Forecasting the spread of COVID19 in Hungary
Khanday, O. M.; Dadvandipour, S.; Lone, M. A.
Show abstract
Time series analysis of the COVID19/ SARS-CoV-2 spread in Hungary is presented. Different methods effective for short-term forecasting are applied to the dataset, and predictions are made for the next 20 days. Autoregression and other exponential smoothing methods are applied to the dataset. SIR model is used and predicted 64% of the population could be infected by the virus considering the whole population is susceptible to be infectious Autoregression, and exponential smoothing methods indicated there would be more than a 60% increase in the cases in the coming 20 days. The doubling of the number of total cases is found to around 16 days using an effective reproduction number.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features 97%
- A new, simple method of describing COVID-19 trajectory and dynamics in any country based on Johnson Cumulative Distribution Function fitting 97%
- Distribution of Incubation Period of COVID-19 in the Canadian Context: Modeling and Computational Study 95%
Similar papers in this journal
- Clustering of Countries for COVID-19 Cases based on Disease Prevalence, Health Systems and Environmental Indicators 96%
- Projections and fractional dynamics of COVID-19 with optimal control analysis 95%
- Detection of Static, Dynamic, and No Tactile Friction Based on Non-linear dynamics of EEG Signals: A Preliminary Study 94%
Similar papers in this journal
- Analysis of the early Covid-19 epidemic curve in Germany by regression models with change points 94%
- The basic reproduction number and prediction of the epidemic size of the novel coronavirus (COVID-19) in Shahroud, Iran 92%
- Estimating the Case Fatality Ratio for COVID-19 using a Time-Shifted Distribution Analysis 92%
"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.