No abstract

Rapid diagnostic testing for COVID-19 is key to guiding social distancing orders and containing emerging disease clusters by contact tracing and isolation. However, communities throughout the US do not yet have adequate access to tests. Pharmacies are already engaged in testing, but there is capacity to greatly increase coverage. Using a facility location optimization model and willingness-to-travel estimates from US National Household Travel Survey data, we find that if COVID-19 testing became available in all US pharmacies, an estimated 94% of the US population would be willing to travel to obtain a test, if warranted. Whereas the largest chain provides high coverage in densely populated states, like Massachusetts, Rhode Island, New Jersey, and Connecticut, independent pharmacies would be required for sufficient coverage in Montana, South Dakota, and Wyoming. If only 1,000 pharmacies in the US are selected to provide testing, judicious selection, using our optimization model, provides estimated access to 29 million more people than selecting pharmacies simply based on population density. COVID-19 testing through pharmacies can improve access across the US. Even if only few pharmacies offer testing, judicious selection of specific sites can simplify logistics and improve access.

Inverse equilibrium modeling fits parameters of an equilibrium model to observations. This allows investigation of whether market structures fit observed outcomes and it has predictive power. We introduce a methodology that leverages relaxed stationarity conditions from Karush-Kuhn-Tucker conditions to set up inverse equilibrium problems. This facilitates reframing of existing equilibrium approaches on power systems into inverse equilibrium programs. We illustrate the methodology on network-constrained and unconstrained Nash-Cournot games between price-making power generators. The inverse equilibrium problems in this paper reformulate into linear programming problems that are flexible and interpretable. Still, inverse equilibrium modeling provides generally inconsistent estimation and econometric approaches are better for this purpose.

Hydropower producers estimate the opportunity value of their water, known as a water value, by comparing current prices to future opportunities. When hydropower dominates the energy mix, the system’s hydrological state predominantly governs supply and thus prices. Despite this intuitive relationship, industry practice is to assume that inflow to reservoirs and prices are independent when they establish operational policies 1–2 years ahead. To investigate the impact of this assumption, we formulate the hydropower scheduling problem as a Markov decision process and develop a novel price model that considers the joint dynamics of forward prices and inflows. We find that producers underestimate their water value when they ignore co-movements between price and inflow. The dependency makes producers more willing to postpone generation and tolerate slightly higher spillage risk. This is because high inflow periods tend to observe low prices and the reservoir capacity is limited. Nevertheless, a case study of a hydropower plant with industry data suggests modest economic losses in practice. Our numerical results suggest a potential gain of 0.17% in expected revenue and approximately unchanged revenue variance if producers consider the co-movements when establishing an operational policy.

Widespread, convenient access to COVID-19 testing has been challenging in the United States. We make a case for provisioning COVID-19 tests through the United States Postal Service (USPS) facilities and demonstrate a simple method for selecting locations to improve access. We provide quantitative evidence that even a subset of USPS facilities could provide broad access, particularly in remote and at-risk communities with limited access to health care. Based on daily travel surveys, census data, locations of USPS facilities, and an established care-seeking model, we estimate that more than 94% of the US population would be willing to travel to an existing USPS facility if warranted. For half of the US population, this would require traveling less than 2.5 miles from home; for 90%, the distance would be less than 7 miles. In Georgia, Illinois, and Minnesota, we estimate that testing at USPS facilities would provide access to an additional 4.1, 3.1, and 1.3 million people and reduce the median travel distance by 3.0, 0.8, and 1.2 miles, respectively, compared with existing testing sites per 28 July 2020. We also discuss the option of distributing test-at-home kits via USPS instead of private carriers. Finally, our proposal provides USPS an opportunity to increase revenues and expand its mission, thus improving its future prospects and relevance.