Introduction: The ease of coronavirus disease 2019 (COVID-19) non-pharmacological interventions and the increased susceptibility during the past COVID-19 pandemic could be a precursor for the resurgence of influenza, potentially leading to a severe outbreak in the winter of 2022 and future seasons. The recent increased availability of data on Electronic Health Records (EHR) in public health systems, offers new opportunities to monitor individuals to mitigate outbreaks. Methods: We introduced a new methodology to rank individuals for surveillance in temporal networks, which was more practical than the static networks. By targeting previously infected nodes, this method used readily available EHR data instead of the contact-network structure. Results: We validated this method qualitatively in a real-world cohort study and evaluated our approach quantitatively by comparing it to other surveillance methods on three temporal and empirical networks. We found that, despite not explicitly exploiting the contacts’ network structure, it remained the best or close to the best strategy. We related the performance of the method to the public health goals, the reproduction number of the disease, and the underlying temporal-network structure (e.g., burstiness). Discussion: The proposed strategy of using historical records for sentinel surveillance selection can be taken as a practical and robust alternative without the knowledge of individual contact behaviors for public health policymakers.