A decision support system to reorganize medical service network in pandemic

The advent of the Covid-19 pandemic has posed severe challenges to health care networks in various countries. The overcrowding of hospitals and the lack of medical staff and beds in multiple wards are among the main problems of governments. A new virus wave also exacerbates these problems. Also, the lack of information and the variability of the incidence rate and severity of the disease in different waves make it difficult to estimate the number of patients accurately. In this respect, this study develops a mixed-integer linear programming model to reorganize the medical service network. A fuzzy approach is employed to estimate the number of patients in each period. The result obtained from the model, apart from preventing the shortage of hospital beds, demonstrates a 60% reduction in visits to these centers.