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In this manuscript we propose a novel method that models the evolution, spread and transmission of COVID 19 pandemic. The proposed model is inspired partly from the evolutionary based state of the art genetic algorithm. The rate of virus evolution, spread and transmission of the COVID 19 and its associated recovery and death rate are modeled using the principle inspired from evolutionary algorithm. Furthermore, the interaction within a community and interaction outside the community is modeled. Using this model, the maximum healthcare threshold is fixed as a constraint. Our evolutionary based model distinguishes between individuals in the population depending on the severity of their symptoms/infection based on the fitness value of the individuals. There is a need to differentiate between virus infected diagnosed (Self isolated) and virus infected non-diagnosed (Highly interacting) sub populations/group. In this study the model results does not compare the number outcomes with any actual real time data based curves. However, the results from the model demonstrates that a strict lockdown, social-distancing measures in conjunction with more number of testing and contact tracing is required to flatten the ongoing COVID-19 pandemic curve. A reproductive number of 2.4 during the initial spread of virus is predicted from the model for the randomly considered population. The proposed model has the potential to be further fine-tuned and matched accurately against real time data.

Technological innovation is viewed as one of the main economic multipliers. In high-technology sectors, with intense competition and short product life cycles, failure to detect an emerging technology could be devastating to both incumbent technology pursuers and innovators. Firms typically employ methods such as a Delphi method, Technology growth curves or technology road-mapping methods to identify and forecast growth. Some of these methods are based on a life cycle approach where a technology is expected to follow an S-curve and then become obsolete in due time, which is analogous to market diffusion of products. In the case of emerging technologies, forecasting methods are not always reliable due to lack of historical data. Hence, both qualitative and quantitative methods are employed to forecast; however, there are risks associated with both of these methods. A general consensus in technology forecasting community is to apply multiple methods for forecasting. In the last few decades there has been explosion of new technologies, especially in the high-technology sector of electronics. Several new applications such as Large Area Displays (several square feet in area), low cost electronics (e.g. RFID tags being manufactured for pennies or cents per unit) and body conformable electronic applications are a few of a long and growing list. The aforementioned applications could broadly be categorized to form a new and emerging field of electronics called flexible electronics. These applications utilize the rugged lightweight plastics to potentially offer attractive characteristics such as low-cost processing, mechanical flexibility, large area coverage, etc. these characteristics are not easily implemented with established silicon technologies. This research analyzes flexible electronics technology by first identifying a few key innovations. Patent and publications data are collected and technology growth curves based on the indicators would be generated, and compared with those of incumbent technology innovations, to assess technology growth potential. Based on the study, technology adoption strategies would also be recommended by which business leaders could anticipate and plan for the effects of these innovations.

In this manuscript we propose a novel method that models the evolution, spread and transmission of COVID 19 pandemic. The proposed model is inspired partly from the evolutionary based state of the art genetic algorithm. The rate of virus evolution, spread and transmission of the COVID 19 and its associated recovery and death rate are modeled using the principle inspired from evolutionary algorithm. Furthermore, the interaction within a community and interaction outside the community is modeled. Using this model, the maximum healthcare threshold is fixed as a constraint. Our evolutionary based model distinguishes between individuals in the population depending on the severity of their symptoms/infection based on the fitness value of the individuals. There is a need to differentiate between virus infected diagnosed (Self isolated) and virus infected non-diagnosed (Highly interacting) sub populations/group. In this study the model results does not compare the number outcomes with any actual real time data based curves. However, the results from the model demonstrates that a strict lockdown, social-distancing measures in conjunction with more number of testing and contact tracing is required to flatten the ongoing COVID-19 pandemic curve. A reproductive number of 2.4 during the initial spread of virus is predicted from the model for the randomly considered population. The proposed model has the potential to be further fine-tuned and matched accurately against real time data.