Introduction to the Special Section on Inference for Infectious Disease Dynamics

The past three decades have seen significant growth in the field of mathematical modeling of infectious diseases, leading to substantial increase in our understanding of epidemiology and control of these diseases. Ability to quickly unravel the dynamics of the spread of infectious diseases is important for effective prevention of future outbreaks and for control of ongoing ones. Recent interest in infectious disease modeling was initially stimulated by the discovery of HIV in the early 1980s and has been maintained by the need to respond to other infectious disease related crises such as, for example, foot-and-mouth disease and SARS outbreaks, healthcare-associated infections, and elevated risks of human influenza pandemics (e.g., risks of global spread of avian and swine flu). Recent Ebola and Zika outbreaks further underscored importance of mathematical and statistical analyses of epidemic dynamics. As a result, mathematical infectious disease modeling remains high on the global scientific agenda. To respond to challenges posed by infectious diseases the epidemic modeling community has become very engaged in public health policy development, further stimulating interest in models of disease transmission. This involvement in practical applications of infectious disease modeling was enabled by algorithmic advances and the continuing increase in computing power. For example, it is now possible to perform simulations based on parameter rich and realistic agentbased models that generate individual-level behavior, infections, and recoveries of millions of individuals. Moreover, analyses on infectious disease outbreak data can now be performed using computationally intensive methods such as maximum likelihood estimation, Markov chain Monte Carlo (MCMC), sequential