Clustering and recent transmission of &lt;em&gt;Mycobacterium tuberculosis&lt;/em&gt; in a Chinese population

Xu, Guisheng; Mao, Xuhua; Wang, Jianming; Pan, Hongqiu

doi:10.2147/idr.s156534

Cited by 17 publications

(16 citation statements)

References 34 publications

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“…The occurrence of TB is often affected and constrained by many complicated factors, which enables the TB incidence series to exhibit complex linear and nonlinear interactions, and thus simply using the single linear or non-linear methods fails to excavate its entire information reasonably well. 29,40 In this study, we devoted to constructing a forecasting method with robust and accurate performance that can be used to analyze and estimate its secular trends and seasonal variation by considering the linear and non-linear clues hidden behind the TB morbidity series in China simultaneously. Moreover, the results emerged from a series of comparative experiments proved that we have succeeded.…”

Section: Discussionmentioning

confidence: 99%

Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework

Wang

Ren

et al. 2020

IDR

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Tuberculosis (TB) is a major public health problem in China, and contriving a long-term forecast is a useful aid for better launching prevention initiatives. Regrettably, such a forecasting method with robust and accurate performance is still lacking. Here, we aim to investigate its potential of the error-trend-seasonal (ETS) framework through a series of comparative experiments to analyze and forecast its secular epidemic seasonality and trends of TB incidence in China. Methods: We collected the TB incidence data from January 1997 to August 2019, and then partitioning the data into eight different training and testing subsamples. Thereafter, we constructed the ETS and seasonal autoregressive integrated moving average (SARIMA) models based on the training subsamples, and multiple performance indices including the mean absolute deviation, mean absolute percentage error, root-mean-squared error, and mean error rate were adopted to assess their simulation and projection effects. Results: In the light of the above performance measures, the ETS models provided a pronounced improvement for the long-term seasonality and trend forecasting in TB incidence rate over the SARIMA models, be it in various training or testing subsets apart from the 48-step ahead forecasting. The descriptive results to the data revealed that TB incidence showed notable seasonal characteristics with predominant peaks of spring and early summer and began to be plunging at on average 3.722% per year since 2008. However, this rate reduced to 2.613% per year since 2015 and furthermore such a trend would be predicted to continue in years ahead. Conclusion: The ETS framework has the ability to conduct long-term forecasting for TB incidence, which may be beneficial for the long-term planning of the TB prevention and control. Additionally, considering the predicted dropping rate of TB morbidity, more particular strategies should be formulated to dramatically accelerate progress towards the goals of the End TB Strategy.

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Section: Discussionmentioning

confidence: 99%

Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework

Wang

Ren

et al. 2020

IDR

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“…The occurrence of tuberculosis is usually affected by various factors, and it is often difficult to identify all the characteristics of the disease sequence 34,35. Simply using the linear or nonlinear model cannot extract adequate information.…”

Section: Discussionmentioning

confidence: 99%

Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population

Li¹,

Wang²,

Song³

et al. 2019

IDR

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Objective: To investigate suitable forecasting models for tuberculosis (TB) in a Chinese population by comparing the predictive value of the autoregressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) hybrid model. Methods: We used the monthly incidence rate of TB in Lianyungang city from January 2007 through June 2016 to construct a fitting model, and we used the incidence rate from July 2016 to December 2016 to evaluate the forecasting accuracy. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to assess the performance of these models in fitting and forecasting the incidence of TB. Results: The ARIMA (10, 1, 0) (0, 1, 1) 12 model was selected from plausible ARIMA models, and the optimal spread value of the ARIMA-GRNN hybrid model was 0.23. For the fitting dataset, the RMSE, MAPE, MAE and MER were 0.5594, 11.5000, 0.4202 and 0.1132, respectively, for the ARIMA (10, 1, 0) (0, 1, 1) 12 model, and 0.5259, 11.2181, 0.3992 and 0.1075, respectively, for the ARIMA-GRNN hybrid model. For the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.2805, 8.8797, 0.2261 and 0.0851, respectively, for the ARIMA (10, 1, 0) (0, 1, 1) 12 model, and 0.2553, 5.7222, 0.1519 and 0.0571, respectively, for the ARIMA-GRNN hybrid model. Conclusions: The ARIMA-GRNN hybrid model was shown to be superior to the single ARIMA model in predicting the short-term TB incidence in the Chinese population, especially in fitting and forecasting the peak and trough incidence.

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“…Our low recent transmission rate (6.8%), determined by the clusters in relation to the total number of isolates, is possibly due to the short time of sampling, same limitation found by Xu et al . [ 52 ]. The comparison of recent transmission rate is a valuable information to monitor strain transmission over the years [ 53 ].…”

Section: Discussionmentioning

confidence: 99%

Molecular characterisation of multidrug-resistantMycobacterium tuberculosisisolates from a high-burden tuberculosis state in Brazil

et al. 2019

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Tuberculosis (TB) is the leading cause of death among infectious diseases worldwide. Among the estimated cases of drug-resistant TB, approximately 60% occur in the BRICS countries (Brazil, Russia, India, China and South Africa). Among Brazilian states, primary and acquired multidrug-resistant TB (MDR-TB) rates were the highest in Rio Grande do Sul (RS). This study aimed to perform molecular characterisation of MDR-TB in the State of RS, a high-burden Brazilian state. We performed molecular characterisation of MDR-TB cases in RS, defined by drug susceptibility testing, using 131 Mycobacterium tuberculosis (M.tb) DNA samples from the Central Laboratory. We carried out MIRU-VNTR 24loci, spoligotyping, sequencing of the katG, inhA and rpoB genes and RDRio sublineage identification. The most frequent families found were LAM (65.6%) and Haarlem (22.1%). RDRio deletion was observed in 42 (32%) of the M.tb isolates. Among MDR-TB cases, eight (6.1%) did not present mutations in the studied genes. In 116 (88.5%) M.tb isolates, we found mutations associated with rifampicin (RIF) resistance in rpoB gene, and in 112 isolates (85.5%), we observed mutations related to isoniazid resistance in katG and inhA genes. An insertion of 12 nucleotides (CCAGAACAACCC) at the 516 codon in the rpoB gene, possibly responsible for a decreased interaction of RIF and RNA polymerase, was found in 19/131 of the isolates, belonging mostly to LAM and Haarlem families. These results enable a better understanding of the dynamics of transmission and evolution of MDR-TB in the region.

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Clustering and recent transmission of <em>Mycobacterium tuberculosis</em> in a Chinese population

Cited by 17 publications

References 34 publications

<p>Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework</p>

<p>Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework</p>

<p>Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population</p>

Molecular characterisation of multidrug-resistantMycobacterium tuberculosisisolates from a high-burden tuberculosis state in Brazil

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