Geographic and demographic heterogeneity of SARS-CoV-2 diagnostic testing in Illinois, USA, March to December 2020

Holden, Tobias M; Richardson, Reese A.K.; Arevalo, Philip; Duffus, Wayne A.; Runge, Manuela; Whitney, Elena; Wise, Leslie; Ezike, Ngozi; Patrick, Sarah; Cobey, Sarah; Gerardin, Jaline

doi:10.1101/2021.04.14.21255476

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…Time-varying detection rates for severe and mild cases were taken from an analysis of Illinois case and death data [35] (S1 Fig 2-3). All Illinois data used for model parameterization, calibration, and validation were obtained from the Illinois Department of Public Health (IDPH).…”

Section: Methodsmentioning

confidence: 99%

Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities

Runge

Richardson

Clay

et al. 2021

Preprint

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In managing COVID-19 with non-pharmaceutical interventions, occupancy of intensive care units (ICU) is often used as an indicator to inform when to intensify mitigation and thus reduce SARS-CoV-2 transmission, strain on ICUs, and deaths. However, ICU occupancy thresholds at which action should be taken are often selected arbitrarily. We propose a quantitative approach using mathematical modeling to identify ICU occupancy thresholds at which mitigation should be triggered to avoid exceeding the ICU capacity available for COVID-19 patients. We used a stochastic compartmental model to simulate SARS-CoV-2 transmission and disease progression, including critical cases that would require intensive care. We calibrated the model for the United States city of Chicago using daily COVID-19 ICU and hospital census data between March and August 2020. We projected ICU occupancies from September to May 2021 under two possible levels of transmission increase. The effect of combined mitigation measures was modeled as a decrease in the transmission rate that took effect when projected ICU occupancy reached a specified threshold. We found that mitigation did not immediately eliminate the risk of exceeding ICU capacity. Delaying action by 7 days increased the probability of exceeding ICU capacity by 10-60% and this increase could not be counteracted by stronger mitigation. Even under modest transmission increase, a threshold occupancy no higher than 60% was required when mitigation reduced the reproductive number Rt to just below 1. At higher transmission increase, a threshold of at most 40% was required with mitigation that reduced Rt below 0.75 within the first two weeks after mitigation. Our analysis demonstrates a quantitative approach for the selection of ICU occupancy thresholds that considers parameter uncertainty and compares relevant mitigation and transmission scenarios. An appropriate threshold will depend on the location, number of ICU beds available for COVID-19, available mitigation options, feasible mitigation strengths, and tolerated durations of intensified mitigation.

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Section: Methodsmentioning

confidence: 99%

Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities

Runge

Richardson

Clay

et al. 2021

Preprint

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Early Pandemic Access to COVID-19 Testing in the Somali Community in King County, Washington, USA: a Mixed-Methods Evaluation

Abdi

Ebengho

Mohamed

et al. 2022

J. Racial and Ethnic Health Disparities

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Background Racial and ethnic disparities in COVID-19 infection and outcomes have been documented, but few studies have examined disparities in access to testing. Methods We conducted a mixed methods study of access to COVID-19 testing in the Somali immigrant community in King County, Washington, USA, early during the COVID-19 pandemic. In September 2020–February 2021, we conducted quantitative surveys in a convenience sample ( n = 528) of individuals who had accessed PCR testing, recruited at King County testing sites near Somali population centers and through social media outreach in the Somali community. We compared self-identified Somali and non-Somali responses using Chi-square and Wilcoxon rank sum tests. We also conducted three Somali-language focus groups ( n = 26) by video conference to explore Somali experiences with COVID-19 testing, and in-depth interviews with King County-based policymakers and healthcare workers ( n = 13) recruited through the research team’s professional network to represent key demographics and roles. Data were analyzed using qualitative rapid analysis to explore the county’s COVID-19 testing landscape. Results Among 420 survey respondents who had received COVID-19 testing in the prior 90 days, 29% of 140 Somali vs. 11% of 280 non-Somali respondents tested because of symptoms ( p = 0.001), with a trend for longer time from symptom onset to testing (a measure of testing access) among Somali respondents (median 3.0 vs. 2.0 days, p = 0.06). Focus groups revealed barriers to testing, including distrust, misinformation, stigma, language, lack of awareness, and transportation. Stakeholders responding from all sectors highlighted the importance of community partnership to improve access. Conclusion Somali communities experience barriers to COVID-19 testing, as evidenced by the longer time from symptom onset to testing and corroborated by our qualitative findings. These barriers, both structural and community-derived, may be overcome through partnerships between government and community to support community-led, multilingual service delivery and racial representation among medical staff. Supplementary Information The online version contains supplementary material available at 10.1007/s40615-022-01470-9.

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Supporting COVID-19 policy response with large-scale mobility-based modeling

Chang

Wilson

Lewis

et al. 2021

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Social distancing measures, such as restricting occupancy at venues, have been a primary intervention for controlling the spread of COVID-19. However, these mobility restrictions place a significant economic burden on individuals and businesses. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures. In this paper, we present our work motivated by our interactions with the Virginia Department of Health on a decision-support tool that utilizes large-scale data and epidemiological modeling to quantify the impact of changes in mobility on infection rates. Our model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges. By perturbing the mobility network, we can simulate a wide variety of reopening plans and forecast their impact in terms of new infections and the loss in visits per sector. To deploy this model in practice, we built a robust computational infrastructure to support running millions of model realizations, and we worked with policymakers to develop an intuitive dashboard interface that communicates our model's predictions for thousands of potential policies, tailored to their jurisdiction. The resulting decision-support environment provides policymakers with much-needed analytical machinery to assess the tradeoffs between future infections and mobility restrictions.

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Geographic and demographic heterogeneity of SARS-CoV-2 diagnostic testing in Illinois, USA, March to December 2020

Cited by 4 publications

References 31 publications

Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities

Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities

Early Pandemic Access to COVID-19 Testing in the Somali Community in King County, Washington, USA: a Mixed-Methods Evaluation

Supporting COVID-19 policy response with large-scale mobility-based modeling

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