Center for Pandemic Decision Science

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Aggregated mobility data could help fight COVID-19
(Science, 2020-03-23) Buckee, Caroline O.; Balsari, Satchit; Chan, Jennifer; Crosas, Mercè; Dominici, Francesca; Gasser, Urs; Grad, Yonatan H.; Grenfell, Bryan; Halloran, M. Elizabeth; Kraemer, Moritz U.G.; Lipsitch, Marc; Metcalf, C. Jessica E.; Meyers, Lauren Ancel; Perkins, T. Alex; Santillana, Mauricio; Scarpino, Samuel V.; Viboud, Cecile; Wesolowski, Amy; Schroeder, Andrew
Austin COVID-19 transmission estimates and healthcare projections
(2020-07) Tec, Mauricio; Lachmann, Michael; Fox, Spencer J.; Pasco, Remy; Woody, Spencer; Starling, Jennifer; Dahan, Maytal; Gaither, Kelly; Scott, James; Meyers, Lauren Ancel
To support public health decision-making and healthcare planning, we developed a model for the five-county Austin-Round Rock Metropolitan Statistical Area (henceforth Austin) that can provide real-time estimates of the prevalence and transmission rate of COVID-19 and project healthcare needs into the future. The model incorporates key epidemiological characteristics of the disease, demographic information for Austin, and local mobility data from anonymized cell phone traces. It uses daily COVID-19 hospitalization data to estimate the changing transmission rate and prevalence of disease. The framework can be readily applied to provide pandemic situational awareness and short-term healthcare projections in other cities around the US. In this report, we use COVID-19 hospitalization data for Austin from March 13 to July 14, 2020 to estimate the state of the pandemic in early July and project hospitalizations through early August of 2020. The projections are based on multiple assumptions about the age-specific severity of COVID-19 and the role of asymptomatic infections in the transmission of the virus. These graphs below do not present the full range of uncertainty for the city of Austin, but are intended to provide basic insight into the changing risks of COVID-19 transmission and healthcare surges in Austin. Our estimates suggest that the pandemic slowed considerably during the March 24-May 1, 2020 stay-home order and reached its lowest transmission rate in mid April. As Texas began relaxing social distancing measures in early May, transmission picked up and continued to increase through mid June. The recent decrease in transmission may have stemmed from mid-June tightening of restrictions and an increase in cautionary messaging. Our projections suggest that, if the pandemic continues to spread at the rate estimated from the second week of July, COVID-19 hospitalizations and/or ICU demands could exceed local capacity in early August. We are posting these results prior to peer review to provide intuition for both policy makers and the public regarding both the immediate threat of COVID-19 and the extent to which continued social distancing, transmission-reducing precautions such as keeping physical distance, wearing cloth face coverings and staying isolated when symptomatic, can mitigate that threat. As new hospitalization data become available, we will provide updated estimates and projections on the UT COVID-19 Modeling Consortium's Austin COVID-19 Dashboard.
Comparative cost-effectiveness of SARS-CoV-2 testing strategies in the USA: a modelling study
(The Lancet Public Health, 2021-02) Du, Zhanwei
Background To mitigate the COVID-19 pandemic, countries worldwide have enacted unprecedented movement restrictions, physical distancing measures, and face mask requirements. Until safe and efficacious vaccines or antiviral drugs become widely available, viral testing remains the primary mitigation measure for rapid identification and isolation of infected individuals. We aimed to assess the economic trade-offs of expanding and accelerating testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the USA in different transmission scenarios. Methods We used a multiscale model that incorporates SARS-CoV-2 transmission at the population level and daily viral load dynamics at the individual level to assess eight surveillance testing strategies that varied by testing frequency (from daily to monthly testing) and isolation period (1 or 2 weeks), compared with the status-quo strategy of symptom-based testing and isolation. For each testing strategy, we first estimated the costs (incorporating costs of diagnostic testing and admissions to hospital, and salary lost while in isolation) and years of life lost (YLLs) prevented under rapid and low transmission scenarios. We then assessed the testing strategies across a range of scenarios, each defined by effective reproduction number (Re), willingness to pay per YLL averted, and cost of a test, to estimate the probability that a particular strategy had the greatest net benefit. Additionally, for a range of transmission scenarios (Re from 1·1 to 3), we estimated a threshold test price at which the status-quo strategy outperforms all testing strategies considered. Findings Our modelling showed that daily testing combined with a 2-week isolation period was the most costly strategy considered, reflecting increased costs with greater test frequency and length of isolation period. Assuming a societal willingness to pay of US$100 000 per YLL averted and a price of $5 per test, the strategy most likely to be cost-effective under a rapid transmission scenario (Re of 2·2) is weekly testing followed by a 2-week isolation period subsequent to a positive test result. Under low transmission scenarios (Re of 1·2), monthly testing of the population followed by 1-week isolation rather than 2-week isolation is likely to be most cost-effective. Expanded surveillance testing is more likely to be cost-effective than the status-quo testing strategy if the price per test is less than $75 across all transmission rates considered. Interpretation Extensive expansion of SARS-CoV-2 testing programmes with more frequent and rapid tests across communities coupled with isolation of individuals with confirmed infection is essential for mitigating the COVID-19 pandemic. Furthermore, resources recouped from shortened isolation duration could be cost-effectively allocated to more frequent testing.
