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This paper investigates the resource allocation algorithm design for intelligent reflecting surface (IRS) aided multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) multicell networks, where a set of base stations cooperate to serve a set of ultra-reliable low-latency communication (URLLC) users. The IRS is deployed to enhance the communication channel and increase reliability by creating a virtual line of sight for URLLC users with unfavorable propagation conditions. This is the first study on IRS-enhanced OFDMA-URLLC systems. The resource allocation algorithm design is formulated as an optimization problem for the maximization of the weighted system sum throughput while guaranteeing the quality of service of the URLLC users. The optimization problem is non-convex and finding the globally optimal solution entails a high computational complexity which is not desirable for real-time applications. Therefore, a suboptimal iterative algorithm is proposed which \textit{jointly} optimizes all optimization variables in each iteration using a new iterative rank minimization approach. The algorithm is guaranteed to converge to a locally optimal solution of the formulated optimization problem. Our simulation results show that the proposed IRS design facilitates URLLC and yields large performance gains compared to two baseline schemes.