BackgroundMajor respiratory infectious diseases, such as influenza, SARS-CoV, and SARS-CoV-2, have caused historic global pandemics with severe disease and economic burdens. Early warning and timely intervention are key to suppress such outbreaks.ObjectiveWe propose a theoretical framework for a community-based early warning (EWS) system that will proactively detect temperature abnormalities in the community based on a collective network of infrared thermometer–enabled smartphone devices.MethodsWe developed a framework for a community-based EWS and demonstrated its operation with a schematic flowchart. We emphasize the potential feasibility of the EWS and potential obstacles.ResultsOverall, the framework uses advanced artificial intelligence (AI) technology on cloud computing platforms to identify the probability of an outbreak in a timely manner. It hinges on the detection of geospatial temperature abnormalities in the community based on mass data collection, cloud-based computing and analysis, decision-making, and feedback. The EWS may be feasible for implementation considering its public acceptance, technical practicality, and value for money. However, it is important that the proposed framework work in parallel or in combination with other early warning mechanisms due to a relatively long initial model training process.ConclusionsThe framework, if implemented, may provide an important tool for important decisions for early prevention and control of respiratory diseases for health stakeholders.