rolling-avg.csv, nyt-us-states-rolling-avg.csv contain COVID-19 case incidence data from the New York Times at different spatial scales (from https://github.com/nytimes/covid-19-data/blob/master/rolling-averages/us.csv)
• OxCGRT_compact_subnational_v1.csv contains Oxford Stringency Index data for each state (from https://github.com/OxCGRT/covid-policy-dataset/tree/main/data)
• state_and_county_fips_master.csv contains each county's corresponding state and name

Intermediate data files with aggregated raw data are provided so that the GAMs and linear regression models can be reproduced.

• group_means_rake provides raw weighted county-week means of contact data truncated at 72 contacts aggregated and disaggregated by age, gender, race, and setting
• output/mobility_19_20_fall_ratio_new_norm.csv contains ratios of Safegraph Social Distancing mobility metrics for the fall of 2019 and 2020
• output/normal_gamma2_72trunc_m1/fitted_predictions.csv provides contact estimates as a result of spatiotemporal GAMs for the main text analysis

Code (scripts/)

Scripts to clean data, rake and aggregate survey responses, run spatiotemporal GAM regression models, baseline linear regression models, and reproduce figures. Scripts for analyzing individual responses are provided for reproducibility but will not run without the original individual-level data (see Individual Data section below). File names briefly describe the purpose of each script. Main figures 1 and 2 are produced by 09_baseline_regression.R.

Individual Data

Individual CTIS survey responses cannot be shared by the authors, but researchers can visit https://cmu-delphi.github.io/delphi-epidata/symptom-survey/data-access.html if they would like to enter an agreement for data usage with CMU Delphi.