Insights on the trend of the Novel Coronavirus 2019 series in some Small Island Developing States: A Thinning-based Modelling Approach

Khan, Naushad Mamode; Bakouch, Hassan S.; Soobhug, Ashwinee Devi; Scotto, Manuel G.

doi:10.1016/j.aej.2020.12.047

Cited by 6 publications

(8 citation statements)

References 50 publications

(43 reference statements)

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“…The relationship between the COVID-19 Stringency index in Mauritius (Refer to https://ourworldindata.org/covid-stringency-index ) and the number of daily new locally acquired COVID-19 active cases also plays a significant role, as proven in [ 4 ] and as shown in Fig 3 above. The COVID-19 Stringency index was near 100 in March 2020 considering the immediate imposition of various sanitary measures like the sanitary curfew and national lockdown, closure of borders, minimal gathering and other health-related measures.…”

Section: Methodsmentioning

confidence: 95%

“…Also, to cater for the long sequence of zeros and high autocorrelation, this paper bring forward the Zero-inflated (ZI) with different Poisson-mixture innovations models and the INAR( p ) models because as illustrated in [ 2 – 4 ], ignoring the excess of zeros will lead to biasedness in the estimated parameters and standard errors [ 5 ]. More on these novel ZI models and the inferential part of the general INAR processes are provided in the subsequent Section.…”

Section: Methodsmentioning

confidence: 99%

“…Apart from these models, the recently studied Cosine-Geometric models [ 4 , 41 ] is also proven useful for count data modelling. The PGF of WCG is given by: where and and .…”

Section: Methodsmentioning

confidence: 99%

“…The COVID-19 new cases series in Mauritius has some distinctive features like a purely unique serial trend with excess of zeros and some oscillations, leading to over-dispersion, while the corresponding COVID-19-related death series describes a preponderance of excess zeros. These series thus imply that the simple integer-valued auto-regressive model (INAR(1)) with Poisson or extra-Poisson innovations is surely insufficient in this context [ 2 – 4 ] and ignoring the excess of zeros will lead to biasedness in the estimated parameters and standard errors [ 5 ]. To remedy, this paper proposes to construct a novel high-ordered integer-valued auto-regressive process (INAR) with Zero-Inflated (ZI) innovation distributions.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the second wave, new covariates like the reproduction rate (ReR), the COVID-19 Risk due to weather conditions (CRW), the major event of vaccination and the COVID-19 Stringency Index need to be assessed in the Mauritian context. Besides, in the European and Asian regions, ReR [ 19 – 21 ], CRW [ 22 , 23 ] and vaccinations [ 24 ] have largely demonstrated their association with COVID-19 transmission while during the first wave in Mauritius, the COVID-19 Stringency index was the most significant factor in curbing the virus [ 4 ]. Further details on the covariates are provided in Section 3.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Re-analyzing the SARS-CoV-2 series using an extended integer-valued time series models: A situational assessment of the COVID-19 in Mauritius

Soobhug¹,

Jowaheer²,

Khan

et al. 2022

PLoS ONE

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This paper proposes some high-ordered integer-valued auto-regressive time series process of order p (INAR(p)) with Zero-Inflated and Poisson-mixtures innovation distributions, wherein the predictor functions in these mentioned distributions allow for covariate specification, in particular, time-dependent covariates. The proposed time series structures are tested suitable to model the SARs-CoV-2 series in Mauritius which demonstrates excess zeros and hence significant over-dispersion with non-stationary trend. In addition, the INAR models allow the assessment of possible causes of COVID-19 in Mauritius. The results illustrate that the event of Vaccination and COVID-19 Stringency index are the most influential factors that can reduce the locally acquired COVID-19 cases and ultimately, the associated death cases. Moreover, the INAR(7) with Zero-inflated Negative Binomial innovations provides the best fitting and reliable Root Mean Square Errors, based on some short term forecasts. Undeniably, these information will hugely be useful to Mauritian authorities for implementation of comprehensive policies.

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Section: Methodsmentioning

confidence: 95%

Section: Methodsmentioning

confidence: 99%

“…Apart from these models, the recently studied Cosine-Geometric models [ 4 , 41 ] is also proven useful for count data modelling. The PGF of WCG is given by: where and and .…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Re-analyzing the SARS-CoV-2 series using an extended integer-valued time series models: A situational assessment of the COVID-19 in Mauritius

Soobhug¹,

Jowaheer²,

Khan

et al. 2022

PLoS ONE

Self Cite

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Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement

Hwang

2022

Chaos, Solitons & Fractals

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This paper is devoted to modelling and predicting COVID-19 confirmed cases through a multiple linear regression. Especially, prediction intervals of the COVID-19 cases are extensively studied. Due to long-memory feature of the COVID-19 data, a heterogeneous autoregression (HAR) is adopted with Growth rates and Vaccination rates; it is called HAR-G-V model. Top eight affected countries are taken with their daily confirmed cases and vaccination rates. Model criteria results such as root mean square error (RMSE), mean absolute error (MAE), , AIC and BIC are reported in the HAR models with/without the two rates. The HAR-G-V model performs better than other HAR models. Out-of-sample forecasting by the HAR-G-V model is conducted. Forecast accuracy measures such as RMSE, MAE, mean absolute percentage error and root relative square error are computed. Furthermore, three types of prediction intervals are constructed by approximating residuals to normal and Laplace distributions, as well as by employing bootstrap procedure. Empirical coverage probability, average length and mean interval score are evaluated for the three prediction intervals. This work contributes three folds: a novel trial to combine both growth rates and vaccination rates in modeling COVID-19; construction and comparison of three types of prediction intervals; and an attempt to improve coverage probability and mean interval score of prediction intervals via bootstrap technique.

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Re-visiting the COVID-19 analysis using the class of high ordered integer-valued time series models with harmonic features

Khan

Soobhug²,

Youssef³

et al. 2022

Healthcare Analytics

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Insights on the trend of the Novel Coronavirus 2019 series in some Small Island Developing States: A Thinning-based Modelling Approach

Cited by 6 publications

References 50 publications

Re-analyzing the SARS-CoV-2 series using an extended integer-valued time series models: A situational assessment of the COVID-19 in Mauritius

Re-analyzing the SARS-CoV-2 series using an extended integer-valued time series models: A situational assessment of the COVID-19 in Mauritius

Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement

Re-visiting the COVID-19 analysis using the class of high ordered integer-valued time series models with harmonic features

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