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© 2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Background: The literature in statistics presents methods by which autocorrelation can identify the best period of measurement to improve the performance of a time-series prediction. The period of measurement plays an important role in improving the performance of disease-count predictions. However, from the operational perspective in public health surveillance, there is a limitation to the length of the measurement period that can offer meaningful and valuable predictions.

Objective: This study aimed to establish a method that identifies the shortest period of measurement without significantly decreasing the prediction performance for time-series analysis of disease counts.

Methods: The data used in this evaluation include disease counts from 2007 to 2017 in northern Nevada. The disease counts for chlamydia, salmonella, respiratory syncytial virus, gonorrhea, viral meningitis, and influenza A were predicted.

Results: Our results showed that autocorrelation could not guarantee the best performance for prediction of disease counts. However, the proposed method with the change-point analysis suggests a period of measurement that is operationally acceptable and performance that is not significantly different from the best prediction.

Conclusions: The use of change-point analysis with autocorrelation provides the best and most practical period of measurement.

Details

Title
Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment
Author
Talaei-Khoei, Amir  VIAFID ORCID Logo  ; Wilson, James M  VIAFID ORCID Logo  ; Seyed-Farzan Kazemi  VIAFID ORCID Logo 
Section
Statistical Methods for Surveillance and Population Health
Publication year
2019
Publication date
Jan-Mar 2019
Publisher
JMIR Publications
e-ISSN
23692960
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2509600475
Copyright
© 2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.