The average seizure count on the day of EQ-5D completion was lower than the average seizure count for the preceding period (2.8865.10 vs. respectively 3.7665.62, 3.8165.69 and 3.7565.39, p , 0.01). The same pattern was seen at the individual measurements. Graphical displays confirm the hypothesis that patients appear to complete the questionnaire on a day that seizure count is relatively low. Conclusions: When HRQoL is measured at regular time intervals, people with an episodic condition such as epilepsy appear to complete these measurements on relatively good days. Consequently, the HRQoL is potentially overestimated in such populations, especially for bad days. When treatment is targeted at reducing the frequency or the intensity of such bad days, measuring HRQoL at regular time intervals might lead to underestimation of the effectiveness of treatment, and therefore to biased estimates of cost-effectiveness.
COVID-19 is an infectious disease, growth of which depends upon the linked stages of the epidemic, the average number of people one person can infect and the time it takes for those people to become infectious themselves. We have studied the COVID-19 time series to understand the growth behaviour of COVID-19 cases series. A structural break occurs in the COVID-19 series at the change time form one stage to another. We have performed the structural break analysis of data available for 207 countries till April 20, 2020. There are 42 countries which have recorded five breaks in COVID cases series. This means that these countries are in the sixth stage of growth transmission and show a downward pattern in reporting in the daily cases, whereas countries with two and three breaks, record the rapid growth pattern in the daily cases. From this study, we conclude that the more the breaks in the series, there is more possibility to determine the constant or decreasing rate of daily cases. It is well fitted using lognormal distribution as this distribution is archived at its highest peak after some period and then suddenly it decreases at a longer time period. This can be seen in various countries like China, Australia, New Zealand and so on.
It has been observed that all over the world, small and medium enterprises (SMEs) are considered as major source for economic growth. Indian SMEs sector is also one of the fastest growing sectors of Indian economy. So it is very important for SMEs to know what their knowledge assets are, and how to manage and make best use of these assets to get maximum return. The effective implementation of Knowledge Management (KM) in SMEs will be governed and facilitated by certain critical variables. The objective of this paper is to identify, discuss and consider the critical variables which are helpful for successful implementation of KM in Indian SMEs. In this paper, based on a review of the literature, eight critical variables have been identified, in order to enhance the competitiveness of SMEs. Interpretive Structural Modeling (ISM) is a methodology for identifying and summarizing relationships among specific variables, which define an issue or problem. It provides a means by which order can be imposed on the complexity of such variables. In the present paper the important variables for the implementation of KM in Indian SMEs has been analyzed to obtain an ISM, which shows the interrelationships of the variables and their levels. These variables have also been categorized depending on their driving power and dependence.
All transmission disease depends on the transmission opportunity or medium like humans in COVID-19. Due to globalization and regular movement of people from one country to another, spread of COVID 19 reached to 208 countries till May 10, 2020. For any society health is major concern for humanity as well as administration. Any pandemic is declared as and when it reached at a particular severity level and control vice versa. So, we have continued the daily COVID 19 cases analysis and segregated till May 10, 2020. We have included at least 25 countries for the analysis purpose due to limitation of number of observations in the analysis. Maximum number of day’s data available for China is for 100 days, followed by Iran for 81 days, minimum number of days data is for 16 days for Western Sahara and Tajikistan.
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