Volume 26, Number 11—November 2020
Research
Nowcasting (Short-Term Forecasting) of Influenza Epidemics in Local Settings, Sweden, 2008–2019
Table 1
Influenza virus activity | Updated* alert threshold, cases/day/100,000 population† | Timeliness‡ | Start according to method | Actual start§ | Interpretation |
---|---|---|---|---|---|
2008–09 A(H3N2), initial retrospective data | |||||
Stockholm | 0.63 | ||||
West Gothia | 0.73 | ||||
Scania |
0.25 |
||||
2009 A(H1N1) | |||||
Stockholm | 0.63 | −5 | 2009 Aug 24 | 2009 Aug 19 | Good |
West Gothia | 0.73 | −6 | 2009 Sep 3 | 2009 Aug 28 | Good |
Scania |
0.25 |
18 |
2009 Aug 13 |
2009 Aug 31 |
Poor |
2010–11 A(H1N1) and B¶ | |||||
Stockholm | 0.63 | −7 | 2010 Dec 30 | 2010 Dec 23 | Good |
West Gothia | 0.73 | −12 | 2011 Jan 9 | 2010 Dec 28 | Poor |
Scania |
0.25 |
2 |
2010 Dec 23 |
2010 Dec 25 |
Excellent |
2011–12 A(H3N2) | |||||
Stockholm | 0.59 | 2 | 2012 Jan 22 | 2012 Jan 24 | Excellent |
West Gothia | 0.43 | 1 | 2012 Jan 31 | 2012 Feb 1 | Excellent |
Scania |
0.27 |
23 |
2012 Jan 9 |
2012 Feb 1 |
Poor |
2012–13 A(H3N2), A(H1N1), and B | |||||
Stockholm | 0.51 | −6 | 2013 Jan 3 | 2012 Dec 28 | Good |
West Gothia | 0.44 | 0 | 2012 Dec 29 | 2012 Dec 29 | Excellent |
Scania |
0.28 |
0 |
2012 Dec 27 |
2012 Dec 27 |
Excellent |
2013–14 A(H3N2), A(H1N1), and B | |||||
Stockholm | 0.52 | 0 | 2014 Jan 30 | 2014 Jan 30 | Excellent |
West Gothia | 0.37 | 1 | 2014 Jan 27 | 2014 Jan 28 | Excellent |
Scania |
0.35 |
0 |
2014 Jan 28 |
2014 Jan 28 |
Excellent |
2014–15 A(H3N2) and B | |||||
Stockholm | 0.52 | −6 | 2015 Jan 13 | 2015 Jan 7 | Good |
West Gothia | 0.39 | 0 | 2015 Jan 17 | 2015 Jan 17 | Excellent |
Scania |
0.35 |
7 |
2015 Jan 16 |
2015 Jan 23 |
Good |
2015–16 A(pH1N1) and B | |||||
Stockholm | 0.52 | 0 | 2016 Jan 2 | 2016 Jan 2 | Excellent |
West Gothia | 0.47 | 16 | 2015 Dec 28 | 2016 Jan 13 | Poor |
Scania |
0.34 |
0 |
2015 Dec 16 |
2015 Dec 16 |
Excellent |
2016–17 A(H3N2) | |||||
Stockholm | 0.34 | −2 | 2016 Dec 1 | 2016 Nov 29 | Excellent |
West Gothia | 0.31 | −2 | 2016 Dec 17 | 2016 Dec 15 | Excellent |
Scania |
0.31 |
0 |
2016 Dec 10 |
2016 Dec 10 |
Excellent |
2017–18 A(H3N2) and B | |||||
Stockholm | 0.38 | 0 | 2017 Dec 12 | 2017 Dec 12 | Excellent |
West Gothia | 0.44 | 4 | 2017 Dec 30 | 2018 Jan 3 | Good |
Scania |
0.34 |
5 |
2017 Dec 22 |
2017 Dec 27 |
Good |
2018–19 A(pH1N1) | |||||
Stockholm | 0.36 | −7 | 2018 Dec 18 | 2018 Dec 5 | Good |
West Gothia | 0.40 | −6 | 2018 Dec 28 | 2018 Dec 22 | Good |
Scania | 0.34 | 5 | 2018 Dec 27 | 2019 Jan 1 | Good |
*Threshold updated after every seasonal influenza (i.e., no updates after pandemic outbreaks). †Threshold determined using clinical influenza-diagnosis data. ‡Positive value means that the algorithm issued an alarm before the local epidemic had started; negative value means that the alarm was raised after the start of the epidemic. §Actual start is the date when the retrospectively calculated intensity level reached the predefined threshold for start of an epidemic (6.3 influenza-diagnosis cases/100,000 population recorded during a floating 7-day period) (7,11). ¶No update of threshold before this seasonal influenza because the previous outbreak was a pandemic.
