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| [email protected] | ||
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| Model projections should be submitted via pull request to the | ||
| [model-output/](./model-output/) folder and associated metadata should be | ||
| submitted at the same time to the [model-metadata/](./model-metadata/) folder | ||
| of this GitHub repository. | ||
| Technical instructions for submission and required file formats can be found | ||
| [here](./model-output/README.md), | ||
| [here, for the metadata file](./model_metadata/README.md). | ||
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| ## Disparities Round | ||
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| We observed disparities in COVID-19 outcomes by sociodemographic factors such as | ||
| age, location, race/ethnicity, occupation, and socioeconomic status, yet most | ||
| epidemiological models do not account for structural inequities that contribute | ||
| to differential transmission and severity risk among these groups. This round | ||
| aims to build multi-model capacity within the Scenario Modeling Hub to model | ||
| and project how disease is distributed differentially among racial/ethnic | ||
| subpopulations. A secondary goal is to retrospectively assess the COVID-19 | ||
| disease burden that could have been averted if various sources of health | ||
| inequities were reduced or mitigated. The round will focus on two US states, | ||
| California and North Carolina, where more detailed epidemiological data are | ||
| available. | ||
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| We have specified a set of scenarios and target outcomes to allow alignment of | ||
| model projections for collective insights. Scenarios have been designed in | ||
| consultation with academic modeling teams and government agencies (e.g., CDC). | ||
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| ### Disparities Round Phase 1: Can we accurately predict COVID-19 death disparities by race/ethnicity? | ||
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| In Phase 1, teams will calibrate to case and death data by race/ethnicity from | ||
| 5/1/2020 – 11/14/2020 and project forward 11/15/2020 – 4/3/2021 in a single | ||
| Scenario A. Teams are required to incorporate health inequities that contribute | ||
| to differential transmission risk and severity by race/ethnicity, where | ||
| severity is defined at the probability of death given infection. Teams will be | ||
| evaluated on their ability to model race/ethnicity-specific death time series | ||
| throughout the projection period in Phase 1. We will assume that we have | ||
| prescribed the perfect scenario conditions; thus full information about new | ||
| variant circulation, non-pharmaceutical interventions (NPIs) such as masking, | ||
| social distancing, and travel restrictions, mobility, and vaccine coverage is | ||
| known over the entire projection period. As a result, we will be able to test | ||
| the ability of models to project disparities over time, after controlling for | ||
| other epidemiological and behavioral uncertainties. Phase 1 features a single | ||
| scenario as follows: | ||
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| <img src= "./rounds/round1_viz/disparities_phase1.png"> | ||
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| ### Disparities Round Phase 2: By how much could we have reduced disparities in COVID-19 deaths? | ||
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| The goals of Phase 2 are to explore the potential impact of reducing inequities | ||
| in transmission risk, severity, or both during the same phase of the pandemic | ||
| projected in Phase 1. In Phase 2, teams will be permitted to calibrate from | ||
| 5/1/2020 – 4/3/2021 to generate projections for Scenario A | ||
| (representing empirically observed disparities). This will ensure that teams | ||
| have captured disparities accurately throughout the entire time period so that | ||
| the impact of reducing sources of inequities can be soundly assessed in | ||
| putative Scenarios B-D. | ||
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| Thus Scenario A can be interpreted as a fitting exercise as opposed to | ||
| projection. | ||
| **In Scenario B, we assume that all racial/ethnic populations were able to | ||
| mitigate their risk of non-household transmission to the degree of White | ||
| populations.** This could have been hypothetically achieved through | ||
| increasing personal protective equipment access and social distancing among | ||
| essential workers and by increased propensity for telework across | ||
| occupational industries. **In Scenario C, we assume that all racial/ethnic | ||
| populations experience age-adjusted severity rates upon infection in line | ||
| with the White population.** This should be viewed as a hypothetical exercise | ||
| in exploring the extent of disease burden that could be averted if long-term | ||
