Increasing urbanization and use of urban areas by synanthropic wildlife has increased human and domestic animal exposure to zoonotic diseases and exacerbated epizootics within wildlife populations. Consequently, there is a need to improve wildlife disease surveillance programs to rapidly detect outbreaks and refine inferences regarding spatiotemporal disease dynamics. Multistate occupancy models can address potential shortcomings in surveillance programs by accounting for imperfect detection and the misclassification of disease states. We used these models to explore the relationship between urbanization, slope, and the spatial distribution of sarcoptic mange in coyotes (Canis latrans) inhabiting Fort Irwin, California, USA. We deployed remote cameras across 180 sites within the desert surrounding the populated garrison and classified sites by mange presence or absence depending on whether a symptomatic or asymptomatic coyote was photographed. Coyotes selected flatter sites closer to the urban area with a high probability of use (0.845, 95% credible interval (CRI): 0.728, 0.944); site use decreased as the distance to urban areas increased (standardized \({\widehat{\beta}}\) = − 1.354, 95% CRI − 2.423, − 0.619). The probability of correctly classifying mange presence at a site also decreased further from the urban area and was probably related to the severity of mange infection. Severely infected coyotes, which were more readily identified as symptomatic, resided closer to the urban area and were most likely dependent on urban resources for survival; urban resources probably contributed to sustaining the disease. Multistate occupancy models represent a flexible framework for estimating the occurrence and spatial extent of observable infectious diseases, which can improve wildlife disease surveillance programs.

伴随人类发展的城市化进程和城市地区对城市地区的利用不断增加，人类和家畜更容易遭受人畜共患疾病的感染，并加剧了野生动物种群中的流行病。因此，需要改进野生动植物疾病监测计划，以迅速发现疫情并完善关于时空疾病动态的推论。通过考虑不完善的检测和疾病状态的错误分类，多状态占用模型可以解决监视程序中的潜在缺陷。我们使用这些模型探索了郊狼（Canis latrans）中城市化，坡度和斜纹岩ge空间分布之间的关系）居住在美国加利福尼亚州的欧文堡。我们在人口稠密的驻军周围的沙漠中的180个地点部署了远程摄像头，并根据是否拍摄了有症状或没有症状的土狼，根据是否有山雀来对这些地点进行分类。郊狼选择了较靠近市区的平坦地点，且使用可能性很高（0.845，95％可信区间（CRI）：0.728，0.944）；随着到城市区域距离的增加，网站使用量减少（标准\（{\ widehat {\ beta}} \）=-1.354，95％CRI-2.423，-0.619）。在市区范围内正确分类大杂烩的可能性也从市区进一步降低，并且可能与大杂烩感染的严重性有关。重度感染的土狼更容易被诊断为有症状，居住在靠近市区的地方，最有可能依赖于城市的资源生存。城市资源可能有助于维持这种疾病。多州居住模型代表了一个灵活的框架，可用于估计可观察到的传染病的发生和空间范围，从而可以改善野生动物疾病的监测程序。