Identification of high-risk disease clusters has been one of the top goals for infectious disease public health surveillance. The proposed metric consists of three components: the area's own risk profile, temporal risk trend, and spatial neighborhood influence. We also introduce a weighting scheme to balance these three components, which accommodates the characteristics of the infectious disease outbreak, and spatial disease trends. Thorough simulation studies were conducted to identify the optimal weighting scheme and evaluate the performance of the proposed cluster prediction surveillance metric. Results indicate that the area’s own risk and the neighborhood influence play an important role to make a highly sensitive metric, and the risk trend term is important for the specificity and the accuracy of prediction. The proposed cluster prediction metric was applied to the COVID-19 case data of South Carolina from March 12th, 2020, and the subsequent 30 weeks of the data.