Physician Perceptions of Catching COVID-19

BackgroundRisk perception, influenced and biased by multiple factors, can affect behavior. ObjectiveTo assess the variability of physician perceptions of catching COVID-19. DesignCross sectional, random stratified sample of physicians registered with Sermo, a global networking platform open to verified and licensed physicians. Main outcome measuresThe survey asked: "What is your likelihood of catching COVID-19 in the next three months?" The physicians were asked to give their best estimate as an exact percentage. ResultsThe survey was completed by 1004 physicians (40 countries, 67 specialties, 49% frontline [e.g. ER, infectious disease, internal medicine]) with a mean (SD) age of 49.14 (12) years. Mean (SD) self-risk estimate was 32.3% {+/-} 26% with a range from 0% to 100% (Figure 1a). Risk estimates were higher in younger (<50 years) doctors and in non-US doctors versus their older and US counterparts (p<0.05 for all) (Figure 1b). Risk estimates were higher among front line versus non-frontline doctors (p<0.05). Risk estimates were higher for women than men (p<0.05) among respondents (60%) reporting gender. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC="FIGDIR/small/20249089v2_fig1A.gif" ALT="Figure 1A"> View larger version (17K): org.highwire.dtl.DTLVardef@7dd844org.highwire.dtl.DTLVardef@17241org.highwire.dtl.DTLVardef@f43cb2org.highwire.dtl.DTLVardef@bcd7f1_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1A.C_FLOATNO Distribution of risk prediction for overall sample (N=1004). Upper panel is a line box whisker and bottom panel shows the frequency distribution. C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/20249089v2_fig1B.gif" ALT="Figure 1B"> View larger version (21K): org.highwire.dtl.DTLVardef@51be7org.highwire.dtl.DTLVardef@16ae5fcorg.highwire.dtl.DTLVardef@12118a2org.highwire.dtl.DTLVardef@1d848c6_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure1B.C_FLOATNO Mean (SD) risk estimates by age, frontline status, and geographic region. C_FIG ConclusionsTo our knowledge, this is the first global study to document physician risk perceptions for catching COVID-19 and how it is impacted by age, gender, practice specialty and geography. Accurate calibration of risk perception is vital since both over- and underestimation of risk could impact physician behavior and have implications for public health.

Are we ready for COVID-19's Golden Passport? Insights from a Global Physician Survey

IntroductionCOVID-19 immunity passports could protect the right to free movement, but critics worry about insufficient evidence, privacy, fraud, and discrimination. We aimed to characterize the global physician communitys opinion regarding immunity passports. MethodsCross sectional, random stratified sample of physicians registered with Sermo, a global networking platform open to verified and licensed physicians. The survey aimed to sample 1,000 physicians divided among the USA, EU and rest of the world. The survey question on immunology asked physicians to offer their insights into whether we know enough about COVID-19 immunity and its duration to offer immunity passports at the present time. ResultsThe survey was completed by 1004 physicians (67 specialties, 40 countries, 49% frontline specialties) with a mean (SD) age of 49.14 (12) years. Overall, 52% answered NO, 17% were UNCERTAIN, and 31% answered YES (P <0.05). EU physicians were more likely to sayYES but even among them it did not exceed 35% approval. US physicians (60%) were more likely to say NO. ConclusionOur findings suggest a current lack of support among physicians for immunity passports. It is hoped that ongoing research and vaccine trials will provide further clarity.

A multiobjective optimization approach to obtain decision thresholds for distributed detection in wireless sensor networks.

For distributed detection in a wireless sensor network, sensors arrive at decisions about a specific event that are then sent to a central fusion center that makes global inference about the event. For such systems, the determination of the decision thresholds for local sensors is an essential task. In this paper, we study the distributed detection problem and evaluate the sensor thresholds by formulating and solving a multiobjective optimization problem, where the objectives are to minimize the probability of error and the total energy consumption of the network. The problem is investigated and solved for two types of fusion schemes: 1) parallel decision fusion and 2) serial decision fusion. The Pareto optimal solutions are obtained using two different multiobjective optimization techniques. The normal boundary intersection (NBI) method converts the multiobjective problem into a number of single objective-constrained subproblems, where each subproblem can be solved with appropriate optimization methods and nondominating sorting genetic algorithm-II (NSGA-II), which is a multiobjective evolutionary algorithm. In our simulations, NBI yielded better and evenly distributed Pareto optimal solutions in a shorter time as compared with NSGA-II. The simulation results show that, instead of only minimizing the probability of error, multiobjective optimization provides a number of design alternatives, which achieve significant energy savings at the cost of slightly increasing the best achievable decision error probability. The simulation results also show that the parallel fusion model achieves better error probability, but the serial fusion model is more efficient in terms of energy consumption.

A motif extraction algorithm based on hashing and modulo-4 arithmetic.

We develop an algorithm to identify cis-elements in promoter regions of coregulated genes. This algorithm searches for subsequences of desired length whose frequency of occurrence is relatively high, while accounting for slightly perturbed variants using hash table and modulo arithmetic. Motifs are evaluated using profile matrices and higher-order Markov background model. Simulation results show that our algorithm discovers more motifs present in the test sequences, when compared with two well-known motif-discovery tools (MDScan and AlignACE). The algorithm produces very promising results on real data set; the output of the algorithm contained many known motifs.

Characterization of a Class of Sigmoid Functions with Applications to Neural Networks.

We study two classes of sigmoids: the simple sigmoids, defined to be odd, asymptotically bounded, completely monotone functions in one variable, and the hyperbolic sigmoids, a proper subset of simple sigmoids and a natural generalization of the hyperbolic tangent. We obtain a complete characterization for the inverses of hyperbolic sigmoids using Euler's incomplete beta functions, and describe composition rules that illustrate how such functions may be synthesized from others. These results are applied to two problems. First we show that with respect to simple sigmoids the continuous Cohen-Grossberg-Hopfield model can be reduced to the (associated) Legendre differential equations. Second, we show that the effect of using simple sigmoids as node transfer functions in a one-hidden layer feedforward network with one summing output may be interpreted as representing the output function as a Fourier series sine transform evaluated at the hidden layer node inputs, thus extending and complementing earlier results in this area. Copyright 1996 Elsevier Science Ltd