Center for Pandemic Decision Science

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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
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.
Serial Interval of COVID-19 among Publicly Reported Confirmed Cases
(Emerging Infectious Diseases, 2020-03-19) Du, Zhanwei; Xu, Xiaoke; Wu, Ye; Wang, Lin; Cowling, Benjamin J.; Meyers, Lauren Ancel
We estimate the distribution of serial intervals for 468 confirmed cases of coronavirus disease reported in China as of February 8, 2020. The mean interval was 3.96 days (95% CI 3.53–4.39 days), SD 4.75 days (95% CI 4.46–5.07 days); 12.6% of case reports indicated presymptomatic transmission.
The secret life of coronavirus: Why we need such drastic social distancing measures
(Economist Impact, 2020-03-20) Meyers, Lauren Ancel
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
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
Hundreds of severe pediatric COVID-19 infections in Wuhan prior to the lockdown
(2020-03-27) Du, Zhanwei; Nugenta, Ciara; Cowling, Benjamin John; Meyers, Lauren Ancel
Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak
(PNAS, 2020-03-31) Wells, Chad R.; Saha, Pratha; Moghadasb, Seyed M.; Pandeya, Abhishek; Shoukata, Affan; Wang, Yaning; Wang, Zheng; Meyers, Lauren Ancel; Singer, Burton H.; Galvani, Alison P.
The novel coronavirus outbreak (COVID-19) in mainland China has rapidly spread across the globe. Within 2 mo since the outbreak was first reported on December 31, 2019, a total of 566 Severe Acute Respiratory Syndrome (SARS CoV-2) cases have been confirmed in 26 other countries. Travel restrictions and border control measures have been enforced in China and other countries to limit the spread of the outbreak. We estimate the impact of these control measures and investigate the role of the airport travel network on the global spread of the COVID-19 outbreak. Our results show that the daily risk of exporting at least a single SARS CoV-2 case from mainland China via international travel exceeded 95% on January 13, 2020. We found that 779 cases (95% CI: 632 to 967) would have been exported by February 15, 2020 without any border or travel restrictions and that the travel lockdowns enforced by the Chinese government averted 70.5% (95% CI: 68.8 to 72.0%) of these cases. In addition, during the first three and a half weeks of implementation, the travel restrictions decreased the daily rate of exportation by 81.3% (95% CI: 80.5 to 82.1%), on average. At this early stage of the epidemic, reduction in the rate of exportation could delay the importation of cases into cities unaffected by the COVID-19 outbreak, buying time to coordinate an appropriate public health response.
Projecting hospital utilization during the COVID-19 outbreaks in the United States
(PNAS, 2020-04) Moghadas, Seyed M.; Shoukat, Affan; Fitzpatrick, Meagan C.; Wells, Chad R.; Sah, Pratha; Pandey, Abhishek; Sachs, Jeffrey D.; Wang, Zheng; Meyers, Lauren Ancel; Singer, Burton H.; Galvani, Alison P.
In the wake of community coronavirus disease 2019 (COVID-19) transmission in the United States, there is a growing public health concern regarding the adequacy of resources to treat infected cases. Hospital beds, intensive care units (ICUs), and ventilators are vital for the treatment of patients with severe illness. To project the timing of the outbreak peak and the number of ICU beds required at peak, we simulated a COVID-19 outbreak parameterized with the US population demographics. In scenario analyses, we varied the delay from symptom onset to self-isolation, the proportion of symptomatic individuals practicing self-isolation, and the basic reproduction number R0. Without self-isolation, when R0 =2.5, treatment of critically ill individuals at the outbreak peak would require 3.8 times more ICU beds than exist in the United States. Self-isolation by 20% of cases 24 h after symptom onset would delay and flatten the outbreak trajectory, reducing the number of ICU beds needed at the peak by 48.4% (interquartile range 46.4-50.3%), although still exceeding existing capacity. When R0 =2, twice as many ICU beds would be required at the peak of outbreak in the absence of self-isolation. In this scenario, the proportional impact of self-isolation within 24 h on reducing the peak number of ICU beds is substantially higher at 73.5% (interquartile range 71.4-75.3%). Our estimates underscore the inadequacy of critical care capacity to handle the burgeoning outbreak. Policies that encourage self-isolation, such as paid sick leave, may delay the epidemic peak, giving a window of time that could facilitate emergency mobilization to expand hospital capacity.
Projections for first-wave COVID-19 deaths across the US using social-distancing measures derived from mobile phones
(2020-04) Woody, Spencer; Tec, Mauricio; Dahan, Maytal; Gaither, Kelly; Lachmann, Michael; Fox, Spencer J.; Meyers, Lauren Ancel; Scott, James
We propose a Bayesian model for projecting first-wave COVID-19 deaths in all 50 U.S. states. Our model's projections are based on data derived from mobile-phone GPS traces, which allows us to estimate how social-distancing behavior is flattening the curve in each state. In a two-week look-ahead test of out-of-sample forecasting accuracy, our model significantly outperforms the widely used model from the Institute for Health Metrics and Evaluation (IHME), achieving 42% lower prediction error: 13.2 deaths per day average error across all U.S. states, versus 22.8 deaths per day average error for the IHME model. Our model also provides an accurate, if slightly conservative, assessment of forecasting accuracy: in the same look-ahead test, 98% of data points fell within the model's 95% credible intervals. Our model's projections are updated daily at https://covid-19.tacc.utexas.edu/projections/.