Conscientious vaccination exemptions in kindergarten to eighth-grade children across Texas schools from 2012 to 2018: A regression analysis
(PLOS Medicine, 2020-03-10) Morrison, Maike; Castro, Lauren A.; Meyers, Lauren Ancel
Background: As conscientious vaccination exemption (CVE) percentages rise across the United States, so does the risk and occurrence of outbreaks of vaccine-preventable diseases such as measles. In the state of Texas, the median CVE percentage across school systems more than doubled between 2012 and 2018. During this period, the proportion of schools surpassing a CVE percentage of 3% rose from 2% to 6% for public schools, 20% to 26% for private schools, and 17% to 22% for charter schools. The aim of this study was to investigate this phenomenon at a fine scale. Methods and findings: Here, we use beta regression models to study the socioeconomic and geographic drivers of CVE trends in Texas. Using annual counts of CVEs at the school system level from the 2012–2013 to the 2017–2018 school year, we identified county-level predictors of median CVE percentage among public, private, and charter schools, the proportion of schools below a high-risk threshold for vaccination coverage, and five-year trends in CVEs. Since the 2012–2013 school year, CVE percentages have increased in 41 out of 46 counties in the top 10 metropolitan areas of Texas. We find that 77.6% of the variation in CVE percentages across metropolitan counties is explained by median income, the proportion of the population that holds a bachelor’s degree, the proportion of the population that self-reports as ethnically white, the proportion of the population that is English speaking, and the proportion of the population that is under the age of five years old. Across the 10 top metropolitan areas in Texas, counties vary considerably in the proportion of school systems reporting CVE percentages above 3%. Sixty-six percent of that variation is explained by the proportion of the population that holds a bachelor’s degree and the proportion of the population affiliated with a religious congregation. Three of the largest metropolitan areas—Austin, Dallas–Fort Worth, and Houston—are potential vaccination exemption "hotspots," with over 13% of local school systems above this risk threshold. The major limitations of this study are inconsistent school-system-level CVE reporting during the study period and a lack of geographic and socioeconomic data for individual private schools. Conclusions: In this study, we have identified high-risk communities that are typically obscured in county-level risk assessments and found that public schools, like private schools, are exhibiting predictable increases in vaccination exemption percentages. As public health agencies confront the reemerging threat of measles and other vaccine-preventable diseases, findings such as ours can guide targeted interventions and surveillance within schools, cities, counties, and sociodemographic subgroups.
Cost-effective proactive testing strategies during COVID-19 mass vaccination: A modelling study
(2022-04) Du, Zhanwei; Wang, Lin; Bai, Yuan; Wang, Xutong; Pandey, Abhishek; Fitzpatrick, Meagan C.; Chinazzi, Matteo; Pastore y Piontti, Ana; Hupert, Nathaniel; Lachmann, Michael; Vespignani, Alessandro; Galvani, Alison P.; Cowling, Benjamin J.; Meyers, Lauren Ancel
Background: As SARS-CoV-2 vaccines are administered worldwide, the COVID-19 pandemic continues to exact significant human and economic costs. Mass testing of unvaccinated individuals followed by isolation of positive cases can substantially mitigate risks and be tailored to local epidemiological conditions to ensure cost effectiveness. Methods: Using a multi-scale model that incorporates population-level SARS-CoV-2 transmission and individual-level viral load kinetics, we identify the optimal frequency of proactive SARS-CoV-2 testing, depending on the local transmission rate and proportion immunized. Findings: Assuming a willingness-to-pay of US$100,000 per averted year of life lost (YLL) and a price of $10 per test, the optimal strategy under a rapid transmission scenario (Re ~ 2.5) is daily testing until one third of the population is immunized and then weekly testing until half the population is immunized, combined with a 10-day isolation period of positive cases and their households. Under a low transmission scenario (Re ~ 1.2), the optimal sequence is weekly testing until the population reaches 10% partial immunity, followed by monthly testing until 20% partial immunity, and no testing thereafter. Interpretation: Mass proactive testing and case isolation is a cost effective strategy for mitigating the COVID-19 pandemic in the initial stages of the global SARS-CoV-2 vaccination campaign and in response to resurgences of vaccine-evasive variants.
