A Simulation-Based Study on the Comparison of Statistical and Time Series Forecasting Methods for Early Detection of Infectious Disease Outbreaks
| Autor: | Lkhagvadorj Munkhdalai, Keun Ho Ryu, Jusim Kim, Hyun Woo Park, Yeon Hwa Choi, Ibrahim Musa, Eun-Joo Yang |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Mean squared error
Health Toxicology and Mutagenesis lcsh:Medicine CUSUM Sensitivity and Specificity 01 natural sciences Article Disease Outbreaks 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine syndromic surveillance outbreak detection aberration detection syndromic diarrhea Republic of Korea Statistics Humans 030212 general & internal medicine Autoregressive integrated moving average 0101 mathematics Time series Mathematics lcsh:R Public Health Environmental and Occupational Health Outbreak Mean absolute percentage error Population Surveillance Symmetric mean absolute percentage error F1 score Sentinel Surveillance Algorithms Forecasting |
| Zdroj: | International Journal of Environmental Research and Public Health; Volume 15; Issue 5; Pages: 966 International Journal of Environmental Research and Public Health, Vol 15, Iss 5, p 966 (2018) International Journal of Environmental Research and Public Health |
| ISSN: | 1660-4601 |
| DOI: | 10.3390/ijerph15050966 |
| Popis: | Early detection of infectious disease outbreaks is one of the important and significant issues in syndromic surveillance systems. It helps to provide a rapid epidemiological response and reduce morbidity and mortality. In order to upgrade the current system at the Korea Centers for Disease Control and Prevention (KCDC), a comparative study of state-of-the-art techniques is required. We compared four different temporal outbreak detection algorithms: the CUmulative SUM (CUSUM), the Early Aberration Reporting System (EARS), the autoregressive integrated moving average (ARIMA), and the Holt-Winters algorithm. The comparison was performed based on not only 42 different time series generated taking into account trends, seasonality, and randomly occurring outbreaks, but also real-world daily and weekly data related to diarrhea infection. The algorithms were evaluated using different metrics. These were namely, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, symmetric mean absolute percent error (sMAPE), root-mean-square error (RMSE), and mean absolute deviation (MAD). Although the comparison results showed better performance for the EARS C3 method with respect to the other algorithms, despite the characteristics of the underlying time series data, Holt⁻Winters showed better performance when the baseline frequency and the dispersion parameter values were both less than 1.5 and 2, respectively. |
| Databáze: | OpenAIRE |
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