The book is based on a class I taught several times at the University of Florida, covering the nitty-gritty of constructing and fitting simple (statistical) ecological models to real data sets. Here is the material for the labs I used in that class; more lab material can be found on the wiki, and I will be likely to update more of it there. (This material is slightly out of date; if you're teaching a course based on the book or otherwise interested, please bug me for updated versions!)

More up-to-date information for the book lives on the book wiki/:

http://emdbolker.wikid...

…this has errata and added notes for each chapter, among other stuff. If you have suggestions for improvements in the R code, or you think you've found an error, please check the wiki and/or contact me (e-mail to bolker "at" mcmaster.ca).

Infectious disease is a moving target: new diseases emerge every year, old diseases evolve into new forms, and ecological and socioeconomic upheavals change the transmission pathways by which diseases spread. By taking an approach focused on the general evolutionary and ecological dynamics of disease, Infectious Disease: A Very Short Introduction considers where particular diseases come from, how they are transmitted from one person to another, why some individuals are more susceptible than others, and what strategies can be used to combat these diseases. It explains the general principles of infection, the management of outbreaks, and the evolutionary and ecological approaches that are now central to much research about infectious disease.

[arXiv, 22 September 2022]

Maruotti et al. 2022 used a mark-recapture approach to estimate bounds on the true number of monkeypox infections in various countries. These approaches are fundamentally flawed; it is impossible to estimate undercounting based solely on a single stream of reported cases. Simulations based on a Richards curve for cumulative incidence show that, for reasonable epidemic parameters, the proposed methods estimate bounds on the ascertainment ratio of ≈0.2−0.5 roughly independently of the true ascertainment ratio. These methods should not be used.

[Journal of Agricultural, Biological and Environmental Statistics, 16 November 2023]

The management of forest pests relies on an accurate understanding of the species’ phenology. Thermal performance curves (TPCs) have traditionally been used to model insect phenology. Many such models have been proposed and fitted to data from both wild and laboratory-reared populations. Using Hamiltonian Monte Carlo for estimation, we implement and fit an individual-level, Bayesian hierarchical model of insect development to the observed larval stage durations of a population reared in a laboratory at constant temperatures. This hierarchical model handles interval censoring and temperature transfers between two constant temperatures during rearing. It also incorporates individual variation, quadratic variation in development rates across insects’ larval stages, and “flexibility” parameters that allow for deviations from a parametric TPC. Using a Bayesian method ensures a proper propagation of parameter uncertainty into predictions and provides insights into the model at hand. The model is applied to a population of eastern spruce budworm (Choristoneura fumiferana) reared at 7 constant temperatures. Resulting posterior distributions can be incorporated into a workflow that provides prediction intervals for the timing of life stages under different temperature regimes. We provide a basic example for the spruce budworm using a year of hourly temperature data from Timmins, Ontario, Canada. Supplementary materials accompanying this paper appear on-line.

[PNAS, 26 January 2024]

If a new pathogen causes a large epidemic, then it might “burn out” before causing a second epidemic. The burnout probability can be estimated from large numbers of computationally intensive simulations, but an easily computable formula for the burnout probability has never been found. Using a conceptually simple approach, we derive such a formula for the standard SIR epidemic model with vital dynamics (host births and deaths). With this formula, we show that the burnout probability is always smaller for diseases with longer infectious periods, but is bimodal with respect to transmissibility (the basic reproduction number). Our analysis shows that the persistence of typical human infectious diseases cannot be explained by births of new susceptibles, clarifying an important epidemiological puzzle…