Modeling for Smart Vaccination against Peste des Petits Ruminants (PPR) in the Emirate of Abu Dhabi, United Arab Emirates

SIMPLE SUMMARY: Peste des petits ruminants (PPR) is an acute viral disease of sheep, goats, camels and some wild ruminant species. It is widespread in Africa, the Middle East and Asia, where it causes heavy economic losses. The disease is currently targeted by the Global Eradication Program (PPR GEP), aimed at eradicating the disease by 2030. The United Arab Emirates (UAE) national animal health plan adopts mass vaccination to control and eradicate PPR from the country. In this study, using the historical and real-time animals’ identification and registration data together with different PPR occurrence risk factors, a mathematical algorithm was developed to intellectually design and implement a PPR vaccination campaign in the Emirate of Abu Dhabi. The algorithm was capable of prioritizing animal holdings at risk of PPR infection to be targeted by vaccination, facilitated the mobilization of vaccination teams in the field and resulted in an increased vaccination coverage of the targeted livestock population in a short time. Such achievements will enhance the UAE national PPR eradication plan to be fulfilled. ABSTRACT: Peste des petits ruminants (PPR) is a contagious and economically important transboundary viral disease of small ruminants. The United Arab Emirates (UAE) national animal health plan aimed to control and eradicate PPR from the country by following the global PPR control and eradication strategy which adopts small ruminants’ mass vaccination to eradicate the disease from the globe by 2030. A smart vaccination approach, which is less expensive and has longer-term sustainable benefits, is needed to accelerate the eradication of PPR. In this study, a mathematical algorithm was developed based on animals’ identification and registration data, belonging to the Abu Dhabi Agriculture and Food Safety Authority (ADAFSA), and other different parameters related to PPR risk occurrence. The latter included animal holding vaccination history, the number of animals per holding, forecasting of the number of animals and newborns per holding, the proximity of an animal holding to a PPR outbreak and the historical animal holding owner vaccination rejection attitude. The developed algorithm successfully prioritized animal holdings at risk of PPR infection within Abu Dhabi Emirate to be targeted by vaccination. This in turn facilitated the mobilization of field vaccination teams to target specific sheep and goat holdings to ensure the generation of immunity against the disease on a risk-based approach. The vaccination coverage of the targeted livestock population was increased to 86% and the vaccination rejection attitude was reduced by 35%. The duration of the vaccination campaign was reduced to 30 compared to 70 working days and hence can alleviate the depletion of human and logistic resources commonly used in classical mass vaccination campaigns. The results obtained from implementing the algorithm-based PPR vaccination campaign will reduce the negative impact of PPR on the UAE livestock sector and accelerate the achievement of the national PPR eradication plan requirements.