An integrated simulation-optimization framework to optimize search and treatment path for controlling a biological invader

Onal, Sevilay; Akhundov, Najmaddin; Büyüktahtakın, İ. Esra; Smith, Jennifer; Houseman, Gregory R.

doi:10.1016/j.ijpe.2019.09.028

“…The majority of mathematical models in the epidemiological literature have used simulation methods to forecast the progression of the epidemic and to study the efficacy of several interventions (Dasaklis et al, 2017;Meltzer et al, 2014;Onal et al, 2019;Pandey et al, 2014;Rivers et al, 2014;Siettos et al, 2015). Several studies have considered stochastic compartmental models to analyze different strategies for controlling epidemic diseases, such as vaccination strategies, behavioral changes that impact the interaction between different groups, and regional intervention strategies (Funk et al, 2017;Lekone and Finkenstädt, 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-Stage Stochastic Programming Approach to Epidemic Resource Allocation with Equity Considerations

Yin

¹

,

Büyüktahtakın

²

2021

Preprint

0

View full text Add to dashboard Cite

Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multistage value of the stochastic solution (VSS), which demonstrates the superiority of the † Cite as: Yin, X. and Büyüktahtakın İ. E., 2021. A multi-stage stochastic programming approach to epidemic resource allocation with equity considerations. Health Care Management Science, pp. 1-57.

show abstract

“…Optimization models are suited to the complexity of invasive species problems because they take account of the biological and behavioral characteristics of an invader while evaluating the costs and benefits of possible management responses [ 43 , 60 ]. Generally, optimization models applied to invasive species combine simulation models, which depict the uncertain invasion process through space and time, with scenario approaches, such that the optimal surveillance is developed with respect to a large number of plausible invasion scenarios, thereby supporting decisions regarding the effective allocation of response resources [ 23 , 43 , 46 , 50 , 56 , 61 ]. For invasive species, optimal surveillance decisions specify timing, spatial intensity and configuration of surveys [ 23 , 50 , 52 , 62 ], and sometimes offer a choice of detection method based on features of potential survey locations (e.g.…”

Section: Optimization Approaches To Surveillance Designmentioning

confidence: 99%

Optimal invasive species surveillance in the real world: practical advances from research

Koch

¹

,

Yemshanov

²

,

Haight

³

et al. 2020

Emerging Topics in Life Sciences

View full text Add to dashboard Cite

When alien species make incursions into novel environments, early detection through surveillance is critical to minimizing their impacts and preserving the possibility of timely eradication. However, incipient populations can be difficult to detect, and usually, there are limited resources for surveillance or other response activities. Modern optimization techniques enable surveillance planning that accounts for the biology and expected behavior of an invasive species while exploring multiple scenarios to identify the most cost-effective options. Nevertheless, most optimization models omit some real-world limitations faced by practitioners during multi-day surveillance campaigns, such as daily working time constraints, the time and cost to access survey sites and personnel work schedules. Consequently, surveillance managers must rely on their own judgments to handle these logistical details, and default to their experience during implementation. This is sensible, but their decisions may fail to address all relevant factors and may not be cost-effective. A better planning strategy is to determine optimal routing to survey sites while accounting for common daily logistical constraints. Adding site access and other logistical constraints imposes restrictions on the scope and extent of the surveillance effort, yielding costlier but more realistic expectations of the surveillance outcomes than in a theoretical planning case.

show abstract

“…The majority of mathematical models in the epidemiological literature have used simulation methods to forecast the progression of the epidemic and to study the efficacy of several interventions (Dasaklis et al, 2017; Meltzer et al, 2014; Onal et al, 2019; Pandey et al, 2014; Rivers et al, 2014; Siettos et al, 2015). Several studies have considered stochastic compartmental models to analyze different strategies for controlling epidemic diseases, such as vaccination strategies, behavioral changes that impact the interaction between different groups, and regional intervention strategies (Funk et al, 2017; Lekone and Finkenstädt, 2006).…”

Section: Introductionmentioning

confidence: 99%

A Multi-Stage Stochastic Programming Approach to Epidemic Resource Allocation with Equity Considerations

Yin

¹

,

Büyüktahtakın

²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multistage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.

show abstract

An integrated simulation-optimization framework to optimize search and treatment path for controlling a biological invader

Cited by 12 publications

References 48 publications

A Multi-Stage Stochastic Programming Approach to Epidemic Resource Allocation with Equity Considerations

A Multi-Stage Stochastic Programming Approach to Epidemic Resource Allocation with Equity Considerations

Optimal invasive species surveillance in the real world: practical advances from research

A Multi-Stage Stochastic Programming Approach to Epidemic Resource Allocation with Equity Considerations

Contact Info

Product

Resources

About