Zero-Inflated Time Series Model for Covid-19 Deaths in Kelantan Malaysia

The development of zero-inflated time series models is well known to account for excessive number of zeros and overdispersion in discrete count time series data. By using Zero-inflated models, we analyzed the daily count of COVID-19 deaths occurrence in Kelantan with excess zeros. Considering factors such as COVID-19 deaths in neighboring state and lag of 1 to 7 days of COVID-19 death in Kelantan, the Zero-Inflated models (Zero-Inflated Poisson (ZIP) and the Zero-Inflated Negative Binomial (ZINB)) were employed to predict the COVID-19 deaths in Kelantan. The ZIP and ZINB were compared with the basic Poisson and Negative Binomial models to find the significant contributing factors from the model. The final results show that the best model was the ZINB model with lag of 1,2,5 and lag of 6 days of Kelantan COVID-19 death, lag of 1-day COVID-19 deaths in neighboring State of Terengganu and Perak significantly influenced the COVID-19 deaths occurrence in Kelantan. The model gives the smallest value of AIC and BIC compared to the basic Poisson and Negative Binomial model. This indicate that the Zero Inflated model predict the excess zeros in the COVID-19 deaths occurrence well compared to the basic count model. Hence, the fitted models for COVID-19 deaths served as a novel understanding on the disease transmission and dissemination in a particular area. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Machine learning models for COVID-19 confirmed cases prediction: A meta-analysis approach

COVID-19, CoronaVirus Disease - 2019, belongs to the genus of Coronaviridae. COVID-19 is no longer pandemic but rather endemic with the number of deaths around the world of more than 3,166,516 cases. This reality has placed a massive burden on limited healthcare systems. Thus, many researchers try to develop a prediction model to further understand this phenomenon. One of the recent methods used is machine learning models that learn from the historical data and make predictions about the events. These data mining techniques have been used to predict the number of confirmed cases of COVID-19. This paper investigated the variability of the effect size on the correlation performance of machine learning models in predicting confirmed cases of COVID-19 using meta-analysis. It explored the correlation between actual and predicted COVID-19 cases from different Neural Network machine learning models by means of estimated variance, chi-square heterogeneity (Q), heterogeneity index (I2) and random effect model. The results gave a good summary effect of 95% confidence interval. Based on chi-square heterogeneity (Q) and heterogeneity index (I2), it was found that the correlations were heterogeneous among the studies. The 95% confidence interval of effect summary also supported the difference in correlation between actual and predicted number of confirmed COVID-19 cases among the studies. There was no evidence of publication bias based on funnel plot and Egger and Begg's test. Hence, findings from this study provide evidence of good prediction performance from the Neural Network model based on a combination of studies that can later serve in the prediction of COVID-19 confirmed cases. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.

The modelling of heteroscedastics IDR-USD exchange rate with intervention and outlier factors

The nonstationary in time series data may be caused by the existence of intervention, outliers, and heteroscedastic effects. The outliers can represent an intervention so that it creates a heteroscedastic process. This research investigates the involvements of these three factors in time series data modelling. It is also reviewed how long the effects of the intervention and outliersfactors will last. The weekly IDR-USD exchange rate in period of May 2015 to April 2020 be evaluated. It is obtained that ARIMA model with the intervention factor gives the best re-estimation result, with smallest average of errors squared. Meanwhile for prediction, the heteroscedastic effect combined with outlier factors gives better results with the lowest percentage of errors. One of the phenomenal interventions in this data is the Covid-19 pandemic, which was started in Indonesia on March 2020. It is found that the effect of the intervention lasts less than five months and the prediction shows that the volatility of IDR-USD exchange rate starts to decline. This shows the stability of the process is starting to be maintained. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.