Dr Glenn Marion

My EPIC research involves using methods from statistics in combination with mathematical models to learn and predict how epidemiological systems behave to inform the design of better disease control. With colleagues I am developing statistical methods that enable such predictions from reported disease cases, even when the at-risk population e.g. of farms or wildlife, is unknown. This will allow analysis of a much wider range of disease threats than is possible with currently available methods, including African Swine Fever in wild boar.

Disease  systems  are  made  up  of  multiple  elements  e.g.  individual  animals  on  different  farms  and associated  wildlife  populations,  that  interact  dynamically  through  a range  of  processes  such  as movement of livestock between farms, transmission of disease between individuals and progression of disease within individuals. The potentially complex nature of  these interactions can result in  less favourable  than  anticipated  outcomes  from actions  aimed  to  control  disease  and,  in  some  cases, totally unintended consequences e.g.  culling of badgers may increase TB risks to cattle.

Mathematical modelling provides a framework which can be used to integrate key aspects of disease systems  in  a  logically  consistent  way.  Modern  computational  methods  now  enable  exploration of the behaviour  of  potentially  very  complex models  under  a  wide  range  of  different  scenarios which, for example, can be used to assess the impact of proposed disease control strategies.  Unfortunately, it  is  often  too  costly  or  impractical  to  measure  the  values  of  critical parameters needed for such models, for example representing disease transmission or duration of infectivity. In addition,  the  inclusion,  or  nature,  of  particular  processes  in  the model  may  be  disputed.  However, modern  statistical  methods  enable  estimates  of  an  increasing  range  of  model  parameters  to  be obtained from incomplete and noisy field data e.g. observations of disease outbreaks. With scientists in EPIC and beyond I am developing statistical computing methods to enable information relevant to increasingly  complex  models  to  be  reliably  extracted  from  a  widening  variety  of  data.  In  addition, these  tools  are  being  extended  to  enable  assessment  of  model  structure  to  be  informed  by  the available data e.g. to determine which processes should be included and how.


Data-Driven Risk Assessment from Small Scale Epidemics: Estimation and Model Choice for Spatio-Temporal Data with Application to a Classical Swine Fever Outbreak. K Gamado, G Marion, T Porphyre

Using Combined Diagnostic Test Results to Hindcast Trends of Infection from Cross-Sectional Data. G Rydevik, GT Innocent, G Marion, RS Davidson, PCL White, C Billinis, P Barrow, PPC Mertens, D Gavier-Widen, MR Hutchings

Website design by Innovation Digital Limited