Real-time pandemic surveillance using hospital admissions and mobility data.

Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and healthcare demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to 0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95% CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities.

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.

Out-of-hospital cardiac arrest outcomes with "pit crew" resuscitation and scripted initiation of mechanical CPR.

OBJECTIVE: To compare OHCA outcomes in patients managed with mechanical versus manual CPR in an EMS system with a "pit crew" approach to resuscitation and a scripted sequence for the initiation of mechanical CPR. METHODS: Through a year-long quality improvement effort we standardized the initial resuscitative efforts for OHCA, prioritizing a "pit crew" approach to high quality manual CPR, early defibrillation and basic airway management ahead of a scripted sequence for initiating mechanical CPR. We then analyzed outcomes for adult, non-traumatic OHCA attended in the following year (2016). We used a propensity score matched analysis to compare ROSC, survival to discharge, and neurologic status among patients managed with manual versus mechanical CPR while controlling for patient demographics and arrest characteristics. RESULTS: Of 444 eligible OHCAs, 217 received manual and 227 received mechanical CPR. Crude ROSC (39.2% vs. 29.1%) and survival to discharge (13.8% vs. 5.7%) were higher with manual CPR. In the propensity matched analysis (n = 176 manual CPR; 176 mechanical CPR), both ROSC (38.6% vs. 28.4%; difference: 10.2%; CI: 0.4% to 20.0%) and survival to discharge (13.6% vs. 6.8%; difference: 6.8%; CI: 0.5% to 13.3%) remained significantly higher for patients receiving manual CPR. CONCLUSIONS: In this EMS system with a standardized, "pit crew" approach to OHCA that prioritized initial high-quality initial resuscitative efforts and scripted the sequence for initiating mechanical CPR, use of mechanical CPR was associated with decreased ROSC and decreased survival to discharge.