New insights into the dynamics of infectious disease<\/strong><\/p>\n When you become infectious, a period of time passes before you are able to infect others, and then once you are able to infect others, a period of time passes before you recover. In more technical language, the natural history of your infection can be modelled mathematically as passing at random times through a series of stages. In various experimental setups, individual cases have the amount of virus they have produced monitored over time since infection – however, determining the rates of transition between stages from these data is not straightforward.<\/p>\n Now, researchers from the Universities of Manchester, Warwick and Bristol have proposed a solution to this problem. They use a Bayesian approach to inference, coupled with ideas from information geometry, to determine the quantities of epidemiological interest.\u00a0 This yields new insight into the dynamics of two of the most prevalent human pathogens – influenza and norovirus – as well as Ebola virus disease.<\/p>\n