The purpose of this paper was to investigate the effects of viral kinetics and exhaled droplet size on indoor transmission dynamics of influenza infection. The target cell-limited model with delayed virus production was adopted to strengthen the inner mechanisms of virus infection on human epithelial cell. The particle number and volume involved in the viral kinetics were linked with Wells-Riley mathematical equation to quantify the infection risk. We investigated population dynamics in a specific elementary school by using the seasonal susceptible - exposed - infected - recovery (SEIR) model. We found that exhaled pulmonary bioaerosol of sneeze (particle diameter <10 microm) have 10(2)-fold estimate higher than that of cough. Sneeze and cough caused risk probabilities range from 0.075 to 0.30 and 0.076, respectively; whereas basic reproduction numbers (R(0)) estimates range from 4 to 17 for sneeze and nearly 4 for cough, indicating sneeze-posed higher infection risk. The viral kinetics and exhaled droplet size for sneeze affect indoor transmission dynamics of influenza infection since date post-infection 1-7. This study provides direct mechanistic support that indoor influenza virus transmission can be characterized by viral kinetics in human upper respiratory tracts that are modulated by exhaled droplet size. Practical Implications This paper provides a predictive model that can integrate the influenza viral kinetics (target cell-limited model), indoor aerosol transmission potential (Wells-Riley mathematical equation), and population dynamic model [susceptible - exposed - infected - recovery (SEIR) model] in a proposed susceptible population. Viral kinetics expresses the competed results of human immunity ability with influenza virus generation. By linking the viral kinetics and different exposure parameters and environmental factors in a proposed school setting with five age groups, the influenza infection risk can be estimated. On the other hand, we implicated a new simple means of inhaling to mitigate exhaled bioaerosols through an inhaled non-toxic aerosol. The proposed predictive model may serve as a tool for further investigation of specific control measure such as the personal protection masks to alter the particle size and number concentration characteristics and minimize the exhaled bioaerosol droplet to decrease the infection risk in indoor environment settings.