Modeling in the COVID-19 Pandemic: Overcoming the Water Sector’s Data Struggles to Realize the Potential of Hydraulic Models

Hydraulic models can provide efficient and cost-effective ways for water utilities to evaluate changes in operating conditions (e.g., population dynamics, disasters), thereby increasing system resiliency during crises. Unfortunately, model development remains out of reach for many utilities because of high software costs, data needs, or personnel requirements. This study seeks to classify hydraulic modeling data needs, identify success factors and challenges associated with model development, and determine whether modeling a subzone of a larger water distribution network can provide useful insights during a crisis, specifically the COVID-19 pandemic. At the pandemic onset, we began developing a hydraulic model of the water distribution system of the University of Texas at Austin campus—a subsystem of the water distribution network of Austin, Texas—to understand how spatiotemporal changes in water demands impacted system performance. We found that the completed model can offer useful insight into the impacts of demand changes within the modeled subsystem (e.g., potential locations of water stagnation). However, the data collection and processing challenges encountered (e.g., siloed collection efforts, lack of standardization, lengthy processing) reflect barriers to model development and use. The amount of time required to gather and process the necessary data shows that model development cannot occur during a time-sensitive crisis, likely rendering any insight too late for use. Here, we make recommendations to address data-related challenges and support utilities in incorporating hydraulic modeling into emergency planning.

Human-Infrastructure Interactions during the COVID-19 Pandemic: Understanding Water and Electricity Demand Profiles at the Building Level.

When engineers design and manage a building's water and electricity utilities, they must make assumptions about resource use. These assumptions are often challenged when unexpected changes in demand occur, such as the spatial and temporal changes observed during the coronavirus (COVID-19) pandemic. Social distancing policies (SDPs) enacted led many universities to close their campuses and implement remote learning, impacting utility consumption patterns. Yet, little is known about how consumption changed at the building level. Here, we aim to understand how water and electricity consumption changed during the pandemic by identifying characteristic weekly demand profiles and understanding how these changes were related to regulatory and social systems. We performed k-means clustering on utility demand data measured before and as the pandemic evolved from five buildings of different types at the University of Texas at Austin. As expected, after SDPs were enacted both water and electricity use shifted, with most buildings seeing a sharp initial decline that remained low until the university partially reopened. In contrast to electricity use, we found that water use was tightly coupled with SDPs. Our study provides actionable information for managers to mitigate negative impacts (e.g., water stagnation) and capitalize on opportunities to minimize resource use.

Association of Human Mobility Restrictions and Race/Ethnicity-Based, Sex-Based, and Income-Based Factors With Inequities in Well-being During the COVID-19 Pandemic in the United States.

Importance: An accurate understanding of the distributional implications of public health policies is critical for ensuring equitable responses to the COVID-19 pandemic and future public health threats. Objective: To identify and quantify the association of race/ethnicity-based, sex-based, and income-based inequities of state-specific lockdowns with 6 well-being dimensions in the United States. Design, Setting, and Participants: This pooled, repeated cross-sectional study used data from 14 187 762 households who participated in phase 1 of the population-representative US 2020 Household Pulse Survey (HPS). Households were invited to participate by email, text message, and/or telephone as many as 3 times. Data were collected via an online questionnaire from April 23 to July 21, 2020, and participants lived in all 50 US states and the District of Columbia. Exposures: Indicators of race/ethnicity, sex, and income and their intersections. Main Outcomes and Measures: Unemployment; food insufficiency; mental health problems; no medical care received for health problems; default on last month's rent or mortgage; and class cancellations with no distance learning. Race/ethnicity, sex, income, and their intersections were used to measure distributional implications across historically marginalized populations; state-specific, time-varying population mobility was used to measure lockdown intensity. Logistic regression models with pooled repeated cross-sections were used to estimate risk of dichotomous outcomes by social group, adjusted for confounding variables. Results: The 1 088 314 respondents (561 570 [51.6%; 95% CI, 51.4%-51.9%] women) were aged 18 to 88 years (mean [SD], 51.55 [15.74] years), and 826 039 (62.8%; 95% CI, 62.5%-63.1%) were non-Hispanic White individuals; 86 958 (12.5%; 95% CI, 12.4%-12.7%), African American individuals; 86 062 (15.2%; 95% CI, 15.0%-15.4%), Hispanic individuals; and 50 227 (5.6%; 95% CI, 5.5%-5.7%), Asian individuals. On average, every 10% reduction in mobility was associated with higher odds of unemployment (odds ratio [OR], 1.3; 95% CI, 1.2-1.4), food insufficiency (OR, 1.1; 95% CI, 1.1-1.2), mental health problems (OR, 1.04; 95% CI, 1.0-1.1), and class cancellations (OR, 1.1; 95% CI, 1.1-1.2). Across most dimensions compared with White men with high income, African American individuals with low income experienced the highest risks (eg, food insufficiency, men: OR, 3.3; 95% CI, 2.8-3.7; mental health problems, women: OR, 1.9; 95% CI, 1.8-2.1; medical care inaccessibility, women: OR, 1.7; 95% CI, 1.6-1.9; unemployment, men: OR, 2.8; 95% CI, 2.5-3.2; rent/mortgage defaults, men: OR, 5.7; 95% CI, 4.7-7.1). Other high-risk groups were Hispanic individuals (eg, unemployment, Hispanic men with low income: OR, 2.9; 95% CI, 2.5-3.4) and women with low income across all races/ethnicities (eg, medical care inaccessibility, non-Hispanic White women: OR, 1.8; 95% CI, 1.7-2.0). Conclusions and Relevance: In this cross-sectional study, African American and Hispanic individuals, women, and households with low income had higher odds of experiencing adverse outcomes associated with the COVID-19 pandemic and stay-at-home orders. Blanket public health policies ignoring existing distributions of risk to well-being may be associated with increased race/ethnicity-based, sex-based, and income-based inequities.