COVID-19 alert stages, healthcare projections and mortality patterns in Austin, Texas, May 2021
(2021-05) Arslan, Nazlican; Sürer, Özge; Morton, David P.; Yang, Haoxiang; Lachmann, Michael; Woody, Spencer; Fox, Spencer J.; Meyers, Lauren Ancel
To support public health decision-making in Austin, Texas, we project COVID-19 healthcare demand as vaccines continue to roll out, and we provide retrospective estimates for in-hospital COVID-19 mortality during surge and non-surge periods of the pandemic. The projections indicate that a return to COVID-19 Alert Stage 2 in May 2021 would be unlikely to cause a healthcare surge that exceeds local ICU capacity. However, our retrospective estimates of in-hospital COVID-19 mortality suggest that even modest surges may increase the COVID-19 fatality rate and that, throughout the pandemic, in-hospital mortality has disproportionately occurred in communities with overlapping socioeconomic, occupational, and health risks. The analyses are based on multiple assumptions about the transmission rate, age-specific severity of COVID-19, and efficacy of vaccines, and thus do not represent the full range of uncertainty that the city of Austin may encounter. We are posting these results prior to peer review to provide insights regarding changing COVID-19 risks as vaccination coverage continues to increase and to guide the relaxation of COVID-19 mitigation measures in the spring and summer of 2021.
COVID-19 Campus Introduction and Gathering Risks for Reopening the University of Texas at Austin
(2020-08) Matsui, Cameron; Johnson, Kaitlyn; Pasco, Remy; Lachmann, Michael; Fox, Spencer J.; Meyers, Lauren Ancel
There are more than 50,000 students enrolled at the University of Texas at Austin (UT), with an estimated 80% from Texas, 93% from the United States, and 7% from abroad. The 2020-2021 academic year is scheduled to begin on August 26th. The university is taking steps to reopen safely in light of four COVID-19-related risks: Introduction risks: UT students returning to Austin from other cities may arrive infected. On-campus transmission risks: Transmission may occur during classes and other organized UT activities. Off-campus transmission risks: Transmission may occur through off-campus interactions among members of the UT community. Community amplification risks: Transmission may spill over from the UT community into the surrounding Austin community. In order to assist the University of Texas at Austin in safely reopening, this report addresses elements of the first three risks. It provides estimates for (i) the prevalence of COVID-19 among returning students, based on the estimated prevalence of the virus in their home communities, (ii) the number of students that could test positive for COVID-19 in the first week of classes, and (iii) the chance that classes/gatherings will include one or more infected attendees, depending on the size of the group. In brief, we assumed that 12,000 students are already in Austin and 10,000 additional students will be returning to Austin by August 26th. We note that this conservatively assumes that more than half of students enrolled at UT elected to remain in their home regions for the fall semester.
COVID-19 Campus Introduction Risks for School Reopenings
(2020-07) Fox, Spencer J.; Lachmann, Michael; Meyers, Lauren Ancel
The COVID-19 pandemic threatens most Texas cities. As of July 21, 2020, Texas has reported 340,000 confirmed cases and nearly 4,200 deaths. School districts statewide are developing plans to offer in-person education that meet the social and educational needs of students while mitigating the risk of COVID-19 to students, staff, faculty, their families and the surrounding communities. The level of risk for a particular school or school system will stem from three factors: 1. Introduction risks: the chance that students and staff will be infected outside of school and arrive at school while infected. 2. On-campus transmission risks: the chance that transmission will occur within schools if and when students or staff arrive infected. 3. Community amplification risks: the chance that individuals infected within schools will subsequently transmit the virus to individuals in the surrounding community. To address the first of these three components, this report provides a simple calculation for estimating the rate at which COVID-19 may appear on school campuses depending on the background prevalence of the virus in the surrounding community.
