Estimating the baseline incidence of a seasonal disease independently of epidemic outbreaks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75010330%3A_____%2F16%3A00011468" target="_blank" >RIV/75010330:_____/16:00011468 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11120/16:43911776

Výsledek na webu

<a href="http://apps.szu.cz/svi/cejph/show_en.php?kat=archiv/2016-3-06" target="_blank" >http://apps.szu.cz/svi/cejph/show_en.php?kat=archiv/2016-3-06</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21101/cejph.a4800" target="_blank" >10.21101/cejph.a4800</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Estimating the baseline incidence of a seasonal disease independently of epidemic outbreaks

Popis výsledku v původním jazyce

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 Serffing'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.

Název v anglickém jazyce

Estimating the baseline incidence of a seasonal disease independently of epidemic outbreaks

Popis výsledku anglicky

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 Serffing'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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

FN - Epidemiologie, infekční nemoci a klinická imunologie

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Central European Journal of Public Health

ISSN

1210-7778

e-ISSN

—

Svazek periodika

24

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

7

Strana od-do

199-205

Kód UT WoS článku

000388551200006

EID výsledku v databázi Scopus

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