Hospital Practices and Research

The Performance of Some Outbreak Detection Algorithms: Using the Reported COVID-19 cases in Iran

Document Type : Original Article

Authors

Mojtaba Sepandi
Yousef Alimohamadi
Mousa Imani

Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran

https://doi.org/10.30491/hpr.2023.182070
Abstract
Background: Outbreak detection algorithms could play a key role in public health surveillance.
Objectives: This study aimed to compare the performance of three algorithms (EWMA, Cumulative Sum (CUSUM), and Poisson Regression) using the reported COVID-19 data for outbreak detection.
Methods: Three outbreak detection algorithms were applied to the data of COVID-19 daily new cases in Iran between 19/02/2020 and 20/06/2022, and 344 simulated outbreak days were injected into the data sequences. The Area Under the Receiver Operating Characteristics (ROC) Curve (AUC) and its 95% confidence intervals (95% CI) were also computed.
Results: EWMA9 had the lowest AUC (51%). Among the different algorithms, EWMA9 with λ = 0.9 and CUSUM 1 had the highest sensitivity with 100 and 87% (95% CI: 84%-91%), respectively.
Conclusion: According to the results, CUSUM, EWMA, and poison regression showed appropriate performance in detecting the COVID-19 outbreaks. These algorithms can be extremely helpful for health practitioners and policymakers in the detection of infectious disease outbreaks.

Keywords

Outbreak
Epidemic
Mathematics Concept
COVID-19
Iran
  1. Paakkari L, Okan O. COVID-19: health literacy is an underestimated problem. Lancet Public Health. 2020;5(5):e249-50. doi:10.1016/S2468-2667(20)30086-4
  2. [08 April 2020]; Available from: https://www.worldometers.info/coronavirus/#countries
  3. Houlihan CF, Whitworth JA. Outbreak science: recent progress in the detection and response to outbreaks of infectious diseases. Clin Med. 2019;19(2):140-4. doi:78 61/clinmedicine.19-2-140
  4. Karami M. Validity of evaluation approaches for outbreak detection methods in syndromic surveillance systems. Iranian J Public Health. 2012;41(11):102-3.
  5. Watkins RE, Eagleson S, Hall RG, Dailey L, Plant AJ. Approaches to the evaluation of outbreak detection methods. BMC Public Health. 2006;6(1):263. doi:10.1186/1471-2458-6-263
  6. Alimohamadi Y, Zahraei SM, Karami M, Yaseri M, Lotfizad M, Holakouie-Naieni K. Alarm thresholds for pertussis outbreaks in Iran: National data analysis. Osong Public Health Res Perspect. 2020;11(5):309-18. doi:24171/ j.phrp.2020.11.5.07
  7. Shu L, Su Y, Jiang W, Tsui KL. A comparison of exponentially weighted moving average-based methods for monitoring increases in incidence rate with varying population size. IIE Trans. 2014;46(8):798-812. doi:10.1080/0740817X.2014.89 4805
  8. Solgi M, Karami M, Poorolajal J. Timely detection of influenza outbreaks in Iran: Evaluating the performance of the exponentially weighted moving average. J Infect Public Health. 2018;11(3):389-92. doi:10.1016/j.jiph.2017.09.011
  9. Lucas JM, Saccucci MS. Exponentially weighted moving average control schemes: properties and enhancements. Technometrics. 1990;32(1):1-12. doi:10.1080/00401706. 1990.10484583
  10. Karami M, Ghalandari M, Poorolajal J, Faradmal J. Early detection of meningitis outbreaks: Application of limited-baseline data. Iranian J Public Health. 2017;46(10):1366-73.
  11. Alimohamadi Y, Mehri A, Janani M, Sepandi M. Aberration detection in influenza trends in Iran by using cumulative sum chart and period regression. J Taibah Univ Medical Sci. 2020;15(6):529-35. doi:10.1016/j.jtumed.2020.09.002
  12. Bédubourg G, Le Strat Y. Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study. PloS One. 2017;12(7):e0181227. doi:10.1371/journal.pone.0181227
  13. Dassanayake SK, French J. An Algorithm for Early Outbreak Detection in Multiple Data Streams. Online J Public Health Inform. 2019;11(1):e226. doi:5210/ojphi.v11i1.9942
  14. Karami M, Soori H, Mehrabi Y, Haghdoost AA, Gouya MM, Esmailnasab N. Evaluating the performance of an outbreak-detection algorithms using semi-synthetic approach: cumulative sum algorithm. Iran J Epidemiol. 2013;9(2):29-38.
  15. Karami M, Soori H, Mehrabi Y, Haghdoost AA, Gouya MM. Real time detection of a measles outbreak using the exponentially weighted moving average: does it work?. J Res Health Sci. 2012;12(1):25-30.
  16. Bowen JR, Callander I, Richards R, Lindrea KB. Decreasing infection in neonatal intensive care units through quality improvement. Arch Dis Child Fetal Neonatal Ed. 2017;102 (1):F51-7. doi:10.1136/archdischild-2015-310165
  17. Ivanov O, Gesteland PH, Hogan W, Mundorff MB, Wagner MM. Detection of pediatric respiratory and gastrointestinal outbreaks from free-text chief complaints. AMIA Annu Symp Proc. 2003;2003:318-22.
  18. Steiner SH, Grant K, Coory M, Kelly HA. Detecting the start of an influenza outbreak using exponentially weighted moving average charts. BMC Med Inform Decis Mak. 2010;10(1):37. doi:10.1186/1472-6947-10-37
  19. Spreco A. Epidemiological and statistical basis for detection and prediction of influenza epidemics. Doctoral dissertation, Link ping University Electronic Press; 2017.
  20. Texier G, Allodji RS, Diop L, Meynard JB, Pellegrin L, Chaudet H. Using decision fusion methods to improve outbreak detection in disease surveillance. BMC Med Inform Decis Mak. 2019;19(1):38. doi:10.1186/s12911-019-0774-3
  21. Dawson P, Gailis R, Meehan A. Detecting disease outbreaks using a combined Bayesian network and particle filter approach. J Theor Biol. 2015;370:171-83. doi:10.1016/j.jtbi.2015.01.023
  22. Faryadres M, Karami M, MOGHIM BA, Esmailnasab N, Pazhouhi K. Levels of alarm thresholds of meningitis outbreaks in Hamadan Province, west of Iran. J Res Health Sci. 2015;15(1):62-5.
  23. Souty C, Jreich R, Le Strat Y, Pelat C, Bo lle PY, Guerrisi C, et al. Performances of statistical methods for the detection of seasonal influenza epidemics using a consensus-based gold standard. Epidemiol Infect. 2018;146(2):168-76. doi:10.1017/S095026881700276X
  24. Toubiana L, Flahault A. A space-time criterion for early detection of epidemics of influenza-like-illness. Eur J Epidemiol. 1998;14:465-70. doi:10.1023/A:1007481929237
Hospital Practices and Research
Volume 8, Issue 1 - Serial Number 29
Winter 2023
Pages 212-216

Home

Guide for Authors

Submit Manuscript

Reviewers

Contact Us