| infrastructural changes were made to increase quality healthcare access, | ||
| debias healthcare settings to address institutional racism, and expand | ||
| institutional/government support systems such as paid leave. **In Scenario D, | ||
| we assume that all racial/ethnic populations experience non-household | ||
| transmission risk and age-adjusted severity in line with the White | ||
| population.** See the round structure below. | ||
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| <img src= "./rounds/round1_viz/disparities_phase2.png"> | ||
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| ### Assumptions | ||
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| In this retrospective round, we are interested in assessing the ability of | ||
| modeling teams to capture the racial and ethnic distribution of deaths in | ||
| California and North Carolina. Therefore, we will provide information on | ||
| external circumstances that impacted disease dynamics throughout the projection | ||
| period, including new variants, state-specific nonpharmaceutical interventions | ||
| (NPIs), mobility, and the ramp up of vaccination during the projection period | ||
| November 2020-April 2021. | ||
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| #### New Variants | ||
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| - The Alpha variant B.1.1.7 emerged and replaced the Wuhan strain during the | ||
| projection period. Alpha B.1.1.7 became the dominant strain mid-March, and was | ||
| reported to be ~40-60% more transmissible than the Wuhan strain with similar | ||
| severity ([Sah et al. 2021](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132270/), | ||
| [Yang et al. 2022](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947836/), | ||
| [Ahmad et al. 2022](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9448317/), | ||
| [Lin et al. 2021](https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2021.775224/full)). | ||
| By mid-April 2020, B.1.1.7 represented [66%](https://www.cdc.gov/mmwr/volumes/70/wr/mm7023a3.htm?s_cid=mm7023a3_w) | ||
| of all sequenced cases in the United States. | ||
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| #### Nonpharmaceutical interventions | ||
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| - [California](https://calmatters.org/health/coronavirus/2021/03/timeline-california-pandemic-year-key-points/) | ||
| - Nov 16, 2020: Mask wearing required in all settings outside of the | ||
| household and non-essential businesses closed. | ||
| - Nov 21, 2020: Curfews from 10pm-5am applied to all non-essential | ||
| businesses and households. | ||
| - Dec 3, 2020: Regional stay at home orders instituted. | ||
| - Jan 25, 2021: Regional stay at home orders and curfew are lifted. | ||
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| - [North Carolina](https://www.huschblackwell.com/north-carolina-state-by-state-covid-19-guidance) | ||
| - Nov 13, 2020: Indoor gatherings limited to 10 individuals. | ||
| - Nov 23, 2020: Masking required in non-household settings. | ||
| - December 11, 2020: Curfew instated from 10pm-5am. | ||
| - Feb 28, 2021: Curfew lifted, masking and social distancing still | ||
| required. | ||
| - March 26, 2021: Restrictions on social distancing are lifted, masking | ||
| remains in place. | ||
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| - Schools were largely closed for the 2020-21 school year in both locations. | ||
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| - Weekly SageGraph mobility data is provided at the census tract level, in the | ||
| [disparities/mobility/](https://github.com/midas-network/covid19-smh-research_resources/tree/main/disparities#mobility) | ||
| folder of | ||
| [covid19-smh-research_resources](https://github.com/midas-network/covid19-smh-research_resources) | ||
| GitHub Repositories. | ||
| Mobility data is sourced from | ||
| [Kang et al. 2021](https://www.nature.com/articles/s41597-020-00734-5). | ||
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| #### Vaccination | ||
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| - Weekly cumulative age- and race/ethnicity-specific vaccination coverage is | ||
| provided at the state level in the | ||
| [disparities/vaccination/](https://github.com/midas-network/covid19-smh-research_resources/tree/main/disparities#vaccination) | ||
| folder of | ||
| [covid19-smh-research_resources](https://github.com/midas-network/covid19-smh-research_resources). Vaccination data is extracted from the | ||
| [CA DPH](https://data.ca.gov/dataset/covid-19-vaccine-progress-dashboard-data) and | ||
| [NCDPH](https://covid19.ncdhhs.gov/dashboard/data-behind-dashboards) vaccine | ||
| dashboards and report vaccination by age and race/ethnicity in separate time | ||
| series. These dashboards report the number of partially (receiving 1 dose) | ||
