After over two years of public reporting, the State Profile Report will no longer be produced and distributed after February 2023. The final release was on February 23, 2023. We want to thank everyone who contributed to the design, production, and review of this report and we hope that it provided insight into the data trends throughout the COVID-19 pandemic. Data about COVID-19 will continue to be updated at CDC’s COVID Data Tracker.

The State Profile Report (SPR) is generated by the Data Strategy and Execution Workgroup in the Joint Coordination Cell, in collaboration with the White House. It is managed by an interagency team with representatives from multiple agencies and offices (including the United States Department of Health and Human Services (HHS), the Centers for Disease Control and Prevention, the HHS Assistant Secretary for Preparedness and Response, and the Indian Health Service). The SPR provides easily interpretable information on key indicators for each state, down to the county level.

It is a weekly snapshot in time that:

• Focuses on recent outcomes in the last seven days and changes relative to the month prior
• Provides additional contextual information at the county level for each state, and includes national level information
• Supports rapid visual interpretation of results with color thresholds

During the week ending July 11, 2021, **** percent of surveyed small businesses in Ohio said in an online survey that they had no change in their number of paid employees due to the COVID-19 pandemic. However, **** percent of small businesses reported aa decrease in paid employment during the same week.

Context

The State of Ohio COVID-19 Dashboard displays the most recent preliminary data reported to the Ohio Department of Health (ODH) about cases, hospitalizations and deaths in Ohio by selected demographics and county of residence. Data for cases and hospitalizations is reported to ODH via the Ohio Disease Reporting System (ODRS), and verified mortality data is reported via the Electronic Death Registration System (EDRS).

Content

Data definitions are published by Ohio.

Acknowledgements

All data is pulled from the state of Ohio COVID-19 Dashboards. Additional documentation can be found there.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

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Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Map showing past 4 weeks of COVID-19 cases in Franklin County Public Health jurisdiction by zip code. Furthermore, this maps also shows cumulative counts, but those data have been discontinued to get a better picture of the current case load. These data are from the start of the pandemic. Data are subject to change as additional information is gathered during case investigations. Zip codes with less than 10 cases are excluded for confidentiality purposes.

United States COVID-19: No. of Deaths: To Date: Ohio data was reported at 43,991.000 Person in 10 Jun 2024. This stayed constant from the previous number of 43,991.000 Person for 09 Jun 2024. United States COVID-19: No. of Deaths: To Date: Ohio data is updated daily, averaging 38,042.000 Person from Jan 2020 (Median) to 10 Jun 2024, with 1602 observations. The data reached an all-time high of 43,991.000 Person in 10 Jun 2024 and a record low of 0.000 Person in 19 Mar 2020. United States COVID-19: No. of Deaths: To Date: Ohio data remains active status in CEIC and is reported by Ohio Department of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table US.D001: Center for Disease Control and Prevention: Coronavirus Disease 2019 (COVID-2019).

View daily updates and historical trends for Ohio Coronavirus Full Vaccination Rate. Source: Our World in Data. Track economic data with YCharts analytics.

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

Weekly COVID-19 Community Levels (CCLs) have been replaced with levels of COVID-19 hospital admission rates (low, medium, or high) which demonstrate >99% concordance by county during February 2022–March 2023. For more information on the latest COVID-19 status levels in your area and hospital admission rates, visit United States COVID-19 Hospitalizations, Deaths, and Emergency Visits by Geographic Area.

This archived public use dataset contains historical case and percent positivity data updated weekly for all available counties and jurisdictions. Each week, the dataset was refreshed to capture any historical updates. Please note, percent positivity data may be incomplete for the most recent time period.

This archived public use dataset contains weekly community transmission levels data for all available counties and jurisdictions since October 20, 2022. The dataset was appended to contain the most recent week's data as originally posted on COVID Data Tracker. Historical corrections are not made to these data if new case or testing information become available. A separate archived file is made available here (: Weekly COVID-19 County Level of Community Transmission Historical Changes) if historically updated data are desired.

