Modelling the spread of pandemic influenza and strategies for its containment and mitigation
The public heath threat posed by novel strains of influenza A gaining transmissibility in people and causing a human pandemic has been recognised as potentially catastrophic, especially since the emergence and global spread of the highly pathogenic avian H5N1 virus. Several mathematical models have been developed to evaluate patterns of spatio-temporal spread of infection, and the effectiveness of various containment strategies. However, these require significant improvements, elaboration, and application, in order to better inform EU-wide policy and responses. Key to the determination of the spatio-temporal patterns of pandemic influenza are data on contact patterns, such as those that are being acquired by the EU projects INFTRANS and POLYMOD. Building on these projects, we will collect detailed data on population structure, workplace sizes, and population movement; while also doing new surveys focused on identifying potential behavioural super-spreaders, and attitudes towards, and potential behavioural changes during, a pandemic. A suite of mathematical models, ranging from deterministic and stochastic differential equations to individual-based microsimulations, will be developed and integrated; taking into account all of the new data that is acquired above. The models will be validated against data on past pandemics and on the dynamics of seasonal and endemic infectious diseases. The effectiveness of control/treatment strategies, including measures to increase social distance (school and workplaces closure, travel reductions), quarantine, antiviral prophylaxis and mass or targeted vaccination, which also consider contact tracing protocols, will be evaluated through these models. An essential ingredient to the usefulness of detailed models is the possibility of updating them on the basis of new information or on the patterns of an emerging epidemic; hence a specific effort will be devoted to develop modular and efficient algorithms allowing for real-time analysis.[+] Read More
The public heath threat posed by novel strains of influenza A gaining transmissibility in people and causing a human pandemic has been recognised as potentially catastrophic, especially since the emergence and global spread of the highly pathogenic avian H5N1 virus. Several mathematical models have been developed to evaluate patterns of spatio-temporal spread of infection, and the effectiveness of various containment strategies. However, several significant improvements (in the data on contact patterns, in the methods to use them in predictive modelling, and in real-time model updating) are needed in order to better inform EU-wide policy and responses.
Main objective of the project is arriving at an accurate and data-based modelling of the expected course of an influenza pandemic, and of the impact of public health measures on its scale and severity. Aims of the project include the study of the social acceptability of public health measures during a pandemic, and of the behavioural changes that are to be expected in such circumstances. Final aim will be the development of a knowledge-based computational environment necessary for real-time analysis and modelling in case of a pandemic.
Improvement of the characterisation of population contact and travel patterns in epidemic models, on the basis of extended data collection, and model-driven extrapolations when data are lacking.
Evaluation of the social acceptance of restriction measures in case of a pandemic, and of the impact of behavioural changes on the expected epidemic course.
Development of a suite of models for the spatio-temporal spread of a new influenza pandemic, that integrate the dual approaches of compartmental modelling and individual-based simulations.
Extensive evaluation of the impact of intervention options for containing and mitigating a pandemic influenza outbreak. Development of an integrated environment for the efficient and extensible simulations of individual-based models.
Providing advice to the health authorities in case of a pandemic.
Development of a research team with rapid analysis capability in case of an epidemic outbreak.