Abstract
This chapter focuses on diffusion, which is a common feature of many social and biological systems. Innovative consumer products frequently “take off” and “go viral”, with sales driven by the word of mouth effect, as their adoption spreads through a population. Infectious diseases can transmit rapidly through a population, accelerating from seemingly low incidence levels, to sizable numbers in a short space of time. Here, the focus is on models of infectious diseases. These have an important decision support function for public health professionals faced with challenge of responding to an infectious disease outbreak. The first model is the classic SIR structure, which divides the population into those who are susceptible, infected and recovered. This model is then extended to cater for multiple age cohorts, so that diverse mixing patterns can be simulated. Finally, a scalable R model of infectious diseases is introduced, combining matrix operations with vectorized differential equations.
The historical and epidemiological literature abound with accounts of infectious diseases invading human communities and of the concomitant effects on population abundance, social organization, and the unfolding patterns of historical events.
R. Anderson, R. May. Infectious Diseases of Humans: Dynamics and Control (1992).
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References
Anderson R, May R (1992) Infectious diseases of humans. Oxford University Press, Oxford
Borgdorff MW, Nagelkerke NJ, Broekmans JF (1999) Transmission of tuberculosis between people of different ages in The Netherlands: an analysis using DNA fingerprinting. Int J Tuberc Lung Dis 3(3):202–206
Breban R, Vardavas R, Blower S (2007) Theory versus data: how to calculate R0? PLoS ONE 2(3): e282 (PMC, Baylis M (ed))
Dangerfield BC, Fang Y, Roberts CA (2001) Model-based scenarios for the epidemiology of HIV/AIDS: the consequences of highly active antiretroviral therapy. Syst Dyn Rev 17(2):119–150
Jackson C, Mangtani P, Vynnycky E, Fielding K, Kitching A, Mohamed H, Maguire H (2011) School closures and student contact patterns. Emerg Infect Dis 17(2):245
Glass LM, Glass RJ (2008) Social contact networks for the spread of pandemic influenza in children and teenagers. BMC Public Health 8(1):61
Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, Edmunds WJ (2008) Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS medicine 5(3):e74
Thompson KM, Tebbens RJD (2008) Using system dynamics to develop policies that matter: global management of poliomyelitis and beyond. Syst Dyn Rev 24(4):433–449
Vynnycky E, White R (2010). An introduction to infectious disease modelling. Oxford University Press, Oxford
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Duggan, J. (2016). Diffusion Models. In: System Dynamics Modeling with R. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-34043-2_5
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DOI: https://doi.org/10.1007/978-3-319-34043-2_5
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