Enhancing risk factor evaluation for infectious diseases using decision-making approach based on linear programming

Abstract

Infectious diseases are defined as the invasion of hazardous agents into the human body that causes injury and has the potential to spread to others. These diseases cause a considerable proportion of illnesses and deaths in any given community. The criteria importance through inter-criteria correlation (CRITIC) approach is used to determine the relative importance of multiple assessment criteria, whereas combinative distance-based assessment (CODAS) is a methodology used to compare and evaluate the efficacy of numerous options. The CRITIC-CODAS method is a linear programming-based tool for estimating the risks of infectious diseases. This method takes into account a number of aspects, including the disease’s characteristics, the population at risk, and the available resources for prevention and treatment. The CRITIC-CODAS technique can provide valuable insights into the possible impact of an infectious disease outbreak and aid in improving decision-making regarding prevention and control measures by assessing these elements in a systematic and quantitative manner. The purpose of this work is to provide an overview of the CRITIC-CODAS technique, which is powered by a q-rung orthopair fuzzy set (q-ROFS), and its use in estimating the risk of infectious disease outbreaks. According to the case study, the infectious disease malaria has a greater possibility of spreading in the city under observation. According to the proposed method, the ranking of infectious cities given as ℐ3ℷ≻ℐ4ℷ≻ℐ5ℷ≻ℐ2ℷ≻ℐ1ℷ. This implies that the likelihood of an epidemic of the infectious disease ℐ3ℷ is higher in the city being observed. The study also addresses the approach’s benefits and shortcomings and makes recommendations for further research and development. Overall, the CRITIC-CODAS approach appears to be a useful tool for better understanding infectious disease risks and guiding appropriate response tactics.