Related data CDC provides the public with two active versions of COVID-19 county-level community transmission level data: this dataset with the levels as originally posted (Weekly Originally Posted dataset), updated weekly with the most recent week’s data since October 20, 2022, and a historical dataset with the county-level transmission data from January 22, 2020 (Weekly Historical Changes dataset).

Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making.

CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2 Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have a transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00).

Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests conducted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have a transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00).

If the two metrics suggest different transmission levels, the higher level is selected.

The reported transmission categories include:

Low Transmission Threshold: Counties with fewer than 10 total cases per 100,000 population in the past 7 days, and a NAAT percent test positivity in the past 7 days below 5%;

Moderate Transmission Threshold: Counties with 10-49 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 5.0-7.99%;

Substantial Transmission Threshold: Counties with 50-99 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 8.0-9.99%;

High Transmission Threshold: Counties with 100 or more total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 10.0% or greater.

Blank: total new cases in the past 7 days are not reported (county data known to be unavailable) and the percentage of positive NAATs tests during the past 7 days (blank) are not reported.

The data in this dataset are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.

This dataset is created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access.

Archived data CDC has archived two prior versions of these datasets. Both versions contain the same 7 data elements reflecting community transmission levels for all available counties and jurisdictions; however, the datasets were updated daily. The archived datasets can be found here:

Archived Originally Posted dataset

Archived Historical Changes dataset

Archived Data Notes:

October 20, 2022: Due to the Mississippi case data dashboard not being updated this week, case rates for all Mississippi counties are reported as 0 in the COVID-19 Community Transmission Level data released on October 20, 2022. This could lead to the COVID-19 Community Transmission Levels metrics for Mississippi counties being underestimated; therefore, they should be interpreted with caution.

October 20, 2022: Due to a data reporting error, the case rate for Philadelphia County, Pennsylvania is lower than expected in the COVID-19 Community Transmission Level data released on October 20, 2022. This could lead to the COVID-19 Community Transmission Level for Philadelphia County being underestimated; therefore, it should be interpreted with caution.

October 28, 2022: Due to a data processing error, case rates for Kentucky appear higher than expected in the weekly release on October 28, 2022. Therefore, the COVID-19 Community Transmission Levels metrics for Kentucky counties may be overestimated and should be interpreted with caution.

November 3, 2022: Due to a reporting cadence issue, case rates for Missouri counties are calculated based on 11 days’ worth of case count data in the COVID-19 Community Transmission Level data released on November 3, 2022, instead of the customary 7 days’ worth of data. This could lead to the COVID-19 Community Transmission Levels metrics for Missouri counties being overestimated; therefore, they should be interpreted with caution.

November 10, 2022: Due to a reporting cadence change, case rates for Alabama counties are calculated based on 13 days’ worth of case count data in the COVID-19 Community Transmission Level data released on November 10, 2022, instead of the customary 7 days’ worth of data. This could lead to the COVID-19 Community Transmission Levels metrics for Alabama counties being overestimated; therefore, they should be interpreted with caution.

November 10, 2022: Per the request of the jurisdiction, cases among non-residents have been removed from all Hawaii county totals throughout the entire time series. Cumulative case counts reported by CDC will no longer match Hawaii’s COVID-19 Dashboard, which still includes non-resident cases. 

November 10, 2022: Due to a reporting cadence issue, case rates for all Mississippi counties are reported as 0 in the COVID-19 Community Transmission data released on November 10, 2022. This could lead to the COVID-19 Community Transmission Levels metrics for Mississippi counties being underestimated; therefore, they should be interpreted with caution. 

November 10, 2022: In the COVID-19 Community Transmission Level data released on November 10, 2022, multiple municipalities in Puerto Rico are reporting higher than expected increases in case counts. CDC is working with territory officials to verify the data submitted. 

November 25, 2022: Due to a reporting cadence change for the Thanksgiving holiday, case rates for all Ohio counties are calculated based on 13 days' worth of case counts in the COVID-19 Community Transmission Level data released on November 25, 2022, instead of the customary 7 days’ worth of data. This could lead to the COVID-19 Community Transmission Levels metrics for all Ohio counties being overestimated; therefore, they should be interpreted with caution.

