Coronavirus epidemic: prediction and controlling measures
Pejman, M. M.; fereidooni, a.
Show abstract
The COVID-19 outbreak has caused over 1.7 million (still increasing) confirmed cases globally as of April 10th, 2020. The levels of spread and severity of the virus lead to a wide-spread political and economic turmoil. We believe that two critical contributing factors need to be taken into account by the authorities to make effective decisions for controlling the spread of the virus: (i) being familiar with the most effective controlling measures and (ii) having a mathematical model to predict the spread of the virus. In this study, we provided information regarding both of these crucial factors. First, we investigated the importance of different measures such as quarantine, isolation, face mask, social distancing, etc. in controlling the virus in various countries. We then present a mathematical model to predict the spread of the virus in different countries. Our prediction shows an excellent match with the actual data up to now.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Distribution of Incubation Period of COVID-19 in the Canadian Context: Modeling and Computational Study 95%
- A Recursive Bifurcation Model for Predicting the Peak of COVID-19 Virus Spread in United States and Germany 95%
- Model Based Estimation of the SARS-CoV-2 Immunization Level in Austria and Consequences for Herd Immunity Effects 95%
Similar papers in this journal
- An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City 94%
- Empiric model for short-time prediction of COVID-19spreading 93%
- An expert judgment model to predict early stages of the COVID-19 outbreak in the United States 93%
Similar papers in this journal
"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.