Community health pathways modeling and scheduling under uncertainty.

Scheduling and coordinating constrained resources in community healthcare settings at a centralized Pathways Community HUB is challenging due to limited resources and the inherent dynamics of the processes and the organizational structures. In this work, we introduce a stochastic programming (SP) approach for connected community health for optimally scheduling community health pathways (CHPs) under uncertainty in resource availability. A CHP is a standardized tool that details multiple steps of a healthcare-related service and the required resources for each step. The SP methodology was implemented and applied to data for a real Pathways Community HUB for a U.S. county involving several CHPs, community health workers, physicians, and other resources. The computational results are promising and they show that client access times depend on the HUB resources uncertain future availability and the level of client demand, with high client demand resulting in relatively longer access time. The study reveals that schedules provided by a deterministic approach where resource availability is assumed to be known can be too optimistic. Several managerial insights are learned from this study, including the observation that the SP model provides client schedules that are equitable across the same type of community health workers.

An integrated chance constraints approach for optimal vaccination strategies under uncertainty for COVID-19.

Despite concerted efforts by health authorities worldwide to contain COVID-19, the SARS-CoV-2 virus has continued to spread and mutate into new variants with uncertain transmission characteristics. Therefore, there is a need for new data-driven models for determining optimal vaccination strategies that adapt to the new variants with their uncertain transmission characteristics. Motivated by this challenge, we derive an integrated chance constraints stochastic programming (ICC-SP) approach for finding vaccination strategies for epidemics that incorporates population demographics for any region of the world, uncertain disease transmission and vaccine efficacy. An optimal vaccination strategy specifies the proportion of individuals in a given household-type to vaccinate to bring the reproduction number to below one. The ICC-SP approach provides a quantitative method that allows to bound the expected excess of the reproduction number above one by an acceptable amount according to the decision-maker's level of risk. This new methodology involves a multi-community household based epidemiology model that uses census demographics data, vaccination status, age-related heterogeneity in disease susceptibility and infectivity, virus variants, and vaccine efficacy. The new methodology was tested on real data for seven neighboring counties in the United States state of Texas. The results are promising and show, among other findings, that vaccination strategies for controlling an outbreak should prioritize vaccinating certain household sizes as well as age groups with relatively high combined susceptibility and infectivity.

COVID-19 vaccination policies under uncertain transmission characteristics using stochastic programming.

We develop a new stochastic programming methodology for determining optimal vaccination policies for a multi-community heterogeneous population. An optimal policy provides the minimum number of vaccinations required to drive post-vaccination reproduction number to below one at a desired reliability level. To generate a vaccination policy, the new method considers the uncertainty in COVID-19 related parameters such as efficacy of vaccines, age-related variation in susceptibility and infectivity to SARS-CoV-2, distribution of household composition in a community, and variation in human interactions. We report on a computational study of the new methodology on a set of neighboring U.S. counties to generate vaccination policies based on vaccine availability. The results show that to control outbreaks at least a certain percentage of the population should be vaccinated in each community based on pre-determined reliability levels. The study also reveals the vaccine sharing capability of the proposed approach among counties under limited vaccine availability. This work contributes a decision-making tool to aid public health agencies worldwide in the allocation of limited vaccines under uncertainty towards controlling epidemics through vaccinations.