November 25, 2022: Due to the Thanksgiving holiday, CDC did not receive updated case data from the following jurisdictions: Rhode Island and Mississippi. As a result, case rates for all counties within these jurisdictions are reported as 0 in the COVID-19 Community Transmission Level Data

United States Excess Deaths excl COVID: Predicted: Avg No. of Deaths: Ohio data was reported at 2,439.000 Number in 16 Sep 2023. This records an increase from the previous number of 2,433.000 Number for 09 Sep 2023. United States Excess Deaths excl COVID: Predicted: Avg No. of Deaths: Ohio data is updated weekly, averaging 2,424.500 Number from Jan 2017 (Median) to 16 Sep 2023, with 350 observations. The data reached an all-time high of 2,656.000 Number in 04 Feb 2023 and a record low of 2,170.000 Number in 15 Jul 2017. United States Excess Deaths excl COVID: Predicted: Avg No. of Deaths: Ohio data remains active status in CEIC and is reported by Centers for Disease Control and Prevention. The data is categorized under Global Database’s United States – Table US.G012: Number of Excess Deaths: by States: All Causes excluding COVID-19: Predicted (Discontinued).

United States Excess Death excl COVID: Predicted: Single Excess Est: Ohio data was reported at 0.000 Number in 16 Sep 2023. This records a decrease from the previous number of 86.000 Number for 09 Sep 2023. United States Excess Death excl COVID: Predicted: Single Excess Est: Ohio data is updated weekly, averaging 0.000 Number from Jan 2017 (Median) to 16 Sep 2023, with 350 observations. The data reached an all-time high of 554.000 Number in 13 Jan 2018 and a record low of 0.000 Number in 16 Sep 2023. United States Excess Death excl COVID: Predicted: Single Excess Est: Ohio data remains active status in CEIC and is reported by Centers for Disease Control and Prevention. The data is categorized under Global Database’s United States – Table US.G012: Number of Excess Deaths: by States: All Causes excluding COVID-19: Predicted (Discontinued).

Excel spread sheet of the 9550 blood donors that were evaluated in this study broken into columns that shows the date of visit to the blood collection center, the State, the geographic region as east (E), west (W) or Kentucky (KY), the blood type, the age, gender, race and raw S protein ELISA OD value. (XLSX)

Introduction: Multiple risk factors of mortality have been identified in patients with COVID-19. Here, we sought to determine the effect of a history of neurological disorder and development of neurological manifestations on mortality in hospitalized patients with COVID-19.Methods: From March 20 to May 20, 2020, hospitalized patients with laboratory confirmed or highly suspected COVID-19 were identified at four hospitals in Ohio. Previous history of neurological disease was classified by severity (major or minor). Neurological manifestations during disease course were also grouped into major and minor manifestations. Encephalopathy, ischemic or hemorrhagic stroke, and seizures were defined as major manifestations, whereas minor neurological manifestations included headache, anosmia, dysgeusia, dizziness or vertigo, and myalgias. Multivariate logistic regression models were used to determine significant predictors of mortality in patients with COVID-19 infection.Results: 574/626 hospitalized patients were eligible for inclusion. Mean age of the 574 patients included in the analysis was 62.8 (SD 17.6), with 298 (51.9%) women. Of the cohort, 240(41.8%) patients had a prior history of neurological disease (HND), of which 204 (35.5%) had a major history of neurological disease (HND). Mortality rates were higher in patients with a major HND (30.9 vs. 15.4%; p = 0.00002), although this was not a significant predictor of death. Major neurological manifestations were recorded in 203/574 (35.4%) patients during disease course. The mortality rate in patients who had major neurological manifestations was 37.4% compared to 11.9% (p = 2 × 10−12) in those who did not. In multivariate analysis, major neurological manifestation (OR 2.1, CI 1.3-3.4; p = 0.002) was a predictor of death.Conclusions: In this retrospective study, history of pre-existing neurological disease in hospitalized COVID-19 patients did not impact mortality; however, development of major neurological manifestations during disease course was found to be an independent predictor of death. Larger studies are needed to validate our findings.

