A Workflow for Software Development within Computational Epidemiology

A critical investigation into computational models developed for studying the spread of communicable disease is presented. The case in point is a spatially explicit micro-meso-macro model for the entire Swedish population built on registry data, thus far used for smallpox and for influenza-like illnesses. The lessons learned from a software development project of more than 100 person months are collected into a check list. The list is intended for use by computational epidemiologists and policy makers, and the workflow incorporating these two roles is described in detail.NOTICE: This is the author’s version of a work that was accepted for publication in Journal of Computationa Science. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Computational Science, VOL 2, ISSUE 3, 6 June 2011 DOI 10.1016/j.jocs.2011.05.004.</p

Innovative in silico approaches to address avian flu using grid technology

The recent years have seen the emergence of diseases which have spread very quickly all around the world either through human travels like SARS or animal migration like avian flu. Among the biggest challenges raised by infectious emerging diseases, one is related to the constant mutation of the viruses which turns them into continuously moving targets for drug and vaccine discovery. Another challenge is related to the early detection and surveillance of the diseases as new cases can appear just anywhere due to the globalization of exchanges and the circulation of people and animals around the earth, as recently demonstrated by the avian flu epidemics. For 3 years now, a collaboration of teams in Europe and Asia has been exploring some innovative in silico approaches to better tackle avian flu taking advantage of the very large computing resources available on international grid infrastructures. Grids were used to study the impact of mutations on the effectiveness of existing drugs against H5N1 and to find potentially new leads active on mutated strains. Grids allow also the integration of distributed data in a completely secured way. The paper presents how we are currently exploring how to integrate the existing data sources towards a global surveillance network for molecular epidemiology.Comment: 7 pages, submitted to Infectious Disorders - Drug Target

The effectiveness of policies to control a human influenza pandemic : a literature review

The studies reviewed in this paper indicate that with adequate preparedness planning and execution it is possible to contain pandemic influenza outbreaks where they occur, for viral strains of moderate infectiousness. For viral strains of higher infectiousness, containment may be difficult, but it may be possible to mitigate the effects of the spread of pandemic influenza within a country and/or internationally with a combination of policies suited to the origins and nature of the initial outbreak. These results indicate the likelihood of containment success in'frontline risk'countries, given specific resource availability and level of infectiousness; as well as mitigation success in'secondary'risk countries, given the assumption of inevitable international transmission through air travel networks. However, from the analysis of the modeling results on interventions in the U.S. and U.K. after a global pandemic starts, there is a basis for arguing that the emphasis in the secondary risk countries could shift from mitigation towards containment. This follows since a mitigation-focused strategy in such developed countries presupposes that initial outbreak containment in these countries will necessarily fail. This is paradoxical if containment success at similar infectiousness of the virus is likely in developing countries with lower public health resources, based on results using similar modeling methodologies. Such a shift in emphasis could have major implications for global risk management for diseases of international concern such as pandemic influenza or a SARS-like disease.Avian Flu,Disease Control&Prevention,Health Monitoring&Evaluation,Population Policies,HIV AIDS

Modeling and Optimization of Dynamical Systems in Epidemiology using Sparse Grid Interpolation

Infectious diseases pose a perpetual threat across the globe, devastating communities, and straining public health resources to their limit. The ease and speed of modern communications and transportation networks means policy makers are often playing catch-up to nascent epidemics, formulating critical, yet hasty, responses with insufficient, possibly inaccurate, information. In light of these difficulties, it is crucial to first understand the causes of a disease, then to predict its course, and finally to develop ways of controlling it. Mathematical modeling provides a methodical, in silico solution to all of these challenges, as we explore in this work. We accomplish these tasks with the aid of a surrogate modeling technique known as sparse grid interpolation, which approximates dynamical systems using a compact polynomial representation. Our contributions to the disease modeling community are encapsulated in the following endeavors. We first explore transmission and recovery mechanisms for disease eradication, identifying a relationship between the reproductive potential of a disease and the maximum allowable disease burden. We then conduct a comparative computational study to improve simulation fits to existing case data by exploiting the approximation properties of sparse grid interpolants both on the global and local levels. Finally, we solve a joint optimization problem of periodically selecting field sensors and deploying public health interventions to progressively enhance the understanding of a metapopulation-based infectious disease system using a robust model predictive control scheme

Disease surveillance systems

Recent advances in information and communication technologies have made the development and operation of complex disease surveillance systems technically feasible, and many systems have been proposed to interpret diverse data sources for health-related signals. Implementing these systems for daily use and efficiently interpreting their output, however, remains a technical challenge. This thesis presents a method for understanding disease surveillance systems structurally, examines four existing systems, and discusses the implications of developing such systems. The discussion is followed by two papers. The first paper describes the design of a national outbreak detection system for daily disease surveillance. It is currently in use at the Swedish Institute for Communicable Disease Control. The source code has been licenced under GNU v3 and is freely available. The second paper discusses methodological issues in computational epidemiology, and presents the lessons learned from a software development project in which a spatially explicit micro-meso-macro model for the entire Swedish population was built based on registry data