Interruption time series analysis using autoregressive integrated moving average model: evaluating the impact of COVID-19 on the epidemic trend of gonorrhea in China

Li, Yanyan; Liu, Xingyan; Li, Xinxiao; Xue, Chenlu; Zhang, Bingjie; Wang, Yongbin

doi:10.1186/s12889-023-16953-5

Cited by 3 publications

(4 citation statements)

References 30 publications

(30 reference statements)

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“…In the event of a suspected case, immediate notification to livestock authorities is essential, emphasizing the need for a swift response from these authorities to the farmers' concerns. This study utilized univariate time series data for BSTS analysis, aligning with methodologies employed in previous studies (40,43).…”

Section: Discussionmentioning

confidence: 99%

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The impact of mass vaccination policy and control measures on lumpy skin disease cases in Thailand: insights from a Bayesian structural time series analysis

Punyapornwithaya,

Arjkumpa,

Buamithup

et al. 2024

Front. Vet. Sci.

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IntroductionIn 2021, Thailand reported the highest incidence of lumpy skin disease (LSD) outbreaks in Asia. In response to the widespread outbreaks in cattle herds, the government's livestock authorities initiated comprehensive intervention measures, encompassing control strategies and a national vaccination program. Yet, the efficacy of these interventions remained unevaluated. This research sought to assess the nationwide intervention's impact on the incidence of new LSD cases through causal impact analysis.MethodsData on weekly new LSD cases in Thailand from March to September 2021 was analyzed. The Bayesian structural time series (BSTS) analysis was employed to evaluate the causal relationship between new LSD cases in the pre-intervention phase (prior to the vaccination campaign) and the post-intervention phase (following the vaccination campaign). The assessment involved two distinct scenarios, each determined by the estimated effective intervention dates. In both scenarios, a consistent decline in new LSD cases was observed after the mass vaccination initiative, while other control measures such as the restriction of animal movement, insect control, and the enhancement of the active surveillance approach remained operational throughout the pre-intervention and the post-intervention phases.Results and discussionAccording to the relative effect results obtained from scenario A and B, it was observed that the incidence of LSD cases exhibited reductions of 119% (95% Credible interval [CrI]: −121%, −38%) and 78% (95% CrI: −126, −41%), respectively. The BSTS results underscored the significant influence of these interventions, with a Bayesian one-sided tail-area probability of p < 0.05. This model-based study provides insight into the application of BSTS in evaluating the impact of nationwide LSD vaccination based on the national-level data. The present study is groundbreaking in two respects: it is the first study to quantify the causal effects of a mass vaccination intervention on the LSD outbreak in Thailand, and it stands as the only endeavor of its kind in the Asian context. The insights collected from this study hold potential value for policymakers in Thailand and other countries at risk of LSD outbreaks.

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Section: Discussionmentioning

confidence: 99%

“…Various methods of ITS have been proposed such as ordinary least square regression, generalized least square, restricted maximum likelihood, and autoregressive integrated moving average (29,(37)(38)(39)(40). Within a Bayesian framework, Bayesian structural time series (BSTS) model is widely used.…”

Section: Introductionmentioning

confidence: 99%

The impact of mass vaccination policy and control measures on lumpy skin disease cases in Thailand: insights from a Bayesian structural time series analysis

Punyapornwithaya,

Arjkumpa,

Buamithup

et al. 2024

Front. Vet. Sci.

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“…Advances in prediction models, artificial intelligence and other computer technologies have enhanced clinical diagnoses, pathogen classification and estimates of epidemic trends of infectious diseases [ 16 – 18 ]. The accuracy and efficiency of these methods have provided important technical support for the prevention and control of respiratory infectious diseases, such as COVID-19 [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings

Wang,

et al. 2024

BMC Infect Dis

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Background Application of accumulated experience and management measures in the prevention and control of coronavirus disease 2019 (COVID-19) has generally depended on the subjective judgment of epidemic intensity, with the quality of prevention and control management being uneven. The present study was designed to develop a novel risk management system for COVID-19 infection in outpatients, with the ability to provide accurate and hierarchical control based on estimated risk of infection. Methods Infection risk was estimated using an auto regressive integrated moving average model (ARIMA). Weekly surveillance data on influenza-like-illness (ILI) among outpatients at Xuanwu Hospital Capital Medical University and Baidu search data downloaded from the Baidu Index in 2021 and 22 were used to fit the ARIMA model. The ability of this model to estimate infection risk was evaluated by determining the mean absolute percentage error (MAPE), with a Delphi process used to build consensus on hierarchical infection control measures. COVID-19 control measures were selected by reviewing published regulations, papers and guidelines. Recommendations for surface sterilization and personal protection were determined for low and high risk periods, with these recommendations implemented based on predicted results. Results The ARIMA model produced exact estimates for both the ILI and search engine data. The MAPEs of 20-week rolling forecasts for these datasets were 13.65% and 8.04%, respectively. Based on these two risk levels, the hierarchical infection prevention methods provided guidelines for personal protection and disinfection. Criteria were also established for upgrading or downgrading infection prevention strategies based on ARIMA results. Conclusion These innovative methods, along with the ARIMA model, showed efficient infection protection for healthcare workers in close contact with COVID-19 infected patients, saving nearly 41% of the cost of maintaining high-level infection prevention measures and enhancing control of respiratory infections.

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“…ARIMA, renowned for its efficiency in capturing linear trends in time series data with minimal computational burden, is often employed to explore variable relationships or serve as a benchmark in testing hybrid models, albeit with mixed outcomes ( 15 ). For instance, Li et al ( 16 ) in China utilized ARIMA to assess the impact of the Corona Virus Disease 2019 (COVID-19) on gonorrhea trends, while Eyles et al ( 17 ) in the United Kingdom (UK) employed it to generate precise short-term forecasts for patient admissions and bed occupancy, thereby improving predictions for medical specialties and lengths of stay. And ARIMA has proven capable of processing and predicting complex but stable time series data.…”

Section: Introductionmentioning

confidence: 99%

Statistical machine learning models for prediction of China’s maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model

Yang,

Cheng,

Zhang

et al. 2024

Front. Public Health

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IntroductionRescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models.MethodsIn this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2) as performance metrics.ResultsIn this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R2 values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R2 can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R2 moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R2, during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA’s predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with ARIMA showing the least accurate predictions.DiscussionWhile the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies.

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Interruption time series analysis using autoregressive integrated moving average model: evaluating the impact of COVID-19 on the epidemic trend of gonorrhea in China

Cited by 3 publications

References 30 publications

The impact of mass vaccination policy and control measures on lumpy skin disease cases in Thailand: insights from a Bayesian structural time series analysis

The impact of mass vaccination policy and control measures on lumpy skin disease cases in Thailand: insights from a Bayesian structural time series analysis

Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings

Statistical machine learning models for prediction of China’s maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model

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