COVID-19 Campus Introduction Risks for Spring 2021 at the University of Texas at Austin
(2021-01) Matsui, Cameron; Johnson, Kaitlyn; Pasco, Remy; Lachmann, Michael; Fox, Spencer J.; Meyers, Lauren Ancel
There are more than 50,000 students enrolled at the University of Texas at Austin (UT), with an estimated 80% from Texas, 93% from the United States, and 7% from abroad. The 2021 spring semester began on January 19th. The university is taking steps to reopen safely in light of four COVID-19-related risks: Introduction risks: UT students returning to Austin from other cities may arrive infected. On-campus transmission risks: Transmission may occur during classes and other organized UT activities. Off-campus transmission risks: Transmission may occur through off-campus interactions among members of the UT community. Community amplification risks: Transmission may spill over from the UT community into the surrounding Austin community. In order to assist the University of Texas at Austin in safely opening for the spring of 2021, this report addresses potential introduction risks. It provides estimates for the number of infected students returning to Austin in the spring semester compared to those expected to have arrived infected in the fall semester. In brief, we assumed that 9,000 students are already in Austin and 21,000 additional students may have returned to Austin by January 19th. We note that this conservatively assumes that 20,000 of the 50,000 enrolled students at UT elected to remain in their home regions for the spring semester. This was based on estimates provided by UT that about 30,000 students were in the Austin area during the fall semester.
COVID-19 Healthcare Demand Projections: 22 Texas Cities
(University of Texas at Austin, 2020-03-26) Du, Zhanwei; Wang, Xutong; Pasco, Remy; Petty, Michaela; Fox, Spencer J.; Meyers, Lauren Ancel
COVID-19 Healthcare Demand Projections: Austin, Texas
(2020-04) Wang, Xutong; Pasco, Remy; Pierce, Kelly; Du, Zhanwei; Fox, Spencer J.; Meyers, Lauren Ancel
To support planning by the city of Austin and Travis County, we analyzed the Austin-Round Rock module of our US COVID-19 Pandemic Model to project the number of hospitalizations under different social distancing scenarios. Note that the results presented herein are based on multiple assumptions about the transmission rate and age-specific severity of COVID-19. There is still much we do not understand about the transmission dynamics of this virus, including the extent of asymptomatic infection and transmission. These results do not represent the full range of uncertainty. Rather, they are meant to serve as plausible scenarios for gauging the likely impacts of social distancing measures in the Austin-Round Rock Metropolitan Area. We have updated our model inputs based on the daily number of COVID-19 hospitalizations in the Austin-Round Rock MSA between March 13 and April 19, 2020. The data suggest that social distancing following the March 24th Stay Home-Work Safe order has resulted in a 94% reduction in COVID-19 transmission, with our uncertainty in this estimate ranging from 55% and 100%. The data also suggest that approximately 13.6% of symptomatic cases are detected (i.e., reported as confirmed cases). We are posting these results prior to peer review to provide intuition for both policy makers and the public regarding both the immediate threat of COVID-19 and the extent to which early social distancing measures are mitigating that threat. Our projections indicate that the Stay Home-Work Safe has delayed and possibly even prevented a COVID-19 healthcare crisis in the region.
