統計的公衆衛生サーベイランス
公衆衛生において，サーベイランスに基づく疾患の迅速な発見と対策は重要な課題の一つである．一方で，人命優先のためのスピードとトレードオフでデータの不完全性や報告遅れなどの問題があり，得られたデータを正確に読み解くための統計的知識が不可欠である．特にCOVID-19や次のパンデミックが喫緊の課題として迫っている現代において，その重要性は増している．本稿では，公衆衛生分野におけるサーベイランスとその時系列データを用いた統計的監視方法および変化の逐次的な検出方法について詳述する．特に，筆者らが注力してきた感染症のアウトブレイクの早期検知に世界的に用いられているFarrington アルゴリズムと空間ス...
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| Published in | 日本統計学会誌 Vol. 55; no. 1; pp. 115 - 135 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
一般社団法人 日本統計学会
05.09.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0389-5602 2189-1478 |
| DOI | 10.11329/jjssj.55.115 |
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| Abstract | 公衆衛生において，サーベイランスに基づく疾患の迅速な発見と対策は重要な課題の一つである．一方で，人命優先のためのスピードとトレードオフでデータの不完全性や報告遅れなどの問題があり，得られたデータを正確に読み解くための統計的知識が不可欠である．特にCOVID-19や次のパンデミックが喫緊の課題として迫っている現代において，その重要性は増している．本稿では，公衆衛生分野におけるサーベイランスとその時系列データを用いた統計的監視方法および変化の逐次的な検出方法について詳述する．特に，筆者らが注力してきた感染症のアウトブレイクの早期検知に世界的に用いられているFarrington アルゴリズムと空間スキャン統計について詳述する．また，サーベイランスの統計的性質を，誤警告の確率，検出遅延，検出成功確率などの指標を用いて評価する方法についても詳述する． |
|---|---|
| AbstractList | 公衆衛生において，サーベイランスに基づく疾患の迅速な発見と対策は重要な課題の一つである．一方で，人命優先のためのスピードとトレードオフでデータの不完全性や報告遅れなどの問題があり，得られたデータを正確に読み解くための統計的知識が不可欠である．特にCOVID-19や次のパンデミックが喫緊の課題として迫っている現代において，その重要性は増している．本稿では，公衆衛生分野におけるサーベイランスとその時系列データを用いた統計的監視方法および変化の逐次的な検出方法について詳述する．特に，筆者らが注力してきた感染症のアウトブレイクの早期検知に世界的に用いられているFarrington アルゴリズムと空間スキャン統計について詳述する．また，サーベイランスの統計的性質を，誤警告の確率，検出遅延，検出成功確率などの指標を用いて評価する方法についても詳述する． |
| Author | 川島 孝行 田上 悠太 米岡 大輔 |
| Author_xml | – sequence: 1 fullname: 川島 孝行 organization: 東京科学大学 – sequence: 1 fullname: 米岡 大輔 organization: 国立健康危機管理研究機構国立感染症研究所 – sequence: 1 fullname: 田上 悠太 organization: 東京海洋大学 |
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| Copyright | 2025 日本統計学会 |
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| References_xml | – reference: Dwass, M. (1957). Modified randomization tests for nonparametric hypotheses, The Annals of Mathematical Statistics, pages 181–187. – reference: Kulldorff, M., Huang, L. and Konty, K. (2009). A scan statistic for continuous data based on the normal probability model, International Journal of Health Geographics, 8, 1–9. – reference: Choi, K. and Thacker, S. B. (1981). An evaluation of influenza mortality surveillance, 1962–1979: I. time series forecasts of expected pneumonia and influenza deaths, American Journal of Epidemiology, 113(3), 215–226. – reference: Allévius, B. and Höhle, M. (2017). An expectation-based space-time scan statistic for zip-distributed data, arXiv preprint arXiv:1712.09188. – reference: Kulldorff, M., Huang, L., Pickle, L. and Duczmal, L. (2006). An elliptic spatial scan statistic, Statistics in Medicine, 25(22), 3929–3943. – reference: Helfenstein, U. (1986). Box-jenkins modelling of some viral infectious diseases, Statistics in Medicine, 5(1), 37–47. – reference: Page, E. S. (1954). Continuous inspection schemes, Biometrika, 41(1/2), 100–115. – reference: Yoneoka, D., Kawashima, T., Makiyama, K., Tanoue, Y., Nomura, S. and Eguchi, A. (2021). Geographically weighted generalized farrington algorithm for rapid outbreak detection over short data accumulation periods, Statistics in Medicine, 40(28), 6277–6294. – reference: Stroup, D. F. and Thacker, S. B. (1993). A bayesian approach to the detection of aberrations in public health surveillance data, Epidemiology, 4(5), 435–443. – reference: 国立感染症研究所(2017). 日本の感染症サーベイランス, 2017年7月. URL https://www.niid.go.jp/niid/ja/nesid-program-summary.html. – reference: Fricker, R. D., Jr., Hegler, B. L. and Dunfee, D. A. (2008). Comparing syndromic surveillance detection methods: Ears’versus a cusum-based methodology, Statistics in Medicine, 27(17), 3407–3429. – reference: Lucas, J. M. (1985). Counted data cusum’s, Technometrics, 27(2), 129–144. – reference: Frisén, M., Andersson, E. and Schiöler, L. (2010). Evaluation of multivariate surveillance, Journal of Applied Statistics, 37(12), 2089–2100. – reference: Radaelli, G. (1996). Detection of an unknown increase in the rate of a rare event, Journal of Applied Statistics, 23(1), 105–114. – reference: Sonesson, C. (2003). Evaluations of some exponentially weighted moving average methods, Journal of Applied Statistics, 30(10), 1115–1133. – reference: Frisén, M., Andersson, E. and Schiöler, L. (2011). Sufficient reduction in multivariate surveillance, Communications in Statistics-Theory and Methods, 40(10), 1821–1838. – reference: Patil, G. P. and Taillie, C. (2004). Upper level set scan statistic for detecting arbitrarily shaped hotspots, Environmental and Ecological Statistics, 11, 183–197. – reference: Roberts, S. W. (2000). Control chart tests based on geometric moving averages, Technometrics, 42(1), 97–101. – reference: 高橋邦彦(2008).