Porras Guillermo, Diaz Elvis W, De la Puente-León Micaela, Gavidia Cesar M, Castillo-Neyra Ricardo
Zoonotic Disease Research Lab, One Health Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia, Lima, Peru.
Facultad de Medicina Veterinaria, Universidad Nacional Mayor de San Marcos, Lima, Perú.
PLoS One. 2025 Jul 31;20(7):e0305154. doi: 10.1371/journal.pone.0305154. eCollection 2025.
Controlling and eliminating zoonotic pathogens such as rabies virus, Echinococcus granulosus, and Leishmania spp. require quantitative knowledge of dog populations. Dog population estimates are fundamental for planning, implementing, and evaluating public health programs. However, dog population estimation is time-consuming, requires many field personnel, may be inaccurate and unreliable, and is not without danger. Our objective was to evaluate a remote method for estimating the population of free-roaming dogs using Google Street View (GSV). Adopting a citizen science approach, participants from Arequipa and other regions in Peru were recruited using social media and trained to use GSV to identify and count free-roaming dogs in 20 urban and 6 periurban communities. We used correlation metrics and negative binomial models to compare the counts of dogs identified in the GSV imagery with accurate counts of free-roaming owned dogs estimated via door-to-door (D2D) survey conducted in 2016. Citizen scientists detected 862 dogs using GSV. After adjusting by the proportion of streets that were scanned with GSV we estimated 1,022 free-roaming dogs, while the 2016 D2D survey estimated 1,536 owned free-roaming dogs across those 26 communities. We detected a strong positive correlation between the number of dogs detected by the two methods in the urban communities (r = 0.85; p < 0.001) and a weak correlation in periurban areas (r = 0.36; p = 0.478). Our multivariable model indicated that for each additional free-roaming dog estimated using GSV, the expected number of owned free-roaming dogs decreased by 2% in urban areas (p < 0.001) and increased by 2% in peri-urban areas (p = 0.004). The type of community (urban vs periurban) had an effect on the predictions, and fitting the models in periurban communities was difficult because of the sparsity of high-resolution GSV images. Using GSV imagery for estimating dog populations is a promising tool, especially in urban areas. Citizen scientists can help to generate information for disease control programs in places with insufficient resources.
控制和消灭狂犬病病毒、细粒棘球绦虫和利什曼原虫属等人畜共患病原体需要了解犬类种群的数量。犬类种群估计对于规划、实施和评估公共卫生项目至关重要。然而，犬类种群估计耗时、需要大量实地工作人员，可能不准确且不可靠，并且并非没有危险。我们的目标是评估一种使用谷歌街景（GSV）估算流浪犬种群数量的远程方法。我们采用公民科学方法，通过社交媒体招募了来自阿雷基帕和秘鲁其他地区的参与者，并对他们进行培训，让他们使用GSV识别和统计20个城市社区和6个城郊社区中的流浪犬。我们使用相关性指标和负二项式模型，将GSV图像中识别出的犬只数量与2016年通过挨家挨户（D2D）调查估算出的流浪犬准确数量进行比较。公民科学家使用GSV检测到862只犬。在用GSV扫描的街道比例进行调整后，我们估计有1022只流浪犬，而2016年的D2D调查估计这26个社区中有1536只家养流浪犬。我们发现，在城市社区中，两种方法检测到的犬只数量之间存在很强的正相关性（r = 0.85；p < 0.001），而在城郊地区相关性较弱（r = 0.36；p = 0.478）。我们的多变量模型表明，在城市地区，使用GSV每多估算出一只流浪犬，预计家养流浪犬数量就会减少2%（p < 0.001），而在城郊地区则会增加2%（p = 0.004）。社区类型（城市与城郊）对预测有影响，并且由于高分辨率GSV图像稀少，在城郊社区拟合模型很困难。使用GSV图像估算犬类种群数量是一种很有前景的工具，尤其是在城市地区。公民科学家可以帮助在资源不足的地方为疾病控制项目提供信息。
