Algorithmic Approach to Determination of Epidemic Thresholds in Infectious Disease Surveillance Systems

Introduction: This review is devoted to the algorithmic approach to establishing epidemic thresholds for a wide range of diseases, including influenza and acute respiratory infections.

Objective: To compare Russian and foreign approaches to the determination of epidemic thresholds within public health surveillance systems.

Materials and methods: To reveal the algorithmic approach to establishing epidemic thresholds in the epidemiological surveillance system, we summarized the results of 14 foreign scientific works and two domestic method guidelines published before December 31, 2023. The literature search was conducted in the eLibrary, CyberLeninka, PubMed, and Google Scholar databases using the keywords “epidemic threshold” and “epidemic”. We compared domestic and foreign algorithms for establishing epidemic thresholds by various characteristics, including the statistical method used, determination of a numerical value of the epidemic threshold, complexity of the algorithm, and the possibility of automating calculations.

Results: Here we discuss the classification and comparative characteristics of the basic algorithms for determining epidemic thresholds used in various countries of the world when carrying out epidemiological surveillance, including the syndromic one. We describe the existing methods for establishing and presenting epidemic thresholds, as well as the sequence of steps for performing the Farrington algorithms, the Early Aberration Detection System C1–C3, the Method of Moving Epidemics, the Method of Moving Percentiles, Multi-level identification of increasing activity by indicators taking into account mixed effects, as well as algorithms provided in Russian Method Guidelines MR 3.1.2.0118–17 and MR 3.1.2.0303–22. We also dwell on the problems of development, accuracy assessment and prospects for the implementation of existing and developed algorithms.

Conclusions: Current algorithms for establishing epidemic thresholds in epidemiological surveillance systems around the world are diverse; they rely on different statistical methods and vary in complexity. To date, there is no convincing evidence of higher efficiency of any algorithm.

1. Langmuir AD. The surveillance of communicable diseases of national importance. N Engl J Med. 1963;268:182-192. doi: 10.1056/NEJM196301242680405

2. Declich S, Carter AO. Public health surveillance: Historical origins, methods and evaluation. Bull World Health Organ. 1994;72(2):285-304.

3. Farrington CP, Andrews N, Beale AD, Catchpole MA. A statistical algorithm for the early detection of outbreaks of infectious disease. J R Statist Soc A. 1996;159(3):547-563. doi: 10.2307/2983331

4. Porta M, ed. A Dictionary of Epidemiology. 5th ed. New York, NY: Oxford University Press; 2008. doi: 10.1093/acref/9780195314496.001.0001

5. 6 Шадрина Я.А., Мокшин А.В. Инновационный подход в управлении элементами здравоохранения при своевременном выявлении предэпидемической ситуации в регионе // Интерэкспо Гео-Сибирь. 2022. №1. С. 261–268.

6. Martin PM, Martin-Granel E. 2,500-year evolution of the term epidemic. Emerg Infect Dis. 2006;12(6):976-980. doi: 10.3201/eid1206.051263

7. Shmueli G, Burkom H. Statistical challenges facing early outbreak detection in biosurveillance. Technometrics. 2010;52(1):39-51. doi: 10.1198/TECH.2010.06134

8. Green MS, Swartz T, Mayshar E, et al. When is an epidemic an epidemic? Isr Med Assoc J. 2002;4(1):3-6.

9. UN Refugee Agency. A UNHCR guide to agile, effective and community-based humanitarian emergency responses. Accessed September 20, 2023. https://emergency.unhcr.org/

10. Unkel S, Farrington CP, Garthwaite PH, Robertson C, Andrews N. Statistical methods for the prospective detection of infectious disease outbreaks: A review. J R Statist Soc A. 2012;175(1):49–82. doi: 10.1111/j.1467-985X.2011.00714.x

11. Noufaily A, Enki DG, Farrington P, Garthwaite P, Andrews N, Charlett A. An improved algorithm for outbreak detection in multiple surveillance systems. Stat Med. 2013;32(7):1206-1222. doi: 10.1002/sim.5595

12. Yoneoka D, Kawashima T, Makiyama K, Tanoue Y, Nomura S, Eguchi A. Geographically weighted generalized Farrington algorithm for rapid outbreak detection over short data accumulation periods. Stat Med. 2021;40(28):6277–6294. doi: 10.1002/sim.9182

13. Hutwagner L, Thompson W, Seeman GM, Treadwell T. The bioterrorism preparedness and response Early Aberration Reporting System (EARS). J Urban Health. 2003;80(2 Suppl 1):i89-96. doi: 10.1007/pl00022319

