Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Quarantine”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

23 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Ventilation in Residential Care Environments

The purpose of this study was to evaluate the effect of ventilation rate, directional airflow (e.g. pressure relationships) and airflow barriers on bioaerosol concentration and movement within assisted-living and residential care environments. Included within this report is a comprehensive literature review, field data collection test plan and, an evaluation of commercially available mitigation technologies related to the transmission of COVID-19 in LTC environments. Additionally, this report includes aerosol testing in an actual LTC facility using the test plan developed herein. Nearly 1.5 million people live in 16,000 nursing homes in the U.S. As of December 20, 2020 the Centers for Medicare and Medicaid Services (CMS) identified a total of 662,549 confirmed and suspected COVID-19 cases in these and other long-term care (LTC) facilities resulting in 92,373 deaths. At this time, COVID-19 cases in LTC facilities accounted for only 3% of the U.S. total of 21M confirmed cases yet nearly a third of the 350,000 total U.S. deaths. Preliminary studies have found little correlation between the quality of care and transmission of COVID-19 in these facilities, suggesting that even the best infection control practices may not be effective in containing the spread of this potentially airborne disease. As a result, many LTC facilities have implemented quarantine procedures and other measures to isolate infectious residents from the general population. Unfortunately, most LTC facilities were not designed for airborne infection control and guidance for retrofitting existing LTC spaces for airborne isolation is limited.

42 ENGINEERING↗
DOE OSTI2026

Zeteotech, LLC TRGR Project Final Report

Reliable detection of aerosolized pathogens is difficult due to need to distinguish between the benign bioaerosols such as dander and pollen and the thousands of pathogens capable of infecting people. Accurate identification of airborne pathogens of concern in the past has required the collection of aerosol samples in a filter, periodic collection of the samples, and processing and identification in the laboratory. This process is labor intensive and expensive. Additionally, this method necessarily has a time to detection window of hours to days depending on the collection frequency. Biological pathogens have an incubation period before the onset of symptoms and severe health effects and/or mortality, people will typically be exposed to them without realizing it. This has resulted in a detect to treat strategy for protection against bio releases. While prophylactic measures can still be effective over these time scales, reducing the time to detection will significantly improve the effectiveness of these measures and subsequently reduce the consequences of a release. Various attempts to reduce the time to detection and identification have been plagued by highly undesirable false-positives which degrade confidence in the system. Zeteotech, LLC has developed a mass spectrometer based bioaerosol sensing system which is capable of autonomously identifying airborne pathogens of concern and alert authorities within minutes instead of hours to days. They have deployed these instruments to protect high-risk facilities by alerting authorities of public health events and intentional bioterrorism events in near real time. This makes it possible to more accurately identify the time and location of the release and minimize the number of people that are exposed through prompt quarantining of affected areas.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2

Severe acute respiratory syndrome coronavirus 2 is the causative agent of the ongoing coronavirus disease pandemic. Initial estimates of the early dynamics of the outbreak in Wuhan, China, suggested a doubling time of the number of infected persons of 6–7 days and a basic reproductive number (R 0 ) of 2.2–2.7. We collected extensive individual case reports across China and estimated key epidemiologic parameters, including the incubation period (4.2 days). We then designed 2 mathematical modeling approaches to infer the outbreak dynamics in Wuhan by using high-resolution domestic travel and infection data. Results show that the doubling time early in the epidemic in Wuhan was 2.3–3.3 days. Assuming a serial interval of 6–9 days, we calculated a median R0 value of 5.7 (95% CI 3.8–8.9). Here, we further show that active surveillance, contact tracing, quarantine, and early strong social distancing efforts are needed to stop transmission of the virus.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Daily Forecasting of Regional Epidemics of Coronavirus Disease with Bayesian Uncertainty Quantification, United States

To increase situational awareness and support evidence-based policymaking, we formulated a mathematical model for coronavirus disease transmission within a regional population. This compartmental model accounts for quarantine, self-isolation, social distancing, a nonexponentially distributed incubation period, asymptomatic persons, and mild and severe forms of symptomatic disease. We used Bayesian inference to calibrate region-specific models for consistency with daily reports of confirmed cases in the 15 most populous metropolitan statistical areas in the United States. We also quantified uncertainty in parameter estimates and forecasts. This online learning approach enables early identification of new trends despite considerable variability in case reporting.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A ``Tail" of Two Cities: Fatality-based Modeling of COVID-19 Evolution in New York City and Cook County, IL

I describe $SIR$ modeling of the COVID-19 pandemic in two U.S. urban environments, New York City (NYC) and Cook County, IL, from onset through the month of June, 2020. Since testing was not widespread early in the pandemic in the U.S., I do not use data on confirmed cases and rely solely on public fatality data to estimate model parameters. Fits to the first 20 days of data determine a degenerate combination of the basic reproduction number, $R_0$, and the mean time to removal from the infectious population, $\gamma^{-1}$, with $\gamma (R_0-1) = 0.25 (0.21)$ inverse days for NYC (Cook County). Equivalently, the initial doubling time was $t_d = 2.8 (3.4)$ days for NYC (Cook). The early fatality data suggest that both locations had infections in early February. I model the mitigation measures implemented in mid-March in both locations (distancing, quarantine, isolation, etc) via a time-dependent reproduction number $R_t$ that declines monotonically from $R_0$ to a sma ller asymptotic value, with a parameterized functional form. The timing (mid-March) and duration (several days) of the transitions in $R_t$ appear well determined by the data. However, the fatality data determine only a degenerate combination of the parameters $R_0$, the percentage reduction in social contact due to mitigation measures, $X$, and the infection fatality rate (IFR), $f$. With flat priors, based on simulations the NYC model parameters have 95.45\% credible intervals of $R_0 = 3.0-5.4$, $X= 80-99.9\%$ and $f=2-6\%$, with $5-13\%$ of the population asymptotically infected. A strong external prior indicating a lower value of $f$ or of $\gamma^{-1}$ would imply lower values of $R_0$ and $X$ and higher percentage infection of the population. For Cook County, the evolution was qualitatively different: after mitigation measures were implemented, the daily fatality counts reached a plateau for about a month before tailing off. This is consistent with an $SIR$ model that exhibits ``critical slowing-down", in which $R_t$ plateaus ! at a value just above unity. For Cook County, the 95.45\% credible intervals for the model parameters are much broader and shifted downward, $R_0=1.4-4.7$, $X=26-54\%$, and $f=0.1-0.6\%$ with $15-88\%$ of the population asymptotically infected. Despite the apparently lower efficacy of its social contact reduction measures, Cook County has had significantly fewer fatalities per population than NYC, $D_\infty/N=100$ vs. 270 per 100,000. In the model, this is attributed to the lower inferred IFR for Cook; an external prior pointing to similar values of the IFR for the two locations would instead chalk up the difference in $D/N$ to differences in the relative growth rate of the disease. I derive a model-dependent threshold, $X^{crit}_e$, for `safe' re-opening, that is, for easing of contact reduction that would not trigger a second wave; for NYC, the models predict that increasing social contact by more than 20\% from post-mitigation levels will lead to renewed spread, while for Cook County the threshold value is very uncertain, given the parameter degeneracies. The timing of 2nd-wave growth will depend on the amplitude of contact increase relative to $X^{crit}_e$ and on the asymptotic growth rate, and the impact in terms of fatalities will depend on the parameter $f$.

Frieman, Joshua↗