Table 1.
The impacts of population mobility on COVID-19 outbreaks in South Carolina.
| Parameters | State level | County level | |||||||||||
|
|
|
Charleston | Greenville | Horry | Spartanburg | Richland | |||||||
| Model training | |||||||||||||
|
|
Time windows (days) | 1-7 | 1-9 | 1-14 | 1-28 | 1-20 | 1-9 | ||||||
|
|
Coefficient of population mobility (95% CI) | 0.818 (0.761-0.876) | 0.486 (0.338-0.634) | 0.278 (0.165-0.390) | 0.395 (0.275-0.515) | 0.270 (0.118-0.422) | 0.157 (0.067-0.246) | ||||||
|
|
Model evaluation (3-day prediction error) | 0.294 | 2.032 | 0.214 | 3.146 | 0.427 | 0.396 | ||||||
| 3-day forecasting | |||||||||||||
|
|
Cumulative difference | 42 | 30 | 28 | 40 | 66 | 81 | ||||||
|
|
Accuracy (%) | 98.7 | 85.1 | 93.3 | 69.0 | 76 | 72.2 | ||||||
| 7-day forecasting | |||||||||||||
|
|
Cumulative difference | 670 | 110 | 147 | 45 | 175 | 144 | ||||||
|
|
Accuracy (%) | 90.9 | 76.7 | 85.2 | 85.9 | 68.3 | 76.8 | ||||||
| 14-day forecasting | |||||||||||||
|
|
Cumulative difference | 2858 | 272 | 541 | 217 | 452 | 329 | ||||||
|
|
Accuracy (%) | 81.6 | 72.1 | 74.5 | 72.6 | 60.3 | 73.6 | ||||||