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Background
The objective was to identify the Spatial-temporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model.
Methods
The data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the China CDC. Joinpoint regression analysis was used to observe the trend. The monthly incidence was predicted by the Seasonal autoregressive integrated moving average (SARIMA) model. Spatial autocorrelation analysis was performed to detect geographic clusters. A multivariate time series model was employed to analyze heterogeneous transmission.
Results
We included 13,991,850 TB cases from 2004 to 2017. The final selected model was the 0 Joinpoint model with an annual average percent change of -3.3. A seasonality was observed across the fourteen years, and the seasonal peaks were in January and March. The best SARIMA model was (0, 1, 1) X (0, 1, 1) 12 , with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5% - 84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences areas were mainly affected by the autoregressive component for the past fourteen years.
Conclusion
A significant decreasing trend was seen from 2004 to 2017. The seasonal peaks were in January and March every year. Obvious clusters were identified in Tibet and Xinjiang Province. A spatial heterogeneity in the component driving the transmission of TB was identified from the multivariate time series model. This suggested that targeted preventive efforts should be made in different provinces based on the main component contributing to the epidemics.
Keywords
Tuberculosis; spatial-temporal; epidemiology; multivariate time series model
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Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Public Health.
No community comments so far
Editorial decision: Accept
On 03 Aug, 2020
Editor assigned
On 30 Jul, 2020
Submission checks complete
On 29 Jul, 2020
Editor invited
On 29 Jul, 2020
Version 2
Posted 10 Jul, 2020
No comments provided
Editorial decision: Minor revision
On 20 Jul, 2020
Editor assigned
On 09 Jul, 2020
Submission checks complete
On 08 Jul, 2020
Editor invited
On 08 Jul, 2020
No comments provided
Editorial decision: Major revision
On 24 Jun, 2020
Review #2 received
Received 22 Jun, 2020
Reviewer #2 agreed
On 11 May, 2020
Review #1 received
Received 06 May, 2020
Reviewer #1 agreed
On 24 Mar, 2020
Reviewers invited
Invitations sent on 17 Mar, 2020
Editor assigned
On 01 Mar, 2020
Submission checks complete
On 29 Feb, 2020
Editor invited
On 29 Feb, 2020
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See the published version of this preprint at BMC Public Health.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Public Health.
No community comments so far
Editorial decision: Accept
On 03 Aug, 2020
Editor assigned
On 30 Jul, 2020
Submission checks complete
On 29 Jul, 2020
Editor invited
On 29 Jul, 2020
Version 2
Posted 10 Jul, 2020
No comments provided
Editorial decision: Minor revision
On 20 Jul, 2020
Editor assigned
On 09 Jul, 2020
Submission checks complete
On 08 Jul, 2020
Editor invited
On 08 Jul, 2020
No comments provided
Editorial decision: Major revision
On 24 Jun, 2020
Review #2 received
Received 22 Jun, 2020
Reviewer #2 agreed
On 11 May, 2020
Review #1 received
Received 06 May, 2020
Reviewer #1 agreed
On 24 Mar, 2020
Reviewers invited
Invitations sent on 17 Mar, 2020
Editor assigned
On 01 Mar, 2020
Submission checks complete
On 29 Feb, 2020
Editor invited
On 29 Feb, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Public Health.
Background
The objective was to identify the Spatial-temporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model.
Methods
The data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the China CDC. Joinpoint regression analysis was used to observe the trend. The monthly incidence was predicted by the Seasonal autoregressive integrated moving average (SARIMA) model. Spatial autocorrelation analysis was performed to detect geographic clusters. A multivariate time series model was employed to analyze heterogeneous transmission.
Results
We included 13,991,850 TB cases from 2004 to 2017. The final selected model was the 0 Joinpoint model with an annual average percent change of -3.3. A seasonality was observed across the fourteen years, and the seasonal peaks were in January and March. The best SARIMA model was (0, 1, 1) X (0, 1, 1) 12 , with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5% - 84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences areas were mainly affected by the autoregressive component for the past fourteen years.
Conclusion
A significant decreasing trend was seen from 2004 to 2017. The seasonal peaks were in January and March every year. Obvious clusters were identified in Tibet and Xinjiang Province. A spatial heterogeneity in the component driving the transmission of TB was identified from the multivariate time series model. This suggested that targeted preventive efforts should be made in different provinces based on the main component contributing to the epidemics.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This preprint is available for download as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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