5th Webinar - Forecast and Prediction
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Dear Colleagues welcome to COVID-19-Response-Webinar website and the upcoming online colloquium titled:
Current Debate 5th
September 18th-19th, 2020
5th Debate COVID-19 Forecast and Prediction
September 18th-19th, 2020
Proceedings of the Past Debates
| 5th Debate COVID-19, Forecast and Prediction, September 18th - 19th, 2020 | ||
|---|---|---|
| # | Lecturer Name | Lecture Title |
| 1 | Giovanni Gallo, INAPP, Italy | Assessing policies related to Covid-19 in hardly reliable data.[1] |
| 2 | Michelangelo Puliga, LinkaLab Italy | Covid-19 early warning signals in social media?[2] |
| 3 | Yurii Dimaschko, Fachhochschule Lübeck | Superspreading as a Regular Factor of the COVID-19 Pandemic: II. Quarantine Measures and the Second Wave.[3] |
| 4 | Bosiljka Tadic, Jozef Stefan Institute | Agent-based modeling of latent infection transmissions.[4] |
| 5 | Roberto Zavatta, Economisti Associati | Territorial patterns in COVID-19 mortality |
| 6 | Kai Nagel, Technische Universität Berlin | Using mobile phone data for epidemiological simulations of lockdowns: government interventions, behavioral changes,
and resulting changes of reinfections.[5] |
| 7 | Jordi Faraudo, Spanish National Research Council | Molecular Dynamics Simulations Of The Interaction Between Sars-Cov-2 And Different Materials. |
| 8 | Giuseppe De Natale, Istituto Nazionale di Geofisica | The evolution of Covid-19 in Italy through statistical analysis: from lethality estimates to seasonal effects. |
| 9 | Elisa Alòs, Universitat Pompeu Fabra, Barcelona | A fractional model for the COVID-19 pandemic: Application to Italian data.[6] |
| 10 | Stanislav Harizanov, Bulgarian Academy of Sciences | Mathematical Modeling of COVID-19 transmission dynamics in Bulgaria by time-dependent inverse SEIR model. |
| 11 | Joeri Schasfoort, University of Cape Town | SABCoM: A Spatial Agent-Based Covid-19 Model.[7] |
| 12 | Giovani L. Vasconcelos, University of Parana | Modelling the primary and secondary waves of COVID-19 with mathematical growth models.[8] |
| 4th Debate COVID-19, Forecast and Prediction, July 24th - 25th, 2020 - 4th Debate COVID-19 Webinar | ||
|---|---|---|
| # | Lecturer Name | Lecture Title |
| 1 | Jesús Barreal Pernas, Madrid University | Hospital impact analysis of initial phase epidemics by means of Beta regression with Spatio-temporal effects. [9] |
| 2 | Björn Johansson, Karolinska Institutet | The effect of masking the general population on COVID-19. [10] |
| 3 | David H. Roberts, Brandeis University | New Models of Epidemics and Their Applications to the COVID Pandemic. [11] |
| 4 | Didier Sornette, ETH Zurich | Analysing, modelling and predicting the COVID-19 epidemics. [12] |
| 5 | Henrik Hult, KTH | Estimates of the proportion of SARS-CoV-2 infected Individuals in Sweden |
| 6 | Juri Dimaschko, Fachhochschule Lubeck | Superspreading as a Regular Factor of the COVID-19 Pandemic |
| 7 | Giovani L. Vasconcelos, U of Parana | Complexity and power laws in the fatality curves of COVID-19. [13] |
| 8 | Beatrize Soane, Sorbonne Université | A Scaling Approach to Estimate the COVID-19 Rate of Infections. |
| 9 | Yuri Nestorov, CORE Belgium | Online analysis of epidemics with variable infection rate. [14] |
| 1st COVID-19 by the Numbers, Models, Big Data, and Reality - April 24th - 25th, 2020 | ||
|---|---|---|
| # | Lecturer Name | Lecture Title |
| 1 | Victoria Lopez, Madrid University |
A COVID-19 mathematical model based on Flow Networks and SIR.[15] |
