✨Introducing Mathematics in Medical and Life Sciences (MMLS) – a multidisciplinary journal at the interface of maths/stats/data science/computing with medical, biological, and broader life sciences. Here's a little bit more about us🧵1/8

👉 Read the full article: https://t.co/FTWeNEcsKJ #MachineLearning #COVID19 #PublicHealth #MassTesting #DataScience #CostaRica #PandemicResponse #HealthTech #OpenAccess #MedicalAI

📈 This work demonstrates how data-driven triage can optimize testing resources and improve public health responses globally.

🧪 Technologies considered: RT-PCR, antigen tests, and RT-LAMP.

🔬 Among the strategies, the hybrid risk-based approach stood out as a cost-effective, scalable, and adaptable solution for future pandemic responses.

🧠 The model achieved an AUC-ROC of 80.48%, showing strong predictive power in classifying risk of infection.

💡 The study uses machine learning to predict COVID-19 contagion risk using health determinants from Costa Rica’s 2021 “Actualidades” survey.

🚨 New Publication Alert! 🚨 A new study is out in @MMLS_journal by Carlos Pasquier, Maikol Solís, Vivian Vilchez & Santiago Núñez-Corrales called “Machine learning-driven COVID-19 early triage and large-scale testing strategies based on the 2021 Costa Rican Actualidades survey.”

We’re excited to have represented in these important discussions and look forward to exploring new collaborations at the intersection of mathematics, engineering, and medicine. Chat to Seb if you're there! #MathInMedicine #DigitalPathology #ImageAnalysis #ComputerVision

Associate Editor @seb_math_bio is representing @MMLS_journal at the @Visiopharm 2nd UK User Group Meeting at the @unibirmingham, UK. This conference for unites unites digital pathology experts for talks, presentations, and networking.

📄 Read the full study here: https://t.co/JAKi0Q06Ic #COVID19 #PublicHealth #UniversityResearch #BayesianModeling #SEIRModel #DataScience #MathInMedicine

Tailored, data-informed models like this can help universities prepare smarter responses in future crises—not just pandemics, but any complex health challenge.

🧩This shows the power of model-data fusion: combining math with real numbers to improve accuracy and make models actually useful for public health decisions.

The results? ✅ 12% error reduction in positive test predictions ✅ 84% error reduction in isolation population estimates ✅ A much closer fit to the real-world situation

📈Using a SEIR model and Bayesian calibration, the authors matched model predictions to real test & isolation data—then improved the model based on where it fell short.

Most models rely on hospital or city-level data. But universities are unique—they're communities of residence, work, and study. This study asked: what happens when we model the campus directly?

🏫 The study, "Enhancing a university community COVID-19 model with Bayesian model-data fusion," focuses on using real surveillance data from Fall 2020 at a northeastern U.S. university.

📢 Publication Highlight 📢 How can universities better respond to health crises like COVID-19? A new study by @Meghanchilds & Tony E. Wong explores how fusing real campus data with math models can boost accuracy. 🔍📊

📄 Read the full study here: https://t.co/GBAA4Fmx5A #CancerResearch #Theramechanics #MathematicalBiology #ComputationalModeling #MedicalInnovation #InSilicoScience

By integrating biology & mechanics, this study opens new possibilities for mechanics-based cancer therapies. Could targeting nuclear stiffness and shape be a game-changer for cancer treatment?

🔑 Key takeaways: ✅Cancer cells must deform their nuclei to invade new tissues. ✅The nucleus acts as a mechanical regulator of movement. ✅ Theramechanics—modifying nuclear properties—could open new treatment avenues.