A Software Development Framework for Agent-Based Infectious Disease Modelling
| Autor: | Luiz C. Mostaço-Guidolin, Aleksander B. Demko, Seyed M. Moghadas, Nick J. Pizzi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
0303 health sciences
medicine.medical_specialty business.industry Computer science Public health Software development Outbreak 02 engineering and technology Disease computer.software_genre Software implementation 3. Good health 03 medical and health sciences Risk analysis (engineering) Infectious disease (medical specialty) Systems management 0202 electrical engineering electronic engineering information engineering medicine Software design 020201 artificial intelligence & image processing business computer 030304 developmental biology |
| Zdroj: | Biomedical Engineering, Trends in Electronics, Communications and Software |
| Popis: | From the Black Death of 1347–1350 (Murray, 2007) and the Spanish influenza pandemic of 1918–1919 (Taubenberger & Morens, 2006), to the more recent 2003 SARS outbreak (Lingappa et al., 2004) and the 2009 influenza pandemic (Moghadas et al., 2009), as well as countless outbreaks of childhood infections, infectious diseases have been the bane of humanity throughout its existence causing significant morbidity, mortality, and socioeconomic upheaval. Advanced modelling technologies, which incorporate the most current knowledge of virology, immunology, epidemiology, vaccines, antiviral drugs, and public health, have recently come to the fore in identifying effective disease mitigation strategies, and are being increasingly used by public health experts in the study of both epidemiology and pathogenesis. Tracing its historical roots from the pioneering work of Daniel Bernoulli on smallpox (Bernoulli, 1760) to the classical compartmental approach of Kermack and McKendrick (Kermack & McKendrick, 1927), modelling has evolved to deal with data that is more heterogeneous, less coarse (based at a community or individual level), and more complex (joint spatial, temporal and behavioural interactions). This evolution is typified by the agent-based model (ABM) paradigm, lattice-distributed collections of autonomous decision-making entities (agents), the interactions of which unveil the dynamics and emergent properties of the infectious disease outbreak under investigation. The flexibility of ABMs permits an effective representation of the complementary interactions between individuals characterized by localized properties and populations at a global level. However, with flexibility comes complexity; hence, the software implementation of an ABM demands more stringent software design requirements than conventional (and simpler) models of the spread and control of infectious diseases, especially with respect to outcome reproducibility, error detection and system management. Outcome reproducibility is a challenge because emergent properties are not analytically tractable, which is further exacerbated by subtle and difficult to detect errors in algorithm logic and software design. System management of software simulating populations/individuals and biological /physical interactions is a serious challenge, as the implementation will involve distributed (parallelized), non-linear, complex, and multiple processes operating in concert. Given these |
| Databáze: | OpenAIRE |
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