14. Salmon M, Schumacher D, Höhle M. Monitoring count time series in R: Aberration detection in public health surveillance. J Stat Softw. 2016;70(10):1–35. doi: 10.18637/jss.v070.i10

15. Morbey RA, Elliot AJ, Charlett A, Verlander NQ, Andrews N, Smith GE. The application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method for syndromic surveillance in England. Bioinformatics. 2015;31(22):3660-3665. doi: 10.1093/bioinformatics/btv418

16. Lake IR, Colón-González FJ, Barker GC, Morbey RA, Smith GE, Elliot AJ. Machine learning to refine decision making within a syndromic surveillance service. BMC Public Health. 2019;19(1):559. doi: 10.1186/s12889-019-6916-9

17. Yang W, Li Z, Lan Y, et al. A nationwide web-based automated system for outbreak early detection and rapid response in China. Western Pac Surveill Response J. 2011;2(1):10-15. doi: 10.5365/WPSAR.2010.1.1.009

18. Vega T, Lozano JE, Meerhoff T, et al. Influenza surveillance in Europe: Establishing epidemic thresholds by the moving epidemic method. Influenza Other Respir Viruses. 2013;7(4):546-558. doi: 10.1111/j.1750-2659.2012.00422.x

19. Biggerstaff M, Kniss K, Jernigan DB, et al. Systematic assessment of multiple routine and near real-time indicators to classify the severity of influenza seasons and pandemics in the United States, 2003–2004 through 2015–2016. Am J Epidemiol. 2018;187(5):1040-1050. doi: 10.1093/aje/kwx334

20. Kang M, Tan X, Ye M, Liao Y, Song T, Tang S. The moving epidemic method applied to influenza surveillance in Guangdong, China. Int J Infect Dis. 2021;104:594-600. doi: 10.1016/j.ijid.2021.01.058

21. Teeluck M, Samura A. Assessing the appropriateness of the Moving Epidemic Method and WHO Average Curve Method for the syndromic surveillance of acute respiratory infection in Mauritius. PLoS ONE. 2021;16(6):e0252703. doi: 10.1371/journal.pone.0252703

22. Global Epidemiological Surveillance Standards for Influenza. World Health Organization; 2013. Accessed September 20, 2023. https://iris.who.int/bitstream/handle/10665/311268/9789241506601-eng.pdf

23. Karpova LS, Pelikh MYu, Volik KM, Popovtseva NM, Stolyarova TP, Lioznov DA. Evaluating the effectiveness of new criteria for early detection of the start and intensity of influenza epidemics in Russian Federation. Epidemiologiya i Vaktsinoprofilaktika. 2023;22(6):4-18 (In Russ.) doi: 10.31631/2073-3046-2023-22-6-4-18

24. Brookmeyer R, Stroup DF, eds. Monitoring the Health of Populations: Statistical Principles and Methods for Public Health Surveillance. New York: Oxford Academic; 2009. doi: 10.1093/acprof:oso/9780195146493.001.0001

25. Feldblium IV, Akimkin VG, Alimov AV, et al. New approaches to assessing and forecasting morbidity with enterovirus (non-polio) infection in the Russian Federation using mathematical models. Health Risk Analysis. 2021;(3):107-115. doi: 10.21668/health.risk/2021.3.10. eng

26. Mikhneva SA, Martinov YuV, Kukhtevich EV, Grishina YuYu. Infectious mononucleosis: A spatiotemporal manifestation of the epidemic process. Zdorov’e Naseleniya i Sreda Obitaniya. 2018;(10(307)):50-54. (In Russ.) doi: 10.35627/2219-5238/2018-307-10-50-54

27. Botvinkin AD, Kravchenko NA, Bayanova TA, Khakimova MI, Gavrilova TA, Likhanova NA. Calculation of epidemic thresholds for incidence of community-acquired pneumonia. Fundamental’naya i Klinicheskaya Meditsina. 2022;7(2):45-55. (In Russ.) doi: 10.23946/2500-0764-2022-7-2-45-55

28. Fricker RD Jr, Hegler BL, Dunfee DA. Comparing syndromic surveillance detection methods: EARS’ versus a CUSUM-based methodology. Stat Med. 2008;27(17):3407-3429. doi: 10.1002/sim.3197

29. 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

30. Choi BY, Kim H, Go UY, Jeong JH, Lee JW. Comparison of various statistical methods for detecting disease outbreaks. Comput Stat. 2010;25(4):603–617. doi: 10.1007/s00180-010-0191-7