ArticleChaos, solitons, and fractals2020
Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed.
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- Disruption of winter influenza activity in Wuxi, China during and after the COVID-19 pandemic (2013-2025): a counterfactual time-series analysis.Frontiers in public health · 2026Article
- Global burden of metabolic dysfunction-associated steatotic liver disease from 1990 to 2021 and the prediction for the next 10 years.Preventive medicine reports · 2025Article
- Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model.BMC public health · 2025Article
- Long-term impact of COVID-19-related nonpharmaceutical interventions on tuberculosis: an interrupted time series analysis using Bayesian method.Journal of global health · 2025Article
- Trends and multi-model prediction of hepatitis B incidence in Xiamen.Infectious Disease Modelling · 2024Article
- A self-driven ESN-DSS approach for effective COVID-19 time series prediction and modelling.Epidemiology and infection · 2024Article
- Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time-series model.Scientific reports · 2024Article
- Multilayer factors associated with excess all-cause mortality during the omicron and non-omicron waves of the COVID-19 pandemic: time series analysis in 29 countries.BMC public health · 2024Article
- Evaluating the impact of COVID-19 outbreak on hepatitis B and forecasting the epidemiological trend in mainland China: a causal analysis.BMC public health · 2024Article
- Analyzing the dynamics of complicated and uncomplicated appendicitis during the COVID-19 pandemic in Seoul, Korea: a multifaceted time series approach.Epidemiology and health · 2024Article
- Comparison of ARIMA and Bayesian Structural Time Series Models for Predicting the Trend of Syphilis Epidemic in Jiangsu Province.Infection and drug resistance · 2024Article
- Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK.Epidemiology and infection · 2023Article
- Analysis of Covid-19 data using discrete Marshall-Olkinin Length Biased Exponential: Bayesian and frequentist approach.Scientific reports · 2023Article
- Causal impacts of the COVID-19 pandemic on daily ridership of public bicycle sharing in Seoul.Sustainable cities and society · 2023Article
- The impact of mass vaccination policy and control measures on lumpy skin disease cases in Thailand: insights from a Bayesian structural time series analysis.Frontiers in veterinary science · 2023Article
- Epidemic efficacy of Covid-19 vaccination against Omicron: An innovative approach using enhanced residual recurrent neural network.PloS one · 2023Article
- Estimation of Lockdowns' Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data.International journal of environmental research and public health · 2022Article
- Assessing the effectiveness of quarantine measures during the COVID-19 pandemic in Chile using Bayesian structural time series models.Infectious Disease Modelling · 2022Article
- Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
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Abstract
backgroundThere are numerous studies dealing with analysis for the future patterns of COVID-19 in different countries using conventional time series models. This study aims to provide more flexible analytical framework that decomposes the important components of the time series, incorporates the prior information, and captures the evolving nature of model parameters.
methodsWe have employed the Bayesian structural time series (BSTS) models to investigate the temporal dynamics of COVID-19 in top five affected countries around the world in the time window March 1, 2020 to June 29, 2020. In addition, we have analyzed the casual impact of lockdown in these countries using intervention analysis under BSTS models.
resultsWe achieved better levels of accuracy as compared to ARIMA models. The forecasts for the next 30 days suggest that India, Brazil, USA, Russia and UK are expected to have 101.42%, 85.85%, 46.73%, 32.50% and 15.17% increase in number of confirmed cases, respectively. On the other hand, there is a chance of 70.32%, 52.54%, 45.65%, 19.29% and 18.23% growth in the death figures for India, Brazil, Russia, USA and UK, respectively. In addition, USA and UK have made quite sagacious choices for lifting/relaxing the lockdowns. However, the pace of outbreak has significantly increased in Brazil, India and Russia after easing the lockdowns.
conclusionOn the whole, the Indian and Brazilian healthcare system is likely to be seriously overburdened in the next month. Though USA and Russia have managed to cut down the rates of positive cases, but serious efforts will be required to keep these momentums on. On the other hand, UK has been successful in flattening their outbreak trajectories.
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