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Wolbachia
Decision-Support 
Tool
Wolbachia

decision-support tool

Aedes aegypti mosquitoes infected with naturally occurring Wolbachia bacteria have a reduced ability to transmit dengue virus. What is not known is how to implement and scale up Wolbachia Replacement Technology to substantially reduce the burden of dengue.

This tool is designed to support decisions related to Wolbachia implementation and scale-up for global dengue control.

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How to use the tool

Please see the descriptions and video tutorials for guidance on how to use the tool for key implementation decisions.

How does this tool work?

This video tutorial provides an overview selected of the key features of the tool. We highlight how the user can select a country, decide on a targeting approach, and input costs, coverage, and effectiveness estimates to understand the potential impact of Wolbachia. We demonstrate the tool's ability to provide estimates for the total target areas of a country, and for each second Global Administrative Unit Layer (GAUL) (e.g., districts) within the country.

How to calculate dengue burden and reduction estimates?

This video tutorial presents how to use the tool to view the estimated dengue burden data. We present how the tool calculates dengue burden overall and in each relevant administrative unit. This data provides a potentially improved understanding of the burden in a country/geography given dengue incidence is often under-reported. We would recommend reviewing this data prior to deciding about Wolbachia implementation.

How to estimate cost?

This video tutorial shows resources within and beyond the tool which may assist a user in deriving their own cost estimates for Wolbachia programs. It also describes how the tool can be used to understand the total costs of Wolbachia programs with differing target approaches and input parameter assumptions such as cost, coverage, and effectiveness. We describe how costs can be refined for different cost scenarios and what the impact might be on the overall costs.

How to calculate benefits from a National Wolbachia scale program?

This video tutorial describes how the tool can be used to determine the impact of Wolbachia in terms of dengue cases averted and Disability-Adjusted Life Years (DALYs) averted. We describe how types of cases averted (i.e., cases requiring hospitalization) are estimated. And also, describes how the tool can be used to scale up Wolbachia in high priority areas. We describe how areas are prioritized based on impact and cost (measured by cost per person covered by the intervention).

Let's do a practice scenario!

Finally! Let's use Indonesia as a practice scenario as we use all the features of the tool to support decision making of Wolbachia Replacement Technology.

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Data Sources

Information about the data sources used in this tool is provided below.
All of the data is available to inspect on GitHub.
Cost ranges for Wolbachia implementation by phase and activity
Costs are a key input parameter for this tool and should be input as the cost in USD per km² of area covered by the program. Costs have been generalized to common program phases which include planning, preparation, production, distribution, release, and monitoring, and common activities within those phases. Prior cost-effectiveness analyses (Brady et al. 2020) have reported costs per km² within these program phases.
Dengue burden
We relied on modelled raster data for burden as asymptomatic and symptomatic dengue are severely underreported. Symptomatic dengue burden was extracted from spatial raster datasets from Bhatt et al. 2013.
Dengue disability
Estimates of DALYs and cases were extracted from a Global Burden of Disease Study (2019). Based on the national estimates, we calculated the DALYs per dengue case. A range of severities with disability weights is not accounted for.
Administrative divisions
Administrative divisions for each secondary administrative unit were obtained from GAUL within the Food and Agriculture organization of the UN.
Population
Population raster spatial datasets were accessed from WorldPop The data used was unconstrained 1 km resolution estimates of population count and population density, adjusted to match UN Population estimates and measured in units of persons per km².
Effectiveness
The default value for effectiveness was extracted from a recent study in Yogyakarta demonstrating that Wolbachia led to a 77% (95% Confidence Interval: 65-85%) reduction in dengue incidence (Utarini et al. 2021).
Healthcare utilization & cost of illness
Estimates of healthcare utilization for each type of case (hospitalized, ambulatory, non-medically attended) were derived from a study evaluating the global economic burden of dengue (Shepard et al. 2016. The study provides estimates for the percentage of people with dengue that receive care in a hospital setting, ambulatory/outpatient setting, and non-medical setting. It also estimates direct, medical costs and indirect (lost wages) associated with each type of case in each country. Costs were adjusted to inflation from 2013 to 2020.
Disease reduction targets
Priority administrative 2 units were selected for 12.5% and 25% national reduction based on the work of one of our collaborators (Tiley, K., Entwistle, J., Thomas, B., Yakob L., Brady, O.J. Using models and maps to inform Target Product Profiles and Preferred Product Characteristics: the example of Wolbachia replacement. Manuscript in preparation). Details regarding the methodology for selecting those areas are described in the manuscript above and in this WHO report (2022).
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Calculations

