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The COVID-19 virus has been recently identified as a new species of virus that can cause severe infections such as pneumonia. The sudden outbreak of this disease is being considered a pandemic. Given all this, it is essential to develop smart biosensors that can detect pathogens with minimum time delay. Surface plasmon resonance (SPR) biosensors make use of refractive index (RI) changes as the sensing parameter. In this work, based on actual data taken from previous experimental works done on plasmonic detection of viruses, a detailed simulation of the SPR scheme that can be used to detect the COVID-19 virus is performed and the results are extrapolated from earlier schemes to predict some outcomes of this SPR model. The results indicate that the conventional Kretschmann configuration can have a limit of detection (LOD) of 2E-05 in terms of RI change and an average sensitivity of 122.4 degRIU-1 at a wavelength of 780 nm.Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. find more COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naïve Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy.At this time, COVID-2019 is spreading its foot in the form of a huge epidemic for the world. This epidemic is spreading its foot very fast in India too. One of the World Health Organization states that COVID-2019 is a serious disease that spreads from one person to another at very fast speed through contact routes and respiratory drops. On this day, India and the world should rise to an effective step to analyze this disease and eliminate the effects of this epidemic. In this paper presented, the growing database of COVID-2019 has been analyzed from March 1, 2020, to April 11, 2020, and the next one is predicted for the number of patients suffering from the rising COVID-2019. Different regression analysis models have been utilized for data analysis of COVID-2019 of India based on data stored by Kaggle in between 1 March 2020 to 11 April 2020. In this study, we have been utilized six regression analysis based models namely quadratic, third degree, fourth degree, fifth degree, sixth degree, and exponential polynomial respectively for the COVID-2019 dataset. We have calculated the root mean square of these six regression analysis models. In these six models, the root mean square error of sixth degree polynomial is very less in compared other like quadratic, third degree, fourth degree, fifth degree, and exponential polynomial. Therefore the sixth degree polynomial regression model is very good models for forecasting the next 6 days for COVID-2019 data analysis in India. In this study, we have found that the sixth degree polynomial regression models will help Indian doctors and the Government in preparing their plans in the next 7 days. Based on further regression analysis study, this model can be tuned for forecasting over long term intervals.What factors affected whether or not a U.S. state governor issued a state-wide stay-at-home order in response to the COVID-19 pandemic of early 2020? Once issued, what factors affected the length of this stay-at-home order? Using duration analysis, we test a number of epidemiological, economic, and political factors for their impact on a state governor's decision to ultimately issue, and then terminate, blanket stay-at-home orders across the 50 U.S. states. Results indicate that while epidemiologic and economic variables had some impact on the delay to initiation and length of the stay-at-home orders, political factors dominated both the initiation and ultimate duration of stay-at-home orders across the United States.The precipitous spread of COVID-19 has created a conflict between human health and economic well-being. To contain the spread of its contagious effect, India imposed a stringent lockdown, and then the stringency was relaxed to some extent in its succeeding phases. We measure social benefits of the lockdown in terms of improved air quality in Indian cities by quantifying the effects with city-specific slope coefficients. We find that the containment measures have resulted in improvement in air quality, but it is not uniform across cities and across pollutants. The level of PM2.5 decreases from about 6 to 25% in many cities. Moreover, we observe that partial relaxations do not help in resuming economic and social activities. It should also be noted that counter-virus measures could not bring levels of the emissions to WHO standards; it highlights the importance of role of green production and consumption activities.The COVID-19 pandemic has caused a massive economic shock across the world due to business interruptions and shutdowns from social-distancing measures. To evaluate the socio-economic impact of COVID-19 on individuals, a micro-economic model is developed to estimate the direct impact of distancing on household income, savings, consumption, and poverty. The model assumes two periods a crisis period during which some individuals experience a drop in income and can use their savings to maintain consumption; and a recovery period, when households save to replenish their depleted savings to pre-crisis level. The San Francisco Bay Area is used as a case study, and the impacts of a lockdown are quantified, accounting for the effects of unemployment insurance (UI) and the CARES Act federal stimulus. Assuming a shelter-in-place period of three months, the poverty rate would temporarily increase from 17.1% to 25.9% in the Bay Area in the absence of social protection, and the lowest income earners would suffer the most in relative terms.