scholarly journals Predicting SARS-CoV-2 Asymptomatic Infection Rate of Inpatients: A Time Series Analysis

2021 ◽  
Vol 1 (S1) ◽  
pp. s51-s52
Author(s):  
Frida Rivera ◽  
Kwang Woo Ahn ◽  
L. Silvia Munoz-Price
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Health System ◽  
Autocorrelation Function ◽  
Infection Rate ◽  
Asymptomatic Infection ◽  
County Level ◽  
Academic Health ◽  
Milwaukee County ◽  
Asymptomatic Patients

Background: Asymptomatic SARS-CoV-2 infections play a crucial role in viral transmission. However, they are often difficult to identify given that widespread surveillance has not been the norm. We sought to determine whether COVID-19 rates reported at the county level could predict the positivity rates for SARS-CoV-2 among asymptomatic patients tested in a large academic health system. Methods: This observational study was conducted from April 23, 2020, to December 10, 2020, at Froedtert Health (FH) system, the largest academic health system in Wisconsin. On April 23, 2020, FH implemented SARS-CoV-2 surveillance among all consecutive admissions not suspected of COVID-19, all patients scheduled for elective procedures and deliveries, and all asymptomatic patients with known exposures. Samples were processed by the FH laboratory using molecular methods (RT-PCR). To obtain the daily number of newly confirmed COVID-19 cases in Milwaukee County, we accessed the Wisconsin Department of Health Services publicly available COVID-19 database. For the purpose of this study, COVID-19 rates were defined as the percentage of positive tests among all daily tests performed at the county level, while SARS-CoV-2 positivity rates were the percentage of positive tests among all daily surveillance tests performed at FH among asymptomatic patients. The association between COVID-19 rates in Milwaukee County and asymptomatic rates at FH were assessed using an autoregressive moving average time series analysis. To examine the association between these rates, we fitted a seventh-order autoregression for the residuals based on autocorrelation function and partial autocorrelation function plots of the residuals from linear regression. Results: From April 23, 2020, to December 10, 2020, there were 2,347 new asymptomatic infections detected at FH and 75,196 new COVID-19 cases reported in Milwaukee County. Figure 1 shows the time-series plot of asymptomatic SARS-CoV-2 positivity rates at FH and Figure 2 shows COVID-19 rates in Milwaukee County. As the COVID-19 rate in Milwaukee County increased by 1 unit, the asymptomatic infection rate in FH decreased by 0.024 unit (95% CI, −0.053 to 0.004; P = .095) after accounting for autocorrelation over time. Thus, there was no association between these rates. Conclusions: The positivity rates among asymptomatic patients at a large medical center were not predicted by the positivity rate at the county level. This finding suggests that the epidemiology at a county level may be determined by pockets in the population who may not interact, and thus not affect, the positivity rates among asymptomatic patients served by a hospital system within the county.Funding: NoDisclosures: None

scholarly journals Predicting asymptomatic SARS-CoV-2 infection rates of inpatients: a time series analysis

2021 ◽  
pp. 1-11
Author(s):  
Frida Rivera ◽  
Kwang Woo Ahn ◽  
L. Silvia Munoz-Price
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Health System ◽  
System Level ◽  
County Level ◽  
Infection Rates ◽  
Series Analysis ◽  
Asymptomatic Patients

Abstract Asymptomatic SARS-CoV-2 infections are often difficult to identify as widespread surveillance has not been the norm. Using time-series analysis, we examined if COVID-19-rates at the county-level could predict positivity rates among asymptomatic-patients at a large Health System. Asymptomatic-positivity rates at the system-level and county-level COVID-19-rates failed to show an association.

scholarly journals Estimating COVID-19 mortality in Italy early in the COVID-19 pandemic

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chirag Modi ◽  
Vanessa Böhm ◽  
Simone Ferraro ◽  
George Stein ◽  
Uroš Seljak
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
Time Series Analysis ◽  
Infection Rate ◽  
Mortality Data ◽  
Cruise Ship ◽  
Series Analysis ◽  
Case Ascertainment ◽  
Population Mortality ◽  
Overall Mortality

