an integrated ahp and system dynamics approach for the evaluation of covid-19 prevention strategies

The covid-19 pandemic has presented a significant challenge to public health systems worldwide, leading to a need for effective strategies to prevent and control its spread. typical decision-making tools have limitations, because each of them requires some historical data for analysis. however, since the covid-19 pandemic has no close precedents, there is a lack of sufficient data. to overcome this limitation, this paper utilizes the analytic hierarchy process (ahp) by combining system dynamics (sd) as a decision-making tool to evaluate different covid-19 prevention strategies based on different criteria. we model the spread of the virus using system dynamics (sd) to calculate pairwise comparison coefficients for the ahp analysis. our findings indicate that social restriction is the most effective strategy for controlling the spread of covid-19 and future pandemics based on three criteria - mortality, cost of implementing the strategy, and psychological impact.