TY  - JOUR
AU  - Talaei-Khoei, Amir
AU  - Wilson, James M
AU  - Kazemi, Seyed-Farzan
PY  - 2019
DA  - 2019/01/15
TI  - Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment
JO  - JMIR Public Health Surveill
SP  - e11357
VL  - 5
IS  - 1
KW  - autocorrelation
KW  - disease counts
KW  - prediction
KW  - public health surveillance
KW  - time-series analysis
AB  - 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. 
SN  - 2369-2960
UR  - http://publichealth.jmir.org/2019/1/e11357/
UR  - https://doi.org/10.2196/11357
UR  - http://www.ncbi.nlm.nih.gov/pubmed/30664479
DO  - 10.2196/11357
ID  - info:doi/10.2196/11357
ER  -