Impact assessment of full and partial stay-at-home orders, face mask usage, and contact tracing: An agent-based simulation study of COVID-19 for an urban region.

PURPOSE: Social intervention strategies to mitigate COVID-19 are examined using an agent-based simulation model. Outbreak in a large urban region, Miami-Dade County, Florida, USA is used as a case study. Results are intended to serve as a planning guide for decision makers. METHODS: The simulation model mimics daily social mixing behavior of the susceptible and infected generating the spread. Data representing demographics of the region, virus epidemiology, and social interventions shapes model behavior. Results include daily values of infected, reported, hospitalized, and dead. RESULTS: Results show that early implementation of complete stay-at-home order is effective in flattening and reversing the infection growth curve in a short period of time. Whereas, using Florida's Phase II plan alone could result in 75% infected and end of pandemic via herd immunity. Universal use of face masks reduced infected by 20%. A further reduction of 66% was achieved by adding contact tracing with a target of identifying 50% of the asymptomatic and pre-symptomatic. CONCLUSIONS: In the absence of a vaccine, the strict stay-at-home order, though effective in curbing a pandemic outbreak, leaves a large proportion of the population susceptible. Hence, there should be a strong follow up plan of social distancing, use of face mask, contact tracing, testing, and isolation of infected to minimize the chances of large-scale resurgence of the disease. However, as the economic cost of the complete stay-at-home-order is very high, it can perhaps be used only as an emergency first response, and the authorities should be prepared to activate a strong follow up plan as soon as possible. The target level for contact tracing was shown to have a nonlinear impact on the reduction of the percentage of population infected. Increase in contact tracing target from 20% to 30% appeared to provide the largest incremental benefit.

A strategic gaming model for health information exchange markets.

Current market conditions create incentives for some providers to exercise control over patient data in ways that unreasonably limit its availability and use. Here we develop a game theoretic model for estimating the willingness of healthcare organizations to join a health information exchange (HIE) network and demonstrate its use in HIE policy design. We formulated the model as a bi-level integer program. A quasi-Newton method is proposed to obtain a strategy Nash equilibrium. We applied our modeling and solution technique to 1,093,177 encounters for exchanging information over a 7.5-year period in 9 hospitals located within a three-county region in Florida. Under a set of assumptions, we found that a proposed federal penalty of up to $2,000,000 has a higher impact on increasing HIE adoption than current federal monetary incentives. Medium-sized hospitals were more reticent to adopt HIE than large-sized hospitals. In the presence of collusion among multiple hospitals to not adopt HIE, neither federal incentives nor proposed penalties increase hospitals' willingness to adopt. Hospitals' apathy toward HIE adoption may threaten the value of inter-connectivity even with federal incentives in place. Competition among hospitals, coupled with volume-based payment systems, creates no incentives for smaller hospitals to exchange data with competitors. Medium-sized hospitals need targeted actions (e.g., outside technological assistance, group purchasing arrangements) to mitigate market incentives to not adopt HIE. Strategic game theoretic models help to clarify HIE adoption decisions under market conditions at play in an extremely complex technology environment.

A MDP model for breast and ovarian cancer intervention strategies for BRCA1/2 mutation carriers.

PURPOSE: Women with BRCA1/2 mutations have higher risk for breast and ovarian cancers. Available intervention actions include prophylactic surgeries and breast screening, which vary significantly in cost, cancer prevention, and in resulting death from other causes. We present a model designed to yield optimal intervention strategies for mutation carriers between the ages of 30 and 65 and any prior intervention history. METHODS: A Markov decision process (MDP) model is developed that considers yearly state transitions for the mutation carriers and state dependent intervention actions. State is defined as a vector comprising mutation type, health states, prior intervention actions, and age. A discounted value iteration algorithm is used to obtain optimal strategies from the MDP model using both cost and quality-adjusted life years (QALYs) as rewards. RESULTS: The results from MDP model show that for 30-year-old women with BRCA1 mutation and no prior intervention history, the cost-optimal strategy is a combination of prophylactic mastectomy (PM) and prophylactic oophorectomy (PO) at age 30 with no screening afterwards. Whereas, the QALYs-optimal strategy suggests PO at age 30 and PM at age 50 with screening afterwards. For BRCA2 mutation carriers at age 30, the cost-optimal strategy is PO at age 30, PM at age 40, and yearly screening only after age 56. Corresponding QALYs-optimal strategy is PM at age 40 with screening. Strategies for all other ages (31 to 65) are obtained and presented. It is also demonstrated that the cost-optimal strategies offer near maximum survival rate and near minimum cancer incidence rates by age 70, when compared to other ad hoc strategies.