Optimal prevention and control strategy of infectious disease: Cost-effectiveness analysis based on a modified...

The large-scale outbreaks of novel infectious diseases threaten public health, while strict intervention measures might slow down the economic activity. The effective prevention and control measures should balance cost and benefit. This study aims to explore the optimal intervention strategy for the infectious diseases by proposing a dynamic model with economic cost based on the modified SEIR model. Seven compartments were expanded as QSEAIRD model according to China's real practice in COVID-19. The parameters were estimated by minimizing the prediction error, and the GDP loss coefficients were introduced to quantify the economic costs of different measures. Thereafter, we formulated a corresponding algorithm to solve for the optimal prevention policies, which could control the epidemic within a specified time with minimized economic loss. Using Shanghai as a case study, we simulated the epidemic trends from March 2022 under different policy scenarios. We found that the government interventions effectively shortened the peak time by 60 % and significantly reduced its magnitude by 90 %. Without these measures, we predicted that Shanghai would reach the peaks of the first and second waves of infections at the end of 2022 and in June 2023, respectively, with the number of infections during the second peak being about 1/7 of that during the first. These results demonstrate that the government's prevention and control measures were effective in containing the epidemic. If relatively loose measures were adopted, the epidemic would not be controlled within one month, which would prolong the implementation of the prevention measures and increase economic loss. By conducting a cost-effectiveness analysis, the proposed model and algorithm can be flexibly applied to optimize the design of infectious disease prevention and control schemes under different scenarios, systematically enhancing the capacity to respond to the novel infectious diseases.