The κ-statistics approach to epidemiology

Giorgio Kaniadakis, Mauro M. Baldi, Thomas S. Deisboeck, Giulia Grisolia, Dionissios T. Hristopulos, Antonio M. Scarfone, Amelia Sparavigna, Tatsuaki Wada & Umberto Lucia

Scientific Reports volume 10, Article number: 19949 (2020)

Abstract
A great variety of complex physical, natural and artificial systems are governed by statistical distributions, which often follow a standard exponential function in the bulk, while their tail obeys the Pareto power law. The recently introduced 𝜅
-statistics framework predicts distribution functions with this feature. A growing number of applications in different fields of investigation are beginning to prove the relevance and effectiveness of 𝜅-statistics in fitting empirical data. In this paper, we use 𝜅-statistics to formulate a statistical approach for epidemiological analysis. We validate the theoretical results by fitting the derived 𝜅-Weibull distributions with data from the plague pandemic of 1417 in Florence as well as data from the COVID-19 pandemic in China over the entire cycle that concludes in April 16, 2020. As further validation of the proposed approach we present a more systematic analysis of COVID-19 data from countries such as Germany, Italy, Spain and United Kingdom, obtaining very good agreement between theoretical predictions and empirical observations. For these countries we also study the entire first cycle of the pandemic which extends until the end of July 2020. The fact that both the data of the Florence plague and those of the Covid-19 pandemic are successfully described by the same theoretical model, even though the two events are caused by different diseases and they are separated by more than 600 years, is evidence that the 𝜅-Weibull model has universal features.

💉 The Oxford coronavirus vaccine has been shown to trigger a robust immune response in healthy adults aged 56-69 and 70+. The data suggest that one of the groups most vulnerable to serious illness and death from COVID-19 could build immunity:
https://t.co/E5uEcHFcrT
#OxfordVaccine

💰 I am currently looking for 2 #PhD Students to work with me on Bayesian workflow topics at the Cluster of Excellence SimTech in Stuttgart, Germany. Deadline ist December 20th and all information can be found here: https://t.co/4FboYYKyJu

"Finland and Norway boast the West’s lowest rates of mortality,..now stand out as the closest Western equivalents to Asian nations that have managed to avoid the worst of the pandemic."

https://www.wsj.com/articles/finland-and-norway-avoid-covid-19-lockdowns-but-keep-the-virus-at-bay-11605704407

"Their recipe: a brief, targeted lockdown in March, followed by tight border controls with mandatory testing and quarantine for all travelers.

"Elsewhere in Europe, strict lockdowns in the spring helped bring infections down, but as most of the continent reopened borders, summer travelers turned into incubators for a new and bigger wave of infections.

https://t.co/XBmrhDqQry

🧑🏻‍🏫 #Renormalization w3.p3 Networks of Renormalization

«مقدمه‌ای بر بازبهنجارش»
هفته سوم: اتوماتای سلولی
قسمت سوم: شبکه‌های بازبهنجارش

یک اتوماتای سلولی شامل یک شبکه منظم از سلول‌های خاموش و روشن است. تحول این سلول‌ها توسط قواعد ثابتی که فقط وابسته به وضعیت قبلی آن سلول و همسایگانش است مشخص می‌شود. در این جلسه ابتدا اتوماتای سلولی را معرفی می‌کنم و به مفاهیمی چون «کامل بودن تورینگ» و «نمودارهای جابه‌جاشوند» می‌پردازم. سپس سراغ درشت-دانه‌بندی اتوماتای سلولی و مقاله ۲۰۰۴ و ۲۰۰۵ گلدنفلد می‌روم و در نهایت در مورد شبکه‌‌های بازبهنجارش بحث خواهم کرد.

🎞 ویدیو در صفحه اینستاگرام سیتپور

🔗 اطلاعات بیشتر:
sitpor.org/2019/09/renorm-week3-ca

~~~~~~
@sitpor
‌instagram.com/sitpor_media
~~~~~~~

🔹 Simons-Emory Workshop on Neural Dynamics

- "What could neural dynamics have to say about neural computation, and do we know how to listen?"
- Friday December 4th from 11am-2pm EST

- Event WebSite
- Register


- The workshop will assume familiarity with Vyas et al., Ann Rev Neuro 2020. Please review before attending.

