A Multi-Fidelity Rollout Algorithm for Dynamic Resource Allocation in Population Disease Management.

Dynamic resource allocation for prevention, screening, and treatment interventions in population disease management has received much attention in recent years due to excessive healthcare costs. In this paper, our goal is to design a model and an efficient algorithm to optimize sequential intervention policies under resource constraints to improve population health outcomes. We consider a discrete-time finite-horizon budget allocation problem with disease progression within a closed birth-cohort population. To address the computational challenges associated with large-state and multiple-period dynamic decision-making problems, we propose a low-fidelity approximation that preserves the population dynamics under a stationary policy. To improve the healthcare interventions in terms of population health outcomes, we then embed the low-fidelity approximation into a high-fidelity optimization model to efficiently identify a good non-stationary sequential intervention policy. Our approach is illustrated by a numerical example of screening and treatment policy implementation for chronic hepatitis C virus (HCV) infection over a budget planning period. We numerically compare our Multi-Fidelity Rollout Algorithm (MF-RA) to a grid search approach and demonstrate the similarity of sequential policy trends and closeness of overall health outcomes measured by quality-adjusted life-years (QALYs) and the total number of individuals that undergo screening and treatment for different annual budgets and birth-cohorts. We also show how our approach scales well to problems with high dimensionality due to many decision periods by studying time to elimination of HCV.

Impact of Vaccination and Nonpharmaceutical Interventions With Possible COVID-19 Viral Evolutions Using an Agent-Based Simulation.

Introduction: The COVID-19 pandemic continues with highly contagious variants and waning immunity. As the virus keeps evolving to be more infectious and immune evasive, some question whether the COVID-19 pandemic can be managed through sustainable public health measures. Methods: We developed an agent-based simulation to explore the impact of COVID-19 mutations, periodic vaccinations, and nonpharmaceutical interventions on reducing COVID-19 deaths. The model is calibrated to the greater Seattle area by observing local epidemic data. We perform scenario analyses on viral mutations that change infectiousness, disease severity, and immune evasiveness from previous infections and vaccination every 6 months. The simulation is run until the end of year 2023. Results: Variants with increased infectivity or increased immune evasion dominate previous strains. With enhanced immune protection from a pancoronavirus vaccine, the most optimistic periodic vaccination rate reduces average total deaths by 44.6% compared with the most pessimistic periodic vaccination rate. A strict threshold nonpharmaceutical intervention policy reduces average total deaths by 71.3% compared with an open society, whereas a moderate nonpharmaceutical intervention policy results in a 33.6% reduction. Conclusions: Our findings highlight the potential benefits of pancoronavirus vaccines that offer enhanced and longer-lasting immunity. We emphasize the crucial role of nonpharmaceutical interventions in reducing COVID-19 deaths regardless of virus mutation scenarios. Owing to highly immune evasive and contagious SARS-CoV-2 variants, most scenarios in this study fail to reduce the mortality of COVID-19 to the level of influenza and pneumonia. However, our findings indicate that periodic vaccinations and a threshold nonpharmaceutical intervention policy may succeed in achieving this goal. This indicates the need for caution and vigilance in managing a continuing COVID-19 epidemic.

Preventing tuberculosis with community-based care in an HIV-endemic setting: a modelling analysis.

INTRODUCTION: Antiretroviral therapy (ART) and tuberculosis preventive treatment (TPT) both prevent tuberculosis (TB) disease and deaths among people living with HIV. Differentiated care models, including community-based care, can increase the uptake of ART and TPT to prevent TB in settings with a high burden of HIV-associated TB, particularly among men. METHODS: We developed a gender-stratified dynamic model of TB and HIV transmission and disease progression among 100,000 adults ages 15-59 in KwaZulu-Natal, South Africa. We drew model parameters from a community-based ART initiation and resupply trial in sub-Saharan Africa (Delivery Optimization for Antiretroviral Therapy, DO ART) and other scientific literature. We simulated the impacts of community-based ART and TPT care programmes during 2018-2027, assuming that community-based ART and TPT care were scaled up to similar levels as in the DO ART trial (i.e. ART coverage increasing from 49% to 82% among men and from 69% to 83% among women) and sustained for 10 years. We projected the number of TB cases, deaths and disability-adjusted life years (DALYs) averted relative to standard, clinic-based care. We calculated programme costs and incremental cost-effectiveness ratios from the provider perspective. RESULTS: If community-based ART care could be implemented with similar effectiveness to the DO ART trial, increased ART coverage could reduce TB incidence by 27.0% (range 21.3%-34.1%) and TB mortality by 34.6% (range 24.8%-42.2%) after 10 years. Increasing both ART and TPT uptake through community-based ART with TPT care could reduce TB incidence by 29.7% (range 23.9%-36.0%) and TB mortality by 36.0% (range 26.9%-43.8%). Community-based ART with TPT care reduced gender disparities in TB mortality rates, with a projected 54 more deaths annually among men than women (range 11-103) after 10 years of community-based care versus 109 (range 41-182) in standard care. Over 10 years, the mean cost per DALY averted by community-based ART with TPT care was $846 USD (range $709-$1012). CONCLUSIONS: By substantially increasing coverage of ART and TPT, community-based care for people living with HIV could reduce TB incidence and mortality in settings with high burdens of HIV-associated TB and reduce TB gender disparities.