Information about calculations and assumptions used in the tool is provided below.
For any 5-, 10-, or 20-year estimates, all costs / benefits are multiplied by the year.

Green: Pre-defined input parameter

Brown: User input

Red: Calculated value

Dengue burden estimates
Total population

Total population in the administrative level 2 area

Target population

Total population in the 'target' of the administrative 2 area

Mean dengue incidence

Total dengue incidence in the administrative 2 area

Total number of cases

Total population in the administrative 2 area × Total dengue incidence in the administrative 2 area
For 5, 10, and 20 year estimates, the cases are multiplied by 5, 10, and 20 respectively.

Total number of hospitalized cases

Total number of cases × percentage of cases seeking care in hospitalized setting

Total number of ambulatory cases

Total number of cases × percentage of cases seeking care in an outpatient setting

Total number of not medically-attended cases

Total number of cases × percentage of cases seeking care in a non-medical setting

Implementation estimates
Area covered by intervention

target area × coverage

Population covered by intervention

target population × coverage

Total cost (phase-based)

(cost of planning + cost of preparation + cost of production + cost of distribution + cost of release + cost of monitoring) × (target area × coverage)
For 5, 10, and 20 year estimates, costs are discounted by 3% each year.
For 5, 10, and 20 year estimates, we assume 100% of the costs for years 1-3, and then 1% of total costs for year 4 and beyond.

Total cost (activity-based)

(define workplan and budget + determine release methodology + enroll community participation + facility setup + mosquito line creation + mosquito production + quality management and control + deliver eggs/adults to distribution points + egg/adult deployments + quality assurance + adaptive management + measure community sentiment + monitoring in the field) × (target area × coverage)
For 5, 10, and 20 year estimates, costs are discounted by 3% each year.
For 5, 10, and 20 year estimates, we assume 100% of the costs for years 1-3, and then 1% of total costs for year 4 and beyond.

Total cost of intervention (phase-based)

cost of phase × area covered by intervention
For 5, 10, and 20 year estimates, costs are discounted by 3% each year.
For 5, 10, and 20 year estimates, we assume 100% of the costs for years 1-3, and then 1% of total costs for year 4 and beyond.

Total cost of intervention (activity-based)

total cost of each activity within the phase × area covered by intervention
For 5, 10, and 20 year estimates, costs are discounted by 3% each year.
For 5, 10, and 20 year estimates, we assume 100% of the costs for years 1-3, and then 1% of total costs for year 4 and beyond.

Cost per person

total cost / population covered by intervention

Cost per case averted

total cost / cases averted

Cost per DALY averted

total cost / DALYs averted

Dengue reduction estimates
Total cases averted

(coverage × dengue incidence) × effectiveness
For 5, 10, and 20 year estimates, cases averted are discounted by 3% each year.

Total DALYs averted

cases averted × DALY_per_case
For 5, 10, and 20 year estimates, cases averted are discounted by 3% each year.