🧑🏻‍🏫 #Renormalization w4.p1 Intro to Ising Model

«مقدمه‌ای بر بازبهنجارش»
هفته چهارم: مدل آیزینگ
قسمت اول: مرور جلسات گذشته و معرفی مدل آیزینگ

مدل آیزینگ، به عنوان معرف‌ترین مدل در فیزیک آماری، یک مدل ساده برای توصیف گذار فاز در مواد مغناطیسی است. این مدل از متغیرهای گسسته (اسپین) به روی یک گراف مشبکه تشکیل شده است. در این قسمت از مجموعه مقدمه‌ای بر بازبهنجارش، نخست مدل آیزینگ را معرفی می‌کنم و سپس به سراغ درشت‌-دانه‌بندی شبکه‌ اسپینی می‌روم. چالش‌های پیش‌رو را مطرح می‌کنم و سرانجام به پدیدارگی جملات مرتبه‌-بالاتر و نقاط ثابت جریان بازبهنجارش می‌پردازم.


🎞 ویدیو در صفحه اینستاگرام سیتپور

🔗 اطلاعات بیشتر:
sitpor.org/2019/10/renorm-week4-ising

~~~~~~
@sitpor
‌instagram.com/sitpor_media
~~~~~~~

💉 واکسن آکسفورد:

Interim data show the #OxfordVaccine is 70.4% effective, & tests on two dose regimens show that it could be 90%, moving us one step closer to supplying it at low cost around the world>>

https://t.co/fnHnKSqftT

داده‌ها نشون دادن که واکسن آکسفورد در تزریق اول حدود ۷۰٪ و در تزریق دوم حدود ۹۰٪ ایمنی ایجاد می‌کنه.

https://twitter.com/UniofOxford/status/1330769567409385474?s=09

The Mathematics and Statistics of Infectious Disease Outbreaks

Slides, R code and video lectures of our 2020 The Mathematics and Statistics of Infectious Disease Outbreaks summer course at Stockholm University are made available to a wider audience.

💰 Open #postdoc position at the Kavli Institute for Systems Neuroscience KISNeuro NTNU to work on applications of statistics and data science to language. Deadline 20/12. Spread the word, apply.

https://t.co/lqFk6qvrNv

😷 ماسک‌های پارچه‌ای با اینکه بهترین گزینه نیستند ولی بسیار موثرتر از نزدن یا نپوشوندن بینی و دهان هستند:

Preprint now at https://t.co/PwTCS6ZaJV Take-home: Cloth masks are not an N95, but they work reasonably well for aerosols 1-2 microns and larger, which is the size that we think mostly mediates transmission. See thread. /1 https://t.co/ae37seFs6r

How compressible is a network? What makes one network more compressible than another?

pre-print: "Compressibility of complex networks"

Network Analysis and Visualization with R and igraph
https://kateto.net/netscix2016.html

Katherine Ognyanova
NetSciX 2016 School of Code Workshop, Wroclaw, Poland

📺 ICML tutorial "Machine Learning with Signal Processing"
https://t.co/MrqstAT0gt

یازدهمین کنفرانس فیزیک آماری، ماده چگال نرم و سیستم‌های پیچیده

⭕️ تاریخ جدید: ۱ و ۲ بهمن‌ماه ۱۳۹۹
دانشگاه شهید بهشتی - مجازی

مهلت ثبت نام تا ۱۳ دی ماه تمدید شده است.

https://www.psi.ir/farsi.asp?page=smc99

@CCNSD

Recurring event
Next on 15.12.2020 11:30 – 12:45 (Tehran Time)
Where Online event

🧠 Nonlinear Independent Component Analysis
Prof. Aapo Hyvärinen, Department of Computer Science, University of Helsinki

🧠 Learning representations from neural time series
Dr. Alexandre Gramfort, Université Paris-Saclay, Inria, Palaiseau, France

🔗 For more information see here:
https://www.aalto.fi/en/events/brain-mind-computational-seminar