COVID-19 Healthcare Demand Projections: Austin, Texas
(2020-03) Pasco, Remy; Wang, Xutong; Petty, Michaela; Du, Zhanwei; Fox, Spencer J.; Pignone, Michael; Johnston, Clay; Meyers, Lauren Ancel
COVID-19 Healthcare Demand Projections: Beaumont-Port Arthur MSA, Texas
(2020-04) Pierce, Kelly; Ho, Ethan; Wang, Xutong; Pasco, Remy; Du, Zhanwei; Fox, Spencer J.; Zynda, Greg; Song, Jawon; Meyers, Lauren Ancel
To support healthcare planning, we analyzed the Beaumont-Port Arthur MSA module of our US COVID-19 Pandemic Model to project the number of cases, healthcare requirements and deaths under different scenarios. Note that the results presented herein are based on multiple assumptions about the transmission rate and age-specific severity of COVID-19. There is still much we do not understand about the transmission dynamics of this virus, including the extent of asymptomatic infection and transmission. These results do not represent the full range of uncertainty. Rather, they are meant to serve as plausible scenarios for gauging the likely impacts of control measures in the Beaumont-Port Arthur MSA. We have updated our model inputs based on the daily number of COVID-19 hospitalizations in Beaumont-Port Arthur between April 2, 2020 and April 20, 2020, provided by the Southeast Texas Regional Advisory Council (SETRAC). The projections assume that schools were closed on March 19, 2020 (start of state mandated school closures) and extensive social distancing began on March 28, 2020 with the Jefferson County Stay at Home order [1]. The data suggest that recent social distancing has reduced transmission by anywhere between 70% and 100% relative to the period prior to March 19th. We make projections for six different scenarios. The first four-70%, 80%, 95% and 100% reductions in transmission fall within this range of current estimates; the other two-0% and 50% reductions in transmission provide more pessimistic projections that could occur with extreme relaxation of social distancing measures. For each of the scenarios, the graphs project COVID-19 cases, hospitalizations, patients requiring ICU care, patients requiring ventilation and deaths. We are posting these results prior to peer review to provide intuition for both policy makers and the public regarding both the immediate threat of COVID-19 and the extent to which continued social distancing, transmission-reducing precautions such as keeping physical distance and wearing cloth face coverings, can mitigate that threat.
COVID-19 Healthcare Demand Projections: Houston-The Woodlands-Sugar Land MSA, Texas
(2020-04) Pierce, Kelly; Ho, Ethan; Wang, Xutong; Pasco, Remy; Du, Zhanwei; Fox, Spencer J.; Zynda, Greg; Song, Jawon; Meyers, Lauren Ancel
To support healthcare planning, we analyzed the Houston-The Woodlands-Sugar Land MSA module of our US COVID-19 Pandemic Model to project the number of cases, healthcare requirements and deaths under different scenarios. Note that the results presented herein are based on multiple assumptions about the transmission rate and age-specific severity of COVID-19. There is still much we do not understand about the transmission dynamics of this virus, including the extent of asymptomatic infection and transmission. These results do not represent the full range of uncertainty. Rather, they are meant to serve as plausible scenarios for gauging the likely impacts of control measures in the Houston-The Woodlands-Sugar Land MSA. We have updated our model inputs based on the daily number of COVID-19 hospitalizations in Houston-The Woodlands-Sugar Land between April 2, 2020 and April 20, 2020, provided by the Southeast Texas Regional Advisory Council (SETRAC). The projections assume that schools were closed on March 19, 2020 (start of state mandated school closures) and extensive social distancing began on March 24, 2020 with Houston and Harris County's Stay Home Work Safe order [1]. The data suggest that recent social distancing has reduced transmission by anywhere between 80% and 100% relative to the period prior to March 19th. We make projections for five different scenarios. The first three 80%, 95% and 100% reductions in transmission fall within this range of current estimates; the other two-0% and 50% reductions in transmission-provide more pessimistic projections that could occur with extreme relaxation of social distancing measures. For each of the scenarios, the graphs project COVID-19 cases, hospitalizations, patients requiring ICU care, patients requiring ventilation and deaths. We are posting these results prior to peer review to provide intuition for both policy makers and the public regarding both the immediate threat of COVID-19 and the extent to which continued social distancing, transmission-reducing precautions such as keeping physical distance and wearing cloth face coverings, can mitigate that threat.
COVID-19 in Austin, Texas: Averting healthcare surges while relaxing social distancing
(2020-04) Duque, Daniel; Morton, David P.; Singh, Bismark; Du, Zhanwei; Pasco, Remy; Meyers, Lauren Ancel
To support planning by the city of Austin and Travis County, we analyzed the Austin-Round Rock module of our US COVID-19 Pandemic Model to project the number of hospitalizations under different social distancing scenarios. Note that the results presented herein are based on multiple assumptions about the transmission rate and age-specific severity of COVID-19. There is still much we do not understand about the transmission dynamics of this virus, including the extent of asymptomatic infection and transmission. These results do not represent the full range of uncertainty. Rather, they are meant to serve as plausible scenarios for designing optimal triggers for enacting stricter social distancing measures in the Austin-Round Rock Metropolitan Area. We have updated our model inputs based on the daily number of COVID-19 hospitalizations in the Austin-Round Rock MSA between March 13 and April 19, 2020. The data suggest that social distancing following the March 24th Stay Home-Work Safe order has resulted in a 94% reduction in COVID-19 transmission, with our uncertainty in this estimate ranging from 55% and 100%. The data also suggest that approximately 13.6% of symptomatic cases are detected (i.e., reported as confirmed cases). We are posting these results prior to peer review to provide intuition for both policy makers and the public regarding both the immediate threat of COVID-19 and the extent to which early social distancing measures are mitigating that threat. Our projections indicate that the Stay Home-Work Safe has delayed and possibly even prevented a COVID-19 healthcare crisis in the region.
