Public health departments need evidence-backed scenario projections to support decision making in infectious disease outbreaks. However, traditional infectious disease models are often not readily deployable or responsive to the urgent questions and priorities of public health departments or health systems. Moreover, uncertainty in model outputs is not always adequately assessed or communicated, potentially undermining trust among public health practitioners and the public. To address these issues, we, the Insight Net Modeling Guidance for Public Health Working Group, used early COVID-19 data from Michigan to illustrate modeling approaches that can be used to answer urgent questions in three key phases of outbreak response: prior to local introduction, early exponential growth, and established transmission with potential interventions. In each phase, we integrate case, hospitalization, and death data and capture ranges of plausible future trajectories. These models, which produce status quo and scenario projections, are intended to inform planning and motivate action rather than forecast precise future outcomes. Importantly, this work offers guidance to focus modeling efforts and provides examples and code for how to fit and implement these models, ultimately serving as both a conceptual guide and practical toolkit to support more transparent, timely, and appropriate use of models in outbreak response.