Hospitalized cases averted

cases averted × percent of cases treated in hospitalized setting

Ambulatory cases averted

cases averted × percent of cases treated in ambulatory setting

Non-medically attended cases averted

cases averted × percent of cases not medically attended

Health system & economic benefit estimates
Total health system costs averted

direct hospitalized costs + direct ambulatory costs + direct non-medically attended costs

Total economic costs averted

indirect hospitalized costs + indirect ambulatory costs + indirect non-medically attended costs

Direct hospitalized costs averted

hospitalized cases averted × direct cost per hospitalized case

Direct ambulatory costs averted

ambulatory cases averted × direct cost per ambulatory case

Direct not-medically attended costs averted

not medically attended cases averted × direct cost per not medically attended case

Indirect hospitalized costs averted

hospitalized cases averted × indirect cost per hospitalized case

Indirect ambulatory costs averted

ambulatory cases averted × indirect cost per ambulatory case

Indirect not-medically attended costs averted

not medically attended cases averted × indirect cost per nonmedically attended case

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About this tool

The goal of this tool is to provide data to support decisions related to the implementation and scale-up of Wolbachia Replacement Technology for dengue control globally. This tool is flexible, allowing the user to input estimated costs, effectiveness, coverage, disease reduction targets, and constraints to estimate the impact and costs for their context.
Efforts were made to ensure that this tool can be used by various decision-makers considering Wolbachia Replacement Technology implementation. Intended users include in-country policy makers, dengue program managers, funders, and implementers.
This tool generates data to support decisions globally. The tool is not intended to provide exact figures but can support decision making by providing plausible values that the user can interpret and estimate by adjusting to local contexts. Without data on the estimated impacts and costs, there is a risk that mosquito release programs will not be implemented in areas where there is the greatest need and payoff for investments.
Limitations
There are a few limitations to note in this tool. Although we used the most recent available estimates for input parameters (i.e., dengue incidence, cost of illness), these may not reflect values in 2022. Given lack of consensus on the best approaches for adjusting costs or modelling dengue incidence spatially, we opted to use estimates from the original data sources. On long-term assumptions, we assume for benefits to begin immediately after programs are initiated. In reality, there might be a slight longer lag to impact given the preparation required before Wolbachia-containing mosquitoes and eggs are released. Relatedly, we had to make some assumptions about the costs over time. We assumed that the program would require high resources for the first three years of implementation and then be sustained at a low cost for the following years. These were based on meetings with implementers and represent the best estimates to date.
App development
This tool was developed by the Strategic Analysis, Research & Training (START) Center in the Department of Global Health at the University of Washington with support from the Bill and Melinda Gates Foundation. Questions regarding the data sources, calculations, and use cases can be directed to Aldina Mesic (amesic@uw.edu). Questions regarding the code and deployment can be directed to Ryan Hafen (rhafen@prevagroup.com)
Acknowledgements
We would like to acknowledge the many organizations and individuals who made this tool and prior iterations of the tool possible:
  • START Center team members (Aldina Mesic, William Sheahan, Erin Ingle, Ana Pereda, Brandon Guthrie, Jairam Lingappa, Andrew Secor, Mohamed Albirair, and Paul Drain).
  • START Center Operations and Leadership (Lauren Adjumani, Noel Daniel, Stephen Hawes).
  • The Bill and Melinda Gates Foundation (Steve Kern, Christy Hanson, Kayla Laserson, and Harmony Chartier).
  • Preva Group (Ryan Hafen)
  • The Arcady Group (Bruce Thomas)
  • The London School of Hygiene and Tropical Medicine (Oliver Brady)
  • Linksbridge SPC (Mike Osberg, Dena Seabrook, Sheldon Halsted, Mira Sytsma)
  • World Mosquito Program (Katie Anders, Bryan Callahan, Reynold Dias)
  • World Health Organization (Raman Velayudhan)
  • Foundation of the National Institutes of Health (Michael Santos, Susan Wiener)
  • Brandeis University (Donald Shepard)
  • Management Sciences for Health (Damian Walker)
  • Imperial College London (Hugo Turner, John Mumford, Megan Quinlan, Adrian Leach, Austin Burt)
  • Keele University (Frederic Tripet)
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