Common colds are one of the principal causes of severe exacerbations in asthmatic people, reflected in epidemic-like waves of asthma hospitalizations. Most studies do not estimate the effect of infectious causes of exacerbations, and cannot account for how this risk changes through time.
We created a model of asthma exacerbation risk, and fitted it to 7 years of daily data from 8 large cities in Texas. To include common cold viruses as a risk factor, we used a dynamic transmission model to simulate common cold prevalence, and we include the changing contact patterns of children in and out of school. We find that school vacation timing is critical to predicting and interpreting the changing risk of asthma exacerbations, and this is due to common cold prevalence. I will describe this study, the model, and our fitting techniques used in our recent paper (doi: 10.1073/pnas.1518677113).
Further, I will give a short primer on the R language, demonstrate how to implement ordinary differential equation models in R, and walk-through the code that generates the common cold infection prevalence used in our asthma paper.
Presenter
Rosalind Eggo, Research Fellow, London School of Hygiene & Tropical Medicine
Rosalind works as an infectious disease modeller in public health epidemiology. She received her PhD in infectious disease modelling from Imperial College London in 2011, on the topic of the spatial and temporal dynamics fo the 1918 influenza pandemic. Rosalind then worked at The University of Texas at Austin for 3 years, on modelling projects relating to seasonal influenza, pandemic influenza preparedness, and the roled of respiratory viruses in asthma exacerbations. After returning to the UK, she began working at the London School of Hygiene and Tropical Medicine in modelling, with a focus on vaccine preventable infections.