COVID-19: Predictive Mathematical Models for the Number of Deaths in South Korea, Italy, Spain, France, UK, Germany, and USA
Fokas, A. S.; Dikaios, N.; Kastis, G. A.
Show abstract
We have recently introduced two novel mathematical models for characterizing the dynamics of the cumulative number of individuals in a given country reported to be infected with COVID-19. Here we show that these models can also be used for determining the time-evolution of the associated number of deaths. In particular, using data up to around the time that the rate of deaths reaches a maximum, these models provide estimates for the time that a plateau will be reached signifying that the epidemic is approaching its end, as well as for the cumulative number of deaths at that time. The plateau is defined to occur when the rate of deaths is 5% of the maximum rate. Results are presented for South Korea, Italy, Spain, France, UK, Germany, and USA. The number of COVID-19 deaths in other counties can be analyzed similarly.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Simple discrete-time self-exciting models can describe complex dynamic processes: a case study of COVID-19 96%
- Modeling the initial phase of COVID-19 epidemic: The role of age and disease severity in the Basque Country, Spain 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 96%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Will an outbreak exceed available resources for control? Estimating the risk from invading pathogens using practical definitions of a severe epidemic 96%
- Modelling preventive measures and their effect on generation times in emerging epidemics 96%
- Evaluating strategies for spatial allocation of vaccines based on risk and centrality 95%
"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.