Motivation Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company for the United States Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL One dimension of critical infrastructure disruptions is the impact on human health. A disruption of electric power, transportation, or chemical industry infrastructure could impair the ability of water treatment facilities to disinfect water, leading to potentially dangerous health risks which must be understood to be effectively mitigated. Mitigation decision makers, in particular, need to understand where the health risks of this infrastructure degradation are the greatest to best prioritize intervention policies. Model Extension Codeço’s model represents dynamics in Southern Asia well, but a small number of outbreaks in Africa occur at smaller populations: Senjojo, Uganda (2002) persons Chadereka, Zimbabwe (1993) persons Mavi Sonii, Liberia (2003) - 9,000 persons Others (Itete, Tanzania and Rumonge, Burundi) with population ~12,000 Given application to US and large range of populations to be examined, we decided to extend Codeço’s model to explicitly track number of cells per infected person, instead of cell density. To avoid introducing new parameter (total volume of mixing body) to the model, we postulate that mixing volume is dependent on population size. A stability analysis of the infection dynamics finds this formulation to be dependent on the extent of ‘crowding’ -- the number of people supported by a given reservoir size. We use cholera as an exemplar water-borne pathogen, and seek to understand where the health risks of various possible infrastructure disruptions are the greatest. We start with a cholera dynamics model published by Cláudia Codeço. A stability analysis of the infection dynamics of Codeço finds the critical point: Summary and Conclusion The approach described here, along with a population to aquatic resources assumption, provides a coarse, high-level characterization of the health risks, in terms of the potential for epidemic outbreaks, of functionally degraded water infrastructure. The NISAC Agent Based Laboratory for Economics (N-ABLE™) can predict both the locations and economic consequences of infrastructure disruptions and outages. SymbolDescription State Variables INumber of infected persons SNumber of susceptible persons BConcentration of v. cholerae (cells/ml) Parameters HTotal human population nHuman birth/death rate (day -1 ) aRate of exposure (day -1 ) KConcentration of cells yielding 50% chance of infection (cells/ml) rRecovery rate (day -1 ) nbGrowth rate of v. cholerae in water (day -1 ) mbLoss rate of v. cholerae in water (day -1 ) eIncrease in v. cholerae density due to infected person (cells/ml person -1 day -1 ) Functions  (B) Dose response relation, B/(K + B) Problem Formulation – Codeço’s Model of Cholera Parameters a0.5 day -1 K10 6 cells/ml r0.2 day -1 nb - mb-0.33 day -1 e10 cells/ml person -1 day -1 Population ServedNumber of Systems 100 or less ,3001,551 3,301-10, , , , , , , More than 500,00066 Number and Size of Surface Water Systems in the US. Calculated alpha projected onto EPA flow data. Codeço’s original model suggests many smaller systems represent a lower risk of epidemic outbreaks, but our revised model is population independent. Applying the per capita critical volume calculated from our model to EPA data suggests that only 218 systems are susceptible to cholera outbreaks in the event of an infrastructure disruption. The average system size is 1811 persons, although a few systems extend to over 60,000 persons. Number of affected systems by size of population served. Applying this calculation to the United States to understand the health risks of degraded water infrastructure reveals there are a limited number of communities falling into this high risk ‘crowded’ category, ranging in size from small to large. H UMAN H EALTH I MPACTS OF I NFRASTRUCTURE D ISRUPTION: S TABILITY A NALYSIS OF C ODEÇO C HOLERA M ODEL Andrew J. Scholand and Mark A. Ehlen Computational Economics Group, National Infrastructure Simulation & Analysis Center, Sandia National Laboratories IA / IP