Researchers outline an approach to support the COVID-19 response with examples rooted in network science and data-driven modeling.
Computed tomography (CT) chest scans have proven invaluable for the diagnosis and prognostication of COVID-19...
Matthew Francis recaps Barbara Han's presentation at the 2020 AAAS Annual Meeting about the emergence of new...
Fractional calculus can help identify pathways for repurposing existing drugs to target important proteases in the COVID-19 virus.
Mathematics indicates that the fast-moving nature of COVID-19 necessitates fast-moving models.
Quantifying the efficacy of contact tracing is crucial to understanding the impact of disease control strategies.
Machine learning for image-based diagnosis and prognosis of COVID-19 is promising but entails numerous pitfalls.
An emergent research front allows mathematical and statistical ideas to enable a rapid expansion of COVID-19 testing.
Researchers employ a method developed for the prediction of chaotic dynamical systems and apply it to COVID-19.
Numerical techniques develop quantitative predictions of pathogen propagation, transmission, and mitigation indoors.
A data-driven artificial intelligence approach supports ongoing COVID-19 pandemic response efforts in real time.
A problem-solving course enabled students to apply mathematical techniques to global challenges related to COVID-19.
During the COVID-19 pandemic, preliminary predictions based on noisy and often incomplete data became elements of a real-time discussion.
Samuel Awoniyi employs Markov chain modeling to compare two possible systems for curbing COVID-19 spread in the U.S.
Lauren Childs uses a modeling framework to compare individual quarantine and active symptom monitoring.
One of the simplest epidemiological models is the SIR model, which divides the population into three groups.
During the COVID-19 pandemic, an online summer school brought students and experts together in a unique way.
Researchers model patient care and resource allocation during COVID-19 and account for a possible second wave.
The DMS issued a set of 15 RAPID awards over the span of several weeks that could have a significant impact in mitigating the spread of COVID-19.
Modelling a pandemic requires work at the third level of ethical engagement: taking a seat at the table of power.
Several differential equations models of COVID-19 use early reported case data to predict the future number of cases.
COVID-19 has caused a potential knowledge gap and taken a mental and emotional toll on students and educators.
Drug repositioning is one of the most feasible strategies for treating COVID-19 patients.
A SIAM task force helped inform the National Science Foundation’s response to the COVID-19 pandemic.
Paul Davis reviews "The Rules of Contagion: Why Things Spread – and Why They Stop" by Adam Kucharski.
At CSE21, Jeremy Smith discussed his efforts to develop a supercomputer-driven pipeline for in-silico COVID-19...
During CSE21, Daniela Calvetti presented a robust metapopulation model that accounts for the complex nuances of...
An expanded compartmental model describes COVID-19 and domestic violence interactions across multiple lockdown...
Coupling probabilistic modeling of underlying data with optimization machinery can help answer epidemiological...
Project INSIGHT aims to improve understanding of the COVID-19 pandemic and its effects on society through math...