Data from: Anticipating the emergence of infectious diseases

In spite of medical breakthroughs, the emergence of pathogens continues to
pose threats to both human and animal populations. We present candidate
approaches for anticipating disease emergence prior to large-scale
outbreaks. Through use of ideas from the theories of dynamical systems and
stochastic processes we develop approaches which are not specific to a
particular disease system or model, but instead have general
applicability. The indicators of disease emergence detailed in this paper
can be classified into two parallel approaches: a set of early-warning
signals based around the theory of critical slowing down and a
likelihood-based approach. To test the reliability of these two approaches
we contrast theoretical predictions with simulated data. We find good
support for our methods across a range of different model structures and
parameter values.