An Integrated Risk Model of a Drinking‐Water – Borne Cryptosporidiosis Outbreak

Abstract

A dynamic risk model is developed to track the occurrence and evolution of a drinking-water-borne cryptosporidiosis outbreak. The model characterizes and integrates the various environmental, medical, institutional, and behavioral factors that determine outbreak development and outcome. These include contaminant delivery and detection, water treatment efficiency, the timing of interventions, and the choices that people make when confronted with a known or suspected risk. The model is used to evaluate the efficacy of alternative strategies for improving risk management during an outbreak, and to identify priorities for improvements in the public health system. Modeling results indicate that the greatest opportunity for curtailing a large outbreak is realized by minimizing delays in identifying and correcting a drinking-water problem. If these delays cannot be reduced, then the effectiveness of risk communication in preemptively reaching and persuading target populations to avoid exposure becomes important.

The sciences of science communication

“…Casman et al (67) found that traditional, trusted distribution systems for "boil water" notices could not possibly reach some consumers in time to protect them from some contaminants. Winterstein and Kimberlin (68) found that the system for distributing consumer medication information sheets puts something in the hands of most patients who pick up their own prescriptions.…”

Climate Change and Children

“…[34][35][36]). Curriero and colleagues [37] and Kistemann and colleagues [38] found that extreme precipitation events increase the loading of contaminants in waterways, and Casman and colleagues [39] concluded that climate change could increase the risk of illness associated with Cryptosporidium parvum.…”

Decision research strategies.

“…Those effects depend on such factors as Cryptosporidium intrusion risk, surveillance procedures, S13 testing capacity, remediation technology, media response, population sensitivity, and institutional coordination (between water utilities, public health authorities, pharmacies, etc.). In modeling these processes, Casman, Fischhoff, Palmgren, Small, and Wu (2000) found that emergency warnings would, typically, have no effect, even if they reached every consumer and achieved perfect compliance. Current tests cannot detect Cryptosporidium in time for people to respond effectively.…”