Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models

Feroze, Navid

doi:10.1016/j.chaos.2020.110196

Cited by 69 publications

(64 citation statements)

References 19 publications

(21 reference statements)

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“… [13] performed a time series forecasting of Covid-19 using deep learning models for India and USA. Feroze [7] forecasted the patterns of COVID-19 using bayesian structural time series models.…”

Section: Introductionmentioning

confidence: 99%

Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods

Ballı

2021

Chaos, Solitons & Fractals

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Section: Introductionmentioning

confidence: 99%

Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods

Ballı

2021

Chaos, Solitons & Fractals

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“…In this section, a more detailed discussion on the results shown in Sect. 4.2.1 , according to comparative analysis of proposed methodology with the approaches in Feroze ( 2020 ), Hazarika and Gupta ( 2020 ), considering the metrics RMSE (Root Mean Square Error), MAE (Mean Absolute Error), RMSPE (Root Mean Square Percentage Error) and coefficient of determination ( ), is presented.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…The approach in Feroze ( 2020 ) is based on Bayesian structural time series (BSTS) models for forecasting the COVID-19 dynamic propagation in Brazil, within the horizon of 30 days. The efficiency of interval type-2 fuzzy Kalman filter, compared to approach proposed in Feroze ( 2020 ), is shown in Table 2 . As it can be seen, although the Bayesian model in approach (Feroze 2020 ) be adaptive, it presents a inferior performance compared to interval type-2 fuzzy Kalman filter, once that it is based on Bayesian inference mechanism influenced by previously computed probability distributions, which contributes for increasing the forecasting errors (Berger 1993 ).…”

Section: Resultsmentioning

confidence: 99%

“…As it can be seen, although the Bayesian model in approach (Feroze 2020 ) be adaptive, it presents a inferior performance compared to interval type-2 fuzzy Kalman filter, once that it is based on Bayesian inference mechanism influenced by previously computed probability distributions, which contributes for increasing the forecasting errors (Berger 1993 ).…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

A novel interval type-2 fuzzy Kalman filtering and tracking of experimental data

Gomes

Serra

2021

Evolving Systems

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In this paper, a methodology for design of fuzzy Kalman filter, using interval type-2 fuzzy models, in discrete time domain, via spectral decomposition of experimental data, is proposed. The adopted methodology consists of recursive parametric estimation of local state space linear submodels of interval type-2 fuzzy Kalman filter for tracking and forecasting of the dynamics inherited to experimental data, using an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm. The partitioning of the experimental data is performed by interval type-2 fuzzy Gustafson–Kessel clustering algorithm. The interval Kalman gains in the consequent proposition of interval type-2 fuzzy Kalman filter are updated according to unobservable components computed by recursive spectral decomposition of experimental data. Computational results illustrate the efficiency of proposed methodology for filtering and tracking the time delayed state variables of Chen’s chaotic attractor in a noisy environment, and experimental results illustrate its applicability for adaptive and real time forecasting the dynamic spread behavior of novel Coronavirus 2019 (COVID-19) outbreak in Brazil.

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The impact of anti‐COVID‐19 nonpharmaceutical interventions on hand, foot, and mouth disease—A spatiotemporal perspective in Xi'an, northwestern China

Shen

Sun

Song

et al. 2022

Journal of Medical Virology

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Growing evidence has shown that anti‐COVID‐19 nonpharmaceutical interventions (NPIs) can support prevention and control of various infectious diseases, including intestinal diseases. However, most studies focused on the short‐term mitigating impact and neglected the dynamic impact over time. This study is aimed to investigate the dynamic impact of anti‐COVID‐19 NPIs on hand, foot, and mouth disease (HFMD) over time in Xi'an City, northwestern China. Based on the surveillance data of HFMD, meteorological and web search data, Bayesian Structural Time Series model and interrupted time series analysis were performed to quantitatively measure the impact of NPIs in sequent phases with different intensities and to predict the counterfactual number of HFMD cases. From 2013 to 2021, a total number of 172,898 HFMD cases were reported in Xi'an. In 2020, there appeared a significant decrease in HFMD incidence (−94.52%, 95% CI: −97.54% to −81.95%) in the first half of the year and the peak period shifted from June to October by a small margin of 6.74% compared to the previous years of 2013 to 2019. In 2021, the seasonality of HFMD incidence gradually returned to the bimodal temporal variation pattern with a significant average decline of 61.09%. In particular, the impact of NPIs on HFMD was more evident among young children (0–3 years), and the HFMD incidence reported in industrial areas had an unexpected increase of 51.71% in 2020 autumn and winter. Results suggested that both direct and indirect NPIs should be implemented as effective public health measures to reduce infectious disease and improve surveillance strategies, and HFMD incidence in Xi'an experienced a significant rebound to the previous seasonality after a prominent decline influenced by the anti‐COVID‐19 NPIs.

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Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models

Cited by 69 publications

References 19 publications

Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods

Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods

A novel interval type-2 fuzzy Kalman filtering and tracking of experimental data

The impact of anti‐COVID‐19 nonpharmaceutical interventions on hand, foot, and mouth disease—A spatiotemporal perspective in Xi'an, northwestern China

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