Table 4.
Multivariate analysis of COVID cases
| Predictors | Estimate | SE | t Stat | p value |
|---|---|---|---|---|
| (Intercept) | 2260.4548 | 795.8747 | 2.8402 | 0.0050 |
| Tests per thousand | 10.2672 | 3.9101 | 2.6258 | 0.0093 |
| Stringency index | 25.7003 | 9.2095 | 2.7906 | 0.0058 |
| Population density | 0.0222 | 0.1920 | 0.1156 | 0.9081 |
| Median age | − 105.1955 | 29.2909 | − 3.5914 | 0.0004 |
| GDP per capita | 0.1074 | 0.0198 | 5.4144 | 1.75e-07 |
| Extreme poverty | − 39.1161 | 18.0948 | − 2.1617 | 0.0318 |
| Hospital beds/thousand | − 186.4294 | 145.8114 | − 1.2786 | 0.2025 |
| BCG Immunization | 1.0827 | 9.1760 | 0.1180 | 0.9062 |
| DTP3 Immunization | 15.1585 | 12.3878 | 1.2237 | 0.2225 |
Coefficient estimates (b) for a generalized linear regression of COVID cases as responses on multiple predictors calculated using MATLAB glmfit function, with standard errors (SE), t statistics, and p values