Contributed by program commitee member Madhav Marathe, Virginia Bioinformatics Institute

News: the synthetic dataset for the case study is now available.

Pandemic diseases such as the avian influenza are extreme infectious disease outbreaks. Pandemic influenza viruses have demonstrated their ability to spread worldwide within months, or weeks, and to cause infections in all age groups. While the ultimate number of infections, illnesses, and deaths is unpredictable, and could vary tremendously depending on multiple factors, it is nonetheless certain that without adequate planning and preparations, an influenza pandemic in the 21st century has the potential to cause enough illnesses to overwhelm current public health and medical care capacities at all levels, despite the vast improvements made in medical technology during the 20th century. Certain modern trends that could increase the potential for pandemics to cause more illnesses and deaths than occurred in earlier pandemics include: (i) larger global population and increased urbanization, (ii) higher levels of long distance travel including international travel, (iii) increased number of elderly individuals and individuals with chronic medical conditions. This combination of factors suggests that the next pandemic may lead to more illnesses occurring more quickly than in the past, overwhelming countries and health systems that are not adequately prepared.

A number of key pandemic response elements and key capabilities for their effective implementation have been identified by the U.S. Department of Health and Human Services and other world bodies such as the World Health Organization. They include: (i) surveillance, investigation, and protective health measures, (ii) viral and anti-viral drugs, (iii) health care and emergency response. Several of the response actions identified directly motivate research and development in spatial data mining.

One of the most effective ways of controlling the spread of infectious diseases is early detection of its onset via surveillance and implementation of control measures (e.g., isolation of cases, quarantine of contacts, antiviral drug treatment and prophylaxis) at points-of-entry to decrease introduction and spread of the pandemic virus in the U.S. This will require quarantine stations and related protections at all major U.S. ports of entry to limit the introduction of pandemic influenza, isolate cases, and trace contacts. Also important is the administration of pre-pandemic stockpiled vaccine, if available, to pre-defined groups critical to the pandemic response. This could provide partial immune protection and/or prime the immune system for a protective response once a targeted pandemic vaccine becomes available.

We identify three challenges that together showcase the methods that must come together to successfully address the scenario envisaged here. First, given a synthetic representation of a city (locations determined by geographical coordinates, people and their movements over time) we must develop a synthetic time-varying social nework capturing collocation and effective contact patterns. Such a model must be simulatable, in order to understand the transmission of disease and characterize the effects of response actions. Second, we must conduct model-based data aggregation using the derived network, to identify the onset of disease and other qualitative indicators of disease spread. Third, we require algorithms to identify critical individuals as well as critical locations based on the structure of the network in order to support targeted vaccination and targeted detection goals (respectively).

A thorough synthetic dataset is available from the Virginia Tech Network Dynamics and Simulation Science Laboratory, for use in exploring these challenges.

For more information, see:

• [HHS] HHS Pandemic Influenza Plan. US Department of Health and Human Services, Novmeber 2005. document.
• [BSE] Barrett C, Smith J, Eubank S, Modern Epidemiology Modeling, Scientific American, March 2005. article.
• [EG+04] Eubank S, Guclu H, Anil Kumar V, Marathe M, Srinivasan A, Toroczkai Z, Wang N, Modeling Disease Outbreaks in Realistic Urban Social Networks, Nature, Vol. 429, pages 180-184, 2004. article.
• [FC+05] Ferguson NM; Cummings DA; Cauchemez S; Fraser C; Riley S; Meeyai A; Iamsirithaworn S; Burke DS. Strategies for Containing an Emerging Influenza Pandemic in Southeast Asia, Nature, Vol. 437, pages 209-214, 08 Sep 2005. article.