With the continued global expansion of COVID-19 transmission and the mounting threat of the disease, the timely analysis of its trend in Nepal and forecasting the potential situation in the country has been deemed necessary. We analyzed the trend, modelling, and impact assessment of COVID-19 cases of Nepal from 23rd January 2020 to 30th April 2020 to portray the scenario of COVID-19 during the first phase of lockdown. Exponential smoothing state-space and autoregressive integrated moving average (ARIMA) models were constructed to forecast the cases. Susceptible-infectious-recovered (SIR) model was fit to estimate the basic reproduction number (Ro) of COVID-19 in Nepal. There has been an increase in the number of cases but the overall growth in COVID-19 was not high. Statistical modelling has shown that COVID-19 cases may continue to increase exponentially in Nepal. The basic reproduction number in Nepal being maintained at a low level of 1.08 for the period of 23rd January to 30th April 2020 is an indication of the effectiveness of lockdown in containing the COVID-19 spread. The models further suggest that COVID-19 might persist until December 2020 with peak cases in August 2020. On the other hand, a basic reproduction number of 1.25 was computed for total cases reported for the 22nd March to 30th April 2020 period implying that COVID-19 may remain for at least a year in the country. Thus, maintaining social distance and stay home policy with an implementation of strict lockdown in the COVID-19 affected district is highly recommended.
With continued global expansion of COVID-19 transmission and mounting threat of the disease, the timely analysis of its trend in Nepal and forecasting the potential situation in the country has been deemed necessary. We analyzed the trend, modelling and impact assessment of COVID-19 cases of Nepal from 23rd January 2020 to 30th April 2020 to portray the scenario of COVID-19 after the first phase of lockdown. Exponential smoothing state-space and autoregressive integrated moving average (ARIMA) models were constructed to forecast the cases. Susceptible-infectious-recovered (SIR) model was fit to estimate the basic reproduction number (Ro) of COVID-19 in Nepal. There has been increase in the number of cases but the overall growth in COVID-19 was not high. Statistical modelling has shown that COVID-19 cases may continue to increase exponentially in Nepal. The basic reproduction number in Nepal being maintained at low level of 1.08 for the period of 23rd January to 30th April 2020 is an indication of effectiveness of lockdown in containing the COVID-19 spread. The models further suggest that COVID-19 might persist until December 2020 with peak cases in August 2020. On the other hand, basic reproduction number of 1.25 was computed for total cases reported for the 22nd March to 30th April 2020 period implying that COVID-19 may remain for at least for a year in the country. Thus, maintaining social distance and stay home policy with an implementation of strict lockdown in COVID-19 affected district is highly recommended.
Conventional method of making statistical inference regarding food quality measure is absolutely based upon experimental data. It refuses to incorporate prior knowledge and historical data on parameter of interest. It is not well suited in the food quality control problems. We propose to use a Bayesian approach inferring the conformance of the data concerning quality run. This approach integrates the facts about the parameter of interest from the historical data or from the expert knowledge. The prior information are used along with the experimental data for the meaningful deduction. In this study, we used Bayesian approach to infer the weight of pouched ghee. Data are taken selecting random samples from a dairy industry. The prior information about average weight and the process standard deviation are taken from the prior knowledge of process specification and standards. Normal-Normal model is used to combine the prior and experimental data in Bayesian framework. We used user-friendly computer programmes, 'First Bayes' and 'WinBUGS' to obtain posterior distribution, estimating the process precision, credible intervals, and predictive distribution. Results are presented comparing with conventional methods. Fitting of the model is shown using kernel density and triplot of the distributions.
Higher education and research for socioeconomic development are well recognized in developed and developing countries. Studies have shown that a high participation rate in higher education with a high share of Science, Technology, Engineering, and Mathematics (STEM) education is critical for competitiveness in the global market. Nepal would not fulfill people’s aspirations for prosperity by perpetuating the status quo scenario of the education system. This study aimed at exploring and assessing the factors influencing science and technology education in Nepal and tried to assess the current status of science education and critically examine the factors affecting the development of science education in Nepal. The study used both primary and secondary data. The primary source of data is from interviews, observations, focused group discussions, and semistructured questionnaires. Secondary data were collected from National Examination Board, universities, colleges, and campuses. The study found a decreasing trend of student enrollment in science and technology (S&T) education in Nepal. In addition, the results revealed a decreasing trend of women students, so it should be taken as a matter of concern. Some key bottlenecks identified were insufficient and broken physical infrastructures (classrooms, laboratories, and libraries); inadequate and incapable human resources; and improper management practices. However, the study results show positive perceptions of society towards S&T education in Nepal. The study recommends developing modern infrastructures, building human resources, and improving management practices for better S&T education.
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