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Summary A simulation study to evaluate the performance of five statistical monitoring methods when applied to different time-series components in the context of control programs for endemic diseases$7.49
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Summary A simulation study to evaluate the performance of five statistical monitoring methods when applied to different time-series components in the context of control programs for endemic diseases
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Course
He (PSYC.101)
Institution
University Of Texas Medical School Houston
Book
Mathematical and Statistical Methods for Actuarial Sciences and Finance
A simulation study to evaluate the performance of five statistical monitoring methods when applied to different time-series components in the context of control programs for endemic diseases
A simulation study to evaluate the performance of five statistical monitoring methods
when applied to different time-series components in the context of control programs
for endemic diseases
Lopes Antunes, Ana Carolina; Jensen, Dan; Hisham Beshara Halasa, Tariq; Toft, Nils
Published in:
P L o S One
Link to article, DOI:
10.1371/journal.pone.0173099
Publication date:
2017
Document Version
Publisher's PDF, also known as Version of record
Link back to DTU Orbit
Citation (APA):
Lopes Antunes, A. C., Jensen, D., Hisham Beshara Halasa, T., & Toft, N. (2017). A simulation study to evaluate
the performance of five statistical monitoring methods when applied to different time-series components in the
context of control programs for endemic diseases. P L o S One, 12(3), [e0173099].
https://doi.org/10.1371/journal.pone.0173099
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, RESEARCH ARTICLE
A simulation study to evaluate the
performance of five statistical monitoring
methods when applied to different time-
series components in the context of control
programs for endemic diseases
Ana Carolina Lopes Antunes1*, Dan Jensen2, Tariq Halasa1, Nils Toft1
a1111111111
a1111111111 1 Division for Diagnostics and Scientific Advice—Epidemiology, National Veterinary Institute–DTU,
a1111111111 Bülowsvej 27, Frederiksberg C, Denmark, 2 Section for Production and Health, Department of Large Animal
a1111111111 Science–KU, Grønnegårdsvej 8, Frederiksberg C, Denmark
a1111111111
* aclan@vet.dtu.dk
Competing interests: The authors have declared
that no competing interests exist.
PLOS ONE | DOI:10.1371/journal.pone.0173099 March 6, 2017
, Statistical methods for monitoring endemic diseases and control programs
Introduction
Surveillance and monitoring systems are critical for the timely and effective detection of
changes in disease status. Over the last decade, several studies have applied different statistical
monitoring methods for detecting outbreaks of (re-)emerging diseases in the context of syn-
dromic surveillance in both human and veterinary medicine [1–3]. Different types of models
(such as linear models, logistic regression and time-series models) have been implemented in
the context of syndromic surveillance in order to evaluate the performance and implementa-
tion of these methods [4].
However, it may not be possible to make generalizations about the performance of these
methods when used for monitoring endemic diseases and control programs. In this case, the
availability of control measures (such as vaccination or health-management programs) results
in lower incidence rates for endemic diseases than for (re)-emerging diseases. The dynamics of
disease spread and immunity within a population from previous exposure also contribute to a
lower incidence, resulting in slow and gradual changes in incidence and prevalence for endemic
diseases [5]. It is important to follow-up on implemented control strategies in order to reduce
and/or eliminate a specific disease [6]. Unexpected changes (such as an increase in disease prev-
alence or a failure to achieve a target value of disease prevalence within a certain period of time)
indicate that the implemented strategies should be revised. When a control program fails to
achieve certain goals, it can have a devastating impact on herds with susceptible animals.
In previous work, we assessed the performance of univariate process control algorithms
(UPCA) in monitoring changes in the burden of endemic diseases based on sentinel surveil-
lance [7]. However, these methods were not tested in the context of voluntary disease control
and monitoring programs. In such cases, the frequency of testing depends on the monetary
value of the animal and not just on the impact of the disease [6]. Programs for monitoring
endemic diseases include the Danish Porcine Reproductive and Respiratory Syndrome Virus
(PRRSV) monitoring program. Despite disease-control efforts, PRRSV has contributed to eco-
nomic losses since its first diagnosis in 1992 [8]. Monitoring of PRRSV is primarily based on
serological testing within the Specific Pathogen Free System (SPF System) [9]. The frequency
of testing depends upon the health status of the herd within this system. As a consequence, the
number of samples is not constant and it is necessary to use methods with a more dynamic
structure, allowing the parameters to change over time, thus taking into account the variation
in sample size. Previous studies have also discussed the influence of variation in the number of
samples (i.e. the noise present in data) on the performance of different monitoring methods
[7,10].
State-space models have a flexible structure, allowing parameters to be updated for each
time step [11]. In addition, they can be decomposed, and changes in the components (such as
trends and seasonal patterns) can be monitored for inference [12]. While state-space models
have been used to monitor influenza in humans [13–15] as well as and for herd-management
decisions [16–19], it has not yet been determined how useful these techniques are for monitor-
ing endemic diseases.
The objectives of this study were to assess the performance of different statistical monitor-
ing methods for endemic disease control programs, and to explore what impact of variation
(noise) in the data had on the performance of these statistical monitoring methods. The simu-
lation study was motivated by the Danish PRRSV monitoring program.
Two state-space models were chosen for this study based on their ability to monitor changes
in different time-series components [11]. Five different statistical monitoring methods were
evaluated for each model: three UPCA used in process-control monitoring [20], and two meth-
ods for monitoring changes based on the trend component of the time series.
PLOS ONE | DOI:10.1371/journal.pone.0173099 March 6, 2017
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