| and fully vaccinated individuals. We describe the details of the vaccination | ||
| rollout and effectiveness assumptions. | ||
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| - We summarize race/ethnicity vaccination data by state into the follow | ||
| sub-populations: | ||
| - California: Latino, White, Asian, Black, Other, Unknown | ||
| - North Carolina: White, Asian, Black, Other, Unknown. Note here | ||
| that non-White Hispanic individuals are included in “Other.” | ||
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| - Vaccination rollout timelines | ||
| 1. California | ||
| ([COVID-19 Vaccination Plan, CADPH](https://www.cdph.ca.gov/Programs/CID/DCDC/CDPH%20Document%20Library/COVID-19/COVID-19-Vaccination-Plan-California-Interim-Draft_V1.0.pdf), | ||
| [California: State-by-State COVID-19 Guidance, Husch Blackwell](https://www.huschblackwell.com/california-state-by-state-covid-19-guidance), | ||
| [Vaccines for People with High-Risk Medical Conditions or Disabilities, CADPH](https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/vaccine-high-risk-factsheet.aspx)) | ||
| - December 15, 2020: Vaccination begins for Phase 1A. Healthcare workers | ||
| and long-term care residents are eligible. | ||
| - January 13, 2021: Individuals 65+ years old are eligible for vaccination. | ||
| - February 3, 2021: Phase 1B: Essential workers with high exposure risk | ||
| (those working in agriculture and food, education and childcare, and | ||
| emergency services) are eligible for vaccination. Vaccination sites are | ||
| also set up in Oakland and Los Angeles to prioritize communities that have | ||
| been heavily impacted by the COVID-19 pandemic. | ||
| - March 4, 2021: The state announces it will direct 40% of its vaccine supply | ||
| to vulnerable communities, based on the Healthy Places Index (HPI). | ||
| - March 15, 2021: Individuals <65 and >16 years with high-risk conditions are | ||
| eligible for vaccination. | ||
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| 2. North Carolina | ||
| ([North Carolina: State-by-State COVID-19 Guidance, Husch Blackwell](https://www.huschblackwell.com/north-carolina-state-by-state-covid-19-guidance)) | ||
| - January 20, 2021: Group 1: North Carolinians over the age of 65 and all health | ||
| care workers who have in-person contact with patients are eligible for vaccination. | ||
| - Feb 9, 2020: Group 2: North Carolina mandates a subset of vaccines must go to every | ||
| geographic region and prioritizes vulnerable communities. | ||
| - Feb 25, 2021: Teachers and other Group 3 essential workers can get vaccinated. | ||
| - March 3, 2021: Additional essential workers in Group 3 can get vaccinated. | ||
| - March 24, 2021: People at higher risk from COVID-19 due to underlying medical | ||
| conditions will become eligible to receive a vaccine, as will people in certain | ||
| congregate-living settings (dorms). | ||
| - March 31, 2021: Group 4 eligible for vaccination. Essential workers now include | ||
| frontline workers who do not have to be in person for work and those in a range of | ||
| sectors such as construction, energy, financial services, public works, and others | ||
| as categorized by the | ||
| [Cybersecurity and Infrastructure Security Agency](https://www.cisa.gov/sites/default/files/publications/ECIW_4.0_Guidance_on_Essential_Critical_Infrastructure_Workers_Final3_508_0.pdf). | ||
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| ### Calibration Data | ||
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| Weekly case and death data by race/ethnicity are available for phase 1 and 2 in the | ||
| [target-data](./target-data/) folder. For more information, please consult the | ||
| documentation associated with the | ||
| [disparities round target data](https://github.com/midas-network/covid19-smh-research/blob/main/target-data/README.md#disparities-round). | ||
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| ### Targets | ||
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| In this round, the required target for trajectories will be **weekly incident | ||
| infections, cases, and deaths in California and North Carolina for a set of | ||
| specified racial/ethnic groups.** Trajectories will need to be paired across | ||
| racial/ethnic groups (i.e., for a given model, location, scenario and horizon, | ||
| all race/ethnicity data for simulation 1 corresponds to the sum of | ||
| race/ethnicity-specific estimates for simulation 1). | ||
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| In California, required racial/ethnic groups are `"latino"`, `"black"`, | ||
| `"white"`, `"asian"`, and `"other"`, where `"other"` represents American Indian | ||
| Alaska Native and Native Hawaiian and Pacific Islander. | ||
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| In North Carolina, required racial/ethnic groups are `"white"`, `"black"`, `"asian"`, | ||
| and `"other"`, where `"other"` represents non-White Hispanic and American Indian | ||
