Major infectious diseases such as COVID-19 have a significant impact on population lives and put enormous pressure on healthcare systems globally. Strong interventions, such as lockdowns and social distancing measures, imposed to prevent these diseases from spreading, may also negatively impact society, leading to jobs losses, mental health problems, and increased inequalities, making crucial the prioritization of riskier areas when applying these protocols. The modeling of mobility data derived from contact-tracing data can be used to forecast infectious trajectories and help design strategies for prevention and control. In this work, we propose a new spatial-stochastic model that allows us to characterize the temporally varying spatial risk better than existing methods. We demonstrate the use of the proposed model by simulating an epidemic in the city of Valencia, Spain, and comparing it with a contact tracing-based stochastic compartment reference model. The results show that, by accounting for the spatial risk values in the model, the peak of infected individuals, as well as the overall number of infected cases, are reduced. Therefore, adding a spatial risk component into compartment models may give finer control over the epidemic dynamics, which might help the people in charge to make better decisions.

COVID-19 等重大传染病对人口生活产生重大影响，并给全球医疗保健系统带来巨大压力。为防止这些疾病传播而实施的强有力的干预措施，例如封锁和社会疏远措施，也可能对社会产生负面影响，导致失业、心理健康问题和不平等加剧，因此在应用这些协议时优先考虑风险较高的地区至关重要。从接触者追踪数据中得出的流动性数据建模可用于预测传染轨迹并帮助设计预防和控制策略。在这项工作中，我们提出了一个新的空间随机模型，使我们能够比现有方法更好地描述随时间变化的空间风险。我们通过模拟西班牙巴伦西亚市的流行病并将其与基于接触者追踪的随机隔间参考模型进行比较来演示所提出模型的使用。结果表明，通过考虑模型中的空间风险值，感染个体的峰值以及感染病例的总数都减少了。因此，在隔间模型中添加空间风险成分可以更好地控制流行病动态，这可能有助于负责人做出更好的决策。