SignificanceForecasts routinely provide critical information for dangerous weather events but not yet for epidemics. Researchers develop computational models that can be used for infectious disease forecasting, but forecasts have not been broadly compared or tested. We collaboratively compared forecasts from 16 teams for 8 y of dengue epidemics in Peru and Puerto Rico. The comparison highlighted components that forecasts did well (e.g., situational awareness late in the season) and those that need more work (e.g., early season forecasts). It also identified key facets to improve forecasts, including using multiple model ensemble approaches to improve overall forecast skill. Future infectious disease forecasting work can build on these findings and this framework to improve the skill and utility of forecasts.
The American Psychological Association (APA) reports 81% of Gen Z teens (ages 13–17) have experienced more intense stress during the COVID-19 pandemic. This study uses a survey-based approach along with robust statistical analyses to identify key stressors from a set of students in a high school in Midwest United States. Our survey includes a broad range of stressors (15 explanatory variables) specific to high schoolers, controls (4 factors for pre-existing conditions), and mental health estimators (7 dependent variables) to identify changes in mental wellbeing during the pandemic. The results (n = 107) show good consistency in our estimators (Cronbach’s α = 0.78) and statistically significant (t = 0.636, p ≪ 0.001) degradation in the mental health. Correlation (r = 0.2, p = 0.034) and regression analysis showed that online learning (β1 = -0.96, p = 0.004) has the most influence on degradation in mental health, with some race-based differences. Exercise time helps reduce mental health degradation (β3 = -0.153, p = 0.037). Many other factors such as gender, homework time, school time, pre-existing mental health issues, and therapy did not have a significant influence on mental health degradation. Analysis of freeform feedback identified the following three recurring themes: increased stress due homework (13.2%), social isolation or lack of social interactions (8.5%), and lack of support for mental wellbeing (12.3%).
Distributed synchronization for parallel simulation is generally classified as being either optimistic or conservative. While considerable investigations have been conducted to analyze and optimize each of these synchronization strategies, very little study on the definition and strictness of causality have been conducted. Do we really need to preserve causality in all types of simulations? This paper attempts to answer this question. We argue that significant performance gains can be made by reconsidering this definition to decide if the parallel simulation needs to preserve causality. We investigate the feasibility of unsynchronized parallel simulation through the use of several queuing model simulations and present a comparative analysis between unsynchronized and Time Warp simulation.
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