It's the very time to learn a pandemic lesson: why have predictive techniques been ineffective when describing long-term events?
Nikitenkova, S.; Kovriguine, D. A.
Show abstract
We have detected a regular component of the monitoring error of officially registered total cases of the spread of the current pandemic. This regular error component explains the reason for the failure of a priori mathematical modelling of probable epidemic events in different countries of the world. Processing statistical data of countries that have reached an epidemic peak has shown that this regular monitoring obeys a simple analytical regularity which allows us to answer the question: is this or that country that has already passed the threshold of the epidemic close to its peak or is still far from it?
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Tracking the Dynamics and Allocating Tests for COVID-19 in Real-Time: an Acceleration Index with an Application to French Age Groups and Départements * 96%
- SARS-CoV-2 infection dynamics in Denmark, February through October 2020: Nature of the past epidemic and how it may develop in the future 95%
- The Acceleration Index as a Test-Controlled Reproduction Number: Application to COVID-19 in France* 95%
Similar papers in this journal
- Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution 94%
- A simple model for how the risk of pandemics from different virus families depends on viral and human traits 93%
- On the design and stability of cancer adaptive therapy cycles: deterministic and stochastic models 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.