Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment.
- Publisher:
- JMIR Publications Inc.
- Publication Type:
- Journal Article
- Citation:
- JMIR public health and surveillance, 2019, 5, (1)
- Issue Date:
- 2019-01-15
Open Access
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is open access.
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.
Please use this identifier to cite or link to this item: