covid 19

Networked Epidemiology for COVID-19

Researchers outline an approach to support the COVID-19 response with examples rooted in network science and data-driven modeling.

Deep Learning for COVID-19 Diagnosis

Computed tomography (CT) chest scans have proven invaluable for the diagnosis and prognostication of COVID-19...

The Forensics of Emerging Diseases

Matthew Francis recaps Barbara Han's presentation at the 2020 AAAS Annual Meeting about the emergence of new...

Networks-Fractional Calculus Alliance versus COVID-19

Fractional calculus can help identify pathways for repurposing existing drugs to target important proteases in the COVID-19 virus.

Modelling Global Outbreaks and Proliferation of COVID-19

Mathematics indicates that the fast-moving nature of COVID-19 necessitates fast-moving models.

Modeling Resource Demands and Constraints for COVID-19 Intervention Strategies

Quantifying the efficacy of contact tracing is crucial to understanding the impact of disease control strategies.

Fighting a Pandemic with Medical Imaging and Machine Learning: Lessons Learned from COVID-19

Machine learning for image-based diagnosis and prognosis of COVID-19 is promising but entails numerous pitfalls.

The Mathematics of Mass Testing for COVID-19

An emergent research front allows mathematical and statistical ideas to enable a rapid expansion of COVID-19 testing.

First-principles Machine Learning for COVID-19 Modeling

Researchers employ a method developed for the prediction of chaotic dynamical systems and apply it to COVID-19.

High-Fidelity Simulation of Pathogen Propagation, Transmission, and Mitigation

Numerical techniques develop quantitative predictions of pathogen propagation, transmission, and mitigation indoors.

Multi-agent Simulations and Vaccine Allocation Strategies

A data-driven artificial intelligence approach supports ongoing COVID-19 pandemic response efforts in real time.

Preparing Future Generations to Address Global Pandemics with Innovative Mathematical Thinking

A problem-solving course enabled students to apply mathematical techniques to global challenges related to COVID-19.

Key Epidemiological Parameters for SARS-CoV-2 Outbreaks and Variant Selection From Noisy Data

During the COVID-19 pandemic, preliminary predictions based on noisy and often incomplete data became elements of a real-time discussion.

An Alternative System for Curbing COVID-19 Spread in the U.S.

Samuel Awoniyi employs Markov chain modeling to compare two possible systems for curbing COVID-19 spread in the U.S.

Choosing Intervention Strategies During an Emerging Epidemic

Lauren Childs uses a modeling framework to compare individual quarantine and active symptom monitoring.

Modeling the Spread of COVID-19

One of the simplest epidemiological models is the SIR model, which divides the population into three groups.

Virtual Summer Schools: Can We Make Them Work?

During the COVID-19 pandemic, an online summer school brought students and experts together in a unique way.

A Mathematical Model to Support Hospital Workflow Management During a Pandemic

Researchers model patient care and resource allocation during COVID-19 and account for a possible second wave.

Mathematicians Quickly Respond to the COVID-19 Pandemic

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.

Questions of Responsibility: Modelling in the Age of COVID-19

Modelling a pandemic requires work at the third level of ethical engagement: taking a seat at the table of power.

A Model to Predict COVID-19 Epidemics with Applications to South Korea, Italy, and Spain

Several differential equations models of COVID-19 use early reported case data to predict the future number of cases.

Exploring COVID-19’s Impact on Undergraduate and Graduate Education

COVID-19 has caused a potential knowledge gap and taken a mental and emotional toll on students and educators.

Math and AI-based Repositioning of Existing Drugs for COVID-19

Drug repositioning is one of the most feasible strategies for treating COVID-19 patients.

The Task Force Report on Future Research Directions for NSF in the Era of COVID-19

A SIAM task force helped inform the National Science Foundation’s response to the COVID-19 pandemic.

When Contagion Rules

Paul Davis reviews "The Rules of Contagion: Why Things Spread – and Why They Stop" by Adam Kucharski.

Structure-based Drug Discovery and Ensemble Docking for SARS-COV-2 Proteins

At CSE21, Jeremy Smith discussed his efforts to develop a supercomputer-driven pipeline for in-silico COVID-19...

Metapopulation Model Accounts for Asymptomatic Spread of COVID-19

During CSE21, Daniela Calvetti presented a robust metapopulation model that accounts for the complex nuances of...

Mathematically Modeling the Dynamics of COVID-19 and Domestic Violence During Lockdown

An expanded compartmental model describes COVID-19 and domestic violence interactions across multiple lockdown...

Optimal Time-dependent Prevalence Estimation and Classification

Coupling probabilistic modeling of underlying data with optimization machinery can help answer epidemiological...

Project INSIGHT: Developing Novel Epidemiological Models with Human Behavior, Interaction, and Collaboration

Project INSIGHT aims to improve understanding of the COVID-19 pandemic and its effects on society through math...

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