Agent-based models (ABMs) can provide realistic dynamics for epidemics at the individual level so that users can observe and predict the spreading pattern and the effectiveness of intervention over time and space. This paper proposes an FPGA-based accelerator for agent-based epidemic modeling for COVID-19. The optimizations enabling the effective acceleration of epidemic modeling are presented. The key idea is to partition the calculation properly to decouple the on-chip resource usage from the population size. Also, an algorithmic adaptation is proposed to reduce the latency caused by conditional branches within loops. An experimental implementation on an Intel Arria 10 GX 10AX115S2F45I1SG FPGA running at 240MHz achieves 2.2 and 1.9 times speed-up respectively over 10 Intel Xeon Gold 6230 CPU cores and an Nvidia GeForce RTX 2080 Ti GPU.