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

Disease monitoring and surveillance play a crucial role in control and eradication programs, as it is important to track implemented strategies in order to reduce and/or eliminate a specific disease. The objectives of this study were to assess the performance of different statistical monitoring meth...

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Published in:PloS one 2017-03, Vol.12 (3), p.e0173099-e0173099
Main Authors: Lopes Antunes, Ana Carolina, Jensen, Dan, Halasa, Tariq, Toft, Nils
Format: Article
Language:English
Subjects:
Algorithms
Animal diseases
Animals
Binomial distribution
Biology and Life Sciences
Computer and Information Sciences
Computer Simulation
Confidence intervals
Consecutive events
Control
Control algorithms
Control charts
Control methods
Control programs
Databases, Factual
Disease control
Endemic diseases
Endemic Diseases - veterinary
Engineering and Technology
Epidemics
Epidemiology
Handbooks
Hogs
Influenza
Kalman filters
Laboratories
Medicine and Health Sciences
Methods
Models, Statistical
Monitoring
Monitoring methods
Monitoring systems
Noise
Physical Sciences
Process control
Reproducibility of Results
Research and Analysis Methods
Science
Sentinel surveillance
Statistical analysis
Statistical methods
Statistics
Studies
Sums
Surveillance
Surveillance systems
Swine
Swine Diseases - epidemiology
Time series
Veterinary medicine
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Jensen, Dan
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description Disease monitoring and surveillance play a crucial role in control and eradication programs, as it is important to track implemented strategies in order to reduce and/or eliminate a specific disease. The objectives of this study were to assess the performance of different statistical monitoring methods for endemic disease control program scenarios, and to explore what impact of variation (noise) in the data had on the performance of these monitoring methods. We simulated 16 different scenarios of changes in weekly sero-prevalence. The changes included different combinations of increases, decreases and constant sero-prevalence levels (referred as events). Two space-state models were used to model the time series, and different statistical monitoring methods (such as univariate process control algorithms-Shewart Control Chart, Tabular Cumulative Sums, and the V-mask- and monitoring of the trend component-based on 99% confidence intervals and the trend sign) were tested. Performance was evaluated based on the number of iterations in which an alarm was raised for a given week after the changes were introduced. Results revealed that the Shewhart Control Chart was better at detecting increases over decreases in sero-prevalence, whereas the opposite was observed for the Tabular Cumulative Sums. The trend-based methods detected the first event well, but performance was poorer when adapting to several consecutive events. The V-Mask method seemed to perform most consistently, and the impact of noise in the baseline was greater for the Shewhart Control Chart and Tabular Cumulative Sums than for the V-Mask and trend-based methods. The performance of the different statistical monitoring methods varied when monitoring increases and decreases in disease sero-prevalence. Combining two of more methods might improve the potential scope of surveillance systems, allowing them to fulfill different objectives due to their complementary advantages.
doi_str_mv 10.1371/journal.pone.0173099
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The objectives of this study were to assess the performance of different statistical monitoring methods for endemic disease control program scenarios, and to explore what impact of variation (noise) in the data had on the performance of these monitoring methods. We simulated 16 different scenarios of changes in weekly sero-prevalence. The changes included different combinations of increases, decreases and constant sero-prevalence levels (referred as events). Two space-state models were used to model the time series, and different statistical monitoring methods (such as univariate process control algorithms-Shewart Control Chart, Tabular Cumulative Sums, and the V-mask- and monitoring of the trend component-based on 99% confidence intervals and the trend sign) were tested. Performance was evaluated based on the number of iterations in which an alarm was raised for a given week after the changes were introduced. Results revealed that the Shewhart Control Chart was better at detecting increases over decreases in sero-prevalence, whereas the opposite was observed for the Tabular Cumulative Sums. The trend-based methods detected the first event well, but performance was poorer when adapting to several consecutive events. The V-Mask method seemed to perform most consistently, and the impact of noise in the baseline was greater for the Shewhart Control Chart and Tabular Cumulative Sums than for the V-Mask and trend-based methods. The performance of the different statistical monitoring methods varied when monitoring increases and decreases in disease sero-prevalence. Combining two of more methods might improve the potential scope of surveillance systems, allowing them to fulfill different objectives due to their complementary advantages.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28264002</pmid><doi>10.1371/journal.pone.0173099</doi><tpages>e0173099</tpages><orcidid>https://orcid.org/0000-0001-8142-8241</orcidid><oa>free_for_read</oa></addata></record>
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1932-6203
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subjects Algorithms
Animal diseases
Animals
Binomial distribution
Biology and Life Sciences
Computer and Information Sciences
Computer Simulation
Confidence intervals
Consecutive events
Control
Control algorithms
Control charts
Control methods
Control programs
Databases, Factual
Disease control
Endemic diseases
Endemic Diseases - veterinary
Engineering and Technology
Epidemics
Epidemiology
Handbooks
Hogs
Influenza
Kalman filters
Laboratories
Medicine and Health Sciences
Methods
Models, Statistical
Monitoring
Monitoring methods
Monitoring systems
Noise
Physical Sciences
Process control
Reproducibility of Results
Research and Analysis Methods
Science
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title 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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