This dataset contains the raw droplet counts resulting from of ddPCR or RT-ddPCR of nucleic acid extracts from wastewater. The wastewater was collected from three different sewersheds in Southwest Ohio (Mill Creek WWTP, Taylor Creek WWTP, and a sub-sewershed, Lick Run). ddPCR counts (positive droplets and total droplets) are provided for the following targets: N1 and N2 (SARS-CoV-2 nucleocapsid genes), crAssphage, PMMoV, HF183 (all fecal indicators), and OC43 (an RNA spike-in from a cultured coronavirus). Other metadata (pH, flow, temperature, TSS, CBOD5) are provided where available. This dataset is associated with the following publication: Nagarkar, M., S. Keely, M. Jahne, E. Wheaton, C. Hart, B. Smith, J. Garland, E. Varughese, A. Braam, B. Wiechman, B. Morris, and N. Brinkman. SARS-CoV-2 Monitoring at three sewersheds of different scales and complexity demonstrates distinctive relationships between wastewater measurements and COVID-19 case data. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 816: 151534, (2022).

Up-to-date information and pre-trained embeddings can be found here.

In order to support NLP research efforts related to COVID-19, we have developed resources for training vector-valued embeddings of COVID-19 related medical concepts, primarily using the CORD-19 dataset.

This resource includes only concept embeddings. Download of the full sets of concept, term, and word embeddings from here requires a valid UMLS Terminology Services account, in order to validate licensed access to SNOMED-CT.

United States Excess Deaths excl COVID: Predicted: Upper Bound: Ohio data was reported at 2,564.000 Number in 16 Sep 2023. This records an increase from the previous number of 2,561.000 Number for 09 Sep 2023. United States Excess Deaths excl COVID: Predicted: Upper Bound: Ohio data is updated weekly, averaging 2,553.500 Number from Jan 2017 (Median) to 16 Sep 2023, with 350 observations. The data reached an all-time high of 2,793.000 Number in 09 Feb 2019 and a record low of 2,287.000 Number in 15 Jul 2017. United States Excess Deaths excl COVID: Predicted: Upper Bound: Ohio data remains active status in CEIC and is reported by Centers for Disease Control and Prevention. The data is categorized under Global Database’s United States – Table US.G012: Number of Excess Deaths: by States: All Causes excluding COVID-19: Predicted (Discontinued).

As of March 10, 2023, there have been 1.1 million deaths related to COVID-19 in the United States. There have been 101,159 deaths in the state of California, more than any other state in the country – California is also the state with the highest number of COVID-19 cases.

The vaccine rollout in the U.S. Since the start of the pandemic, the world has eagerly awaited the arrival of a safe and effective COVID-19 vaccine. In the United States, the immunization campaign started in mid-December 2020 following the approval of a vaccine jointly developed by Pfizer and BioNTech. As of March 22, 2023, the number of COVID-19 vaccine doses administered in the U.S. had reached roughly 673 million. The states with the highest number of vaccines administered are California, Texas, and New York.

Vaccines achieved due to work of research groups Chinese authorities initially shared the genetic sequence to the novel coronavirus in January 2020, allowing research groups to start studying how it invades human cells. The surface of the virus is covered with spike proteins, which enable it to bind to human cells. Once attached, the virus can enter the cells and start to make people ill. These spikes were of particular interest to vaccine manufacturers because they hold the key to preventing viral entry.

As of October 2023, the state with the highest number of COVID-19 related breach of contract lawsuits was New York, where ** were recorded. The states of California and Ohio were next in the list, where ***** COVID-19 related breach of contract lawsuits were recorded in each state respectively.

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.

Assessment of raw S protein OD values in donors with 2 or more donations analyzed over 2 time periods from August 13th to December 8th of 2020.