PAI Pandemics, Detection and Management

P PAI imity for aerosol-borne disease, sexual contact for sexu- ally transmitted diseases, and insect feeding patterns for Positional Accuracy Improvement (PAI) mosquito-borne diseases. In general, then, computational epidemiology creates computer models of diffusive processes spreading across interaction networks. Pandemics, Detection The basic goal of epidemiological modeling is to under- and Management stand the dynamics of disease spread well enough to control it. Potential interventions for controlling infectious CHRISTOPHER L. BARRETT,STEPHEN EUBANK, disease include pharmaceuticals for treatment or prophy- BRYAN LEWIS,MADHAV V. M ARATHE laxis, social interventions designed to change transmis- Virginia Bioinformatics Institute, Virginia Tech, sion rates between individuals, physical barriers to trans- Blacksburg, VA, USA mission, and eradication of vectors. Efficient use of these interventions requires targeting subpopulations that are on Synonyms the critical path of disease spread. Computational models Epidemiology, computational; Epidemics; Public health; can be used to identify those critical subpopulations and to Information management systems; Distributed informa- assess the feasibility and effectiveness of proposed inter- tion systems ventions. Definition Historical Background Epidemiology is the study of patterns of health in a population and the factors that contribute to these patterns. It Epidemiology did not emerge as a distinct discipline until plays an essential role in public health through the elu- the mid-19th century as the medical sciences sought to cidation of the processes that lead to ill health as well determine the efficacy of different medical practices. John as the evaluation of strategies designed to promote good Snow famously interrupted the 1854 cholera outbreak in health. Epidemiologists are primarily concerned with pub- London by removing the handle of the Broad Street pump, lic health data, which includes the design of studies, evalu- an event that is widely credited with bringing epidemi- ation and interpretation of public health data, and the main- ology into the mainstream. His studies along with those tenance of data collection systems. of many others were responsible for bringing about wide- Computational Epidemiology is the development and use ranging public health reforms and laid the foundation for of computer models for the spatio-temporal diffusion of the development of the germ theory of disease causation. disease through populations. The models may range from Once etiological agents were identified as the cause of descriptive, e. g. static estimates of correlations within disease, the sanitary reforms of the late 19th and early large databases, to generative, e. g. computing the spread 20th centuries greatly reduced the incidence of infectious of disease via person-to-person interactions through a large disease in the human population. Epidemiology contin- population. The disease may represent an actual infec- ued to identify novel causes of disease but also matured tious disease, or it may represent a more general reac- and began to consider the social determinants of health. tion-diffusion process, such as the diffusion of innova- Aided by improved statistical tools epidemiologists were tion. The populations of interest depend on the disease, able to focus on more nuanced analysis of population-wide including humans, animals, plants, and computers. Simi- health data, for example, linking lung cancer to smok- larly, the interactions that must be represented depend on ing. The pharmaceutical revolution of the mid-20th centu- the disease and the populations, including physical prox- ry required epidemiology to assess the efficacies of the new 840 Pandemics, Detection and Management Urban Infrastructure Population therapies being created. Epidemiology has further gained . Transportation Network . Demographics . Transit Routes/Schedules from the ability to manipulate large bodies of data and per- . Household Structure form complex calculations on this data using computers. Land Use & Accessibility As technology has improved the sophistication of tech- Activities niques to analyze public health data used by epidemiol- . Times ogists has kept pace. Recent years have seen the emer- . Types . Location gence of computational epidemiology which focuses on . Travel Mode Database Creation using complex computer models and innovative forms of Query Processing Data Fusion & Filtering data analysis. At its core, epidemiology is still focused on Route Plans . Times improving the health of the public, however, recently, epi- . Locations Specialized demiologically inspired methods and techniques have been Population Dynamics harnessed to analyze computer security, network rout- .Id ing, distributed databases, marketing strategies, and other Traffi c Simulation . Time . Times . Type social phenomena. Traveler Locations . Geo Coordinates . Vehicle Status . Other information Scientific Fundamentals Device Ownership Model Additional . HH Ownership Probabilities Information The spread of infectious disease depends both on prop- . Optional Specification erties of the pathogen and the host. An important factor that greatly influences an outbreak of an infectious disease Pandemics, Detection and Management, Figure 1 Schematic diagram showing how various databases and integrated to create a synthetic popula- is the structure of the interaction network across which it tion. GIS plays an important role in constructing these synthetic populations spreads. Descriptive models are useful for estimating prop- erties of the disease, but the structure of the interaction network changes with time and is often affected by the pres- Step 1 Creating a set of (agent) synthetic interactors, ence of disease and public health interventions. Thus gen- Step 2 Generating (time varying) interaction networks, erative models are most often used to study the effects of Step 3 Detailed simulation of the epidemic process. public health policies on the control of disease. Aggregate or collective computational epidemiology mod- Step 1 creates a synthetic urban populations [3,4,5,6], and els often assume a population is partitioned into a few is done by integrating a variety of databases from commer- subpopulations (e. g. by age) with a regular interaction cial and public sources into a common architecture for data structure within and between subpopulations. The result- exchange that preserves the confidentiality of the original ing model can typically be expressed as a set of coupled data sets, yet produces realistic attributes and demograph- ordinary differential equations. Such models focus on esti- ics for the synthetic individuals. Figure 1 shows a schemat- mating the number of infected individuals as a function of ic diagram. The synthetic population is a set of synthetic time, and have been useful in understanding population- people, each associated with demographic variables drawn wide interventions. For example, they can be used to deter- from any of the demographics available in the census [3,7]. mine the level of immunization required to create herd Joint demographic distributions can be reconstructed from immunity. the marginal distributions available in typical census data Disaggregate or individual-based models, in contrast, rep- using an iterative proportional fitting (IPF) technique. Each resent each interaction between individuals, and can thus synthetic individual is placed in a household with other be used to study critical pathways. Disaggregate models synthetic people and each household is located geograph- require neither partitions of the population nor assump- ically in such a way that a census of our synthetic popu- tions about large scale regularity of interactions; instead, lation yields results that are statistically indistinguishable they require detailed estimates of transmissibility between from the original census data, if they are both aggregated individuals. The resulting model is typically a stochastic to the block group level. Synthetic populations are thus sta- finite discrete dynamical system. For more than a few indi- tistically indistinguishable from the census data; neverthe- viduals, the state space of possible configurations of the less, since they are synthetic they respect privacy of indi- dynamical system is so large that they are best studied viduals within the population. Note that, census tables are using computer simulation. precisely constructed so as to respect privacy. The synthet- GIS tools and techniques play an important role in building ic individuals carry with them a complete range of demo- these computational tools. The overall approach followed graphic attributes collected in the census data. This include by disaggregate models consists of the following steps: variables such as income level, age, etc. Pandemics, Detection and Management 841 In Step 2, a set of activity templates for households on the current state of the individual and the disease state are determined based on several thousand responses to of his neighbors. This state transition is probabilistic and an activity or time-use survey. These activity templates depends on the duration of contact. It may also depend include the sort of activities each household member per- on the attributes of the people involved (age, profession, forms and the time of day they are performed. Each health status, etc.) as well as the type of contact (intimate, synthetic household is matched with one of the survey casual, etc.),

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