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UNSTRUCTURED Mathematical modeling of COVID-19 transmission has been instrumental in the global pandemic, particularly to governments in many low- and middle-income countries (LMICs), where empirical data and active surveillance is limited. Simulations can be used to understand disease trajectory and impacts of policy options at little additional cost. We describe an automated, open-source, and web-based dashboard designed to empower Malawi government officials to understand the ramifications of certain policy interventions from a mathematical model of COVID-19 transmission. The dynamic dashboard allows policymakers to simulate scenarios of various COVID responses by allowing them to manipulate model inputs like percentage of population using cloth masks, percentage of population abiding by physical distancing recommendations, and the length of both these interventions in days. The dashboard’s outputs include COVID-19 cases, hospitalizations, intensive care unit (ICU) stays, and deaths based on the user-defined parameters and level of interest for the data (i.e., administrative levels like traditional authority-, district-, or national-level). It also compares the outputs from the user-defined simulation with a baseline scenario that conservatively estimates 15% of the population masking and 8% abiding by physical distancing guidelines. Our dynamic dashboard not only generates pertinent outputs to formulating a pandemic policy response, but it also offers an intuitive, streamlined, and real-time user experience to policymakers who may lack backgrounds in epidemiology and programming. LMICs should continue to invest in their country’s data collection, digital infrastructures, and technical training for key stakeholders to support widespread use of these evidence-based, decision-support tools in the future.