References
- Nsoesie EO, Brownstein JS, Ramakrishnan N, Marathe MV. A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza Other Respir Viruses. 2014;8:309–16. DOIPubMedGoogle Scholar
- Wu JT, Ho A, Ma ES, Lee CK, Chu DK, Ho PL, et al. Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data. PLoS Med. 2011;8:
e1001103 . DOIPubMedGoogle Scholar - Viboud C, Vespignani A. The future of influenza forecasts. Proc Natl Acad Sci U S A. 2019;116:2802–4. DOIPubMedGoogle Scholar
- Reich NG, Brooks LC, Fox SJ, Kandula S, McGowan CJ, Moore E, et al. A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States. Proc Natl Acad Sci U S A. 2019;116:3146–54. DOIPubMedGoogle Scholar
- Schmid F, Wang Y, Harou A. Nowcasting guidelines—a summary. Geneva: World Meteorological Organization; 2019 [cited 2019 Jul 15]. https://public.wmo.int/en/resources/bulletin/nowcasting-guidelines-%E2%80%93-summary
- Spreco A, Eriksson O, Dahlström Ö, Cowling BJ, Timpka T. Integrated detection and prediction of influenza activity for real-time surveillance: algorithm design. J Med Internet Res. 2017;19:
e211 . DOIPubMedGoogle Scholar - Spreco A, Eriksson O, Dahlström Ö, Cowling BJ, Timpka T. Evaluation of nowcasting for detecting and predicting local influenza epidemics, Sweden, 2009-2014. Emerg Infect Dis. 2018;24:1868–73. DOIPubMedGoogle Scholar
- Timpka T, Spreco A, Dahlström Ö, Eriksson O, Gursky E, Ekberg J, et al. Performance of eHealth data sources in local influenza surveillance: a 5-year open cohort study. J Med Internet Res. 2014;16:
e116 . DOIPubMedGoogle Scholar - Timpka T, Spreco A, Eriksson O, Dahlström Ö, Gursky EA, Strömgren M, et al. Predictive performance of telenursing complaints in influenza surveillance: a prospective cohort study in Sweden. Euro Surveill. 2014;19:20966. DOIPubMedGoogle Scholar
- World Health Organization. International statistical classification of diseases and related health problems. 10th revision. Volume 2. Geneva: The Organization; 2010 [cited 2019 Jun 1]. https://www.who.int/classifications/icd/ICD10Volume2_en_2010.pdf
- Vega T, Lozano JE, Meerhoff T, Snacken R, Beauté J, Jorgensen P, et al. Influenza surveillance in Europe: comparing intensity levels calculated using the moving epidemic method. Influenza Other Respir Viruses. 2015;9:234–46. DOIPubMedGoogle Scholar
- Chen Y, Ong JHY, Rajarethinam J, Yap G, Ng LC, Cook AR. Neighbourhood level real-time forecasting of dengue cases in tropical urban Singapore. BMC Med. 2018;16:129. DOIPubMedGoogle Scholar
- García-Basteiro AL, Chaccour C, Guinovart C, Llupià A, Brew J, Trilla A, et al. Monitoring the COVID-19 epidemic in the context of widespread local transmission. Lancet Respir Med. 2020;8:440–2. DOIPubMedGoogle Scholar
- Timpka T, Eriksson H, Gursky EA, Nyce JM, Morin M, Jenvald J, et al. Population-based simulations of influenza pandemics: validity and significance for public health policy. Bull World Health Organ. 2009;87:305–11. DOIPubMedGoogle Scholar
- Soliman M, Lyubchich V, Gel YR. Complementing the power of deep learning with statistical model fusion: Probabilistic forecasting of influenza in Dallas County, Texas, USA. Epidemics. 2019;28:
100345 . DOIPubMedGoogle Scholar - Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet. 2019;393:1577–9. DOIPubMedGoogle Scholar
- Ly S, Arashiro T, Ieng V, Tsuyuoka R, Parry A, Horwood P, et al. Establishing seasonal and alert influenza thresholds in Cambodia using the WHO method: implications for effective utilization of influenza surveillance in the tropics and subtropics. Western Pac Surveill Response J. 2017;8:22–32. DOIPubMedGoogle Scholar
- Rakocevic B, Grgurevic A, Trajkovic G, Mugosa B, Sipetic Grujicic S, Medenica S, et al. Influenza surveillance: determining the epidemic threshold for influenza by using the Moving Epidemic Method (MEM), Montenegro, 2010/11 to 2017/18 influenza seasons. Euro Surveill. 2019;24:
1800042 . DOIPubMedGoogle Scholar
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