Gitanjali Rahul Shinde - Quality of Work-Life During Pandemic: Data Analysis and Mathematical Modeling

HAPPINESS is a state of mind that has nothing to do with the external world.

Bhagwad Gita

This uncertain spread of COVID-19 is creating an emotional challenge for many people leading to their daily routine in very unprecedented ways. Every stakeholders of the society like employer and employees are being affected to large extent and are responsible to play important role in controlling this spread. Preventing and mitigating COVID-19 at the workplace are very important and require proper guidelines and recommendations to prevent and mitigate COVID-19 at the workplace. Work-life balance and mental health considerations are very important as per the guidelines issued by World Health Organization (WHO) during this COVID-19 outbreak. As per the guidelines stated, the recommendations are issues to the different groups of people which include the general population, healthcare workers, team leaders in healthcare organizations, older adults, people with underlying health conditions and their careers, people in isolation, etc.

Due to this pandemic, work from home culture has become a policy priority for most state and central governments and there should be policy in place considering both employers and employees. In view of this, an exploratory study is required to investigate the ongoing experiences of employees and employers faced in India. A question-based survey for employees in different sectors like education, information technology, agriculture, healthcare and real estate sector is required to collect their experiences on the different types of questions. On the responses received for these questions, mathematical tools can play an important role to build mathematical models for employees in different sectors. The application of appropriate tools from mathematics and mathematical model depends on the set of a questionnaire prepared for different domains. In addition to this, machine learning-based analysis is also important to draw inference from the outcomes resulted from clustering or classification algorithms in machine learning. The normative inferences are also one of the important outcomes of these machine learning-based inferences to decide what is ought to be? The selection of an appropriate machine learning algorithm will completely depend on the descriptive statistics of the data set, and accordingly, the outcomes can be analyzed for different purposes.