Chronic wasting disease (CWD) is a fatal disease of cervids with significant ecological and economic impacts. State wildlife agencies spend millions each year to test deer and elk for CWD, more so if they are one of 26 states that have previously detected the disease. Therefore, maximizing sampling efficiency and improving its effectiveness are critical. Several modeling efforts have already examined risk factors including sex, age, sample source, genetics, geophysical features, captive cervids, hunter-harvested carcasses, and disposal methods to “sample smarter” and increase detection power; however, a rigorous integration of these various models has not happened. We will evaluate strengths and weaknesses of available analytical tools and determine which can be synthesized to derive a more powerful sampling strategy. The products of this synthesis will be a tool that integrates local harvest and disease prevalence data with data science, mathematical and statistical modeling techniques. This toolset will allow MI and others to more fully explore and optimize disease surveillance efforts. By identifying risk factors for CWD, states can tailor sampling protocols to maximize efficiency and confidence in disease prevalence. The strength of this project is to form a regional collaboration that will allow for standardization, comparison, and integration of CWD surveillance streams. All states involved will benefit from improved surveillance effectiveness, minimized cost of sampling, and maximize the probability of discovering new infections. By bringing together some of the best scientists and agency personnel involved with CWD, we will bolster our collective knowledge and understanding of the biology and ecology of this shared threat. Although the model and resulting tool will prioritize MI and CWD, our novel approach to disease surveillance optimization will be transferrable to other states and will enhance infrastructure to address management challenges of other wildlife disease issues.