A concise framework to facilitate open COVID pledge of non-disclosed technologies: In terms of non-disclosed patent applications and trade secrets.

BACKGROUND: As a result of the COVID-19 global pandemic, many intellectual property (IP) owners have signed on to the "Open COVID Pledge", an agreement that makes corporate and university IP available free of charge for the purpose of facilitating the development of technologies that will end the pandemic and minimize the impact of disease. Joining this pledge is relatively straightforward for already-disclosed IPs. However, few, if any, has considered how to encourage owners of "non-disclosed patent applications" and "trade secrets" to sign on to this meaningful pledge. In other words, so far there is no proposal to extend the Open COVID Pledge for confidential pending patents and trade secrets. METHODS: We propose an innovative and flexible framework to cover both non-disclosed patent applications and trade secrets to mobilize inventors to participate in the Open COVID Pledge. RESULTS: By focusing on immediate publication of the patent-applying technology and extending provisional right to such applications which is subject to the Open Pledge during this pandemic, our recommendations are workable for inventors who would like to pledge their non-disclosed technologies for the detection, prevention and treatment of the COVID-19, in the meantime preserving their IP rights for the post-pledge period. CONCLUSION: This paper offers a way forward to guide pledgers and implementers who are interested in supporting the effort by addressing some of the issues associated with the free sharing of non-disclosed patent applications and trade secrets in the fight against COVID-19.

Forecasting Future World Events with Neural Networks (preprint)

Forecasting future world events is a challenging but valuable task. Forecasts of climate, geopolitical conflict, pandemics and economic indicators help shape policy and decision making. In these domains, the judgment of expert humans contributes to the best forecasts. Given advances in language modeling, can these forecasts be automated? To this end, we introduce Autocast, a dataset containing thousands of forecasting questions and an accompanying news corpus. Questions are taken from forecasting tournaments, ensuring high quality, real-world importance, and diversity. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts (avoiding leakage from the future). Motivated by the difficulty of forecasting numbers across orders of magnitude (e.g. global cases of COVID-19 in 2022), we also curate IntervalQA, a dataset of numerical questions and metrics for calibration. We test language models on our forecasting task and find that performance is far below a human expert baseline. However, performance improves with increased model size and incorporation of relevant information from the news corpus. In sum, Autocast poses a novel challenge for large language models and improved performance could bring large practical benefits.

Identification of driver genes for severe forms of COVID-19 in a deeply phenotyped young patient cohort (preprint)

The etiopathogenesis of severe COVID-19 remains unknown. Indeed given major confounding factors (age and co-morbidities), true drivers of this condition have remained elusive. Here, we employ an unprecedented multi-omics analysis, combined with artificial intelligence, in a young patient cohort where major co-morbidities have been excluded at the onset. Here, we established a three-tier cohort of individuals younger than 50 years without major comorbidities. These included 47 "critical" (in the ICU under mechanical ventilation) and 25 "non-critical" (in a noncritical care ward) COVID-19 patients as well as 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cells proteomics, cytokine profiling and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing and structural causal modeling led to key findings. Critical patients were characterized by exacerbated inflammation, perturbed lymphoid/myeloid compartments, coagulation and viral cell biology. Within a unique gene signature that differentiated critical from noncritical patients, several driver genes promoted severe COVID-19 among which the upregulated metalloprotease ADAM9 was key. This gene signature was replicated in an independent cohort of 81 critical and 73 recovered COVID-19 patients, as were ADAM9 transcripts, soluble form and proteolytic activity. Ex vivo ADAM9 inhibition affected SARS-CoV-2 uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, COVID-19 cohort, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. The key driver, ADAM9, interfered with SARS-CoV-2 biology. A repositioning strategy for anti-ADAM9 therapeutic is feasible. One sentence summaryEtiopathogenesis of severe COVID19 in a young patient population devoid of comorbidities.

COVID-19 projections for reopening Connecticut (preprint)

Closure of schools and the statewide "Stay Safe, Stay Home" order have effectively reduced COVID-19 transmission in Connecticut, with model projections estimating incidence at about 1,300 new infections per day. If close interpersonal contact increases quickly in Connecticut following reopening on May 20, the state is at risk of a substantial increase of COVID-19 infections, hospitalizations, and deaths by late Summer 2020. Real-time metrics including case counts, hospitalizations, and deaths may fail to provide enough advance warning to avoid resurgence. Substantial uncertainty remains in our knowledge of cumulative COVID-19 incidence, the proportion of infected individuals who are asymptomatic, infectiousness of children, the effects of testing and contact tracing on isolation of infected individuals, and how contact patterns may change following reopening.

A model for COVID-19 transmission in Connecticut (preprint)

To support public health policymakers in Connecticut as they begin phased lifting of social distancing restrictions, we developed a county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. We calibrated this model to the local dynamics of deaths and hospitalizations and the exact timing of state interventions, including school closures and stay-at-home order. In this technical report, we describe the details of the model design, implementation and calibration, and show projections of epidemic development through the Summer of 2020 under different assumptions about the increase in contact rates following partial state reopening. Our model results are consistent with high effectiveness of state lockdown measures, but changes in human interaction patterns during the coming months are unknown. In addition, a lot of uncertainty remains with respect to several key epidemiological parameters and the effectiveness of increased testing and contact tracing capacity. As more information becomes available, we will update the projections presented in this report. Reports in this series are posted to https://crawford-lab.github.io/covid19_ct/.