Difference between revisions of "Covid"
From Nanopedia
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| 4 | | 4 | ||
| Marco Paggi, IMT School, Lucca | | Marco Paggi, IMT School, Lucca | ||
| − | | Simulation of Covid-19 epidemic evolution: are compartmental models really predictive?<br /> | + | | Simulation of Covid-19 epidemic evolution: are compartmental models really predictive?[https://www.youtube.com/watch?v=81ZCQyjvIKo&list=PL_2_Wdyw43YN7MjG33OlWgsLGV1LxhvcF&index=4]<br /> |
|- style="font-size:12px;" | |- style="font-size:12px;" | ||
| 5 | | 5 | ||
| Venkatesha Prasad, Delft University<br /> | | Venkatesha Prasad, Delft University<br /> | ||
| − | | A simple Stochastic SIR model for COVID-19<br /> | + | | A simple Stochastic SIR model for COVID-19.[https://www.youtube.com/watch?v=81ZCQyjvIKo&list=PL_2_Wdyw43YN7MjG33OlWgsLGV1LxhvcF&index=5]<br /> |
|- style="font-size:12px;" | |- style="font-size:12px;" | ||
| 6 | | 6 | ||
| Ali Nasseri, British Columbia University<br /> | | Ali Nasseri, British Columbia University<br /> | ||
| − | | Planning as Inference in Epidemiological Dynamic Models<br /> | + | | Planning as Inference in Epidemiological Dynamic Models.[https://www.youtube.com/watch?v=81ZCQyjvIKo&list=PL_2_Wdyw43YN7MjG33OlWgsLGV1LxhvcF&index=6]<br /> |
|- | |- | ||
| 7 | | 7 | ||
| Line 37: | Line 37: | ||
| 8 | | 8 | ||
| V. K. Jindal, Penjab University | | V. K. Jindal, Penjab University | ||
| − | | COVID-19 – a realistic model for saturation, growth and decay of the India specific disease<br /> | + | | COVID-19 – a realistic model for saturation, growth and decay of the India specific disease.[https://www.youtube.com/watch?v=81ZCQyjvIKo&list=PL_2_Wdyw43YN7MjG33OlWgsLGV1LxhvcF&index=7]<br /> |
|- style="font-size:12px;" | |- style="font-size:12px;" | ||
| 9 | | 9 | ||
| Sebastian Gonçalves, Physics Institute<br /> | | Sebastian Gonçalves, Physics Institute<br /> | ||
| − | | Trends and Urban scaling in the COVID-19 pandemic<br /> | + | | Trends and Urban scaling in the COVID-19 pandemic.[https://www.youtube.com/watch?v=81ZCQyjvIKo&list=PL_2_Wdyw43YN7MjG33OlWgsLGV1LxhvcF&index=8]<br /> |
|- style="font-size:12px;" | |- style="font-size:12px;" | ||
| 10 | | 10 | ||
| Josimar Chire, ICMC Brasil<br /> | | Josimar Chire, ICMC Brasil<br /> | ||
| − | | Social Sensors to Monitor COVID-19 South American Countries<br /> | + | | Social Sensors to Monitor COVID-19 South American Countries.[https://www.youtube.com/watch?v=81ZCQyjvIKo&list=PL_2_Wdyw43YN7MjG33OlWgsLGV1LxhvcF&index=9]<br /> |
|- | |- | ||
| colspan="3" style="text-align:center; font-size:18px; font-family:'Arial Black', Gadget, sans-serif !important;; background-color:#dae8fc;" | COVID-19 Forecast and Prediction - May 15th -16th, 2020<br /> | | colspan="3" style="text-align:center; font-size:18px; font-family:'Arial Black', Gadget, sans-serif !important;; background-color:#dae8fc;" | COVID-19 Forecast and Prediction - May 15th -16th, 2020<br /> | ||
Revision as of 08:08, 27 May 2020
| COVID-19 by the Numbers, Models, Big Data, and Reality - April 24th - 25th, 2020 | ||
|---|---|---|
| # | Lecturer Name | Lecture Title |
| 1 | Victoria Lopez, Madrit University |
A COVID-19 mathematical model based on Flow Networks and SIR.[1] |
| 2 | Axel Branderburg, KTH Stockholm | Piecewise quadratic growth during the 2019 novel coronavirus epidemic.[2] |
| 3 | Alessio Muscillo, University of Sienna |
Disease spreading in social networks and unintended consequences of weak social distancing.[3] |
| 4 | Marco Paggi, IMT School, Lucca | Simulation of Covid-19 epidemic evolution: are compartmental models really predictive?[4] |
| 5 | Venkatesha Prasad, Delft University |
A simple Stochastic SIR model for COVID-19.[5] |
| 6 | Ali Nasseri, British Columbia University |
Planning as Inference in Epidemiological Dynamic Models.[6] |
| 7 | Anand Sahasranaman, Imperial College London |
Data and models of COVID-19 in India |
| 8 | V. K. Jindal, Penjab University | COVID-19 – a realistic model for saturation, growth and decay of the India specific disease.[7] |
| 9 | Sebastian Gonçalves, Physics Institute |
Trends and Urban scaling in the COVID-19 pandemic.[8] |
| 10 | Josimar Chire, ICMC Brasil |
Social Sensors to Monitor COVID-19 South American Countries.[9] |
| COVID-19 Forecast and Prediction - May 15th -16th, 2020 | ||
| # | Lecturer Name |
Lecture Title |
| 1 | David S. Jones, Harvard University | History in a Crisis—Lessons for Covid-19 |
| 2 | Christofer Brandt, Universität Greifswald |
Transparent comparison and prediction of corona numbers |
| 3 | Gaetano Perone, University of Bergamo | An Arima Model to Forecast the Spread and the final size of COVID-2019 Epidemic in Italy |
| 4 | Keno Krewer, Max Planck Institute |
Time-resolving an ongoing outbreak with Fourier analysis |
| 5 | Gerry Killeen, University College Cork |
Pushing past the tipping points in containment trajectories of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) epidemics: A simple arithmetic rationale for crushing the curve instead of merely flattening it. |
| 6 | Michael Li, University of Alberta | Why it is difficulty to make accurate predictions of COVID-19 epidemics? |
| 7 | V.K. Jindal, Panjab University |
COVID-19 Primary and secondary infection as order parameter – a unifying global model. |
| 8 | Ashis Das, World Bank |
Rapid development of an open-access artificial intelligence decision support tool for CoVID-19 mortality prediction |
| 9 | Fulgensia Mbabazi, Busitema University |
A Mathematical Model Approach for Prevention and Intervention Measures of the COVID-19 Pandemic in Uganda |
