About this data
It changes rapidly
This data changes rapidly, so what’s shown may be out of date. Table totals may not always represent an accurate sum. Information about reported cases is also available on the World Health Organization site.
It doesn’t include all cases
Confirmed cases aren’t all cases. They only include people who tested positive. Testing rules and availability vary by country.
It changes rapidly
This data changes rapidly, so what’s shown may be out of date. Table totals may not always represent an accurate sum. Information about reported cases is also available on the World Health Organization site.
It doesn’t include all cases
Confirmed cases aren’t all cases. They only include people who tested positive. Testing rules and availability vary by country.
🎞 Doyne Farmer - How can we understand our complex economy?
https://www.youtube.com/watch?v=GPKmFEDLh5E
We are getting better at predicting things about our environment - the impact of climate change for example. But what about predicting our collective effect on ourselves? We can predict the small things, but we fail miserably when it comes to many of the big things. The financial crisis cost the world trillions, yet our ability to forecast and mitigate the next economic crisis is very low. Is this inherently impossible? Or perhaps we are just not going about it the right way?
The complex systems approach to economics, which brings in insights from the physical and natural sciences, presents an alternative to standard methods. Doyne will explain this new approach and give examples of its successes. He will present a vision of the economics of the future as it confronts the serious problems that our world will face.
https://www.youtube.com/watch?v=GPKmFEDLh5E
We are getting better at predicting things about our environment - the impact of climate change for example. But what about predicting our collective effect on ourselves? We can predict the small things, but we fail miserably when it comes to many of the big things. The financial crisis cost the world trillions, yet our ability to forecast and mitigate the next economic crisis is very low. Is this inherently impossible? Or perhaps we are just not going about it the right way?
The complex systems approach to economics, which brings in insights from the physical and natural sciences, presents an alternative to standard methods. Doyne will explain this new approach and give examples of its successes. He will present a vision of the economics of the future as it confronts the serious problems that our world will face.
YouTube
How can we understand our complex economy?
Oxford Mathematics Public Lectures: Doyne Farmer - How can we understand our complex economy?
We are getting better at predicting things about our environment - the impact of climate change for example. But what about predicting our collective effect on…
We are getting better at predicting things about our environment - the impact of climate change for example. But what about predicting our collective effect on…
متاسفانه خبردار شدیم که امروز صبح، فلیپ اندرسون فوت کرده. اندرسون برنده جایزه نوبل در فیزیک و از پیشگامان فیزیک ماده چگال و سیستمهای پیچیده بود. جمله معروف «بیشتر، متفاوت است.» از او به ارمغان مانده.
https://en.wikipedia.org/wiki/Philip_Warren_Anderson
روحش شاد و یادش گرامی
https://en.wikipedia.org/wiki/Philip_Warren_Anderson
روحش شاد و یادش گرامی
Complex Systems Studies
متاسفانه خبردار شدیم که امروز صبح، فلیپ اندرسون فوت کرده. اندرسون برنده جایزه نوبل در فیزیک و از پیشگامان فیزیک ماده چگال و سیستمهای پیچیده بود. جمله معروف «بیشتر، متفاوت است.» از او به ارمغان مانده. https://en.wikipedia.org/wiki/Philip_Warren_Anderson …
Princeton University
Nobel laureate and Princeton physicist Philip Anderson dies at age 96
Philip Warren Anderson, the Joseph Henry Professor of Physics, Emeritus, at Princeton University and one of the greatest theoretical physicists of the postwar era, died Sunday, March 29, at age 96.
Students interested in big data and social problems, here's a great online introductory course in modern applied economics, taught by Raj Chetty (Harvard)
No prior background in economics or statistics required!
Videos of all 18 lectures (open access): https://t.co/3TeVumpCGH
No prior background in economics or statistics required!
Videos of all 18 lectures (open access): https://t.co/3TeVumpCGH
💰 My department at Stockholm University is looking for #PhD students in Statistics. Join us in building the most modern stats department in Sweden!
