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The use of a CUSUM residual chart to monitor respiratory syndromic data

https://doi.org/10.1080/0740817x.2012.761369
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/qre.1056openalex-snapshot
A comparison of CUSUM, EWMA, and temporal scan statistics for detection of increases in poisson rates
inferredcites →https://doi.org/10.1002/qre.1444openalex-snapshot
CUSUM Procedures for Health Care Surveillance
inferredcites →https://doi.org/10.1002/sim.2209openalex-snapshot
Temporal surveillance using scan statistics
inferredcites →https://doi.org/10.1002/sim.3019openalex-snapshot
A comparison of surveillance methods for small incidence rates
inferredcites →https://doi.org/10.1002/sim.3197openalex-snapshot
Comparing syndromic surveillance detection methods: EARS' versus a CUSUM‐based methodology
inferredcites →https://doi.org/10.1002/sim.4122openalex-snapshot
A comparison of weighted CUSUM procedures that account for monotone changes in population size
inferredcites →https://doi.org/10.1007/978-1-4757-1862-1openalex-snapshot
Sequential Analysis
inferredcites →https://doi.org/10.1007/978-1-4757-3460-7openalex-snapshot
Scan Statistics
inferredcites →https://doi.org/10.1016/j.wem.2010.09.015openalex-snapshot
Morbidity and Mortality Weekly Report
inferredcites →https://doi.org/10.1037/e307182005-001openalex-snapshot
What is Syndromic Surveillance?
inferredcites →https://doi.org/10.1037/e307182005-016openalex-snapshot
Approaches to Syndromic Surveillance When Data Consist of Small Regional Counts
inferredcites →https://doi.org/10.1046/j.1440-1762.2001.00423.xopenalex-snapshot
The application of statistical process control charts to the detection and monitoring of hospital‐acquired infections
inferredcites →https://doi.org/10.1057/jors.1971.52openalex-snapshot
Time Series Analysis Forecasting and Control
inferredcites →https://doi.org/10.1073/pnas.042117499openalex-snapshot
Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales
inferredcites →https://doi.org/10.1080/00224065.2005.11980327openalex-snapshot
Introduction to Statistical Quality Control, Fifth Edition
inferredcites →https://doi.org/10.1080/00224065.2006.11918593openalex-snapshot
The Use of Control Charts in Health-Care and Public-Health Surveillance
inferredcites →https://doi.org/10.1080/08982110802334138openalex-snapshot
A Review of Healthcare, Public Health, and Syndromic Surveillance
inferredcites →https://doi.org/10.1093/ije/dyl162openalex-snapshot
Methods for monitoring influenza surveillance data
inferredcites →https://doi.org/10.1109/tr.2010.2104192openalex-snapshot
Recent Research and Developments in Temporal and Spatiotemporal Surveillance for Public Health
inferredcites →https://doi.org/10.1111/1467-985x.00186openalex-snapshot
Prospective Time Periodic Geographical Disease Surveillance Using a Scan Statistic
inferredcites →https://doi.org/10.1111/j.1467-985x.2007.00502.xopenalex-snapshot
On the use and Evaluation of Prospective Scan Methods for Health-Related Surveillance
inferredcites →https://doi.org/10.1111/j.1467-985x.2011.00714.xopenalex-snapshot
Statistical Methods for the Prospective Detection of Infectious Disease Outbreaks: A Review
inferredcites →https://doi.org/10.1186/1472-6947-3-2openalex-snapshot
Time series modeling for syndromic surveillance
inferredcites →https://doi.org/10.2307/1269900openalex-snapshot
Sequential Analysis, Tests and Confidence Intervals
inferredcites →https://doi.org/10.2307/2988198openalex-snapshot
Time Series Analysis: Forecasting and Control.
inferredcites →https://doi.org/10.3201/eid0303.970322openalex-snapshot
Using Laboratory-Based Surveillance Data for Prevention: An Algorithm for Detecting Salmonella Outbreaks
inferredcites →https://doi.org/10.3201/eid1005.030646openalex-snapshot
Syndromic Surveillance in Public Health Practice, New York City
inferredcites →https://doi.org/10.3201/eid1010.030789openalex-snapshot
Syndromic Surveillance for Influenzalike Illness in Ambulatory Care Setting
inferredcites →https://doi.org/10.5705/ss.2011.027aopenalex-snapshot
Early detection of a change in Poisson rate after accounting for population size effects
inferredcites →https://doi.org/10.6339/jds.2005.03(3).229openalex-snapshot
Monitoring the SARS Epidemic in China: A Time Series Analysis

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4477088openalex-extracted
Gan-Based Statistical Process Control for the Time Series Data
inferred← citeshttps://doi.org/10.1002/qre.3140openalex-snapshot
Residual‐based CUSUM beta regression control chart for monitoring double‐bounded processes
inferred← citeshttps://doi.org/10.1016/j.cie.2019.02.030openalex-snapshot
Risk-adjusted Bernoulli chart in multi-stage healthcare processes based on state-space model with a latent risk variable and dynamic probability control limits
inferred← citeshttps://doi.org/10.1016/j.cie.2022.108536openalex-snapshot
Copula-based CUSUM charts for monitoring infectious disease using Markovian Poisson processes
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.112613openalex-snapshot
GAN-based statistical process control for the time series data
inferred← citeshttps://doi.org/10.1061/9780784484296.022openalex-snapshot
Diagnosing and Quantifying Post-Disaster Pipe Material Cost Fluctuations
inferred← citeshttps://doi.org/10.1080/03610926.2022.2077961openalex-snapshot
Design and implementation of distribution-free Phase-II charting schemes based on unconditional run-length percentiles
inferred← citeshttps://doi.org/10.1080/23737484.2022.2087121openalex-snapshot
Surveilling public health through statistical process monitoring: A literature review and a unified framework
inferred← citeshttps://doi.org/10.1515/jom-2020-0129openalex-snapshot
Evaluating disease outbreaks with syndromic surveillance using medical student clinical rotation patient encounter logs
inferred← citeshttps://doi.org/10.3389/fpubh.2023.1141688openalex-snapshot
Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends
inferred← citeshttps://doi.org/10.3390/ijerph191912375openalex-snapshot
Early Detection of SARS-CoV-2 Epidemic Waves: Lessons from the Syndromic Surveillance in Lombardy, Italy
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