AbstractEstimating rates of COVID-19 infection and associated mortality is challenging due to uncertainties in case ascertainment. We perform a counterfactual time series analysis on overall mortality data from towns in Italy, comparing the population mortality in 2020 with previous years, to estimate mortality from COVID-19. We find that the number of COVID-19 deaths in Italy in 2020 until September 9 was 59,000–62,000, compared to the official number of 36,000. The proportion of the population that died was 0.29% in the most affected region, Lombardia, and 0.57% in the most affected province, Bergamo. Combining reported test positive rates from Italy with estimates of infection fatality rates from the Diamond Princess cruise ship, we estimate the infection rate as 29% (95% confidence interval 15–52%) in Lombardy, and 72% (95% confidence interval 36–100%) in Bergamo.

State Recognition Technology and Application on Milling Tool Wear

2007 ◽  
Vol 10-12 ◽  
pp. 869-873
Author(s):  
Chuang Wen Xu ◽  
Hua Ling Chen ◽  
Z. Liu
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Tool Wear ◽  
Time Series Analysis ◽  
Autocorrelation Function ◽  
Feature Vector ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
State Recognition ◽  
Milling Tool

A new method of state recognition of milling tool wear was presented based on time series analysis and fuzzy cluster analysis. After calculating, verifying liberation signal of tool state, and analyzing cutoff property, trailing property, periodicity of the sample autocorrelation function and partial autocorrelation function as well as estimating parameter of model. It can be decided that dynamic data serial is suit AR(p) (autoregression) model. Taking p equal to 12 as a feature vector extraction, based on the fuzzy cluster analysis the similarity relation between the feature vector of the tool working state and the sample feature vector was obtained. Working state of tool wear was determined according to the similarity relation of feature vector. This method was used to recognize initial wear state, normal wear state and acute wear state of milling tool. The result indicates that this method of tool wear recognition based on time series analysis and fuzzy cluster is effective.

scholarly journals Rapid design and implementation of an integrated patient self-triage and self-scheduling tool for COVID-19

2020 ◽  
Vol 27 (6) ◽  
pp. 860-866 ◽  
Author(s):  
Timothy J Judson ◽  
Anobel Y Odisho ◽  
Aaron B Neinstein ◽  
Jessica Chao ◽  
Aimee Williams ◽  
...  
Keyword(s):  
Health System ◽  
San Francisco ◽  
Self Care ◽  
Severe Illness ◽  
Patient Portal ◽  
Academic Health ◽  
Phone Calls ◽  
Asymptomatic Patients ◽  
Telephone Hotline ◽  
Scheduling Tool

Abstract Objective To rapidly deploy a digital patient-facing self-triage and self-scheduling tool in a large academic health system to address the COVID-19 pandemic. Materials and Methods We created a patient portal-based COVID-19 self-triage and self-scheduling tool and made it available to all primary care patients at the University of California, San Francisco Health, a large academic health system. Asymptomatic patients were asked about exposure history and were then provided relevant information. Symptomatic patients were triaged into 1 of 4 categories—emergent, urgent, nonurgent, or self-care—and then connected with the appropriate level of care via direct scheduling or telephone hotline. Results This self-triage and self-scheduling tool was designed and implemented in under 2 weeks. During the first 16 days of use, it was completed 1129 times by 950 unique patients. Of completed sessions, 315 (28%) were by asymptomatic patients, and 814 (72%) were by symptomatic patients. Symptomatic patient triage dispositions were as follows: 193 emergent (24%), 193 urgent (24%), 99 nonurgent (12%), 329 self-care (40%). Sensitivity for detecting emergency-level care was 87.5% (95% CI 61.7–98.5%). Discussion This self-triage and self-scheduling tool has been widely used by patients and is being rapidly expanded to other populations and health systems. The tool has recommended emergency-level care with high sensitivity, and decreased triage time for patients with less severe illness. The data suggests it also prevents unnecessary triage messages, phone calls, and in-person visits. Conclusion Patient self-triage tools integrated into electronic health record systems have the potential to greatly improve triage efficiency and prevent unnecessary visits during the COVID-19 pandemic.

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