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Background
The COVID-19 pandemic has initiated several initiatives to better understand its behavior, and some projects are monitoring its evolution across countries, which naturally leads to comparisons made by those using the data. However, most “at a glance” comparisons may be misleading because the curve that should explain the evolution of COV...
Citations
... While molecular biology has attempted to predict new mutations, no successful method has been developed thus far. Therefore, we still needed a developed way of forecasting outbreaks of the new coronavirus variants because the evolution of COVID-19 variants differed across countries depending on socio-economic characteristics (10). ...
The occurrences and domination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are still crucial factors for determining the coronavirus disease 19 (COVID-19) policies. We collected weekly Phylogenetic Assignment of Named Global Outbreak sub-lineages, naming genetically distinct lineages of SARS-CoV-2, including variants of concern, in the United Kingdom, South Africa, South Korea, Denmark, Germany, the United States, and worldwide. This study included 12,296,756 samples of the max share of the sub-lineages from the 33rd week of 2020 to the 40th week of 2022. This study conducted a two-state Markov-switching model to estimate the probability of the phase shift state and predicted the probability of each regime with the Hamilton filter and Kim’s smoothing algorithm. We discovered different weekly patterns based on dominant SARS-CoV-2 variants in target area. Due to differences in containment policies and outbreak waves, we observed a time lag in dominant variants in these countries. Using the inferred probability of the phase shift regime for forecasting, it showed significant probabilities that the stable phase will still be stable in the next week. It also showed significant probabilities that the unstable phase will still be unstable in the next week. Our findings present the probability of observing the phase shift regime each week. Until a new SARS-CoV-2 variant occurs, the regime tended to stay with a low probability of phase shift regime. When a new SARS-CoV-2 variant would occur, the regime would immediately react and change the probability. We propose the Markov-switching model to determine COVID-19 policies and predict SARS-CoV-2 variants.
IMPORTANCE
Using regime-switching models, we attempted to determine whether there is a link between changes in severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) variants and infection waves, as well as forecasting new SARS-Cov-2 variants. We believe that our study makes a significant contribution to the field because it proposes a new approach for forecasting the ongoing pandemic, and the spread of other infectious diseases, using a statistical model which incorporates unpredictable factors such as human behavior, political factors, and cultural beliefs.
... Then, despite the measures taken to alleviate the crisis, efforts were made to reduce its negative social and economic effects, but the loss of well-being was felt in many cases [17]. Previous studies have shown that when national governments tend to adopt stricter measures to fight against COVID-19, the outcomes can be more meaningful [45][46][47]. Risks can be more easily counteracted through institutional transparency, open communication between stakeholders and closeness to citizens. In the context of such challenges, effective crisis management is decisive, and the ability to anticipate possible system disruptions, the speed of response, the coordinated action by institutions and cooperation make the difference between states in terms of the adverse implications of crises [9]. ...
Background
Global crises, regardless of the place where they started to spread or of the factors that triggered them, require a comprehensive approach, primarily based on good communication, cooperation and mutual support. No individual and no institution should remain indifferent to crises but, on the contrary, be fully aware that any involvement in curbing them matters. Although humanity can be affected by various types of crises, in this paper we refer to the one related to COVID-19 pandemic. There are certain reasons that come to justify our choice: first of all, being a shock with a strong impact on people, its analysis should be performed from several angles; this may bring to light an image with its disparate propagation and measures to counteract it both in developed countries, and especially in those with a shortage of resources. Secondly, in the context of the emergence of vaccines against COVID-19, it is helpful to have an overview of COVID-19 through the lens of the relationship between the vaccination process and the elements that characterize governance, with a differentiated dashboard by country categories worldwide: low, middle and high-income countries. Our study is far from capturing the complexity arising from such social problem, but rather aims to outline the defining role of governance when it comes to providing firm reactions to the COVID-19 crisis.
Methods
Given that our sample consists of a large number of countries, namely 170, first, examined all together, and then, split into three groups (high, middle and low-income), it is challenging to address governance in association with COVID-19 vaccination, in order to see how much they interact and how each of the six aggregate governance indicators of the World Bank (Worldwide Governance Indicators) is reflected in this process. Even if they do not oscillate strongly over relatively short periods of time, reporting on health issues requires a sequential inventory, considering closer time intervals, so as to be able to act promptly. Thus, to better distinguish how the COVID-19 vaccination process evolved in low, middle and high-income countries, but also how it was imprinted by governance, we present the situation quarterly (March, June, September and December), in 2021, the year when the immunization campaigns were the most intense at the global level. Regarding the applied methods, we mention both OLS regressions with robust estimators and a panel model, used to investigate the determinants of COVID-19 vaccination, some of them describing the good governance, as well as other dimensions.
Results
The findings point out that the influence of governance on COVID-19 vaccination differs depending on whether a country belongs to high, middle or low-income typology: the strongest determinism of governance on vaccination is encountered in high-income countries, and the weakest in low-income ones; in some cases, governance does not matter significantly. However, exploring the three groups of states included in the research, it is observed that the most relevant factors in this relationship are government effectiveness, regulatory quality and control of corruption.
