Multiple models for outbreak decision support in the face of uncertainty.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Disproportionate impacts of COVID-19 in a large US city.

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5-24.8%) infection rate and 29.4% (95% CrI: 28.0-31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3-12.0%] vs 25.1% [95% CrI: 23.7-26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49-57%] vs 28% [95% CrI: 27-30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0-3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC's social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.

COVID-19 Test Allocation Strategy to Mitigate SARS-CoV-2 Infections across School Districts.

In response to COVID-19, schools across the United States closed in early 2020; many did not fully reopen until late 2021. Although regular testing of asymptomatic students, teachers, and staff can reduce transmission risks, few school systems consistently used proactive testing to safeguard return to classrooms. Socioeconomically diverse public school districts might vary testing levels across campuses to ensure fair, effective use of limited resources. We describe a test allocation approach to reduce overall infections and disparities across school districts. Using a model of SARS-CoV-2 transmission in schools fit to data from a large metropolitan school district in Texas, we reduced incidence between the highest and lowest risk schools from a 5.6-fold difference under proportional test allocation to 1.8-fold difference under our optimized test allocation. This approach provides a roadmap to help school districts deploy proactive testing and mitigate risks of future SARS-CoV-2 variants and other pathogen threats.

A Searchable Database for Prediction of Emission Compositions from Upstream Oil and Gas Sources.

Atmospheric emissions from oil and gas production operations are composed of multiple hydrocarbons and can have large variations in composition. Accurate estimates of emission compositions are needed to estimate the fate and impacts of emissions and to attribute emissions to sources. This work presents a database, constructed with empirical data and thermodynamic models, that can be queried to estimate hydrocarbon compositions from emission sources present at oil and gas production sites. The database can be searched for matches using between two and seven well parameters as query inputs (gas-to-oil ratio, API gravity, separator pressure, separator temperature, methane molar fraction in produced gas, ethane molar fraction of produced gas, and propane molar fraction in produced gas). Database query performance was characterized by comparing returns from database queries to a test data set. Application of the database to well parameters for tens of thousands of wells in the Barnett, Eagle Ford, and Fayetteville production regions demonstrates variations in emission compositions. Ethane to methane ratio varies by more than an order of magnitude from well to well and source to source. VOC to methane ratios are comparable in variability to ethane to methane ratios for most emission sources, but have a higher variability for emissions from flashing of liquid hydrocarbon tanks.

On the set of emotions with facial signals.

Which, if any, emotions have a facial signal? Studies from AI to Zoology sometimes presuppose an answer to this question. According to one important and influential research program, the basic (fundamental and discrete) emotions can be identified by their possession of a biologically based unique and universally recognized facial signal. To the classic set of six such emotions, researchers recently advanced 12 new candidates, which were examined in the present study with a standard free-labeling procedure in three samples: English-speaking Americans (n = 200), Mandarin-speaking Chinese (n = 101), and Malayalam-speaking Indians (n = 200). In the three samples, respectively, a majority of respondents chose the predicted label for only one, one, and none of the 12 faces. That is, a majority of respondents failed to choose the predicted label for 11 of the 12 faces in the English-speaking (proportion of respondents range for the 11: .04 to .45) and Mandarin-speaking (proportion of respondents range for the 11: .00 to .44) samples; a majority of respondents failed to choose the predicted label for any of the 12 faces in the Malayalam-speaking sample (proportion of respondents range: .00 to .42). The modal choice in the three samples was the predicted label for five, six, and one, respectively, of the 12 faces. "Recognition" of the predicted emotion was negligible (< 15% of respondents) for five, eight (two of which were modal), and 10, respectively, of the 12 faces. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Design of COVID-19 staged alert systems to ensure healthcare capacity with minimal closures.

Community mitigation strategies to combat COVID-19, ranging from healthy hygiene to shelter-in-place orders, exact substantial socioeconomic costs. Judicious implementation and relaxation of restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. With public compliance, the policy triggers ensure adequate intensive care unit capacity with high probability while minimizing the duration of strict mitigation measures. In comparison, we show that other sensible COVID-19 staging policies, including France's ICU-based thresholds and a widely adopted indicator for reopening schools and businesses, require overly restrictive measures or trigger strict stages too late to avert catastrophic surges. As proof-of-concept, we describe the optimization and maintenance of the staged alert system that has guided COVID-19 policy in a large US city (Austin, Texas) since May 2020. As cities worldwide face future pandemic waves, our findings provide a robust strategy for tracking COVID-19 hospital admissions as an early indicator of hospital surges and enacting staged measures to ensure integrity of the health system, safety of the health workforce, and public confidence.

Design of COVID-19 Staged Alert Systems to Ensure Healthcare Capacity with Minimal Closures.

Community mitigation strategies to combat COVID-19, ranging from healthy hygiene to shelter-in-place orders, exact substantial socioeconomic costs. Judicious implementation and relaxation of restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. With public compliance, the policy triggers ensure adequate intensive care unit capacity with high probability while minimizing the duration of strict mitigation measures. In comparison, we show that other sensible COVID-19 staging policies, including France's ICU-based thresholds and a widely adopted indicator for reopening schools and businesses, require overly restrictive measures or trigger strict stages too late to avert catastrophic surges. As cities worldwide face future pandemic waves, our findings provide a robust strategy for tracking COVID-19 hospital admissions as an early indicator of hospital surges and enacting staged measures to ensure integrity of the health system, safety of the health workforce, and public confidence.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support.

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.

Software-Assisted Manual Review of Clinical Next-Generation Sequencing Data: An Alternative to Routine Sanger Sequencing Confirmation with Equivalent Results in >15,000 Germline DNA Screens.

Clinical genomic tests increasingly use a next-generation sequencing (NGS) platform due in part to the high fidelity of variant calls, yet rare errors are still possible. In germline DNA screening, failure to correct such errors could have serious consequences for patients, who may follow an unwarranted screening or surgical management path. It has been suggested that routine orthogonal confirmation by Sanger sequencing is required to verify NGS results, especially low-confidence positives with depressed allele fraction (<30% of alternate allele). We evaluated whether an alternative method of confirmation-software-assisted manual call review-performed comparably with Sanger confirmation in >15,000 samples. Licensed reviewers manually inspected both raw and processed data at the batch, sample, and variant levels, including raw NGS read pileups. Of ambiguous variant calls with <30% allele fraction (1707 total calls at 38 unique sites), manual call review classified >99% (n = 1701) as true positives (enriched for long insertions or deletions and homopolymers) or true negatives (often conspicuous NGS artifacts), with the remaining <1% (n = 6) being mosaic. Critically, results from software-assisted manual review and retrospective Sanger sequencing were concordant for samples selected from all ambiguous sites. We conclude that the confirmation required for high confidence in NGS-based germline testing can manifest in different ways; a trained NGS expert operating platform-tailored review software achieves quality comparable with routine Sanger confirmation.