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Reducing dengue fever cases at the lowest budget: a constrained optimization approach applied to Thailand

Reducing dengue fever cases at the lowest budget: a constrained optimization approach applied to Thailand
Reducing dengue fever cases at the lowest budget: a constrained optimization approach applied to Thailand
Background: With the challenges that dengue fever (DF) presents to healthcare systems and societies, public health officials must determine where best to allocate scarce resources and restricted budgets. Constrained optimization (CO) helps to address some of the acknowledged limitations of conventional health economic analyses and has typically been used to identify the optimal allocation of resources across interventions subject to a variety of constraints.
Methods: A dynamic transmission model was developed to predict the number of dengue cases in Thailand at steady state. A CO was then applied to identify the optimal combination of interventions (release of Wolbachia-infected mosquitoes and paediatric vaccination) within the constraints of a fixed budget, set no higher than cost estimates of the current vector control programme, to minimize the number of dengue cases and disability-adjusted life years (DALYs) lost. Epidemiological, cost, and effectiveness data were informed by national data and the research literature. The time horizon was 10 years. Scenario analyses examined different disease management and intervention costs, budget constraints, vaccine efficacy, and optimization time horizon.
Results: Under base-case budget constraints, the optimal coverage of the two interventions to minimize dengue incidence was predicted to be nearly equal (Wolbachia 50%; paediatric vaccination 49%) with corresponding coverages under lower bound (Wolbachia 54%; paediatric vaccination 10%) and upper bound (Wolbachia 67%; paediatric vaccination 100%) budget ceilings. Scenario analyses indicated that the most impactful situations related to the costs of Wolbachia and paediatric vaccination with decreases/ increases in costs of interventions demonstrating a direct correlation with coverage (increases/ decreases) of the respective control strategies under examination.
Conclusions: Determining the best investment strategy for dengue control requires the identification of the optimal mix of interventions to implement in order to maximize public health outcomes, often under fixed budget constraints. A CO model was developed with the objective of minimizing dengue cases (and DALYs lost) over a 10-year time horizon, within the constraints of the estimated budgets for vector control in the absence of vaccination and Wolbachia. The model provides a tool for developing estimates of optimal coverage of combined dengue control strategies that minimize dengue burden at the lowest budget.
Dengue, Vaccination, Wolbachia, constrained optimisation
1935-2727
Knerer, Gerhart
c9a2532c-59ee-4a07-9546-ee425f99e14a
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Brailsford, Sally
634585ff-c828-46ca-b33d-7ac017dda04f
Knerer, Gerhart
c9a2532c-59ee-4a07-9546-ee425f99e14a
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Brailsford, Sally
634585ff-c828-46ca-b33d-7ac017dda04f

Knerer, Gerhart, Currie, Christine and Brailsford, Sally (2021) Reducing dengue fever cases at the lowest budget: a constrained optimization approach applied to Thailand. PLoS Neglected Tropical Diseases, 21, [807]. (doi:10.1186/s12889-021-10747-3).

Record type: Article

Abstract

Background: With the challenges that dengue fever (DF) presents to healthcare systems and societies, public health officials must determine where best to allocate scarce resources and restricted budgets. Constrained optimization (CO) helps to address some of the acknowledged limitations of conventional health economic analyses and has typically been used to identify the optimal allocation of resources across interventions subject to a variety of constraints.
Methods: A dynamic transmission model was developed to predict the number of dengue cases in Thailand at steady state. A CO was then applied to identify the optimal combination of interventions (release of Wolbachia-infected mosquitoes and paediatric vaccination) within the constraints of a fixed budget, set no higher than cost estimates of the current vector control programme, to minimize the number of dengue cases and disability-adjusted life years (DALYs) lost. Epidemiological, cost, and effectiveness data were informed by national data and the research literature. The time horizon was 10 years. Scenario analyses examined different disease management and intervention costs, budget constraints, vaccine efficacy, and optimization time horizon.
Results: Under base-case budget constraints, the optimal coverage of the two interventions to minimize dengue incidence was predicted to be nearly equal (Wolbachia 50%; paediatric vaccination 49%) with corresponding coverages under lower bound (Wolbachia 54%; paediatric vaccination 10%) and upper bound (Wolbachia 67%; paediatric vaccination 100%) budget ceilings. Scenario analyses indicated that the most impactful situations related to the costs of Wolbachia and paediatric vaccination with decreases/ increases in costs of interventions demonstrating a direct correlation with coverage (increases/ decreases) of the respective control strategies under examination.
Conclusions: Determining the best investment strategy for dengue control requires the identification of the optimal mix of interventions to implement in order to maximize public health outcomes, often under fixed budget constraints. A CO model was developed with the objective of minimizing dengue cases (and DALYs lost) over a 10-year time horizon, within the constraints of the estimated budgets for vector control in the absence of vaccination and Wolbachia. The model provides a tool for developing estimates of optimal coverage of combined dengue control strategies that minimize dengue burden at the lowest budget.

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More information

Accepted/In Press date: 25 March 2021
e-pub ahead of print date: 27 April 2021
Keywords: Dengue, Vaccination, Wolbachia, constrained optimisation

Identifiers

Local EPrints ID: 449118
URI: http://eprints.soton.ac.uk/id/eprint/449118
ISSN: 1935-2727
PURE UUID: 3331c705-b6db-444d-8459-70f9ca523b36
ORCID for Christine Currie: ORCID iD orcid.org/0000-0002-7016-3652
ORCID for Sally Brailsford: ORCID iD orcid.org/0000-0002-6665-8230

Catalogue record

Date deposited: 17 May 2021 16:34
Last modified: 20 May 2021 01:39

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