Health Life

Why COVID-19 infection curves behave so unexpectedly

Contact network of employees in an office building in France, thresholded according to the number of contacts. In (a) all links are shown that connect two persons with more than 100 encounters, (b) and (c) show the cases for 200 and 500 encounters. In the densest network (a) almost all employees get infected (red). Only a few with weak ties to the network’s core stay healthy (green).The same is true for the less dense network (b). Below a certain link density, things change abruptly (c): only a few infection clusters appear, while the majority remains healthy. This is the typical pattern observed in the COVID-19 pandemic when implementing social distancing measures. That shows the importance of reducing the network density below the critical point. The proof of the existence of such a critical point is the main contribution of the paper. The practical message is: Non pharmaceutical interventions should be able to bring social network density below that point. Credit: CSH Vienna

With the first COVID-19 epidemic peak behind them, many countries explained the decrease of infection numbers through non-pharmaceutical interventions. Phrases like “social distancing” and “flatten the curve” have become part of common vocabulary. Yet some explanations fell short: How could one explain the linear rise of infection curves, which many countries display after the first peak, in contrast to the S-shaped curves, expected from epidemiological models?

In a new paper published in the Proceedings of the National Academy of Sciences, scientists at the Complexity Science Hub Vienna (CSH) offer an explanation for the of the infection .

“At the beginning of the pandemic, COVID-19 infection curves showed the expected ,” says Stefan Thurner, CSH president and professor for Science of Complex Systems at the Medical University of Vienna. This can be well

Health Life

A.I. tool promises faster, more accurate Alzheimer’s diagnosis

PET scan of a human brain with Alzheimer’s disease. Credit: public domain

By detecting subtle differences in the way that Alzheimer’s sufferers use language, researchers at Stevens Institute of Technology have developed an A.I. algorithm that promises to accurately diagnose Alzheimer’s without the need for expensive scans or in-person testing. The software not only can diagnose Alzheimer’s, at negligible cost, with more than 95 percent accuracy, but is also capable of explaining its conclusions, allowing physicians to double check the accuracy of its diagnosis.

“This is a real breakthrough,” said the tool’s creator, K.P. Subbalakshmi, founding director of Stevens Institute of Artificial Intelligence and professor of electrical and computer engineering at the Charles V. Schaeffer School of Engineering. “We’re opening an exciting new field of research, and making it far easier to explain to patients why the A.I. came to the conclusion that it did, while diagnosing patients. This addresses the important question of trustability of A.I. systems in the medical field”

It has long been known that Alzheimer’s can affect a person’s use of language. People with Alzheimer’s typically replace nouns with pronouns, such as by saying ‘He sat on it’ rather than ‘The boy sat on the chair.’ Patients might also use awkward circumlocutions, saying “My stomach feels bad because I haven’t eaten” instead of simply “I’m hungry.” By designing an explainable A.I. engine which uses attention mechanisms and convolutional neural network— a form of A.I. that learns over time—Subbalakshmi and her students were able to develop software that could not only accurately identify well-known telltale signs of Alzheimer’s, but also detect subtle linguistic patterns previously overlooked.

Subbalakshmi and her team trained her algorithm using texts produced by both healthy subjects and known Alzheimer’s sufferers as they described a drawing of children stealing cookies from a jar. Using

Health article

School Decision-Making Tool for Parents, Caregivers, and Guardians

Introduction

Schools play an important role in students’ educational achievement, health, and wellbeing. Working with local health officials and with parents and caregivers, schools also have an important role in slowing the spread of SARS-CoV-2 (the virus that causes COVID-19) while protecting students, teachers, and staff and helping ensure students have safe and healthy learning environments.

As schools begin to reopen across the nation, parents, guardians, and caregivers will be making decisions based on numerous factors, such as individual preferences, health concerns, work situations, and school considerations. When making decisions about school for your family, there are many things to think about beyond academics, such as access to school meal programs, social services, extended day childcare, extra-curricular activities, social-emotional support from peers and educators, and transportation. Parents, guardians, and caregivers will be thinking about numerous factors, such as individual preferences, health concerns, work situations, and school considerations.

Many schools are offering parents and guardians a choice between in-person and virtual modes of instruction. CDC’s Decision-Making Tool for Parents and Guardians is designed to help you think through school re-entry and the choices that your child’s school is offering. ​

Consider the risks and benefits

Because of the COVID-19 public health emergency (PHE), instructional formats such as class size, setting, and daily schedules will likely look different than in past years. Consider the risks and benefits of these different instructional formats. For example, in-person instruction may offer easier access to school services, improved educational efficacy, more opportunities for social interaction and return to work for some parents and caregivers, but it also has a higher risk of COVID-19 exposure for your child than virtual instruction. Families will differ in their choice of instructional formats based on whether  the student or members of the household are at increased risk of severe illness,