Modelling the factors affecting the probability for local rabies elimination by strategic control

Authors

Johann L. Kotzé, University of PretoriaFollow
John Duncan Grewar, University of Pretoria
Aaron M. Anderson, APHISFollow

ORCID IDs

Johann L. Kotzé https://orcid.org/0000-0002-9632-5904

John Duncan Grewar https://orcid.org/0000-0002-4496-8051

Aaron Anderson https://orcid.org/0000-0003-0108-689X

Document Type

Article

Date of this Version

2021

Citation

Kotzé JL, Duncan Grewar J, Anderson A (2021) Modelling the factors affecting the probability for local rabies elimination by strategic control. PLoS Negl Trop Dis 15(3): e0009236. https://doi.org/10.1371/journal.pntd.0009236

Comments

The work is made available under the Creative Commons CC0 public domain dedication.

Abstract

Dog rabies has been recognized from ancient times and remains widespread across the developing world with an estimated 59,000 people dying annually from the disease. In 2011 a tri-partite alliance consisting of the OIE, the WHO and the FAO committed to globally eliminating dog-mediated human rabies by 2030. Regardless of global support, the responsibility remains with local program managers to implement successful elimination programs. It is well known that vaccination programs have a high probability of successful elimination if they achieve a population-coverage of 70%. It is often quoted that reducing population turnover (typically through sterilizations) raises the probability for local elimination by maintaining herd immunity for longer. Besides this, other factors that affect rabies elimination are rarely mentioned. This paper investigates the probability for local elimination as it relates to immunity, fecundity, dog population size, infectivity (bite rates), in-migration of immunenaïve dogs, and the initial incidence. To achieve this, an individual-based, stochastic, transmission model was manipulated to create a dataset covering combinations of factors that may affect elimination. The results thereof were analysed using a logistic regression model with elimination as the dependent variable. Our results suggest that smaller dog populations, lower infectivity and lower incidence (such as when epidemics start with single introductions) strongly increased the probability for elimination at wide ranges of vaccination levels. Lower fecundity and lower in-migration had weak effects. We discuss the importance of these findings in terms of their impact and their practical application in the design of dogmediated rabies control programs.