Estimating the secondary attack rate and serial interval of influenza-like illnesses using social media
作者： Elad Yom-Tov , Ingemar Johansson-Cox , Vasileios Lampos , Andrew C. Hayward
DOI: 10.1111/IRV.12321
关键词:
摘要: Objectives Knowledge of the secondary attack rate (SAR) and serial interval (SI) influenza is important for assessing severity seasonal epidemics virus. To date, such estimates have required extensive surveys target populations. Here, we propose a method estimating intrafamily SAR SI from postings on Twitter social network. This estimate derived large number people reporting ILI symptoms in them and\or their immediate family members. Design We analyze data 2012–2013 2013–2014 seasons England find that increases estimated precede rates reported by physicians. Results hypothesize observed variations peak value are related to appearance specific strains virus demonstrate this comparing changes values over time relation known virology. In addition, (the average between cases) as 2� 41 days 2012 48 2013.
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N Okabe, T Toyokawa, F Odaira, T Sunagawa, Y Tsuchihashi, K Taniguchi, Y Yahata, H Takahashi, T Kodama, Assessment of secondary attack rate and effectiveness of antiviral prophylaxis among household contacts in an influenza A(H1N1)v outbreak in Kobe, Japan, May-June 2009. Eurosurveillance. ,vol. 14, pp. 19320- ,(2009) , 10.2807/ESE.14.35.19320-EN
D Carcione, L S Goggin, P Effler, G K Dowse, D B Mak, K Sh Kwan, C M Giele, D W Smith, Secondary attack rate of pandemic influenza A(H1N1) 2009 in Western Australian households, 29 May-7 August 2009. Eurosurveillance. ,vol. 16, pp. 19765- ,(2011) , 10.2807/ESE.16.03.19765-EN
M. Elizabeth Halloran, Secondary Attack Rate Encyclopedia of Biostatistics. ,(2005) , 10.1002/0470011815.B2A04049
Vasileios Lampos, Tijl De Bie, Nello Cristianini, Flu detector: tracking epidemics on twitter european conference on machine learning. ,vol. 6323, pp. 599- 602 ,(2010) , 10.1007/978-3-642-15939-8_42
Gunther Eysenbach, Infodemiology: tracking flu-related searches on the web for syndromic surveillance. american medical informatics association annual symposium. ,vol. 2006, pp. 244- 248 ,(2006)
H Zhao, H Green, A Lackenby, M Donati, J Ellis, C Thompson, A Bermingham, J Field, P Sebastianpillai, M Zambon, J M Watson, R Pebody, A new laboratory-based surveillance system (Respiratory DataMart System) for influenza and other respiratory viruses in England: results and experience from 2009 to 2012 Eurosurveillance. ,vol. 19, pp. 20680- ,(2014) , 10.2807/1560-7917.ES2014.19.3.20680
Y. Yang, J. D. Sugimoto, M. E. Halloran, N. E. Basta, D. L. Chao, L. Matrajt, G. Potter, E. Kenah, I. M. Longini, The Transmissibility and Control of Pandemic Influenza A (H1N1) Virus Science. ,vol. 326, pp. 729- 733 ,(2009) , 10.1126/SCIENCE.1177373
Arthur E. Hoerl, Robert W. Kennard, Ridge Regression: Applications to Nonorthogonal Problems Technometrics. ,vol. 12, pp. 69- 82 ,(1970) , 10.1080/00401706.1970.10488635
Arthur E. Hoerl, Robert W. Kennard, Ridge regression: biased estimation for nonorthogonal problems Technometrics. ,vol. 42, pp. 80- 86 ,(2000) , 10.2307/1271436
Andrew C Hayward, Ellen B Fragaszy, Alison Bermingham, Lili Wang, Andrew Copas, W John Edmunds, Neil Ferguson, Nilu Goonetilleke, Gabrielle Harvey, Jana Kovar, Megan SC Lim, Andrew McMichael, Elizabeth RC Millett, Jonathan S Nguyen-Van-Tam, Irwin Nazareth, Richard Pebody, Faiza Tabassum, John M Watson, Fatima B Wurie, Anne M Johnson, Maria Zambon, None, Comparative community burden and severity of seasonal and pandemic influenza: results of the Flu Watch cohort study The Lancet Respiratory Medicine. ,vol. 2, pp. 445- 454 ,(2014) , 10.1016/S2213-2600(14)70034-7