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The design of a sensor-placement scheme capable of detecting all possible contamination events for a water distribution system before consumers are put at risk is essentially impossible given current technologies and budgets. It is, however, possible to design sensor-placement schemes that optimize related objectives (e.g., minimize expected volume of contaminated water consumed prior to detection), but this requires the availability of hydraulic and water quality models for the distribution network and significant computational power, which are the main obstacles to the identification of optimal sensor locations. This paper describes a different approach that reduces the problem's complexity by expressing a water distribution system as different graphs based on the information readily available from most, if not all, water utilities. The approach provides critical policy and decision support for utilities when hydraulic and water quality models are not available and/or when simulation-based techniques are computationally infeasible. CE Database subject headings: Water distribution systems; Network analysis; Security; Water quality; Water pollution.

While structured by social and institutional networks, disease outbreaks are modulated by physical, economical, technological, communication, health, and governmental infrastructures. To systematically reason about the nature of outbreaks, the potential outcomes of media, prophylaxis, and vaccination campaigns, and the relative value of various early warning devices, social context, and infrastructure, must be considered. Numerical models provide a cost-effective ethical system for reasoning about such events. BioWar, a scalable citywide multiagent network numerical model, is described in this paper. BioWar simulates individuals as agents who are embedded in social, health, and professional networks and tracks the incidence of background and maliciously introduced diseases. In addition to epidemiology, BioWar simulates health-care-seeking behaviors, absenteeism patterns, and pharmaceutical purchases, information useful for syndromic and behavioral surveillance algorithms. Index Terms--Bioterrorism, multiagent network, social network, syndromic and behavioral surveillance.

Environmental DNA (eDNA) sampling, the detection of species‐specific genetic material in water samples, is an emerging tool for monitoring aquatic invasive species. Optimizing eDNA sampling protocols can be challenging because there is imperfect understanding of how each step of the protocol influences its sensitivity. This paper develops a probabilistic model that characterizes each step of an eDNA sampling protocol to evaluate the protocol's overall detection sensitivity for one sample. The model is then applied to analyse how changes over time made to the eDNA sampling protocol to detect bighead (BH) and silver carp (SC) eDNA have influenced its sensitivity, and hence interpretation of the results. The model shows that changes to the protocol have caused the sensitivity of the protocol to fluctuate. A more efficient extraction method in 2013, new species‐specific markers with a qPCR assay in 2014, and a more efficient capture method in 2015 have improved the sensitivity, while switching to a larger elution volume in 2013 and a smaller sample volume in 2015 have reduced the sensitivity. Overall, the sensitivity of the current protocol is higher for BH eDNA detection and SC eDNA detection compared to the original protocol used from 2009 to 2012. The paper shows how this model of eDNA sampling can be used to evaluate the effect of proposed changes in an eDNA sampling and analysis protocol on the sensitivity of that protocol to help researchers optimize their design. [ABSTRACT FROM AUTHOR]