covid19-smh-research_resources
所属分类:生物医药技术
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sh-1993
说明: COVID-19场景建模中心研究-资源
(COVID-19 Scenario Modeling Hub Research - Resources)
文件列表:
disparities/
LICENSE
# COVID-19 Scenario Modeling Hub Research - Resources
The repository contains auxiliary data and code relevant to the modeling
effort for the [COVID-19 Scenario Modeling Hub Research rounds](https://github.com/midas-network/covid19-smh-research)
## Disparities Rounds
The repository contains a [disparities](./disparities/) folder regrouping
all the auxiliary code and data associated with the disparities rounds.
The folder contains multiple sub-folders:
- [case_imputation](./disparities/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](https://arxiv.org/abs/2206.08161) 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.
- [vaccination](./disparities/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
- [serology](./disparities/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)](https://covid.cdc.gov/covid-data-tracker/#nationwide-blood-donor-seroprevalence).
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.
- [population_data](./disparities/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.
- [hospitalization_data](./disparities/hospitalization_data/): hospitalization data
by race/ethnicity, in a rate per 100,000 people for California and
number of hospitalizations for North Carolina.
- [contact_matrix](./disparities/contact_matrix/): synthetic daily contact
matrices by race/ethnicity in the household, school, community, workplace
setting using methodology described in
[Mistry et al. 2021](https://www.nature.com/articles/s41467-020-20544-y) and
[Aleta et al. 2022](https://www.pnas.org/doi/10.1073/pnas.2112182119).
We produce two contact matrices reflecting the pre-pandemic and pandemic
periods and code and data to manually produce contact matrices.
- [mobility](./disparities/mobility): weekly mobility data at the census tract level
from [Kang et al. 2021](https://www.nature.com/articles/s41597-020-00734-5)
For more information, please consult the associated [README](./disparities/README.md)
More information about the disparities rounds is available on the
[COVID-19 Scenario Modeling Hub - Research](https://github.com/midas-network/covid19-smh-research)
GitHub repository.
## License
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.
## Contributing
Please feel free to open an issue if you identify any issue or would like to
suggest an idea/improvement.
## Funding
Scenario modeling groups are supported through grants to the contributing
investigators.
The Scenario Modeling Hub site is supported by the
[MIDAS Coordination Center](https://midasnetwork.us/),
NIGMS Grant U24GM132013 to the University of Pittsburgh.
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