Food designing for Covid-19 patients with the help of applied mathematical optimization technique in LINDO Software

Objective: Due to the alarming rise in COVID-19 cases every day in India, numerous academics are already developing a variety of math-based estimation models to forecast the pandemic future course. This paper makes use of publicly available data to anticipate certain COVID-19 trajectories in India. Methods: We used the Auto-Regressive Integrated Moving Average Model, a time series model, to anticipate the number of COVID-19 infected cases every day soon. People need to consume nutritious food that is well-balanced and contains the right number of calories, nutrients, as well as vitamins for strong development, bearing in mind that sustaining and repairing bodily tissues is the goal while preventing unfavorable illnesses and disease. Results: Recent studies have shown that healthy eating can help lower the possibilities of developing cancer, cardiovascular disease and for COVID patients. Therefore, an optimization strategy and LINDO software was used to solve the model. This study offers an exhaustive daily meal plan created especially for hospital patients, acting as a helpful resource for school administrators. With six different food choices, the diet plan makes sure that you get all the nutrients you need each day at a reasonable price. Conclusions: The model is solved using LINDO software in the study, which shows how effective it is when compared to other heuristic techniques like biological algorithms. It is determined through thorough investigation that the chosen meals are both financially and nutritionally feasible to serve in ho spital settings. Hospital patients make up the study’s participants, and each day’s total cost comes to Rs109.34. This cost makes it possible to deliver meals that are minimally more costly but of higher quality, improving the patients’ overall nutritional value.