@Article{info:doi/10.2196/11357,
author="Talaei-Khoei, Amir
and Wilson, James M
and Kazemi, Seyed-Farzan",
title="Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment",
journal="JMIR Public Health Surveill",
year="2019",
month="Jan",
day="15",
volume="5",
number="1",
pages="e11357",
keywords="autocorrelation; disease counts; prediction; public health surveillance; time-series analysis",
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. ",
issn="2369-2960",
doi="10.2196/11357",
url="http://publichealth.jmir.org/2019/1/e11357/",
url="https://doi.org/10.2196/11357",
url="http://www.ncbi.nlm.nih.gov/pubmed/30664479"
}