The ongoing SARS-CoV-2 (Covid-19) pandemic has ushered an unforeseen level of global health and economic burden. As a respiratory infection, Covid-19 is known to have a dominant airborne transmission modality, wherein fluid flow plays a central role. Quantification of complex non-intuitive dynamics and transport of pathogen laden respiratory particles in indoor flows has been of specific interest. Here we present a Lagrangian computational approach towards quantification of human-to-human exposure quantifiers, and identification of pathways by which flow organizes transmission. We develop a Lagrangian viral exposure index in a parametric form, accounting for key parameters such as building and layout, ventilation, occupancy, biological variables. We also employ a Lagrangian computation of the Finite Time Lyapunov Exponent field to identify hidden patterns of transport. A systematic parametric study comprising a set of 120 simulations, yielding a total of 1,320 different exposure index computations are presented. Results from these simulations enable: (a) understanding the otherwise hidden ways in which air flow organizes the long-range transport of such particles; and (b) translating the micro-particle transport data into a quantifier for understanding infection exposure risks.