Infected individuals must be aware of disease symptoms to seek care, so outreach and education programs are critical to disease control. However, public health organizations often only have limited resources for outreach and must carefully design campaigns to maximize effectiveness, potentially leveraging word-of-mouth information spread. We show how classic epidemiological models can be reformulated such that identifying an efficient disease control resource allocation policy in the context of information spread becomes a submodular maximization problem. This means that our framework can simultaneously handle multiple, interacting dynamic processes coupled through the likelihood of disease clearance, allowing our framework to provide insight into optimal resource allocation while considering social dynamics in addition to disease dynamics (e.g., knowledge spread and disease spread). We then demonstrate that this problem can be algorithmically solved and can handle stochasticity in input parameters by examining a numerical example of tuberculosis control in India.