「疾病地図から疾病集積性へ」『保健医療科学』57(2), 86–92. – reference: Assuncao, R., Costa, M., Tavares, A. and Ferreira, S. (2006). Fast detection of arbitrarily shaped disease clusters, Statistics in Medicine, 25(5), 723–742. – reference: Unkel, S., Farrington, C. P., Garthwaite, P. H., Robertson, C. and Andrews, N. (2012). Statistical methods for the prospective detection of infectious disease outbreaks: a review, Journal of the Royal Statistical Society Series A: Statistics in Society, 175(1), 49–82. – reference: Frisén, M. (2009). Optimal sequential surveillance for finance, public health, and other areas, Sequential Analysis, 28(3), 310–337. – reference: Serfling, R. E. (1963). Methods for current statistical analysis of excess pneumonia-influenza deaths, Public Health Reports, 78(6), 494. – reference: Tango, T. and Takahashi, K. (2005). A flexibly shaped spatial scan statistic for detecting clusters, International Journal of Health Geographics, 4, 1–15. – reference: Rahman, M. O., Yoneoka, D., Murano, Y., Yorifuji, T., Shoji, H., Gilmour, S., Yamamoto, Y. and Ota, E. (2023). Detecting geographical clusters of low birth weight and/or preterm birth in japan, Scientific Reports, 13(1), 1788. – reference: Sonesson, C. and Bock, D. (2003). A review and discussion of prospective statistical surveillance in public health, Journal of the Royal Statistical Society Series A: Statistics in Society, 166(1), 5–21. – reference: Frisén, M. and De Maré, J. (1991). Optimal surveillance, Biometrika, 78(2), 271–280. – reference: Salmon, M., Schumacher, D. and Höhle, M. (2016). Monitoring count time series in R: Aberration detection in public health surveillance, Journal of Statistical Software, 70(10), 1–35. doi: 10.18637/jss.v070.i10. URL https://www.jstatsoft.org/index.php/jss/article/view/v070i10. – reference: Lawson, A. B., Biggeri, A., Böhning, D., Lesaffre, E., Viel, J.-F. and Bertollini, R. (1999). Disease mapping and risk assessment for public health. URL https://api.semanticscholar.org/CorpusID:70669438. – reference: Costagliola, D. (1994). When is the epidemic warning cut-off point exceeded?, European Journal of Epidemiology, 10, 475–476. – reference: Aue, A. and Kirch, C. (2024). The state of cumulative sum sequential changepoint testing 70 years after page, Biometrika, 111(2), 367–391. – reference: Rogerson, P. A. and Yamada, I. (2004). Monitoring change in spatial patterns of disease: comparing univariate and multivariate cumulative sum approaches, Statistics in Medicine, 23(14), 2195–2214. – reference: Kulldorff, M. (1999). Spatial scan statistics: Models, calculations, and applications, Scan Statistics and Applications, pages 303–322. – reference: Shewhart, W. A. (1930). Economic quality control of manufactured product, Bell System Technical Journal, 9(2), 364–389. doi: https://doi.org/10.1002/j.1538-7305.1930.tb00373.x. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/j.1538-7305.1930.tb00373.x. – reference: Lawson, A. B. (2013). Statistical methods in spatial epidemiology, John Wiley & Sons. – reference: Cho, H. and Kirch, C. (2021). Data segmentation algorithms: Univariate mean change and beyond, Econometrics and Statistics. – reference: Neill, D., Moore, A. and Cooper, G. (2005). A bayesian spatial scan statistic, Advances in Neural Information Processing Systems, 18. – reference: Farrington, C. P., Andrews, N. J., Beale, A. D. and Catchpole, M. A. (1996). A statistical algorithm for the early detection of outbreaks of infectious disease, Journal of the Royal Statistical Society: Series A (Statistics in Society), 159(3), 547–563. – reference: Tango, T. (2008). 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Spatial disease clusters: detection and inference, Statistics in Medicine, 14(8), 799–810. |
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