Using data-driven approaches to improve delivery of animal health care interventions for public health
- aThe Epidemiology, Economics and Risk Assessment Group, The Roslin Institute and The Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Veterinary Centre, Roslin EH25 9RG, United Kingdom;
- bMission Rabies, Blantyre, Malawi;
- cWorldwide Veterinary Service, Blantyre, Malawi;
- dDepartment of Animal Health and Livestock Development, Lilongwe, Malawi;
- eMission Rabies, Cranborne BH21 5PZ, United Kingdom;
- fDivision of Veterinary Clinical Studies, The Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Hospital for Small Animals, Easter Bush Veterinary Centre, Roslin EH25 9RG, United Kingdom
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Edited by Simon A. Levin, Princeton University, Princeton, NJ, and approved December 7, 2020 (received for review February 27, 2020)

Significance
Rabies is arguably the exemplar of the One Health Agenda in which preventative health care in one species can improve health of other species. Interrogation of large epidemiology datasets offers the potential to deliver health care initiatives in a more efficient and cost-effective manner. However, real-life examples demonstrating this potential are limited. Here, we report a real-time, data-driven approach to improve cost effectiveness of dog vaccination campaigns in urban sub-Saharan African settings, which eliminates the need of expensive door-to-door vaccination by replacing them with strategically positioned fixed and roaming static points (SPs). This approach has the potential to act as a template for future successful and sustainable urban SP-only dog vaccination campaigns.
Abstract
Rabies kills ∼60,000 people per year. Annual vaccination of at least 70% of dogs has been shown to eliminate rabies in both human and canine populations. However, delivery of large-scale mass dog vaccination campaigns remains a challenge in many rabies-endemic countries. In sub-Saharan Africa, where the vast majority of dogs are owned, mass vaccination campaigns have typically depended on a combination of static point (SP) and door-to-door (D2D) approaches since SP-only campaigns often fail to achieve 70% vaccination coverage. However, D2D approaches are expensive, labor-intensive, and logistically challenging, raising the need to develop approaches that increase attendance at SPs. Here, we report a real-time, data-driven approach to improve efficiency of an urban dog vaccination campaign. Historically, we vaccinated ∼35,000 dogs in Blantyre city, Malawi, every year over a 20-d period each year using combined fixed SP (FSP) and D2D approaches. To enhance cost effectiveness, we used our historical vaccination dataset to define the barriers to FSP attendance. Guided by these insights, we redesigned our vaccination campaign by increasing the number of FSPs and eliminating the expensive and labor-intensive D2D component. Combined with roaming SPs, whose locations were defined through the real-time analysis of vaccination coverage data, this approach resulted in the vaccination of near-identical numbers of dogs in only 11 d. This approach has the potential to act as a template for successful and sustainable future urban SP-only dog vaccination campaigns.
Footnotes
- ↵1To whom correspondence may be addressed. Email: stella.mazeri{at}roslin.ed.ac.uk.
↵2L.G. and R.J.M. contributed equally to this work.
Author contributions: S.M., J.L.B.B., D.M., P.C., J.C., P.O.G., F.L., A.D.G., I.G.H., B.M.d.B., L.G., and R.J.M. designed research; S.M., J.L.B.B., D.M., and F.L. performed research; S.M. contributed new reagents/analytic tools; S.M. analyzed data; S.M., J.L.B.B., D.M., P.C., J.C., P.O.G., F.L., A.D.G., I.G.H., B.M.d.B., L.G., and R.J.M. wrote the paper; and L.G. acquired funding for vaccination campaign.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2003722118/-/DCSupplemental.
Data Availability.
All study data are included in the article and supporting information.
- Copyright © 2021 the Author(s). Published by PNAS.
This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
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