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Volume 26, Number 11—November 2020
Research

Nowcasting (Short-Term Forecasting) of Influenza Epidemics in Local Settings, Sweden, 2008–2019

Armin SprecoComments to Author , Olle Eriksson, Örjan Dahlström, Benjamin John Cowling, Matthew Biggerstaff, Gunnar Ljunggren, Anna Jöud, Emanuel Istefan, and Toomas Timpka
Author affiliations: Linköping University Department of Health, Medicine, and Caring Sciences, Linköping, Sweden (A. Spreco, E. Istefan, T. Timpka); Center for Health Services Development, Region Östergötland, Linköping (A. Spreco, T. Timpka); Linköping University Department of Computer and Information Science, Linköping (O. Eriksson, T. Timpka); Linköping University Department of Behavioral Sciences and Learning, Linköping (Ö. Dahlström); World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, The University of Hong Kong School of Public Health, Hong Kong (B.J. Cowling); Centers for Disease Control and Prevention, Atlanta, Georgia, USA (M. Biggerstaff); Karolinska Institutet Department of Neurobiology, Care Sciences, and Society, Huddinge, Sweden (G. Ljunggren); Public Health Care Services Committee Administration, Region Stockholm, Stockholm, Sweden (G. Ljunggren); Lund University Faculty of Medicine, Department of Laboratory Medicine, Division of Occupational and Environmental Medicine, Lund, Sweden (A. Jöud); Lund University Faculty of Medicine, Clinical Sciences, Division of Orthopedics, Lund (A. Jöud); Scania University Hospital Department for Research and Development, Lund (A. Jöud)

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Table 1

Performance of the detection algorithm displayed with alert thresholds updated by using data from previous nonpandemic influenza seasons in evaluation of nowcasting for detection and prediction of local influenza epidemics, Sweden, 2008–2019

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.

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References
  1. 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;8:30916. DOIPubMed
  2. 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. DOIPubMed
  3. Viboud  C, Vespignani  A. The future of influenza forecasts. Proc Natl Acad Sci U S A. 2019;116:28024. DOIPubMed
  4. 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;116:314654. DOIPubMed
  5. 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
  6. 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. DOIPubMed
  7. 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;24:186873. DOIPubMed
  8. 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. DOIPubMed
  9. 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. DOIPubMed
  10. 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
  11. 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;9:23446. DOIPubMed
  12. 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. DOIPubMed
  13. 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;8:4402. DOIPubMed
  14. 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;87:30511. DOIPubMed
  15. 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. DOIPubMed
  16. Collins  GS, Moons  KGM. Reporting of artificial intelligence prediction models. Lancet. 2019;393:15779. DOIPubMed
  17. 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;8:2232. DOIPubMed
  18. 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. DOIPubMed

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