Epidemic time series have an important role in epidemiology, allowing past patterns to be identified and future forecasts to be made. They are also vital in syndromic surveillance, with the aim of detecting epidemics in their early stages. There is a growing number of detailed long epidemic time series available for analysis. The Mathematical Challenges for Long Epidemic Time Series Workshop organised by the at Warwick Mathematics Institute aimed to bring together statisticians and epidemic modellers in order to focus on the interesting scientific challenges these large data sets pose.

This technical two-day workshop on 16th-17th December was preceded by a more general half-day workshop on “Big Data and Google Flu” on 15th December. This workshop marked the launch of the Warwick Data Science Institute supported by Mathematics, Statistics and Computer Science, which they hope will be able to take advantage of the opportunities posed by large, complex datasets.

Daniela De Angelis, Programme Leader at the MRC Biostatistics Unit, invited speaker at the Mathematical Challenges for Long Epidemic Time Series Workshop gave a talk entitled “Current Challenges in Inference for Infectious Disease Models Using Data from Multiple Sources”.

Abstract: Health-related policy decision-making for epidemic control is increasingly evidence-based, exploiting multiple sources of data. Policy makers rely on models, which are required to approximate realistically the process of interest and use all relevant information. This requirement poses a number of statistically challenging problems. One of these challenges is the computationally efficient estimation of model parameters when the interest is in producing timely assessment and prediction of an epidemic evolution as new data become available.

In this talk current attempts at addressing this computationally efficient parameter estimation problem were discussed in the context of the H1N1 influenza pandemic in England.

List of invited speakers at Mathematical Challenges for Long Epidemic Time Series Workshop:

• Daniela De Angelis (University of Cambridge)
• Gavin Gibson (Heriot-Watt University)
• Theodore Kypraios (The University of Nottingham)
• Angela Noufaily (The Open University)
• Trevelyan J. McKinley (University of Cambridge)
• Tim Kinyanjui (The University of Warwick)
• Ganna Rozhnova (Utrecht Center for Infection Dynamics)

Weblink to Workshop webpage (including abstracts)