COVID-19 in Austin, Texas: Epidemiological Assessment of Grocery Shopping
(2020-04) Pasco, Remy; Wang, Xutong; Du, Zhanwei; Fox, Spencer J.; Meyers, Lauren Ancel
There are an estimated 24,000 grocery store workers in the Austin-Round Rock metropolitan area (MSA) representing 2% of the labor force [1]. The Austin Stay Home - Work Safe order that was issued on March 24, 2020 and extended on April 13, 2020 restricts non-essential work, but permits work in grocery stores and public grocery shopping [2,3]. Daily interactions between grocery workers and the general population may undermine efforts to reduce person-to-person contact, and exacerbate the individual and city-wide risks associated with COVID-19 transmission. In response to a request from the city of Austin, we projected the epidemiological impacts of grocery work under different assumptions regarding the effectiveness of precautionary measures taken by workers and shoppers in grocery stores. To do so, we modified the Austin-Round Rock module of our US COVID-19 Pandemic Model to explicitly include a population subgroup representing grocery workers and contacts that occur between members of the general public and grocery workers in stores. As a base case scenario, we assumed that grocery workers would maintain typical workforce contact rates, estimated as twice the average workplace contacts for 18-49 year olds in the general population. Our analysis suggests that grocery shopping can considerably increase the community-wide risk of COVID-19 and that both shoppers and workers can and should do their part to protect themselves and others from transmission in stores. Furthermore, the risk of COVID-19 hospitalizations within the population of grocery workers is expected to be much higher than that in the non-working 18-49 year old population.
COVID-19 in Austin, Texas: Relaxing Social Distancing Measures
(2020-04) Wang, Xutong; Du, Zhanwei; Huang, George; Fox, Spencer J.; Meyers, Lauren Ancel
To support planning by the city of Austin and Travis County, we analyzed the Austin-Round Rock module of our US COVID-19 Pandemic Model to project the number of hospitalizations under different scenarios for relaxing social distancing measures following the March 24th Stay Home-Work Safe order. Note that the results presented herein are based on multiple assumptions about the transmission rate and age-specific severity of COVID-19. There is still much we do not understand about the transmission dynamics of this virus, including the extent of asymptomatic infection and transmission. These results do not represent the full range of uncertainty. Rather, they are meant to serve as plausible scenarios for gauging the likely impacts of social distancing measures in the Austin-Round Rock Metropolitan Area. We have updated our model inputs based on the daily number of COVID-19 hospitalizations in the Austin-Round Rock MSA between March 13 and April 19, 2020. The data suggest that social distancing following the March 24th Stay Home-Work Safe order has resulted in a 94% reduction in COVID-19 transmission, with our uncertainty in this estimate ranging from 70% and 100%. The data also suggest that approximately 13.6% of symptomatic cases are detected (i.e., reported as confirmed cases). We are posting these results prior to peer review to provide intuition for both policy makers and the public regarding both the threat of COVID-19 and the extent to which social distancing measures can mitigate that threat. Our projections indicate that the Stay Home-Work Safe has likely prevented a COVID-19 healthcare crisis in the region during the first wave of the pandemic. When current measures are relaxed, we may see more COVID-19 transmission in the area leading to a second pandemic wave. Whether or not and how quickly COVID-19 cases and hospitalizations rise in the second wave will critically depend on the extent to which individuals and communities continue to take steps to reduce the risks of transmission.
