Evaluation the Performance of Exponentially Weighted Moving Average in the Detection of Cholera Outbreaks: Using the Reported Cholera Outbreaks in Literature
Abstract
Introduction: Timely Detection of outbreaks of infectious diseases can have a very important role in
surveillance systems. the presence of appropriate methods can have a very important role for this purpose, the aim of the current study was to Evaluation The Performance of Exponentially Weighted Moving Average in the detection of cholera outbreaks using the reported cholera outbreaks in literature.
Methods: In the current study the EWMA method was evaluated. To assess the performance of the mentioned methods the six real outbreaks algorithm reported in the literature were used. These reported outbreaks were the daily counts of cholera cases in different countries. After insertion of each outbreak, 7 days inserted as nonoutbreaks days. All analyses performed by MedCalc18.11, Stata version15 and excel 2010.
Results: the sensitivity of EWMA was 56.4% (95% CI: 54.3%- 58.5%). The highest sensitivity for outbreak detection was seen in EWMA1 79.18(73.56-84.09) and the lowest was seen in EWMA4 12.2(8.4-17.0). EWMA2 with λ= 0.2 had the best performance with sensitivity 69.8 (63.6-75.5) and specificity 91.4(76.9-98.2) and AUC= 0.80.
Conclusion: The EWMA method can be very useful in the detection of outbreaks, but the use of this method along the other models may increase the sensitivity of outbreaks detection.
2.Islam MT, Khan AI, Sayeed MA, Amin J, Islam K, Alam N, et al. Field evaluation of a locally produced rapid diagnostic test for early detection of cholera in Bangladesh. PLoS neglected tropical diseases 2019;13(1):e0007124.
3.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.
4.Shu L, Su Y, Jiang W, Tsui K-L. A comparison of exponentially weighted moving average-based methods for monitoring increases in incidence rate with varying population size. IIE Transactions 2014;46(8):798-812.
5.Solgi M, Karami M, Poorolajal J. Timely detection of influenza outbreaks in Iran: Evaluating the performance of the exponentially weighted moving average. Journal of infection and public health 2018;11(3):389-392.
6.Karami M. Validity of evaluation approaches for outbreak detection methods in syndromic surveillance systems. Iranian journal of public health 2012;41(11):102-103.
7.Dan-Nwafor CC, Ogbonna U, Onyiah P, Gidado S, Adebobola B, Nguku P, et al. A cholera outbreak in a rural north central Nigerian community: an unmatched case-control study. BMC public health 2019;19(1):112.
8.Oguttu DW, Okullo A, Bwire G, Nsubuga P, Ario A. Cholera outbreak caused by drinking lake water contaminated with human faeces in Kaiso Village, Hoima District, Western Uganda, October 2015. Infectious diseases of poverty 2017;6(1):146.
9.Gidado S, Awosanya E, Haladu S, Ayanleke HB, Idris S, Mamuda I, et al. Cholera outbreak in a naïve rural community in Northern Nigeria: the importance of hand washing with soap, September 2010. The Pan African Medical Journal 2018;30.
10.Pande G, Kwesiga B, Bwire G, Kalyebi P, Riolexus A, Matovu JK, et al. Cholera outbreak caused by drinking contaminated water from a lakeshore water-collection site, Kasese District, south-western Uganda, June-July 2015. PloS one 2018;13(6):e0198431.
11.Moradi G, Rasouli MA, Mohammadi P, Elahi E, Barati H. A cholera outbreak in Alborz Province, Iran: a matched case-control study. Epidemiology and health 2016;38.
12.Karami M. Maosumi Asl H, Mohammadin M, Rae-ofi H, Saghafipour A, Noroozi M, Khedmati E. Qom cholera outbreak in 2011: influential and determinant factors. IJE 2012;8(3):84-92.
13.Lucas JM, Saccucci MS. Exponentially weighted moving average control schemes: properties and enhancements. Technometrics 1990;32(1):1-12.
14.Faryadres M, Karami M, Moghimbeigi A, 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.
15.Moscarelli M, Athanasiou T, Sevdalis N, Vescovi F, Fattouch K, Nasso G, et al. Controlled Exponentially Weighted Moving Average Chart in Cardiac Surgery: A Simulation Study Across 9 Italian Cardiac Centers. Semin Thorac Cardiovasc Surg 2016;28(2):253-258.
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-F57.
17.Silva GS, Schwartz M, Morrison RB, Linhares DCL. Monitoring breeding herd production data to detect PRRSV outbreaks. Prev Vet Med 2017;148:89-93.
18.Steiner SH, Grant K, Coory M, Kelly HA. Detecting the start of an influenza outbreak using
ponentially weighted moving average charts. BMC Med Inform Decis Mak 2010;10:37.
19.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.
20.Texier G, Alldoji RS, Diop L, Meynard J-B, Pellegrin L, Chaudet H. Using decision fusion methods to improve outbreak detection in disease surveillance. BMC medical informatics and decision making 2019;19(1):38.
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.
| Files | ||
| Issue | Vol 5 No 3 (2019) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/jbe.v5i3.3617 | |
| Keywords | ||
| EWMA; Sensitivity; Specificity; Outbreak | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
