Abstract
BackgroundInfluenza is a well known and common human respiratory infection, causing significant morbidity and mortality every year. Despite Influenza variability, fast and reliable outbreak detection is required for health resource planning. Clinical health records, as published by the Diagnosticat database in Catalonia, host useful data for probabilistic detection of influenza outbreaks.MethodsThis paper proposes a statistical method to detect influenza epidemic activity. Non-epidemic incidence rates are modeled against the exponential distribution, and the maximum likelihood estimate for the decaying factor λ is calculated. The sequential detection algorithm updates the parameter as new data becomes available. Binary epidemic detection of weekly incidence rates is assessed by Kolmogorov-Smirnov test on the absolute difference between the empirical and the cumulative density function of the estimated exponential distribution with significance level 0 ≤ α ≤ 1.ResultsThe main advantage with respect to other approaches is the adoption of a statistically meaningful test, which provides an indicator of epidemic activity with an associated probability. The detection algorithm was initiated with parameter λ0 = 3.8617 estimated from the training sequence (corresponding to non-epidemic incidence rates of the 2008-2009 influenza season) and sequentially updated. Kolmogorov-Smirnov test detected the following weeks as epidemic for each influenza season: 50−10 (2008-2009 season), 38−50 (2009-2010 season), weeks 50−9 (2010-2011 season) and weeks 3 to 12 for the current 2011-2012 season.ConclusionsReal medical data was used to assess the validity of the approach, as well as to construct a realistic statistical model of weekly influenza incidence rates in non-epidemic periods. For the tested data, the results confirmed the ability of the algorithm to detect the start and the end of epidemic periods. In general, the proposed test could be applied to other data sets to quickly detect influenza outbreaks. The sequential structure of the test makes it suitable for implementation in many platforms at a low computational cost without requiring to store large data sets.
Highlights
Influenza is a well known and common human respiratory infection, causing significant morbidity and mortality every year
Diagnosticat is introduced we provide insights on the statistical distribution of influenza incidence rates, as well as how the relevant parameters can be estimated from the observations
We used the open database described earlier to test the detector. We validated in this experiment the proposed Maximum Likelihood (ML) data fitting that characterizes non-epidemic cases as exponentially distributed
Summary
Influenza is a well known and common human respiratory infection, causing significant morbidity and mortality every year. As published by the Diagnosticat database in Catalonia, host useful data for probabilistic detection of influenza outbreaks. Influenza is a well known and common human respiratory infection. It is responsible of significant morbidity and mortality every year. In Catalonia, primary care doctors have been routinely registering their activity in eCAP (an electronic health recording system) since 2006. This accounts for over 3,500 physicians collecting data of nearly 6 million people (80% of the population) [2,3]. SISAP publishes weekly information about all cases of those infectious diseases, available on Diagnosticat (http://4.sisap. cat/diagnosticat)
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