Mathematical models of infectious disease transmission are increasingly used to guide public health and policy decisions. Hence, it is important that every effort is made to ensure that models are ‘correct', made difficult by the frequent need to simulate a model numerically. The best we can do in most cases is to be able to replicate a model i.e. generate the same results from the same inputs (model plus parameters), or failing that, reproduce results that are similar. This can be achieved by sharing the computer code, and/or providing a sufficiently detailed description of the model. I will illustrate that it is often difficult to replicate or reproduce results of modeling publications, using case studies that highlight some of the many causes of this failure. I will argue that the FAIR principles proposed for data – that they should be Findable, Accessible, Interoperable and Reusable – are equally valid for modeling studies, and go a long way towards ensuring reproducibility. I will present Epirecipes (http://epirecip.es) a FAIR platform that both allows models to be replicated exactly, while fostering the idea that a wide variety of approaches are needed to ensure the robustness of model results. The added value from this platform includes resources for teaching, acting as a ‘Rosetta Stone' - allowing models from one computer language to be ported to another, and as a repository of best practices, potential pitfalls, and technical tricks that are all too often tucked away in papers or textbooks. As quoted from ‘The Turing Way' (https://the-turing-way.netlify.com), a handbook for reproducible science, reproducing models of infectious disease should be ‘too easy not to do'.[-]

Low-dimensional compartment models for biological systems can be fitted to time series data using Monte Carlo particle filter methods. As dimension increases, for example when analyzing a collection of spatially coupled populations, particle filter methods rapidly degenerate. We show that many independent Monte Carlo calculations, each of which does not attempt to solve the filtering problem, can be combined to give a global filtering solution with favorable theoretical scaling properties under a weak coupling condition. The independent Monte Carlo calculations are called islands, and the operation carried out on each island is called adapted simulation, so the complete algorithm is called an adapted simulation island filter. We demonstrate this methodology and some related algorithms on a model for measles transmission within and between cities.[-]

Antibiotic resistance is a serious public health concern. Responding to this problem effectively requires characterising the factors (i.e. evolutionary and ecological processes) that determine resistance frequencies. At present, we do not have ecologically plausible models of resistance that are able to replicate observed trends - we are therefore unable to make credible predictions about resistance dynamics. In this talk, I will present work motivated by three tends observed in Streptococcus pneumoniae resistance data: the stable coexistence of antibiotic sensitivity and resistance, variation between resistance frequencies between pneumococcal lineages and correlation in resistance to different antibiotics. I will propose that variation in the fitness benefit gained from resistance arising from variation in the duration of carriage of pneumococcal lineages is a parsimonious explanation for all three trends. This eco-evolutionary framework could allow more accurate prediction of future resistance levels and play a role in informing strategies to prevent the spread of resistance.[-]

In an epidemic model, the basic reproduction number $ R_{0}$ is a function of the parameters (such as infection rate) measuring disease infectivity. In a large population, if $ R_{0}> 1$, then the disease can spread and infect much of the population (supercritical epidemic); if $ R_{0}< 1$, then the disease will die out quickly (subcritical epidemic), with only few individuals infected.
For many epidemics, the dynamics are such that $ R_{0}$ can cross the threshold from supercritical to subcritical (for instance, due to control measures such as vaccination) or from subcritical to supercritical (for instance, due to a virus mutation making it easier for it to infect hosts). Therefore, near-criticality can be thought of as a paradigm for disease emergence and eradication, and understanding near-critical phenomena is a key epidemiological challenge.
In this talk, we explore near-criticality in the context of some simple models of SIS (susceptible-infective-susceptible) epidemics in large homogeneous populations.[-]

In recent years, new pandemic threats have become more and more frequent (SARS, bird flu, swine flu, Ebola, MERS, nCoV...) and analyses of data from the early spread more and more common and rapid. Particular interest is usually focused on the estimation of $ R_{0}$ and various methods, essentially based estimates of exponential growth rate and generation time distribution, have been proposed. Other parameters, such as fatality rate, are also of interest. In this talk, various sources of bias arising because observations are made in the early phase of spread will be discussed and also possible remedies proposed.[-]

Interview at Cirm: Tom Britton
Dean of Mathematics & Physics, Stockholm University
Research interests: applied probability models and statistical inference for such, in particular epidemic models, networks and applications towards genetics and molecular biology including phylogenetics.

Erdös number: 2

Associate editor for Journal of Mathematical Biology

The math departments of Stockholm University and The Royal Institute of Technology together form the Stockholm Mathematics Center (SMC)

Currently chairman of the Cramér Society (Swedish association for academic statisticians)[-]

Professor Julia Gog is a British mathematician, David N. Moore Fellow and Director of Studies in Mathematics at Queens' College, Cambridge and Professor of mathematical biology in the University of Cambridge Department of Applied Mathematics and Theoretical Physics. She is also a member of the Cambridge Immunology Network and the Cambridge Infectious Diseases Interdisciplinary Research Centre.Her research specialises in using mathematical techniques to study infectious diseases, particularly influenza. Current projects include:

Models of influenza strain dynamics
Spatial spread of influenza
Within-host dynamics of influenza
In vitro dynamics of Salmonella
Bioinformatic methods to detect RNA signals in viruses

http://www.damtp.cam.ac.uk/research/dd/[-]

Reaction diffusion equations have been introduced during the early 20th century to model the density of populations undergoing range expansions in various contexts. These equations commonly admit travelling wave solutions, i.e. the population expands at a constant speed with a stationary profile. These deterministic models can be obtained as rescaling limits of stochastic population models when the population density tends to infinity. But do these stochastic models also admit such (random) travelling fronts? If so, what is the asymptotic speed of these fronts, and how does the nature of the front affect this speed? These questions have been the subject of many studies in the case of the Fisher-Kolmogorov-Petrovsky-Piskunov equation, and in this talk I will give some partial answers in the case of reaction-diffusion equations with a bistable reaction term.
The latter type of equations arises when one is interested in the motion of hybrid zones or the expansion of populations with an Allee effect. We shall see that their behaviour is in sharp contrast with that of the stochastic F-KPP equation.
joint work with Alison Etheridge and Sarah Penington[-]