Estimating the baseline and threshold for the incidence of diseases with seasonal and long-term trends

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75010330%3A_____%2F15%3A00011084" target="_blank" >RIV/75010330:_____/15:00011084 - isvavai.cz</a>

Výsledek na webu

<a href="http://apps.szu.cz/svi/cejph/show_en.php?kat=archiv/2015-4-12" target="_blank" >http://apps.szu.cz/svi/cejph/show_en.php?kat=archiv/2015-4-12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21101/cejph.a4392" target="_blank" >10.21101/cejph.a4392</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Estimating the baseline and threshold for the incidence of diseases with seasonal and long-term trends

Popis výsledku v původním jazyce

In epidemiology, it is very important to estimate the baseline incidence of infectious diseases. From this baseline, the epidemic threshold can be derived as a clue to recognize an excess incidence, i.e. to detect an epidemic by mathematical methods. Nevertheless, a problem is posed by the fact that the incidence may vary during the year, as a rule, in a season dependent manner. To model the incidence of a disease, some authors use seasonal trend models. For instance, Serfling applies the sine function with a phase shift and amplitude. A similar model based on the analysis of variance with kernel smoothing and Serflingĺs higher order models, i.e. models composed of multiple sine-cosine function pairs with a variably long period, will be presented below. Serflingĺs model uses a long-term linear trend, but the linearity may not be always acceptable. Therefore, a more complex, long-term trend estimation will also be addressed, using different smoothing methods. In addition, the issue of the time unit (mostly a week) used in describing the incidence is discussed.

Název v anglickém jazyce

Estimating the baseline and threshold for the incidence of diseases with seasonal and long-term trends

Popis výsledku anglicky

In epidemiology, it is very important to estimate the baseline incidence of infectious diseases. From this baseline, the epidemic threshold can be derived as a clue to recognize an excess incidence, i.e. to detect an epidemic by mathematical methods. Nevertheless, a problem is posed by the fact that the incidence may vary during the year, as a rule, in a season dependent manner. To model the incidence of a disease, some authors use seasonal trend models. For instance, Serfling applies the sine function with a phase shift and amplitude. A similar model based on the analysis of variance with kernel smoothing and Serflingĺs higher order models, i.e. models composed of multiple sine-cosine function pairs with a variably long period, will be presented below. Serflingĺs model uses a long-term linear trend, but the linearity may not be always acceptable. Therefore, a more complex, long-term trend estimation will also be addressed, using different smoothing methods. In addition, the issue of the time unit (mostly a week) used in describing the incidence is discussed.

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

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

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

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Svazek periodika

23

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

8

Strana od-do

352-359

Kód UT WoS článku

000370311200012

EID výsledku v databázi Scopus

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