Table 1.
Linear regression models to predict rates of cases and deaths on the 60th and the 80th day since the first confirmed case, based on demographic, health and economic data, and on PM2.5 concentrations and exposure (percentages of population exposed to PM2.5 levels exceeding WHO guideline value) in 2017*.
| Predicted value | Model p-value | Model RMSE (Root mean square error) | %old | density | GDP | Physician** | Hospital Bed** | PM2.5 concentration 2017 |
PM2.5 exposure 2017 |
|---|---|---|---|---|---|---|---|---|---|
| Rate of cases on day 60 | 0.025 | 0.41 | 0.02 (0.52) | 0.0001 (0.17) | 0.001 (0.06) | 1.69 (0.07) | −0.91 (0.02) | 0.02 (0.17) | 0.0001 (0.96) |
| Rate of deaths on day 60 | 0.037 | 0.39 | 0.05 (0.1) | 0.00005 (0.47) | 0.001 (0.02) | 0.81 (0.39) | −1.0 (0.009) | 0.03 (0.1) | 0.002 (0.53) |
| Rate of cases on day 80 | 0.009 | 0.46 | 0.03 (0.34) | 0.0001 (0.126) | 0.001 (0.02) | 0.23 (0.79) | −1.0 (0.006) | 0.02 (0.17) | −0.002 (0.32) |
| Rate of deaths on day 80 | 0.007 | 0.47 | 0.03 (0.13) | 0.0001 (0.06) | 0.001 (0.04) | 0.06 (0.95) | −1.3 (0.0005) | 0.009 (0.58) | 0.001 (0.65) |
*Regression coefficients (p-values) ** per 10,000 population.