<p>This paper investigates the role of virus detection in the prevention and control of epidemics. The objective is to identify more effective measures for preventing and controlling outbreaks with minimal costs, thereby mitigating the impact on socio-economic development. This study, inspired by invasive species management, provides an innovative decision-making framework based on computing experiments for the prevention and control of epidemic. The novel framework not only fully considers the multiple heterogeneities in the process of the spread of epidemic but also organically unified the two prevention and control strategies—the tight control strategy and the loose control strategy—through the design of a virus detection strategy. It is found that adhering to the tight control strategy is more effective than the loose control strategy in terms of reducing the number of infected individuals, the proportion of severe and critical illness, and economic damage. According to the desired level of epidemic control set by decision-makers, the Epidemic-detection model can flexibly provide the optimal decisions on when and where to conduct the detection. Furthermore, the results also reveal that the contact coefficient, virus detection efficiency, and virus detection frequency have marginal decreasing effects. Increasing virus detection frequency alleviates the impact of the contact coefficient on the epidemic, but it amplifies the impact of virus detection efficiency on the epidemic on it.</p>