Model Assisted Statistics and Applications

COVID-19 is a severe acute respiratory syndrome caused by the new Coronavirus. COVID-19 outbreak is a Public Health Emergency of International Concern, declared by WHO, that killed more than 2 million people worldwide. Since there are no specific drugs available and vaccination campaigns are in the initial phase, or even have not begun in some countries, the main way to fight the outbreak worldwide is still based on non-pharmacological strategies, such as the use of protective equipment, social isolation and mass testing. Modeling of the disease epidemics have gained pivotal importance to guide health authorities on the decision making and applying of those strategies. Here, we present the use of the Weibull distribution to model predictions of the COVID-19 outbreak based on daily new cases and deaths data, by non-linear regression using Metropolis-Markov Chain Monte Carlo simulations. It was possible to predict the evolution of daily new cases and deaths of COVID-19 in many countries as well as the overall number of cases and deaths in the future. Modeling predictions of COVID-19 pandemic may be of importance on the evaluation of governments and health authorities mitigation procedures, since it allows one to extract parameters that may help to guide those decisions and measures, slowing down the spread of the disease.

We construct a model for the progress of the 2020 coronavirus epidemic in the United States of America, using probabilistic methods rather than the traditional compartmental model. We employ the generalized beta family of distributions, including those supported on bounded intervals and those supported on semi-infinite intervals. We compare the best-fit distributions for daily new cases and daily new deaths in America to the corresponding distributions for United Kingdom, Spain, and Italy. We explore how such a model might be justified theoretically in comparison to the apparently more natural compartmental model. We compare forecasts based on these models to observations, and find the forecasts useful in predicting total pandemic deaths.

The Auto Regressive Integrated Moving Average (ARIMA) model seems not to easily capture the nonlinear patterns exhibited by the 2019 novel coronavirus (COVID-19) in terms of daily confirmed cases. As a result, Artificial Neural Network (ANN) and Error, Trend, and Seasonality (ETS) modeling have been successfully applied to resolve problems with nonlinear estimation. Our research suggests that it would be ideal to use a single model of ETS or ARIMA for COVID-19 time series forecasting rather than a complicated Hybrid model that combines several models. We compare the forecasting performance of these models using real, worldwide, daily COVID-19 data for the period between January 22, 2020 till June 19, and June 20 till January 2, 2021 which marks two stages, each stage indicating the first and the second wave respectively. We discuss various forecasting approaches and the criteria for choosing the best forecasting technique. The best forecasting model selected was compared using the forecasting assessment criterion known as Mean Absolute Error (MAE). The empirical results show that the ETS and ARIMA models outperform the ANN and Hybrid models. The main finding from the ETS and ARIMA models analysis indicate that the magnitude of the increase in total confirmed cases over time is declining and the percentage change in the death rate is also on the decline. Our results shows that the chosen forecaste models are consistent during the first and second wave of of the pandemic. These forecasts are encouraging as the world struggles to contain the spread of COVID-19. This may be the result of the social distancing measures mandated by governments worldwide.

This paper explains the epidemic spread using social network analysis, based on data from the first three months of the 2020 COVID-19 outbreak across the world and in Canada. A network is defined and visualization is used to understand the spread of coronavirus among countries and the impact of other countries on the spread of coronavirus in Canada. The degree centrality is used to identify the main influencing countries. Exponential Random Graph Models (ERGM) are used to identify the processes that influence link creation between countries. The community detection is done using Infomap, Label propagation, Spinglass, and Louvain algorithms. Finally, we assess the community detection performance of the algorithms using adjusted rand index and normalized mutual information score.

The aim of this study was to jointly model COVID-19 test positivity rates and hospitalizations in Texas using Bayesian joinpoint regression. The data for both test positivity rates and hospitalizations were obtained from the Texas Department of State Health Services between April 5 and October 19, 2020. The stage 1 model identifies four significant shifts in test positivity rates, three of which occur roughly 9 days after documented policy or behavioral changes statewide. Estimated positivity rates from the first model were then used to predict hospitalization rates and to estimate lag time between changes in positivity and hospitalization. The resulting lag time is 9.056 days ( ± 3.808). Both models are valuable to policy makers and public health officials as they study the impact of behavioral patterns on disease prevalence and resulting hospitalizations.

Human race is under the COVID-19 pandemic menace since beginning of the year 2020. Even though the disease is easily transmissible, a massive fraction of the affected people is recovering. Most of the recovered patients will not experience death due to COVID-19, even if they observed for a long period. They can be treated as long term survivors in the context of lifetime data analysis. In this article, we present statistical methods to estimate the proportion of long term survivors (cure fraction) of the COVID-19 patient population in India. The proportional hazards mixture cure model is used to estimate the cure fraction and the effect of the covariates gender and age, on lifetime. We can see that the cure fraction of the COVID-19 patients in India is more than 90%, which is indeed an optimistic information.

This article aims at identifying the high risk provinces in Iraq using a finite Poisson mixture. Through this methodology, the levels of relative risk is determined through identifying the number of components. In this article we do not investigate spatial correlation among regions and assume that the levels of risk observed in different regions are independent each other. The estimation of the model parameters and the model selection are performed using the Bayesian approach which allow to allocate each province to an identified risk level. We consider the data of the Coronavirus disease (COVID-19) infections in 18 provinces in Iraq and determining the levels of relative risks of this pandemic. The results are spatially shown in map which illustrates that the best Bayesian model fitted the data is 3 components model (high, medium and low risk).

Reporting of daily new cases and deaths on COVID-19 is one of the main tools to understand and menage the pandemic. However, governments and health authorities worldwide present divergent procedures while registering and reporting their data. Most of the bias in those procedures are influenced by economic and political pressures and may lead to intentional or unintentional data corruption, what can mask crucial information. Benford’s law is a statistical phenomenon, extensively used to detect data corruption in large data sets. Here, we used the Benford’s law to screen and detect inconsistencies in data on daily new cases of COVID-19 reported by 80 countries. Data from 26 countries display severe nonconformity to the Benford’s law ( p < 0.01), what may suggest data corruption or manipulation.