Preventing tuberculosis with community-based care in an HIV-endemic setting: a modeling analysis.

Introduction: Antiretroviral therapy (ART) and TB preventive treatment (TPT) both prevent tuberculosis (TB) disease and deaths among people living with HIV. Differentiated care models, including community-based care, can increase uptake of ART and TPT to prevent TB in settings with a high burden of HIV-associated TB, particularly among men. Methods: We developed a gender-stratified dynamic model of TB and HIV transmission and disease progression among 100,000 adults ages 15-59 in KwaZulu-Natal, South Africa. We drew model parameters from a community-based ART initiation and resupply trial in sub-Saharan Africa (Delivery Optimization for Antiretroviral Therapy, DO ART) and other scientific literature. We simulated the impacts of community-based ART and TPT care programs during 2018-2027, assuming that community-based ART and TPT care were scaled up to similar levels as in the DO ART trial (i.e., ART coverage increasing from 49% to 82% among men and from 69% to 83% among women) and sustained for ten years. We projected the number of TB cases, deaths, and disability-adjusted life years (DALYs) averted relative to standard, clinic-based care. We calculated program costs and incremental cost-effectiveness ratios from the provider perspective. Results: If community-based ART care could be implemented with similar effectiveness to the DO ART trial, increased ART coverage could reduce TB incidence by 27.0% (range 21.3% - 34.1%) and TB mortality by 36.0% (range 26.9% - 43.8%) after ten years. Increasing both ART and TPT uptake through community-based ART with TPT care could reduce TB incidence by 29.7% (range 23.9% - 36.0%) and TB mortality by 36.0% (range 26.9% - 43.8%). Community-based ART with TPT care reduced gender disparities in TB mortality rates by reducing TB mortality among men by a projected 39.8% (range 32.2% - 46.3%) and by 30.9% (range 25.3% - 36.5%) among women. Over ten years, the mean cost per DALY averted by community-based ART with TPT care was $846 USD (range $709 - $1,012). Conclusions: By substantially increasing coverage of ART and TPT, community-based care for people living with HIV could reduce TB incidence and mortality in settings with high burdens of HIV-associated TB and reduce TB gender disparities.

Route Optimization Tool (RoOT) for distribution of vaccines and health products.

Delivery of health products from provinces or districts to health facilities, including temperature-sensitive vaccines, is one of the most effective interventions to ensure availability of supplies and save lives in low- and middle-income countries. Currently, routes are hand drawn by logisticians that are adjusted based on vehicle availability and quantity of products. Easy-to-use supply chain tools are needed that planners can use in real-time to create or adjust routes for available vehicles and road conditions. Efficient and optimized distribution is even more critical with the COVID-19 vaccine distribution. We develop a Route Optimization Tool (RoOT) using a variant of a Vehicle Routing and Scheduling Algorithm (VeRSA) that is coded in Python, but reads and writes Excel files to make data input and using outputs easier. The tool takes into account cold chain distribution, is easy-to-use, and provides routes quickly within two minutes. RoOT can be used for routine operations or in emergency situations, such as delivery of new COVID-19 vaccine. The tool has a user-centric design with easy dropdown menus and the ability to optimize on time, risk, or combination of both. Training of logisticians in Mozambique indicate that RoOT is easy to use and provides a tool to improve planning and efficient distribution of health products, especially vaccines. We illustrate using RoOT in an emergency situation, such as a cyclone. RoOT is an open-source tool for optimal routing of health products. It provides optimized routes faster than most commercial software, and is tailored to meet the needs of government stakeholders. Currently, RoOT does not allow multi-day routes, and is designed for trips that can be completed within twenty-four hours. Areas for future development include multi-day routing and integration with mapping software to facilitate distance calculations and visualization of routes.

