Using data-driven approaches to improve delivery of animal health care interventions for public health

Stella Mazeri, Jordana L Burdon Bailey, Dagmar Mayer, Patrick Chikungwa, Julius Chulu, Paul Orion Grossman, Fred Lohr, Andy Gibson, Ian Handel, Mark Bronsvoort, Luke Gamble, Richard Mellanby

Research output: Contribution to journalArticlepeer-review

Abstract

Rabies kills approximately 60,000 people a 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 most 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, labour-intensive and logistically challenging raising the need to develop approaches that increase attendance at SP.
Here, we report a real-time, data-driven approach to improve efficiency of an urban dog vaccination campaign. Historically, we vaccinated approximately 35,000 dogs in Blantyre city, Malawi every year over a 20-day period using combined fixed static point (SP) and door-to-door (D2D) approaches. To enhance cost-effectiveness, we used our historical vaccination data set to define the barriers to fixed SP attendance. Guided by these insights, we redesigned our vaccination campaign by increasing the number of fixed SP and eliminating the expensive and labour-intensive D2D component. Combined with roaming SP, whose location was defined through the real time analysis of vaccination coverage data, this novel approach resulted in the vaccination of near identical numbers of dogs in only 11 days. This approach has the potential to act as a template for successful and sustainable future urban SP only dog vaccination campaigns.
Original languageEnglish
JournalProceedings of the National Academy of Sciences
Early online date19 Jan 2021
DOIs
Publication statusPublished - 2 Feb 2021

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