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Background and Objective: COVID-19 has engulfed the entire world, with many countries struggling to contain the pandemic. In order to understand how each country is impacted by the virus compared with what would have been expected prior to the pandemic and the mortality risk on a global scale, a multi-factor weighted spatial analysis is presented. Method: A number of key developmental indicators across three main categories of demographics, economy, and health infrastructure were used, supplemented with a range of dynamic indicators associated with COVID-19 as independent variables. Using normalised COVID-19 mortality on 13 May 2020 as a dependent variable, a linear regression (N = 153 countries) was performed to assess the predictive power of the various indicators. Results: The results of the assessment show that when in combination, dynamic and static indicators have higher predictive power to explain risk variation in COVID-19 mortality compared with static indicators alone. Furthermore, as of 13 May 2020 most countries were at a similar or lower risk level than what would have been expected pre-COVID, with only 44/153 countries experiencing a more than 20% increase in mortality risk. The ratio of elderly emerges as a strong predictor but it would be worthwhile to consider it in light of the family makeup of individual countries. Conclusion: In conclusion, future avenues of data acquisition related to COVID-19 are suggested. The paper concludes by discussing the ability of various factors to explain COVID-19 mortality risk. The ratio of elderly in combination with the dynamic variables associated with COVID-19 emerge as more significant risk predictors in comparison to socio-economic and demographic indicators.

The COVID-19 pandemic has caused unprecedented crisis across the world, with many countries struggling with the pandemic. In order to understand how each country is impacted by the virus and assess the risk on a global scale we present a regression based analysis using two pre-existing indexes, namely the Inform and Infectious Disease Vulnerability Index, in conjunction with the number of elderly living in the population. Further we introduce a temporal layer in our modeling by incorporating the stringency level employed by each country over a period of 6 time intervals. Our results show that the indexes and level of stringency are not ideally suited for explaining variation in COVID-19 risk, however the ratio of elderly in the population is a stand out indicator in terms of its predictive power for mortality risk. In conclusion, we discuss how such modeling approaches can assist public health policy.

Academic conferences offer numerous submission tracks to support the inclusion of a variety of researchers and topics. Work in progress papers are one such submission type where authors present preliminary results in a poster session. They have recently gained popularity in the area of Human Computer Interaction (HCI) as a relatively easier pathway to attending the conference due to their higher acceptance rate as compared to the main tracks. However, it is not clear if these work in progress papers are further extended or transitioned into more complete and thorough full papers or are simply one-off pieces of research. In order to answer this we explore self-citation patterns of four work in progress editions in two popular HCI conferences (CHI2010, CHI2011, HRI2010 and HRI2011). Our results show that almost 50% of the work in progress papers do not have any self-citations and approximately only half of the self-citations can be considered as true extensions of the original work in progress paper. Specific conferences dominate as the preferred venue where extensions of these work in progress papers are published. Furthermore, the rate of self-citations peaks in the immediate year after publication and gradually tails off. By tracing author publication records, we also delve into possible reasons of work in progress papers not being cited in follow up publications. In conclusion, we speculate on the main trends observed and what they may mean looking ahead for the work in progress track of premier HCI conferences.

pubushar(iiriris,cu , I V l W IWIFAbsrrucl-Ceo-information Technologics (GIT) are playing P significant role for an eliicient management of natural disasters all over the world. Among them space technologies are prominent for geo-information acquisition in an efficient and timely manner. This paper is focused on the potential use5 of CIT for natural disaster management with particular examples in Patiistan which is vulnerable to various natural hazards and disasters, such at droughts, floods, earthquakes, landslides. cyclones etc; are causing a devastating impact on human life, economy and environment.The CIT include5 Remote Sensing (RS), Geographical Information Systems (GIS), GPS, Information andCommunication Technology (ICT) etc. The use of remote sensing and CIS ha5 become an integrated, well developed and successful tool in disaster management, Spatial anaiyris of hazard is a complex task, as a number of factors play important role in the occurrence of the disastrous event. Therefore, analysis requires a large number of input parametcrs for predisaster. disaster and post-disaster phases. The increased availability of Remote Sensing data and CIS Iunctionalities in there times have created opportunities for a more detailed and rapid analysis of natural hazards. These enabling tcchnologies are air0 the core of comprehensive natural disaster management system that covers disaster's moniioring, modcling, mirigntion, rescues operation management, and rehabilitation strategies development etc. It is almost impossible to fulty control the disasters, but a suitable strategy can be developed for disasters management using GIT in conjunction with conventional techniques. Numerous projects have been initiated in the country for these purposes. The proper structure of information system for disaster management should be available to tackle the disaster and to integrate all the data sets in all such projects.

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