Airborne transmission of infectious agents is governed by a complex interplay of biological, environmental, and behavioral parameters that remain poorly unified by existing epidemiologic models.  Here theta (Θ) is proposed, a hybridized, logarithmic, refractory index designed to dynamically characterize the airborne transmissibility of pathogens across the lifespan of an outbreak spanning from initial autochthonous emergence through epidemic acceleration and eventual resolution. Θ integrates empirical, real-time, and assumed data streams to quantify the probability of disease transmission between an infectious and a susceptible host via aerosol. The notional equation components supporting Θ accounts for source contribution including particle size distribution, exhaled breath aerosol production, and biologic load probability; environmental airborne transport including humidity, temperature, UV, particle dynamics, and pathogen resilience; and host response encompassing susceptibility, behavioral modifiers, immunization status, and mutational adaptation. Θ is adaptable to incomplete data, incorporates evolutionary mutation rate and bioterrorism contingencies, and is scalable across pathogens, environments, and user participation levels. The utility of Θ is demonstrated in modeled outbreaks of H5 influenza, SARS-CoV-2 variants, and airborne bacterial agents, showing its utility in capturing the trajectory of transmissibility over time. As a refractory index, Θ resists oversimplification while remaining interpretable to public health systems. Its integration into aspirational WiFi-anchored hyperlocal sensing networks, and users provisioning volunteer data from mobile devices offers real-time, location-specific insights into transmission risk, opening a new frontier for algorithm-enabled epidemic intelligence.  Neural network-based models harness data sets to provide a swarm of recursively updated Θ estimates rather than a recalcitrant point transmission estimates.