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 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.
The unseen and pervasive threat of COVID-19 throughout the US
(2020-04) Javan, Emily; Fox, Spencer J.; Meyers, Lauren Ancel
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 serial interval estimates based on confirmed cases in public reports from 86 Chinese cities
(2020-04) Du, Zhanwei; Xu, Xiaoke; Wu, Ye; Wang, Lin; Cowling, Benjamin J.; Meyers, Lauren Ancel
As a novel coronavirus (COVID-19) continues to spread widely and claim lives worldwide, its transmission characteristics remain uncertain. Here, we present and analyze the serial intervals-the time period between the onset of symptoms in an index (infector) case and the onset of symptoms in a secondary (infectee) case-of 339 confirmed cases of COVID-19 identified from 264 cities in mainland China prior to February 19, 2020. Here, we provide the complete dataset in both English and Chinese to support further COVID-19 research and modeling efforts.
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 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.
Impact of Social Distancing Measures on COVID-19 Healthcare Demand in Central Texas
(2020-04) Wang, Xutong; Pasco, Remy; Du, Zhanwei; Petty, Michaela; Fox, Spencer J.; Galvani, Alison P.; Pignone, Michael; Johnston, S. Claiborne; Meyers, Lauren Ancel
Background: A novel coronavirus (SARS-CoV-2) emerged in Wuhan, China in late 2019 and rapidly spread worldwide. In the absence of effective antiviral drugs and vaccines, well-targeted social distancing measures are essential for mitigating the COVID-19 pandemic, reducing strain on local health systems, and preventing mortality. Here, we provide a quantitative assessment of the efficacy of social distancing to slow COVID-19 transmission and reduce hospital surge, depending on the timing and extent of the measures imposed for a metropolitan region and its health care systems. Methods and Findings: We built a granular mathematical model of COVID-19 transmission that incorporated age-specific and risk-stratified heterogeneity, estimates for the transmission, and severity of COVID-19 using current best evidence. We performed thousands of stochastic simulations of COVID-19 transmission in the Austin-Round Rock Metropolitan Area to project the impact of school closures coupled with social distancing measures that were estimated to reduce non-household contacts by 0%, 25%, 50%, 75% or 90%. We compare early versus late implementation and estimate the number of COVID-19 hospitalizations, ICU patients, ventilator needs and deaths through mid-August, 2020. We queried local emergency services and hospital systems to estimate total hospital bed, ICU, and ventilator capacity for the region. We expected COVID-19 hospital beds and ICU requirements would surpass local capacity by mid-May if no intervention was taken. Assuming a four-day epidemic doubling time, school closures alone would be expected to reduce peak hospitalizations by only 18% and cumulative deaths by less than 3%. Immediate social distancing measures that reduced non-household contacts by over 75%, such as stay-at-home orders and closing of non-essential businesses, would be required to ensure that COVID-19 cases do not overwhelm local hospital surge capacity. Peak ICU bed demand prior to mid August 2020 would be expected to be reduced from 2,121 (95% CI: 2,0-2,208) with no intervention to 698 (95% CI:204-1,100) with 75% social distancing and 136 (95% CI: 38-308) with 90% social distancing; current ICU bed capacity was estimated at 680. A two-week delay in implementation of such measures is projected to accelerate a local ICU bed shortage by four weeks. Conclusions: School closures alone hardly impact the epidemic curve. Immediate social distancing measures that reduce non-household contacts by over 75% were required to ensure that COVID-19 cases do not overwhelm local hospital surge capacity. These findings helped inform the Stay Home-Work Safe order enacted by the city of Austin, Texas on March 24, 2020 as a means of mitigating the emerging COVID-19 epidemic.
Staged Strategy to Avoid Hospital Surge and Preventable Mortality, while Reducing the Economic Burden of Social Distancing
(2020-05) Duque, Daniel; Tec, Mauricio; Morton, David P.; Scott, James; Yang, Haoxiang; Pasco, Remy; Pierce, Kelly; Fox, Spencer J.; Pignone, Michael; Hudson, Parker; Meyers, Lauren Ancel
In order to balance the goals of preventing death from COVID-19 while also minimizing other negative (societal and economic) impacts, it is important to have a clear monitoring strategy that can guide public policy. Such a strategy can be used to help guide the application of the range of non-pharmacological interventions that have been shown to reduce disease transmission and death. Here, we propose a coherent data-driven strategy for triggering both the tightening and relaxation of policies to ensure Measures COVID-19 hospitalizations do not exceed healthcare capacity in cities while minimizing the duration of restrictions on commerce, healthcare, recreation, and schooling. As US states reopen during the COVID-19 pandemic, it is expected that cases will increase. The goal of this strategy is to reduce economic hardship while maintaining access to hospital care for both COVID-19 patients and all other patients and to avoid excess serious complications or death for those with COVID-19 or for those with other medical conditions like cancer or cardiovascular disease, who may not receive timely or safe care if health system overwhelmed by COVID-19. In addition, an overwhelmed health system creates a more dangerous environment for our health care providers who will be more likely to be infected and hence exacerbate health care shortages. It could also seriously compromise the public confidence in their health and safety.

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