Conclusions
Besides the order of importance of governance indicators on COVID-19 vaccination, our study indicates that, overall, governance positively shapes the vaccination rate at the level of the chosen sample. In normative terms, these findings can be translated particularly by the fact that they can serve as information to raise awareness on the relevance of the existence of an institutional framework that allows the formulation of strategies according to the patterns of each country, especially since the actionable tools depend on the available resources. As a general conclusion, public policies should be designed in such a way as to strengthen trust in vaccination regulations and in governments, to reduce the multifaceted negative effects of this health crisis and to hope for its total end.
... AIC can be calculated for each possible combination of explanatory variables, and the model with the lowest AIC is selected as the most optimal model [24]. The Cumulative Deviance Explained percentage measured how well the models fit the data [25]. ...
The Makassar Strait (MS) is characterized by water mass from the Pacific Ocean and is one of the ITF (Indonesia Throughflow) branches. It carries warm water masses from the Pacific Ocean to the Indian Ocean. This research aims to analyze the relationship between CPUE of Eastern Little Tuna (Euthynnus affinis) and oceanographic variables, likewise predict the fishing area using the Generalized Additive Model (GAM). The research method used is spatial and temporal analysis. The data was used from 2015 to 2020. The data processed were sea surface temperature, chlorophyll-a, salinity, currents, sea level as predictor variables, and Eastern Little Tuna production as a response. Eastern Little Tuna catch data were normalized into Catch per Unit Effort, while the oceanographic data were extracted using ArcGIS. Based on the results of the GAM model, it was found that the model with five variables is the most suitable predictive model, with 16.4% CDE. Salinity is the most influential parameter on the catch of Eastern Little Tuna with a significance value of <2.00 x 10-16 ***. The optimum value for SST is 30–31 °C, chlorophyll-a is 1–2 mg/m3, salinity is 29–30 ppt, current velocity is 0.3–0.5 m/s and sea level is between 0.6–0.7 m. Based on the GAM prediction results, a high CPUE value will be obtained in the southwest monsoon (March to May). Fishing activity carried out in the best season will implement the adoption of harvest control measures.
... The half-life of volatility persistence should be less than 100 days, and the volatility half-life of the first regime must be less than that of the second regime; 7. The model with the highest mean ( p 11 , p 22 ) should be selected because the higher this value, the more persistent the regime. Otherwise, regime-switching might occur too often, which would not be plausible for MS-GARCH models, as even a singleregime GARCH model could perform better to explain periods of high and low volatility in stock indices [67]; Figure 4 shows the best-fitting models that meet the filtering protocol for each of the 16 stock indices. The purple dashed line is the 5% conditional VaR. ...
The onset of the COVID-19 pandemic has increased volatility in financial markets, motivating researchers to investigate its impact. Some use the GARCH family of models to focus on long-memory persistence, while others use Markov chain models to better identify structural breaks and regimes. However, no study has addressed the occurrence of these two phenomena in a unified framework. Since both are important features of the data, to ignore one or the other could lead to poorly specified models. The outcome would be incorrect risk measurement, with implications for risk management, Value at risk, portfolio decisions, forecasting, and option pricing. This paper aims to fill this gap in the literature. We assemble an international dataset for 16 stock market indices in three continents over the period from August 1, 2019 to February 18, 2022, totalling 669 business days. Using R, we estimate 80 GARCH family models, 16 pure Markov-Switching models, and 900 combined GARCH/ Markov-Switching models using daily stock market log-returns. We allow for two volatility regimes (low and high). We also measure and incorporate News Impact Curves, which show how past shocks affect contemporaneous volatility. Our main finding, across estimated models, is that COVID-19 affected both long-memory persistence and volatility regimes in most markets. To describe the specific impact in each market, we report News Impact Curves. Lastly, the first wave of COVID-19 had a much greater impact on volatility than did subsequent waves linked to the emergence of new variants.
... And in addition to the analyses dealing with the COVID-19 analyses, in several cases also the parallel for example with the Spanish flue are analyzed [14]. As it was mentioned, the spread of the pandemic can be eliminated or at least slow down by different kind of restrictions, their impact was analyzed in many case studies, such for example in the study dealing with the GAM functions and Markov-Switching models in the evaluation framework to assess countries' performance in controlling of the pandemic [15]. A comparative study of five countries dealing with the optimal control model of the pandemic is presented in [16]. ...
Pandemics have the potential to cause immense disruption of our everyday activities and has impact on the communities and societies mainly through the restrictions applied to the business activities, services, manufacturing, but also education, transportation etc. Therefore, it is important to create suitable prediction models to establish convenient methods for the planning of the operations and processes to cope with the difficulty. In this paper, the prediction model for the spread of the viral disease in term of the estimated maximal weekly confirmed cases and weekly deaths using the Weibull distribution as a theoretical model for statistical data processing is presented. The theoretical prediction model was applied and confirmed on the data available for the whole world and compared to the situation in Europe and Slovakia for the pandemic waves and can be used for the more precise prediction of the pandemic situation and to enhance planning of the activities and processes regarding to the restrictions applied during the worsening pandemic situation.
An introduction to this Special Issue on Data Science for COVID-19 is included in this paper. It contains a general overview about methods and applications of nonparametric inference and other flexible data science methods for the COVID-19 pandemic. Specifically, some methods existing before the COVID-19 outbreak are surveyed, followed by an account of survival analysis methods for COVID-related times. Then, several nonparametric tools for the estimation of certain COVID rates are revised, along with the forecasting of most relevant series counts, and some other related problems. Within this setup, the papers published in this special issue are briefly commented in this introductory article.
