A Modification of the Sampling Series with a Gaussian Multiplier

2006 ◽  
Vol 5 (1) ◽  
pp. 2-20
Author(s):  
Qian Liwen ◽  
Dennis B. Creamer
Keyword(s):  
Sampling Series

scholarly journals Translating surveillance data into incidence estimates

2018 ◽  
Author(s):  
Yoann Bourhis ◽  
Timothy R. Gottwald ◽  
Frank van den Bosch
Keyword(s):  
Disease Incidence ◽  
Surveillance Data ◽  
Estimation Methods ◽  
Full Model ◽  
Estimation Model ◽  
Sampling Series ◽  
Wide Range ◽  
Incidence Estimation

AbstractMonitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which to estimate the disease incidence,i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence is not changing between monitoring rounds, resulting in underestimation of the disease incidence. In this paper we develop an incidence estimation model accounting for epidemic growth with monitoring rounds sampling varying incidence. We also show how to accommodate the asymptomatic period characteristic to most diseases. For practical use, we produce an approximation of the model, which is subsequently shown accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations.

scholarly journals A bivariate sampling series involving mixed partial derivatives

2017 ◽  
Vol 41 ◽  
pp. 387-403 ◽  
Author(s):  
Rashad Mudhish ASHARABI ◽  
Hamoud AL-HADDAD
Keyword(s):  
Partial Derivatives ◽  
Sampling Series

scholarly journals Translating surveillance data into incidence estimates

2019 ◽  
Vol 374 (1776) ◽  
pp. 20180262 ◽  
Author(s):  
Y. Bourhis ◽  
T. Gottwald ◽  
F. van den Bosch
Keyword(s):  
Infectious Disease ◽  
Disease Incidence ◽  
Disease Outbreaks ◽  
Estimation Methods ◽  
Estimation Model ◽  
Theme Issue ◽  
Online Application ◽  
Infectious Disease Outbreaks ◽  
Sampling Series ◽  
Wide Range

Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which we may estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence does not change between monitoring rounds, resulting in an underestimation of the disease incidence. In this paper, we develop an incidence estimation model accounting for epidemic growth with monitoring rounds that sample varying incidence. We also show how to accommodate the asymptomatic period that is the characteristic of most diseases. For practical use, we produce an approximation of the model, which is subsequently shown to be accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations. The estimation model is made available as an online application. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

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