The repository contains auxiliary data and code relevant to the modeling effort for the COVID-19 Scenario Modeling Hub Research rounds
The repository contains a disparities folder regrouping all the auxiliary code and data associated with the disparities rounds.
The folder contains multiple sub-folders:
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case_imputation: imputed cases by race/ethnicity at the state level to infer missing case demographic information. A high proportion of COVID-19 cases are not reported with demographic information such as race/ethnicity. Populations with reduced access to quality healthcare and testing resources are more likely to experience Covid-19 morbidity and mortality, and thus is it not appropriate to omit or distribute missing case data randomly. We adapt methods described in Trangucci et al. 2022 to infer the distribution of missing cases by race/ethnicity. Here, we solely consider the cases that were reported without race/ethnicity and do not consider underreporting issues.
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vaccination: weekly vaccination data by key demographics for California and North Carolina. We provide the number of individuals receiving at least 1 dose ("partial_vax") and fully vaccinated ("full_vax") by age and by race/ethnicity
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serology: monthly serology data was extracted from the CDC COVID Data Tracker, 2020-2021 Nationwide COVID-19 Infection- and Vaccination-Induced Antibody Seroprevalence (Blood donations). The nationwide blood donor seroprevalence survey estimates the percentage of the U.S. population ages 16 and older that have developed antibodies against SARS-CoV-2.
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population_data: state-level population structure data by age and race/ethnicity both separately and jointly. Age groups include ‘0-17’, ‘18-64’, and ‘65+’ years.
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hospitalization_data: hospitalization data by race/ethnicity, in a rate per 100,000 people for California and number of hospitalizations for North Carolina.
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contact_matrix: synthetic daily contact matrices by race/ethnicity in the household, school, community, workplace setting using methodology described in Mistry et al. 2021 and Aleta et al. 2022. We produce two contact matrices reflecting the pre-pandemic and pandemic periods and code and data to manually produce contact matrices.
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mobility: weekly mobility data at the census tract level from Kang et al. 2021
For more information, please consult the associated README
More information about the disparities rounds is available on the COVID-19 Scenario Modeling Hub - Research GitHub repository.
All source code specific to the overall project is available under an open-source MIT license. Some items might be available under different terms and licenses. Please consult these licenses before using it to ensure that you follow the terms under which they were released.
Please feel free to open an issue if you identify any issue or would like to suggest an idea/improvement.
Scenario modeling groups are supported through grants to the contributing investigators.
The Scenario Modeling Hub site is supported by the MIDAS Coordination Center, NIGMS Grant U24GM132013 to the University of Pittsburgh.