Modeling and forecasting the COVID-19 pandemic time-series data

Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data

The coronavirus disease 2019 (COVID-19) pandemic continues to destroy human life around the world. Almost every country throughout the globe suffered from this pandemic, forcing various governments to apply different restrictions to reduce its impact. In this study, we compare different time-series models with the neural network autoregressive model (NNAR). The study used COVID-19 data in Pakistan from February 26, 2020, to February 18, 2022, as a training and testing data set for modeling. Different models were applied and estimated on the training data set, and these models were assessed on the testing data set. Based on the mean absolute scaled error (MAE) and root mean square error (RMSE) for the training and testing data sets, the NNAR model outperformed the autoregressive integrated moving average (ARIMA) model and other competing models indicating that the NNAR model is the most appropriate for forecasting. Forecasts from the NNAR model showed that the cumulative confirmed COVID-19 cases will be 1,597,180 and cumulative confirmed COVID-19 deaths will be 32,628 on April 18, 2022. We encourage the Pakistan Government to boost its immunization policy.

Novel statistics predict the COVID-19 pandemic could terminate in 2022

Many people want to know when the COVID-19 pandemic will end and life will return to normal. This question is highly elusive and distinct predictions have been proposed. In this study, the global mortality and case fatality rate of COVID-19 were analyzed using the nonlinear regression. The analysis showed that the COVID-19 pandemic could terminate in 2022, but COVID-19 could be one or two times more deadly than seasonal influenza by 2023. The prediction considered the possibility of emergence of new variants of SARS-CoV-2 and was supported by the features of the Omicron variant and other facts. Since the herd immunity against COVID-19 established through natural infections and mass vaccination is distinct among countries, COVID-19 could be more or less deadly in some countries in the coming years than the prediction. Although the future of COVID-19 will have multiple possibilities, this statistics-based prediction could aid to make proper decisions and establish an example on prediction of infectious diseases. This article is protected by copyright. All rights reserved.

Forecasting Facing Economic Shifts, Climate Change and Evolving Pandemics

By its emissions of greenhouse gases, economic activity is the source of climate change which affects pandemics that in turn can impact badly on economies. Across the three highly interacting disciplines in our title, time-series observations are measured at vastly different data frequencies: very low frequency at 1000-year intervals for paleoclimate, through annual, monthly to intra-daily for current climate; weekly and daily for pandemic data; annual, quarterly and monthly for economic data, and seconds or nano-seconds in finance. Nevertheless, there are important commonalities to economic, climate and pandemic time series. First, time series in all three disciplines are subject to non-stationarities from evolving stochastic trends and sudden distributional shifts, as well as data revisions and changes to data measurement systems. Next, all three have imperfect and incomplete knowledge of their data generating processes from changing human behaviour, so must search for reasonable empirical modeling approximations. Finally, all three need forecasts of likely future outcomes to plan and adapt as events unfold, albeit again over very different horizons. We consider how these features shape the formulation and selection of forecasting models to tackle their common data features yet distinct problems.