Mass screening models for contagious diseases with no latent period

A simulation–optimization framework for optimizing response strategies to epidemics☆

Epidemics require dynamic response strategies that encompass a multitude of policy alternatives and that balance health, economic and societal considerations. We propose a simulation–optimization framework to aid policymakers select closure, protection and travel policies to minimize the total number of infections under a limited budget. The proposed framework combines a modified, age-stratified SEIR compartmental model to evaluate the health impact of response strategies and a Genetic Algorithm to effectively search for better strategies. We implemented our framework on a real case study in Nova Scotia to devise optimized response strategies to COVID-19 under different budget scenarios and found a clear trade-off between health and economic considerations. Closure policies seem to be the most sensitive to policy restrictions, followed by travel policies. On the other hand, results suggest that practising social distancing and wearing masks are necessary whenever their economic impacts are bearable. The framework is generic and can be extended to encompass vaccination policies and to use different epidemiological models and optimization methods.

Optimization methods for decision making in disease prevention and epidemic control

This paper investigates problems of disease prevention and epidemic control (DPEC), in which we optimize two sets of decisions: (i) vaccinating individuals and (ii) closing locations, given respective budgets with the goal of minimizing the expected number of infected individuals after intervention. The spread of diseases is inherently stochastic due to the uncertainty about disease transmission and human interaction. We use a bipartite graph to represent individuals' propensities of visiting a set of location, and formulate two integer nonlinear programming models to optimize choices of individuals to vaccinate and locations to close. Our first model assumes that if a location is closed, its visitors stay in a safe location and will not visit other locations. Our second model incorporates compensatory behavior by assuming multiple behavioral groups, always visiting the most preferred locations that remain open. The paper develops algorithms based on a greedy strategy, dynamic programming, and integer programming, and compares the computational efficacy and solution quality. We test problem instances derived from daily behavior patterns of 100 randomly chosen individuals (corresponding to 195 locations) in Portland, Oregon, and provide policy insights regarding the use of the two DPEC models.

Breast Cancer Screening Services: Trade-offs in Quality, Capacity, Outreach, and Centralization

This work combines and extends previous work on breast cancer screening models by explicitly incorporating, for the first time, aspects of the dynamics of health care states, program outreach, and the screening volume-quality relationship in a service system model to examine the effect of public health policy and service capacity decisions on public health outcomes. We consider the impact of increasing standards for minimum reading volume to improve quality, expanding outreach with or without decentralization of service facilities, and the potential of queueing due to stochastic effects and limited capacity. The results indicate a strong relation between screening quality and the cost of screening and treatment, and emphasize the importance of accounting for service dynamics when assessing the performance of health care interventions. For breast cancer screening, increasing outreach without improving quality and maintaining capacity results in less benefit than predicted by standard models.

Repeat screening for HIV: when to test and why

This paper presents models for repeat HIV screening under conditions of constant low HIV incidence. The models reveal a direct link between the prevalence of undetected HIV infection and the screening interval between repeat HIV tests. We show how to select screening intervals that either achieve a given HIV prevalence level, or optimally balance the cost of repeat HIV testing against the cost of HIV infection. Alternatively, given an existing repeat screening program, the model implies that cost of infection for which the given screening interval is optimal. The method also suggests how to select an HIV testing technology. The models are applied to existing repeat testing programs in the U.S. Army and among legal commercial sex workers in the state of Nevada in the Far West of the United States.

Strategic decision support in preventive health care

This nontechnical interdisciplinary paper highlights major principles of strategic decision support in preventive health care and points to selected key literature. It begins by looking at preventive health care from strategic perspectives such as determinants of health, health care costs, objectives of prevention, political order constellations and available data. Next, the paper identifies strategic decisions and decision-making to be supported. Having gained strategic perspective, it discusses tools for decision support. These tools are here related to two planning steps (i) assessment, problem and goal analysis; and (ii) design and choice of alternative interventions. Qualitative as well as quantitative aspects of computer-assisted policy analysis in preventive health care are outlined and examples presented.

A mathematical model for scheduling screening tests for progressive diseases

In this paper, a mathematical model describing the stochastic process underlying the etiology of noncontagious, curable, progressive diseases is developed. In our previous study of progressive diseases, the reliabilities of screening tests were assumed to be constants, independent of how long the individual has had the disease. The purpose of this study is to develop more insight into modeling the disease process, and the interaction of screening tests with the natural behavior of the disease. To accomplish this task, more realistic screening parameters are selected, and optimum policies for mass screening programs under different reliabilities of screening tests are developed. These modifications make the model general enough to be applicable to a larger class of progressive diseases that includes breast cancer. Solutions to two simplified versions of the model for childhood neuroblastoma illustrate the use of our proposed procedure.