COVID-19 Pandemic Response Simulation in a Large City: Impact of Nonpharmaceutical Interventions on Reopening Society.

As the novel coronavirus (COVID-19) pandemic continues to expand, policymakers are striving to balance the combinations of nonpharmaceutical interventions (NPIs) to keep people safe and minimize social disruptions. We developed and calibrated an agent-based simulation to model COVID-19 outbreaks in the greater Seattle area. The model simulated NPIs, including social distancing, face mask use, school closure, testing, and contact tracing with variable compliance and effectiveness to identify optimal NPI combinations that can control the spread of the virus in a large urban area. Results highlight the importance of at least 75% face mask use to relax social distancing and school closure measures while keeping infections low. It is important to relax NPIs cautiously during vaccine rollout in 2021.

A light-touch routing optimization tool (RoOT) for vaccine and medical supply distribution in Mozambique.

Planning vaccine distribution in rural and urban poor communities is challenging, due in part to inadequate vehicles, limited cold storage, road availability, and weather conditions. The University of Washington and VillageReach jointly developed and tested a user-friendly, Excel spreadsheet based optimization tool for routing and scheduling to efficiently distribute vaccines and other medical commodities to health centers across Mozambique. This paper describes the tool and the process used to define the problem and obtain feedback from users during the development. The distribution and routing tool, named route optimization tool (RoOT), uses an indexing algorithm to optimize the routes under constrained resources. Numerical results are presented using five datasets, three realistic and two artificial datasets. RoOT can be used in routine or emergency situations, and may be easily adapted to include other products, regions, or logistic problems.

Optimizing Implementation of Hepatitis C Birth-Cohort Screening and Treatment Strategies: Model-Based Projections.

Background: Chronic hepatitis C (HCV) is a significant public health problem affecting more than three million Americans. The US health care systems are ramping up costly HCV screening and treatment efforts with limited budget. We determine the optimal implementation of HCV birth-cohort screening and treatment strategies under budget constraints and health care payer's perspective. Methods: Markov model and scenario-based simulation optimization. The target population is birth cohort born between 1945 and 1975. The interventions are allocating annual budget to screen a proportion of the target population and treat a proportion of the identified chronic HCV-positive patients over 10 years. Outcomes measure is to maximize lifetime discounted quality-adjusted life-years. Results: Allocate a percentage of the annual budget to screening, then treat patients with the remaining budget and prioritize the sickest patients. When the budget is $1 billion/year, the best strategy is to allocate the entire budget to treatment. When the budget is $5 billion/year, it is optimal to allocate 60% of the budget to screening in the first 2 years and 0% thereafter for age cohort 40 to 49; and allocate 20% of the budget to screening starting in year 3 for age cohorts 50 to 59 and 60 to 69. Health benefits are sensitive to budget in the first 2 years. Results are not sensitive to distribution of fibrosis stages by awareness of HCV. Conclusion: When budget is limited, all efforts should be focused on early treatment. With higher budget, better population health outcomes are achieved by reserving some budget for HCV screening while implementing a priority-based treatment strategy. This work has broad applicability to diverse health care systems and helps determine how much effort should be devoted to screening versus treatment under resource limitations.

Design and implementation of a combined influenza immunization and tuberculosis screening campaign with simulation modelling.

RATIONALE, AIMS AND OBJECTIVES: Design and implement a concurrent campaign of influenza immunization and tuberculosis (TB) screening for health care workers (HCWs) that can reduce the number of clinic visits for each HCW. METHOD: A discrete-event simulation model was developed to support issues of resource allocation decisions in planning and operations phases. RESULTS: The campaign was compressed to100 days in 2010 and further compressed to 75 days in 2012 and 2013. With more than 5000 HCW arrivals in 2011, 2012 and 2013, the 14-day goal of TB results was achieved for each year and reduced to about 4 days in 2012 and 2013. CONCLUSION: Implementing a concurrent campaign allows less number of visiting clinics and the compressing of campaign length allows earlier immunization. The support of simulation modelling can provide useful evaluations of different configurations.