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Abstract Threats to water supply systems have increased in number and intensity. Natural disasters such as earthquakes have caused different types of damage to water distribution networks (WDN), particularly for those with aged infrastructure. This paper investigates the resilience of an existing water distribution network under seismic hazard. An earthquake generation model coupled with a probabilistic flow-based pressure driven demand hydraulic model is investigated and applied to an existing WDN. A total of 27 earthquake scenarios and 2 repair strategies were simulated. The analysis examined hydraulic resilience metrics such as pressure, leak demand, water serviceability, and population impacted. The results show that nodal pressure drops below nominal pressure and reaches zero in some earthquake scenarios. Leak demand could reach to more than 10 m3/s within hours following an earthquake. Water serviceability drops to a low of 40% and population impacted reaches up to 90% for a 6.5 M earthquake, for example. This study highlights and quantifies vulnerabilities within the simulated WDN. The tools outlined here illustrate an approach that can: (1) ultimately help to better inform utility water safety plans, and (2) prepare proactive strategies to mitigate/repair before a hazard of this nature occurs.

Governments have implemented different interventions and response models to combat the spread of COVID-19. The necessary intensity and frequency of control measures require us to project the number of infected cases. Three short-term forecasting models were proposed to predict the total number of infected cases in Canada for a number of days ahead. The proposed models were evaluated on how their performance degrades with increased forecast horizon, and improves with increased historical data by which to estimate them. For the data analyzed, our results show that 7 to 10 weeks of historical data points are enough to produce good fits for a two-weeks predictive model of infected case numbers with a NRMSE of 1% to 2%. The preferred model is an important quick-deployment tool to support data-informed short-term pandemic related decision-making at all levels of governance.