Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China

Zheng, Yanling; Zhang, Liping; Zhang, Xueliang; Wang, Kai; Zheng, Yusheng

doi:10.1371/journal.pone.0116832

Cited by 68 publications

(66 citation statements)

References 34 publications

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“…Disease Method(s) (Guan et al, 2004) HAV ARIMA, ANNs (Earnest et al, 2005) SARS ARIMA (Gaudart et al, 2009) Malaria ARIMA (Liu et al, 2011) HFRS ARIMA (Zhang et al, 2013) Typhoid Fever SARIMA, BPNN, RBFNN, and ERNN (Ren et al, 2013) HEV ARIMA, BPNN (Nsoesie et al, 2013) HPS ARIMA (Zheng et al, 2015) Tuberculosis ARIMA (Wu et al, 2015) HFRS ARIMA, GRNN, and NARNN (Zeng et al, 2016) Pertussis ARIMA, ETS (Wei et al, 2016) Hepatitis ARIMA, GRNN (Sun et al, 2018) SFTS ARIMA, NBM, and GAM (Wang et al, 2018a and France over the next ten days with 95% relative confidence intervals.…”

Section: Referencementioning

confidence: 99%

Estimation of COVID-19 prevalence in Italy, Spain, and France

Ceylan

2020

Science of The Total Environment

495

423

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Europe has become the epicentre of the virus and hit the continent harder than China.• The apparent mortality rate of COVID-19 is approximately 13% in Italy, 11% in Spain, and 15% in France. • Time series models are significant in predicting the prevalence of the COVID-19 pandemic. • ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) were chosen as the best models for Italy, Spain, and France, respectively. were collected from the World Health Organization website. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) models with the lowest MAPE values (4.7520, 5.8486, and 5.6335) were selected as the best models for Italy, Spain, and France, respectively. This study shows that ARIMA models are suitable for predicting the prevalence of COVID-19 in the future. The results of the analysis can shed light on understanding the trends of the outbreak and give an idea of the epidemiological stage of these regions. Besides, the prediction of COVID-19 prevalence trends of Italy, Spain, and France can help take precautions and policy formulation for this epidemic in other countries.

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Section: Referencementioning

confidence: 99%

Estimation of COVID-19 prevalence in Italy, Spain, and France

Ceylan

2020

Science of The Total Environment

495

423

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“…Time series analysis has been used to predict TB morbidity or mortality, but most were conducted in one city or one region and based on one or two models without incorporating meteorological factors in the model [13][14][15][16]. Our previous studies have observed that the incidence of TB exhibited seasonal fluctuations, indicating a potential relationship with meteorological factors [17].…”

Section: Introductionmentioning

confidence: 99%

Exploring the role of meteorological factors in predicting incident pulmonary tuberculosis: A time-series study in eastern China

Pan²,

et al. 2020

Preprint

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BackgroundMany studies have compared the performance of time-series models in predicting pulmonary tuberculosis (PTB). Few studies regarding the role of meteorological factors in predicting PTB are available. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting pulmonary tuberculosis (PTB).MethodsWe collected the monthly number of PTB cases registered in three cities of China from 2005 to 2018, and data of six meteorological factors in the same period. We constructed three time-series models, including the autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and the recurrent neural network (RNN) model. The construction of the ARIMA model did not incorporate meteorological factors, while the construction of ARIMAX and RNN models incorporated meteorological factors. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to compare the performance of models in predicting the monthly number of PTB cases in 2018.ResultsBoth the cross-correlation analysis and spearman rank correlation test showed that PTB was related to meteorological factors in the three cities. The prediction performance of both ARIMA and RNN models was improved after incorporating the meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.536%, 11.957%, and 12.360% in Xuzhou, 15.568%, 11.155%, and 14.087% in Nantong, and 9.700%, 9.660% and 12.501% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956 and 34.785 in Xuzhou, 34.073, 25.884 and 31.828 in Nantong, and 19.545, 19.026 and 26.019 in Wuxi, respectively.ConclusionsOur study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration may increase the accuracy of time series models in predicting PTB.

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“…The ARIMA model can take into account changing trends, periodic changes, and random disturbances in a time series. In epidemiology, ARIMA models have been successfully used to predict the incidence of tuberculosis [ 18], dengue [ 19], as well as other infectious diseases [ 20].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Heterogeneity Analysis of Haemorrhagic Fever with Renal Syndrome in Anqiu City, China, 2000-2014

Shi

et al. 2019

Preprint

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Background: The purpose of this study was to explore the dynamics of the occurrence of haemorrhagic fever with renal syndrome (HFRS) and find the potential spatiotemporal factors leading to the incidence of HFRS in Anqiu City. Methods: Monthly reported cases of HFRS and climatic data for 2000–2014 in Anqiu City were obtained. An autoregressive integrated moving average (ARIMA) model was used to fit the HFRS incidence prediction model and predict the epidemic trend in Anqiu City. Multiple linear regression method was used to analyze the temporal relationship between HFRS incidence and meteorological factors during the study period. Results: Spatial analysis results indicated that the annualized average incidence at the town level ranged from 2.18 to 6.09 per 100, 000 among 14 towns, and the western towns in Anqiu City exhibited high endemic levels during the study periods. With high validity, the optimal ARIMA (0, 1, 1) × (0, 1, 1)12 model could be used to predict the HFRS incidence in Anqiu City in 2014. The monthly trend in HFRS incidence was negatively associated with temperature and precipitation and positively associated with average wind speed. Multiple linear regression models showed that precipitation and relative wind speed were key climatic factors contributing to the transmission of HFRS. Conclusions: This study provides evidence that the ARIMA model can be used to fit the fluctuations in HFRS frequency in Anqiu City. Our findings add to the knowledge of the role played by climate factors in HFRS transmission in Anqiu City and can assist local health authorities in the development/refinement of a better strategy to prevent HFRS transmission.

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Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China

Cited by 68 publications

References 34 publications

Estimation of COVID-19 prevalence in Italy, Spain, and France

Estimation of COVID-19 prevalence in Italy, Spain, and France

Exploring the role of meteorological factors in predicting incident pulmonary tuberculosis: A time-series study in eastern China

Spatiotemporal Heterogeneity Analysis of Haemorrhagic Fever with Renal Syndrome in Anqiu City, China, 2000-2014

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