As COVID-19 outbreak has created a global crisis, treating patients with minimum resources and traditional methods has become a hectic task. In this technological era, the rapid growth of coronavirus has affected the countries in lightspeed manner. Therefore, the present study proposes a model to analyse the resource allocation for the COVID-19 pandemic from a pluralistic perspective.
The present study has combined data analytics with the K-mean clustering and probability queueing theory (PQT) and analysed the evolution of COVID-19 all over the world from the data obtained from public repositories. By using K-mean clustering, partitioning of patients’ records along with their status of hospitalization can be mapped and clustered. After K-mean analysis, cluster functions are trained and modelled along with eigen vectors and eigen functions.
After successful iterative training, the model is programmed using R functions and given as input to Bayesian filter for predictive model analysis. Through the proposed model, disposal rate; PPE (personal protective equipment) utilization and recycle rate for different countries were calculated.
Using probabilistic queueing theory and clustering, the study was able to predict the resource allocation for patients. Also, the study has tried to model the failure quotient ratio upon unsuccessful delivery rate in crisis condition.
The study has gathered epidemiological and clinical data from various government websites and research laboratories. Using these data, the study has identified the COVID-19 impact in various countries. Further, effective decision-making for resource allocation in pluralistic setting has being evaluated for the practitioner's reference.
Further, the proposed model is a two-stage approach for vulnerability mapping in a pandemic situation in a healthcare setting for resource allocation and utilization.
