This study aims to provide insights into predicting future cases of COVID-19 infection and rates of virus transmission in the UK by critically analyzing and visualizing historical COVID-19 data, so that healthcare providers can prepare ahead of time. In order to achieve this goal, the study invested in the existing studies and selected ARIMA and Fb-Prophet time series models as the methods to predict confirmed and death cases in the following year. In a comparison of both models using values of their evaluation metrics, root-mean-square error, mean absolute error and mean absolute percentage error show that ARIMA performs better than Fb-Prophet. The study also discusses the reasons for the dramatic spike in mortality and the large drop in deaths shown in the results, contributing to the literature on health analytics and COVID-19 by validating the results of related studies.
In this paper, the analysis of a genealogical network is presented. The source database was constructed from the records of birth, marriage and death registers of a medium-sized Hungarian town covering some centuries. This genealogical network contains ca. 100.000 individuals. The topological features of this acyclic directed graph were analyzed by computer software in order to draw conclusions about the community. The results illustrate how network science can help the social sciences. A new measure is also defined to quantify the degree of pedigree collapse of a person having a partially known ancestor graph. The network was analyzed from the point of view of this ancestor-loss coefficient.
In this paper, we analyze a dataset including more than 189 million tweets related to the first month of the 2022 war in Ukraine. Our analysis especially focuses on communities of Twitter users and their collective behavior. In particular, we applied the InfoMap community detection algorithm and found on average 44079.63 communities of Twitter users per day. Our behavioral analysis especially focuses on the five largest daily communities (i.e. the communities that have been detected for each day during the first month of the war). We found that: 1) hashtags played an essential role in framing conversations, 2) communities often publicly called on international organizations or offices such as @potus, @NATO, or @UN to aid in conflict resolution, 3) anger was the dominant emotion in all communities and 4) negative tweets spread wider than the positive ones.
It has not been far, over a century, since humankind conceived that hazardous incidents should be substantially managed to procrastinate the future could-be hazards.In the middle of the twentieth century, nonetheless, safety measures were passed by officials and introduced to authorities, and private sectors, so as to reduce risks, environmental impacts of the hazards and to evaluate probable outcomes.Therefore, the concept of ALARP, meaning 'as low as reasonably practicable' presented back then, has been implemented in risk reduction management to make decisions upon acceptability and tolerability of risks.In order to do so, a few so-called tools, such as Cost-Benefit Analysis, are specified to societal and other types of risks so that we could weigh the balance of the amount of capital to be invested on safety on the one hand, and the extracted benefit attained out of the investment on the other.This implementation opaquely carries on several social, socio-economic, political and even environmental implications.Nevertheless, it has brought up some concerns into proponents' mindset, ranging from practicality and political reality to calling into question whether ALARP is mainly theoretical.The aim of this study is to figure out whether Cost-Benefit Analysis can be an appropriate tool to analyse the true outcome(s) of ALARP.This paper will offer a critical point of view over the risk-evaluating concept to discern how much it has been practically efficient.
