ImportanceAsymptomatic and presymptomatic carriers of SARS-CoV-2 are an ongoing and significant risk for community spread of the virus, especially with the majority of the world still unvaccinated and new variants emerging.ObjectiveTo quantify the presence and effects of symptom presentation (or lack thereof) on the community transmission of SARS-CoV-2.DesignA cohort of 12,960 young adults participated in health reporting, contact tracing, and COVID-19 testing for 103 days between August 10 and November 20, 2020.SettingA mid-sized university campus in Indiana, United States.ParticipantsUniversity students, most of whom are 18-23 years old (67%) and living in congregate on-campus housing (60%). Of the 12,960 students, 1,556 (12.0%) tested positive for COVID-19 during the 103 day period. Of the positive cases, 1,198 reported sufficient health check data (7 days prior and 7 days post diagnosis) to be classified as asymptomatic or symptomatic.Main OutcomeSecondary attack rate, based on presentation or absence of symptoms and type of symptoms calculated with respect to confirmed close contacts and a 14-day incubation period, varies on the type of symptom, timing of symptoms, and absence of symptoms. A quantifiable understanding of SAR on the longitudinal data of more than one thousand subjects in a university environment provides keen insights about developing strategies to respond to the continued prevalence of COVID-19 in the unvaccinated world and growth of variants.Results32.5% of all cases reported no symptoms within a 15-day window centered on their positive test (7 days prior, the day of the positive test, and 7 days after). The secondary attack rate (SAR) of asymptomatic COVID-19 index cases was 19.1%. The SAR of symptomatic index cases was 25.4%, and while the onset timing of symptoms did not affect transmission, the presence of certain symptoms like fever, shortness of breath, and dry cough increased the SAR as high as 30.0%.Conclusions and RelevanceAsymptomatic rates of transmission of SARS-CoV-2 are much higher than has been estimated in prior studies and continue to pose a significant and ongoing risk in the pandemic, especially with the prevalence of variants like the Delta variant. In addition, different symptoms are associated with varying rates of transmission, posing a significant challenge in how to diagnose or assess risk through mechanisms such as daily health checks for symptom reporting, a practice commonly in place for entry into schools, offices, restaurants, etc. Given the uncertain nature of symptoms and varied transmission rates, this study suggests a broader embrace of masking, social distancing and testing might be needed to counter the variants until higher global vaccination rates can be achieved.
Terrorism is a major problem worldwide, causing thousands of fatalities and billions of dollars in damage every year. To address this threat, we propose a novel feature representation method and evaluate machine learning models that learn from localized news data in order to predict whether a terrorist attack will occur on a given calendar date and in a given state. The best model (a Random Forest aided by a novel variable-length moving average method) achieved area under the receiver operating characteristic (AUROC) of ≥ 0.667 (statistically significant w.r.t. random guessing with p ≤ .0001) on four of the five states that were impacted most by terrorism between 2015 and 2018. These results demonstrate that treating terrorism as a set of independent events, rather than as a continuous process, is a fruitful approach—especially when historical events are sparse and dissimilar—and that large-scale news data contains information that is useful for terrorism prediction. Our analysis also suggests that predictive models should be localized (i.e., state models should be independently designed, trained, and evaluated) and that the characteristics of individual attacks (e.g., responsible group or weapon type) were not correlated with prediction success. These contributions provide a foundation for the use of machine learning in efforts against terrorism in the United States and beyond.
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