During transmission of seasonal endemic diseases such as measles and influenza, spatial
waves of infection have been observed between large distant populations. Also, during the …

Modern infectious disease epidemiology has a strong history of using mathematics both for
prediction and to gain a deeper understanding. However the study of infectious diseases is …

For epidemiologists, evolutionary biologists, and health-care professionals, real-time and
predictive modeling of infectious disease is of growing importance. This book provides a …

Mathematical modelling and statistical inference provide a framework to evaluate different
non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has …

Sudden oak death, caused by Phytophthora ramorum, has killed millions of oak and tanoak
in California since its first detection in 1995. Despite some localized small-scale …

Infectious disease incidence data are increasingly available at the level of the individual and
include high-resolution spatial components. Therefore, we are now better able to challenge …

Likelihood-based inference for epidemic models can be challenging, in part due to
difficulties in evaluating the likelihood. The problem is particularly acute in models of large …

Parasitism and predation have long been considered analogous interactions. Yet by and
large, ecologists continue to study parasite–host and predator–prey ecology separately …

Like rest of the world, the South Asian region is facing enormous challenges with the
coronavirus disease 2019 (COVID-19) pandemic. The socioeconomic context of the eight …

Across many fields, scenario modeling has become an important tool for exploring long-term
projections and how they might depend on potential interventions and critical uncertainties …