SBIDER: Forward Prediction
Prediction is generally a key concept in much of science, and is closely linked to data analysis. The general steps are to create a mathematical or statistical model that we believe describes a particular system, to match this model to the available data, and then to predict forwards in time to obtain forecasts of the future. Much of the work in SBIDER uses mechanisitic mathematical models, which attempt to capture (and quantify) the underlying real-world processes.
Work in SBIDER focuses on understanding and predicting the spread and control of many infectious diseases, bringing together the latest methodology with biological understanding to generate novel insights. Our research spans from high profile pathogens including pandemic influenza, malaria, bovine tuberculosis and foot-and-mouth disease, to less well-publicised diseases such as respiratory syncytical virus, foot-rot and European foulbrood. A major theme of our work is to provide scientific policy advice, particularly on vaccination cost-effectiveness and neglected tropical diseases.
Research into a range of human infectious diseases, often driven by an applied need to address pressing public health problems. Examples include: a range of Neglected Tropical Diseases, childhood infections, vaccine cost-effectiveness, and influenza.
Combinations of field-work, lab studies, statistics and mathematical modelling seeks to better understand these diseases and effect better control. Examples include: bovine TB, foot-and-mouth disease, footrot in sheep and a range of honey-bee infections.
Development of novel techniques, motivated by problems in infectious diseases. Much of this research feeds into our more applied work. Examples include: using networks, capturing stochasticity and modern statistical methods for inference from reported cases and genomic data.