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Introduction: This article describes the experience of the Singapore Armed Forces (SAF) implementing telemedicine consultations for COVID-19–positive patients recovering within community recovery facilities (CRFs) in a semi-inpatient setting. Materials and Methods: The SAF adopted a systematic approach to telemedicine implementation and scaling up, with Phase 1 being the deployment of medical teams operating on-site clinics daily at six CRFs and telemedicine only provided for after-hours medical consultations on an ad hoc basis. Subsequently in Phase 2, most clinical consultations in the CRFs were conducted virtually. Results: Phases 1 and 2 recorded 1,902 and 449 clinical consultations, respectively. The mean number of clinical encounters was 33 per 1,000 occupants per day in Phase 1, and 12 per 1,000 occupants per day in Phase 2 (p < 0.001). Acute respiratory illness (52.3% in Phase 1 and 46.7% in Phase 2) was the most common reason for consultations. With full telemedicine in Phase 2, there was reduction in the mean number of clinical encounters per 1,000 occupants per day (p = 0.001), lower man-hours in personal protective equipment (PPE) (p < 0.001), and rise in escalation of care (p < 0.001) but without adverse events reported. Conclusions: Telemedicine for patients was safe, improved medical manpower efficiency, and reduced man-hours in PPE. The increased escalation of care in Phase 2 due to the lack of physical examination capabilities was to be expected to ensure patients' safety. Overall, it is recommended that for stable and mild medical conditions, telemedicine is a viable, safe, and efficient health care delivery tool in crisis situations similar to COVID-19. [ABSTRACT FROM AUTHOR]

The probability hypothesis density (PHD) filter, which is used for multi-target tracking based on sensor measurements, relies on the propagation of the first-order moment, or intensity function, of a point process. This algorithm assumes that targets behave independently, an hypothesis which may not hold in practice due to potential target interactions. In this paper, we construct a second-order PHD filter based on determinantal point processes which are able to model repulsion between targets. Such processes are characterized by their first- and second-order moments, which allows the algorithm to propagate variance and covariance information in addition to first-order target count estimates. Our approach relies on posterior moment formulas for the estimation of a general hidden point process after a thinning operation and a superposition with a Poisson point process, and on suitable approximation formulas in the determinantal point process setting. The repulsive properties of determinantal point processes apply to the modeling of negative correlation between distinct measurement domains. Monte Carlo simulations with correlation estimates are provided. [ABSTRACT FROM AUTHOR]