Surveillance of Community Outbreaks of Respiratory Tract Infections Based on House-Call Visits in the Metropolitan Area of Athens, Greece

Abstract

BackgroundThe traditional Serfling-type approach for influenza-like illness surveillance requires long historical time-series. We retrospectively evaluated the use of recent, short, historical time-series for recognizing the onset of community outbreaks of respiratory tract infections (RTIs).MethodsThe data used referred to the proportion of diagnoses for upper or lower RTIs to total diagnoses for house-call visits, performed by a private network of medical specialists (SOS Doctors) in the metropolitan area of Athens, Greece, between January 01, 2000 and October 12, 2008. The reference standard classification of the observations was obtained by generating epidemic thresholds after analyzing the full 9-year period. We evaluated two different alert generating methods [simple regression and cumulative sum (CUSUM), respectively], under a range of input parameters, using data for the previous running 4–6 week period. These methods were applied if the previous weeks contained non-aberrant observations.ResultsWe found that the CUSUM model with a specific set of parameters performed marginally better than simple regression for both groups. The best results (sensitivity, specificity) for simple regression and CUSUM models for upper RTIs were (1.00, 0.82) and (0.94, 0.93) respectively. Corresponding results for lower RTIs were (1.00, 0.80) and (0.93, 0.91) respectively.ConclusionsShort-term data for house-call visits can be used rather reliably to identify respiratory tract outbreaks in the community using simple regression and CUSUM methods. Such surveillance models could be particularly useful when a large historical database is either unavailable or inaccurate and, thus, traditional methods are not optimal.

Mathematical modeling of infectious disease dynamics

“…Statistical process control methods including cumulative sum (CUSUM) charts [36][37][38][39][40][41] and exponentially weighted moving average (EWMA) 42,43 -based methods. CUSUM is probably the most common used technique for the detection of disease outbreaks.…”

SBDiEM: A new mathematical model of infectious disease dynamics

“…The prevailing taxonomy proposed by Siettos and Russo [58] encompasses three general categories: (1) statistical methods of outbreaks and their identification of spatial patterns in real epidemics, (2) state-space models of the evolution of a "hypothetical" or on-going epidemic spread, and (3) machine learning methods, all utilized also for predictability purposes vis-à-vis an ongoing epidemic. In particular, the first category includes i) regression methods [59][60][61][62][63][64] , ii) times series analysis, namely ARIMA and seasonal ARIMA approaches [65][66][67][68] , iii) process control methods including cumulative sum (CUSUM) charts [69][70][71][72][73][74] and exponentially weighted moving average (EWMA) methods [75,76] , as well as iv) Hidden Markov models (HMM) [77,78] . The second category incorporates i) "continuum" models in the form of differential and/or (integro)-partial differential equations [79][80][81][82] , ii) discrete and continuous-time Markov-chain models [83][84][85] , iii) complex network models which relax the hypotheses of the previous stochastic models that interactions among individuals are instantaneous and homogeneous [86][87][88][89][90][91] , and iv) Agent-based models [92][93][94][95] .…”

Enhancing Public Health Surveillance: Outbreak Detection Algorithms Deployed for Syndromic Surveillance During Arbaeenia Mass Gatherings in Iraq

“…Control charts are quicker, which is one of the known algorithms for the early detection of outbreaks that can identify slight changes in the number of syndromes [ 16 ]. CUSUM is under the umbrella of statistical process control-based methods [ 17 ], and it is used especially when historical data are not available [ 18 ]. The CUSUM procedure has become a standard tool for process control and is the recommended method for the timely detection of small step changes.…”