Deadline: April 27.
https://t.co/wUy21a0yQn
Deadline: April 27.
https://t.co/wUy21a0yQn
Forecasts by Country
On this page, we present the 6-day forecasts of COVID-19 case counts by country based on a novel epidemiological model that integrates the effect of population behavior changes due to government measures and social distancing.
http://rocs.hu-berlin.de/corona/docs/forecast/results_by_country/
On this page, we present the 6-day forecasts of COVID-19 case counts by country based on a novel epidemiological model that integrates the effect of population behavior changes due to government measures and social distancing.
http://rocs.hu-berlin.de/corona/docs/forecast/results_by_country/
Forwarded from Complex Networks (SBU)
ایسنا
سه استراتژی برای شکست کرونا در ایران + هزینهها
کشور در آستانه یک بحران فراگیر قرار دارد و به اعتقاد بسیاری از کارشناسان پاسخ درست و یا اشتباه به بحران کرونا میتواند مسیر تاریخی ایران را تغییر دهد، اهمیت مساله تا جاییست که بسیاری از کشورهای جهان کرونا را بزرگترین بحران خود پس از جنگ جهانی دوم میدانند،…
🕯 Epidemiological models aren't crystal balls that predict the future, nor are we sure if their parameters are correct! That's okay because that's not what they're for. Let's treat them as what they are: the future imploring us to choose our fate.
https://t.co/Zgq0oR6S4a
https://t.co/Zgq0oR6S4a
The Atlantic
Don’t Believe the COVID-19 Models
That’s not what they’re for.
Network Epidemiology Online Workshop Series
Join us for Understanding and Exploring Network Epidemiology in the Time of Coronavirus, a special online workshop series presented by the University of Maryland’s COMBINE program in network biology in partnership with Vermont’s Complex Systems Center.
🦠 Apply by 5pm ET on Tuesday, April 7th for full consideration (see “Signing up” below)
http://www.combine.umd.edu/network-epidemiology/
Join us for Understanding and Exploring Network Epidemiology in the Time of Coronavirus, a special online workshop series presented by the University of Maryland’s COMBINE program in network biology in partnership with Vermont’s Complex Systems Center.
🦠 Apply by 5pm ET on Tuesday, April 7th for full consideration (see “Signing up” below)
http://www.combine.umd.edu/network-epidemiology/
A call to honesty in pandemic modeling - Wesley Pegden - Medium
https://medium.com/@wpegden/a-call-to-honesty-in-pandemic-modeling-5c156686a64b
https://medium.com/@wpegden/a-call-to-honesty-in-pandemic-modeling-5c156686a64b
Medium
A call to honesty in pandemic modeling
Maria Chikina and Wesley Pegden
Covid-19: Global summary
Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally.
https://epiforecasts.io/covid/posts/global/
Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally.
https://epiforecasts.io/covid/posts/global/
Bridging the gap between graphs and networks
Gerardo Iñiguez, Federico Battiston, Márton Karsai
https://arxiv.org/pdf/2004.01467
Network science has become a powerful tool to describe the structure and dynamics of real-world complex physical, biological, social, and technological systems. Largely built on empirical observations to tackle heterogeneous, temporal, and adaptive patterns of interactions, its intuitive and flexible nature has contributed to the popularity of the field. With pioneering work on the evolution of random graphs, graph theory is often cited as the mathematical foundation of network science. Despite this narrative, the two research communities are still largely disconnected. In this Commentary we discuss the need for further cross-pollination between fields -- bridging the gap between graphs and networks -- and how network science can benefit from such influence. A more mathematical network science may clarify the role of randomness in modeling, hint at underlying laws of behavior, and predict yet unobserved complex networked phenomena in nature.
Gerardo Iñiguez, Federico Battiston, Márton Karsai
https://arxiv.org/pdf/2004.01467
Network science has become a powerful tool to describe the structure and dynamics of real-world complex physical, biological, social, and technological systems. Largely built on empirical observations to tackle heterogeneous, temporal, and adaptive patterns of interactions, its intuitive and flexible nature has contributed to the popularity of the field. With pioneering work on the evolution of random graphs, graph theory is often cited as the mathematical foundation of network science. Despite this narrative, the two research communities are still largely disconnected. In this Commentary we discuss the need for further cross-pollination between fields -- bridging the gap between graphs and networks -- and how network science can benefit from such influence. A more mathematical network science may clarify the role of randomness in modeling, hint at underlying laws of behavior, and predict yet unobserved complex networked phenomena in nature.