%0 Journal Article
%@ 2369-2960
%I JMIR Publications
%V 5
%N 1
%P e11357
%T Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment
%A Talaei-Khoei,Amir
%A Wilson,James M
%A Kazemi,Seyed-Farzan
%+ Department of Information Systems, University of Nevada Reno, Ansari Business Building, 1664 N Virginia Street, Room 314F, Reno, NV, 89557, United States, 1 7754407005, atalaeikhoei@unr.edu
%K autocorrelation
%K disease counts
%K prediction
%K public health surveillance
%K time-series analysis
%D 2019
%7 15.01.2019
%9 Original Paper
%J JMIR Public Health Surveill
%G English
%X 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. 
%M 30664479
%R 10.2196/11357
%U http://publichealth.jmir.org/2019/1/e11357/
%U https://doi.org/10.2196/11357
%U http://www.ncbi.nlm.nih.gov/pubmed/30664479