Name
Title | ||
|---|---|---|
COVID-19 modelling by time-varying transmission rate associated with mobility trend of driving via Apple Maps |
Abstract | ||
|---|---|---|
•Proposed dynamic transmission rate with a control rate for COVID-19 modelling.•Modified an “exponential decay” model to capture subexponential growth dynamics.•Improved performance in model fitting and prediction based on six EU countries’ data.•Explored how to associate the global mobility trend with control rate in the model.•Positive correlation found within average change of mobility trend and control rate. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.jbi.2021.103905 | Journal of Biomedical Informatics |
Keywords | DocType | Volume |
Infectious disease modelling,COVID-19,Dynamic transmission rate,Mobility trend,Data integration | Journal | 122 |
ISSN | Citations | PageRank |
1532-0464 | 1 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Jing | 1 | 14 | 3.09 |
| Kok Yew Ng | 2 | 1 | 1.02 |
| Brian Mac Namee | 3 | 1 | 0.34 |
| Pardis Biglarbeigi | 4 | 1 | 2.03 |
| Rob Brisk | 5 | 1 | 2.03 |
| Raymond Bond | 6 | 1 | 0.34 |
| Dewar Finlay | 7 | 1 | 0.68 |
| James McLaughlin | 8 | 1 | 0.68 |