Example 1: Estimating the PAF for the proportion of dementia attributable to smoking | Example 2: Combining subpopulations into a total | Example 3: Estimating the PIF for the proportion of dementia reduced by a 15% reduction in smoking. | Example 4: A categorical population attributable fraction | Example 5: Combining fractions of different risks into an ensemble | Example 6: Adjusted fractions | Example 7: Ensuring non-negative fractions | Example 8: Computing averted and attributable cases | Example 9: Computing averted cases (strictly positive) | Example 10: Using Hazard Ratios (HR) and Odds Ratios (OR) instead of Relative Risks (RR) | References

Core Concepts: PAF and PIF | Key assumption: Independent (summary) data sources. | Usage | Population Attributable Fraction (PAF) | Incorporating Uncertainty | Potential Impact Fraction (PIF) | Attributable and averted cases | Combining fractions from subpopulations | Combining fractions from multiple risks | Adjusting fractions for commonality | Additional information | References

Introduction | Comparing the Performance with and without DoW Effect | DoW Coefficients Estimates | Employing DoW Estimates as Priors for Future Nowcasts | Conclusion

Introduction | Nowcasting Scenarios | Comparing the Nowcast Estimates of the Poisson and negative binomial Models | Extracting Quantile Estimates | Calculating and Plotting the Weighted Interval Score | Conclusion

Introduction | Data Preparation | Nowcasting Scenarios | Delay Distribution Estimates | Incidence Estimates | Conclusion