| 2 | Axel Branderburg, KTH Stockholm | Piecewise quadratic growth during the 2019 novel coronavirus epidemic.[16] |
| 3 | Alessio Muscillo, University of Sienna |
Disease spreading in social networks and unintended consequences of weak social distancing.[17] |
| 4 | Marco Paggi, IMT School, Lucca | Simulation of Covid-19 epidemic evolution: are compartmental models really predictive?[18] |
| 5 | Venkatesha Prasad, Delft University |
A simple Stochastic SIR model for COVID-19.[19] |
| 6 | Ali Nasseri, British Columbia University |
Planning as Inference in Epidemiological Dynamic Models.[20] |
| 7 | Anand Sahasranaman, Imperial College London |
Data and models of COVID-19 in India.[21] |
| 8 | V. K. Jindal, Penjab University | COVID-19 – a realistic model for saturation, growth and decay of the India specific disease.[22] |
| 9 | Sebastian Gonçalves, Physics Institute |
Trends and Urban scaling in the COVID-19 pandemic.[23] |
| 10 | Josimar Chire, ICMC Brasil |
Social Sensors to Monitor COVID-19 South American Countries.[24] |
| 2nd 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[25] |
| 2 | Christofer Brandt, Universität Greifswald |
Transparent comparison and prediction of corona numbers[26] |
| 3 | Gaetano Perone, University of Bergamo | An Arima Model to Forecast the Spread and the final size of COVID-2019 Epidemic in Italy[27] |
| 4 | Keno Krewer, Max Planck Institute |
Time-resolving an ongoing outbreak with Fourier analysis[28] |
| 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.[29] |
| 6 | Michael Li, University of Alberta | Why it is difficulty to make accurate predictions of COVID-19 epidemics?[30] |
| 7 | V.K. Jindal, Panjab University |
COVID-19 Primary and secondary infection as order parameter – a unifying global model.[31] |
| 8 | Ashis Das, World Bank |
Rapid development of an open-access artificial intelligence decision support tool for CoVID-19 mortality prediction.[32] |
| 9 | Fulgensia Mbabazi, Busitema University |
A Mathematical Model Approach for Prevention and Intervention Measures of the COVID-19 Pandemic in Uganda.[33] |
| 3rd Debate COVID-19, Forecast and Prediction, June 26th - 27th, 2020 | ||
| # | Lecturer Name | Lecture Title |
| 1 | Francesco Piazza, CNRS-Orleans | COVID-19: The unreasonable effectiveness of simple models |
| 2 | Martijn J. Hoogeveen, Open Universiteit | Pollen Explains Flu-like & Covid-19 Seasonality: developing a predictive model |
| 3 | Henrik Hult, KTH | Estimates of the proportion of SARS-CoV-2 infected individuals in Sweden |
| 4 | Reyer Gerlagh, Tilburg University | Closed-Form Solutions for Optimal Social Distancing in a SIR Model of COVID-19 Suppression |
| 5 | Maziar Nekovee, Sussex University | Understanding the spreading patterns of COVID-19 in UK and its impact on exit strategies. |
| 6 | Benjamin Ambrosio, Universite du Havre | On a coupled time-dependent SIR models fitting with New York and New-Jersey states COVID-19 data |
| 7 | Konstantinos Gkiotsalitis, U of Twente | Optimal frequency setting of metro services in the age of COVID-19 distancing measures |
| 8 | Beatrize Soane, Sorbonne Université | A Scaling Approach to Estimate the COVID-19 Rate of Infections. |
| 9 | Benedetta Cerruti, Independent | Did lockdowns serve their purpose? |
| 10 | Oliver Johnson, Bristol University | Using non-standard measures of population density to predict the spread of COVID-19 |
| 11 | Subir Das, JNCASR | Spread of COVID-19: How robust are the universal features? |
| 12 | Andrew Hart, University of Chile | An agent-based model for COVID-19, lockdown in Santiago and the reproduction Matrix |
