OBJECTIVE: The study sought to investigate the disease state-dependent risk profiles of patient demographics and medical comorbidities associated with adverse outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. MATERIALS AND METHODS: A covariate-dependent, continuous-time hidden Markov model with 4 states (moderate, severe, discharged, and deceased) was used to model the dynamic progression of COVID-19 during the course of hospitalization. All model parameters were estimated using the electronic health records of 1362 patients from ProMedica Health System admitted between March 20, 2020 and December 29, 2020 with a positive nasopharyngeal PCR test for SARS-CoV-2. Demographic characteristics, comorbidities, vital signs, and laboratory test results were retrospectively evaluated to infer a patient's clinical progression. RESULTS: The association between patient-level covariates and risk of progression was found to be disease state dependent. Specifically, while being male, being Black or having a medical comorbidity were all associated with an increased risk of progressing from the moderate disease state to the severe disease state, these same factors were associated with a decreased risk of progressing from the severe disease state to the deceased state. DISCUSSION: Recent studies have not included analyses of the temporal progression of COVID-19, making the current study a unique modeling-based approach to understand the dynamics of COVID-19 in hospitalized patients. CONCLUSION: Dynamic risk stratification models have the potential to improve clinical outcomes not only in COVID-19, but also in a myriad of other acute and chronic diseases that, to date, have largely been assessed only by static modeling techniques.

Efficient algorithms that generate a detailed operation plan for satellite-switched time-division multiple access (SS/TDMA) systems are proposed. A burst time plan generation problem is analyzed and two algorithms for burst scheduling are presented. The first method is an algorithm based upon a bin pack problem. The other algorithm schedules new bursts while reassigning already scheduled bursts by a single machine scheduling model. These algorithms are shown to be applicable to practical systems operating with transponder hopping and multidestination bursts. Simulation results for a number of example problems are presented.>

Increased frequency reuse in satellite systems will result in increased levels of cochannel interference. Such interference can in fact become the limiting factor affecting system performance. In this paper, the effects of cochannel interference on the bit error rate (BER) of a QPSK satellite system are analyzed. A nonlinear satellite transponder having a specified AM-AM and AM-PM transfer characteristic is assumed. An arbitrary number of up- and downlink interferers is considered. The theoretical results are compared with results obtained from time-domain computer simulations. Two facets of the cochannel interference problem which are of particular interest are investigated: the non-Gaussian nature of the interference and the small-signal suppression which may occur for uplink interferers.

The results of an extended series of propagation measurements to determine the envelope frequency correlation coefficients at 4.6 and 7.6 GHz over four paths ranging from 140 to 200 km are reported. Comparison is made with previous experimental findings and with the models of Rice [6], Sunde [9], and Bello et al. [11]. The correlation coefficients measured simultaneously at the two frequencies were found nearly equivalent although a marked time dependence was evident over periods as short as 30 min. A discussion is given of the several expressions that have been referred to as envelope correlation functions, a distinction that is necessary when comparing the results of various workers and empirical models. An attempt is made to modify the empirical parameters entering the models to agree best with the experimental findings.