COVID-19 projections for the reopening of the University of Texas at Austin in fall of 2021
(2021-08) Johnson, Kaitlyn; Pasco, Remy; Woody, Spencer; Lachmann, Michael; Bhavnani, Darlene; Klima, Jessica; Fox, Spencer J.; Meyers, Lauren Ancel
There are more than 50,000 students enrolled at the University of Texas at Austin (UT), with an estimated 80% from Texas and 93% from the United States. During the 2020-21 academic year, UT offered hybrid and online courses to mitigate the risks of COVID-19 transmission on campus. The 2021-22 academic year is scheduled to begin on August 25, 2021. Given the wide availability of COVID-19 vaccines in the US, UT is planning to resume in-person classes and on-campus activities. UT will urge vaccination for all unvaccinated students, make COVID-19 testing readily available to all students, staff and faculty, strongly encourage masking and conduct contact tracing when viral cases are detected. In order to assist UT in safely reopening, this report estimates the SARS-CoV-2 vaccination coverage and infection prevalence among students at the start of the academic year and then provides projections under a variety of vaccination and testing levels. For each scenario, we project infections, costs associated with testing, and required isolation facilities from August 25 through December 16, 2021. We also derive the level of proactive testing needed to keep COVID-19 levels below the very high transmission threshold of 140 cases per 100,000 people over seven days. Assuming that SARS-CoV-2 will spread at levels estimated prior to interventions such as widespread masking and increased vaccinations, we project that COVID-19 risks will depend on vaccination rates among UT students and that proactive testing can be scaled to mitigate those risks.
COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support
(2020-11) Shea, Katriona; Borchering, Rebecca K.; Probert, William J.M.; Howerton, Emily; Bogich, Tiffany L.; Li, Shouli; van Panhuis, Willem G.; Viboud, Cecile; Aguás, Ricardo; Belov, Artur; Bhargava, Sanjana H.; Cavany, Sean; Chang, Joshua C.; Chen, Cynthia; Chen, Jinghui; Chen, Shi; Chen, YanqQuan; Childs, Lauren M.; Chow, Carson C.; Crooker, Isabel; De Valle, Sara Y.; España, Guido; Fairchild, Geoffrey; Gerkin, Richard C.; Germann, Timothy C.; Gu, Quanquan; Guan, Xiangyang; Guo, Lihong; Hart, Gregory R.; Hladish, Thomas J.; Hupert, Nathaniel; Janies, Daniel; Kerr, Cliff C.; Klein, Daniel J.; Klein, Eili; Lin, Gary; Manore, Carrie; Meyers, Lauren Ancel; Mittler, John; Mu, Kunpeng; Núñez, Rafael C.; Oidtman, Rachel; Pasco, Remy; Pastore y Piontti, Ana; Paul, Rajib; Pearson, Carl A.B.; Perdomo, Dianela R.; Perkins, T. Alex; Pierce, Kelly; Pillai, Alexander N.; Rael, Rosalyn Cherie; Rosenfeld, Katherine; Ross, Chrysm Watson; Spencer, Julie A.; Stoltzfu, Arlin B.; Toh, Kok Ben; Vattikuti, Shashaank; Vespignani, Alessandro; Wang, Lingxiao; White, Lisa; Xu, Pan; Yang, Yupeng; Yogurtcu, Osman N.; Zhang, Weitong; Zhao, Yanting; Zou, Difan; Ferrari, Matthew; Pannell, David; Tildesley, Michael; Seifarth, Jack; Johnson, Elyse; Biggerstaff, Matthew; Johansson, Michael; Slayton, Rachel B.; Levander, John; Stazer, Jeff; Salerno, Jessica; Runge, Michael C.
Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid- sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.
COVID-19 risk assessment for public events - May 2022
(2022-05) Fox, Spencer J.; Johnson, Kaitlyn; Owirodu, Briana; Johnson-Leung, Jennifer; Elizondo, Marcel; Walkes, Desmar; Meyers, Lauren Ancel
We describe a risk assessment framework to support event planning during COVID-19 waves. The method was developed in partnership with public health officials in Austin, Texas. The framework is based on a previously published model [1]. The inputs to our calculations include the following: the local prevalence of COVID-19 [2], epidemiological properties of current variants, the structure of the event, including the number of attendees, types and duration of activities, density of interactions, and ventilation, COVID-related precautions for the event, including vaccine, testing, and face mask requirements, and local demographic information. The risk assessment framework uses the above inputs to estimate the following quantities: the number of attendees likely to arrive infected, the reproduction number of COVID-19 at the event, the number of attendees likely to become infected at the event, and the number of additional infections that will occur in Austin in the subsequent four weeks, stemming from infections occurring at the event. This report considers two case studies in Travis county (Austin, TX): (1) a business conference with 3,000 attendees and (2) an outdoor festival with 50,000 attendees.

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