Diarrheal disease attributable to water and sanitation can be prevented using point-of-use water treatment. In Ethiopia, a small number of households treat water at point-of-use with appropriate methods. However, evidence on factors associated with household use of these treatment methods is scarce. Therefore, this study is intended to explore the household use of appropriate point-of-use water treatment and associated factors in Ethiopia. The data of 2005, 2011, and 2016 Ethiopian demographic and health surveys were used for analysis. Households reportedly treating water with bleach, boiling, filtration, and solar disinfection in each survey are considered as treating with appropriate treatment methods. Household water treatment with these treatment methods and factors associated was assessed using bivariate and multivariable regression. In addition, a region level difference in the treatment use was assessed by using multilevel modeling. The number of households that reported treating water with appropriate water treatment methods was 3.0%, 8.2%, and 6.5% respectively in 2005, 2011, and 2016. Household heads with higher education had 5.99 (95% CI = 3.48, 10.33), 3.61 (95% CI = 2.56, 5.07), and 3.43 (95% CI = 2.19, 6.37) times higher odds of using the treatment methods respectively in 2005, 2011, and 2016 compared to household heads who had no education. There was a significantly high number of households that used appropriate water treatment methods in 2011 (AOR = 2.78, 95% CI = 2.16, 3.57) and 2016 (AOR = 2.18, 95% CI = 1.64, 3.89) compared to 2005 data. In pooled data analysis, the reported use of the treatment methods is associated with household head education, residency, drinking water sources, and owning radio and television. From a multilevel modeling, within-region variation is higher than between-region variations in the use of treatment methods in each survey. Below 10% of households reportedly treating water at point-of-use in each survey attributable to different factors. Designing intervention strategies for wide-scale use of treatment methods at the country level is fundamental.

Extreme weather can cause severe damage and widespread power outages across utility service areas. The restoration process can be long and costly and emergency managers may have limited computational resources to optimize the restoration process. This study takes an agent based modeling (ABM) approach to optimize the utility storm recovery process in Connecticut. The ABM is able to replicate past storm recoveries and can test future case scenarios. We found that parameters such as the number of outages, repair time range and the number of utility crews working can substantially impact the estimated time to restoration (ETR). Other parameters such as crew starting locations and travel speeds had comparatively minor impacts on the ETR. The ABM can be used to train new emergency managers as well as test strategies for storm restoration optimization.

Power outage restoration following extreme storms is a complicated process that couples engineering processes and human decisions. Emergency managers typically rely on past experiences and have limited access to computer simulations to aid in decision-making. Climate scientists predict that although hurricane frequency may decrease, the intensity of storms may increase. Increased damage from hurricanes will result in new restoration challenges that emergency managers may not have experience solving. Our study uses agent-based modeling (ABM) to determine how restoration might have been impacted for 30 different scenarios of Hurricane Sandy for a climate in 2112 (Sandy2112). These Sandy2112 scenarios were obtained from a previous study that modeled how outages from Hurricane Sandy in 2012 might have been affected in the future as climate change intensified both wind and precipitation hazards. As the number of outages increases, so does the expected estimated time to restoration for each storm. The impact of increasing crews is also studied to determine the relationship between the number of crews and outage durations (or restoration curves). Both the number of outages and the number of crews impact the variability in time to restoration. Our results can help emergency managers and policy makers plan for future hurricanes that are likely to become stronger and more impactful to critical infrastructure.