Workshop on Agent-based Models, 17 and 18 December 2024Info Location Attendee Categories Contact More Info Event Information
DescriptionThe MRC Biostatistics Unit is delighted to be holding a special two-day workshop that will explore the use of agent-based models (ABMs) to enhance epidemic preparedness, by offering fine-scaled insights into disease transmission. Traditional compartmental models, like those used during the COVID-19 pandemic, segment populations broadly by age or geography but struggle to address finer details such as the transmission within particular socio-economic groups. ABMs, by simulating transmission at the individual level, provide a solution by allowing more precise evaluation of how interventions could affect different groups and regions. ABMs can capture complex systems ranging from large-scale vaccine studies to localised outbreaks. However, their complexity requires careful calibration to real-world data, such as infection trends and population behaviours, to give credence to any insights generated and to improve the accuracy of predictions. University of Cambridge Students and Staff, whose department will be covering the participation cost, please raise a Purchase Order (or contact a member of your department who can assist with this) and send a PDF of the Purchase Order to [email protected] . You will then be sent the passcode which you will need to use along with the Purchase Order number to complete registration below, selecting the 'UoC Student/Staff Registration' option.
Event Location
Attendee Categories2. UoC Student and Staff Registration: Internal Cross-charge (£30)
Additional ItemsMore InformationThis is intended to be a community-building workshop aimed to discuss the challenges of building, calibrating, and deploying these models in real time during outbreaks, highlighting their potential to inform more effective public health responses. Invited presenters have been selected to span a range of disciplines and career stages. It is international in focus, though numbers are limited to improve the likelihood of open discussion and increase the potential for future collaboration.
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