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Estimating the Baseline Incidence of a Seasonal Disease Independently of Epidemic Outbreaks.

In epidemiology, it is very important to estimate the baseline incidence of infectious diseases, but the available data are often subject to outliers due to epidemic outbreaks. Consequently, the estimate of the baseline incidence is biased and so is the predicted epidemic threshold which is a crucia...

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Published in:Central European Journal of Public Health Vol. 24; no. 3; pp. 199 - 206
Main Authors: Procházka, Bohumír, Kynčl, Jan
Format: Journal Article
Published: Central European Journal of Public Health Sep2016
Online Access:View this record in EBSCOhost
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        12107778
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      jtl: Central European Journal of Public Health
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      dt: Sep2016
      vid: 24
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      pub: Central European Journal of Public Health
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        10.21101/cejph.a4800
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        atl: Estimating the Baseline Incidence of a Seasonal Disease Independently of Epidemic Outbreaks.
      aug:
        au:
          Procházka, Bohumír
          Kynčl, Jan
        affil: Unit for Biostatistics, National Institute of Public Health, Prague, Czech Republic
      sug:
        subj:
          Disease Outbreaks
          Models, Statistical
          Communicable Diseases Epidemiology
          Seasons
          Bias (Research)
          Incidence
          Clinical Assessment Tools
      ab: In epidemiology, it is very important to estimate the baseline incidence of infectious diseases, but the available data are often subject to outliers due to epidemic outbreaks. Consequently, the estimate of the baseline incidence is biased and so is the predicted epidemic threshold which is a crucial reference indicator used to suspect and detect an epidemic outbreak. Another problem is that the "usual" incidence varies in a season dependent manner, i.e. it may not be constant throughout the year, is often periodic, and may also show a trend between years. To take account of these factors, more complicated models adjusted for outliers are used. If not adjusted for outliers, the baseline incidence estimate is biased. As a result, the epidemic threshold can be overestimated and thus can make the detection of an epidemic outbreak more difficult. Classical Serfling's model is based on the sine function with a phase shift and amplitude. Multiple approaches are applied to model the long-term and seasonal trends. Nevertheless, none of them controls for the effect of epidemic outbreaks. The present article deals with the adjustment of the data biased by epidemic outbreaks. Some models adjusted for outliers, i.e. for the effect of epidemic outbreaks, are presented. A possible option is to remove the epidemic weeks from the analysis, but consequently, in some calendar weeks, data will only be available for a small number of years. Furthermore, the detection of an epidemic outbreak by experts (epidemiologists and microbiologists) will be compared with that in various models.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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