Eltahir Yassir M, Aburizq Wael, Bensalah Oum Keltoum, Mohamed Meera S, Al Shamisi Aysha, AbdElkader Ayman I, Al-Majali Ahmad
Animals Health and Extension Division, Abu Dhabi Agriculture and Food Safety Authority (ADAFSA), Abu Dhabi 52150, United Arab Emirates.
Data and Artificial Intelligence Division, Abu Dhabi Agriculture and Food Safety Authority (ADAFSA), Abu Dhabi 52150, United Arab Emirates.
Animals (Basel). 2023 Oct 18;13(20):3248. doi: 10.3390/ani13203248.
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.
小反刍兽疫（PPR）是一种具有传染性且对经济有重要影响的小反刍动物跨界病毒性疾病。阿拉伯联合酋长国（阿联酋）的国家动物卫生计划旨在通过遵循全球PPR控制和根除战略，在该国控制并根除PPR。该战略采用小反刍动物大规模疫苗接种，目标是到2030年在全球根除这种疾病。需要一种更经济且具有长期可持续效益的智能疫苗接种方法，以加速PPR的根除。在本研究中，基于属于阿布扎比农业和食品安全局（ADAFSA）的动物识别和登记数据以及与PPR风险发生相关的其他不同参数，开发了一种数学算法。后者包括养殖场的动物疫苗接种历史、每个养殖场的动物数量、每个养殖场动物和新生动物数量的预测、养殖场与PPR疫情爆发地的距离以及养殖场主过去拒绝疫苗接种的态度。所开发的算法成功地对阿布扎比酋长国内有PPR感染风险的养殖场进行了优先排序，以便针对这些养殖场进行疫苗接种。这反过来又有助于调动现场疫苗接种团队，针对特定的绵羊和山羊养殖场，以确保基于风险的方法产生针对该疾病的免疫力。目标牲畜群体的疫苗接种覆盖率提高到了86%，疫苗接种拒绝态度降低了35%。疫苗接种活动的持续时间从70个工作日减少到了30个工作日，因此可以缓解传统大规模疫苗接种活动中常用的人力和后勤资源的消耗。实施基于算法的PPR疫苗接种活动所获得的结果将减少PPR对阿联酋畜牧业的负面影响，并加速实现国家PPR根除计划的要求。