| Alaska Native. | ||
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| Given the missingness in demographic disease data and limited data available on | ||
| case reporting rates by race/ethnicity, infections and cases will not be evaluated. | ||
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| Teams will be submitting cases and infections for the purpose of model comparison and | ||
| weekly death targets will be evaluated. | ||
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| ### Additional Information | ||
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| Auxiliary data and code are available in the | ||
| [covid19-smh-research_resources](https://github.com/midas-network/covid19-smh-research_resources) | ||
| GitHub repository, | ||
| [disparities folder](https://github.com/midas-network/covid19-smh-research_resources/tree/main/disparities) | ||
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| The folder contains multiple sub-folders: | ||
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| - Case Imputation: imputed case time series by race/ethnicity at the state level to | ||
| infer missing case demographic information | ||
| - Vaccination: vaccination time series by age and race/ethnicity | ||
| - Serology: monthly infection-based and combined vaccination/infection seroprevalence | ||
| time series | ||
| - Population Data: state-level population structure data by age and race/ethnicity | ||
| - Hospitalization: hospitalization time series by race/ethnicity, in a rate per 100,000 | ||
| people for California and number of hospitalizations for North Carolina | ||
| - Contact Matrix: synthetic daily contact matrices by race/ethnicity in the household, | ||
| school, community, workplace setting in the pre-pandemic and pandemic period | ||
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| ### Submission Information | ||
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| | Phase | Type | Scenario | Scenario name | Scenario ID for submission file (`scenario_id`) | | ||
| |:--------:|:--------:| ---------------------------------------------- |:-----------------:|:--------------------:| | ||
| |1| Projection | Scenario A. Inequity-driven transmission and severity by race/ethnicity | phase_one | A-2020-05-01 | | ||
| |2| Calibration | Scenario A. Inequity-driven transmission and severity by race/ethnicity | inTran_inSev | B-2020-11-15 | | ||
| |2| Projection | Scenario B. Inequity-mitigated transmission and inequity-driven severity by race/ethnicity | mitTran_inSev | C-2020-11-15 | | ||
| |2| Projection | Scenario C. Inequity-driven transmission and inequity-mitigated severity and by race/ethnicity | inTran_mitSev | D-2020-11-15 | | ||
| |2| Projection | Scenario D. Inequity-mitigated transmission and inequity-mitigated severity by race/ethnicity | mitTran_mitSev | E-2020-11-15 | | ||
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| - End date for fitting data for Phase 1: Saturday November 14, 2020 | ||
| - Start date for scenarios: Sunday November 15, 2020 (first date of simulated | ||
| transmission/outcomes) | ||
| - Simulation end date: April 3, 2021 (20-week horizon) | ||
| - **Phase 1 projections due: March 26, 2024** | ||
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| ##### Submission requirements | ||
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| - Must consist of a subset of weekly targets from Sunday, November 15, 2020 - | ||
| Saturday, April 3, 2021 (20 week projection period). Weeks follow epi-weeks | ||
| (Sun-Sat) dated by the last day of the week. | ||
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| - Weekly targets: Weekly incident infections, cases, and deaths by location | ||
| and major racial/ethnic group. We require the following racial/ethnic groups | ||
| by state: | ||
| - California: `"latino"`, `"black"`, `"white"`, `"asian"`, and `"other"`. | ||
| - North Carolina: `"black"`, `"white"`, `"asian"`, and `"other"`. | ||
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| - 100-300 individual trajectories for each target. Trajectories should be sampled | ||
| in such a way that they will be most likely to produce the uncertainty of the | ||
| simulated process. | ||
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| - Metadata: We will require a brief meta-data from all teams. | ||
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| ## Target Data | ||
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| The repository contains a [target-data](./target-data/) folder relevant to the modeling efforts. | ||
| It contains COVID-19 case and death time series by race/ethnicity. | ||
| It contains: | ||
| - COVID-19 case and death time series by race/ethnicity for the disparities round. | ||
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| For more information, please consult the associated [README file](./target-data/README.md) | ||
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