Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models
Bibliographic Data
| ID | 15354575 |
|---|---|
| Authors | Ta‐chien Chan (0000-0002-1685-783X, Research Center for Humanities and Social Sciences, Academia Sinica, corresponding author), Yung-Chu Teng (Institute of Statistical Science, Academia Sinica), Jing-Shiang Hwang (0000-0002-5807-7583, Institute of Statistical Science, Academia Sinica) |
| Year | 2015 |
| Volume | 15 |
| Issue | 1 |
| Pages | 168-168 |
| Publication date | 2015-02-20 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | BMC Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1471-2458 • E-ISSN: 1471-2458 |
| Publisher | BioMed Central (PUBLISHER • GB) |
| DOI | 10.1186/s12889-015-1500-4 |
| PMID | 25886316 |
| PMCID | PMC4352259 |
| OpenAlex | W2024431914 |
| Language | EN |
| References cited | 32 |
The proposed simple approach was able to filter out temporal trends, adjust for temperature, and issue warning signals for the first wave of the influenza epidemic in a timely and accurate manner
Biostatistics · CUSUM · Environmental health · Medical emergency · Negative binomial distribution · Outbreak · Pearson product-moment correlation coefficient · Public health · Public health surveillance · Regression analysis · Statistics · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Influenza Virus Research Studies · Medicine · Nursing · Virology
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| Citation velocity | historical |
|---|---|
| Highly cited | No |