Predicting an epidemic trajectory is difficult

Predicting the trajectory of a novel emerging pathogen is like waking in the middle of the night and finding yourself in motion—but not knowing where you are headed, how fast you are traveling, how far you have come, or even what manner of vehicle conveys you into the darkness. A few scattered anomalies resolve into defined clusters. Cases accumulate. You assemble information about temporal sequences, identify a causative agent, piece together a few transmission histories, try to estimate mortality rates. A few weeks pass. Everyone wants to know how far the disease is going to spread, how fast that is going to happen, and what the final toll will be. They want predictive models. However, predictive models are difficult to develop and subject to wide uncertainties. In PNAS, Castro et al. (1) demonstrate one reason why. They show that based on early data it is nearly impossible to determine precisely whether interventions will be sufficient to quell an epidemic or whether the epidemic will continue to grow unabated. At best, we can predict the likelihoods of these opposing scenarios—scenarios that describe completely different worlds. In one a disaster has been narrowly averted, but in the other millions of people become infected and the global economy is upended. The fundamental problem in predicting epidemic trajectories is the way in which uncertainties compound. To begin with, epidemics are stochastic processes. Luck matters—–particularly when superspreading is important—and happenstance shapes patterns of geographic spread (2). Early on, little is known about the parameters describing the spread of infection. What is the generation interval, how long is the infectious window, and what is the basic reproductive number in any particular setting? How is the disease transmitted, how long does it remain in the environment, and what are the effects of seasonality? Without answers to these … [↵][1]1To whom correspondence may be addressed. Email: wilke{at}austin.utexas.edu or cbergst{at}uw.edu. [1]: #xref-corresp-1-1