| from __future__ import annotations |
|
|
| from dataclasses import dataclass |
| from typing import Dict, Iterable, List, Optional, Tuple |
|
|
| import numpy as np |
| import pandas as pd |
|
|
| from .sexual_debut import SexualDebutModel |
|
|
|
|
| @dataclass(slots=True) |
| class Campaign: |
| age_from: int |
| age_to: int |
| coverage: float |
|
|
|
|
| def age_coverage( |
| ages: Iterable[int], |
| routine_coverage: float, |
| vaccine_efficacy_before: float, |
| vaccine_efficacy_after: float, |
| campaigns: Optional[List[Campaign]], |
| sexual_debut_model: SexualDebutModel, |
| age_at_vaccination: int, |
| protection_duration_years: Optional[int] = None, |
| ) -> pd.DataFrame: |
| """ |
| Calculate age-specific vaccination coverage. |
| |
| Args: |
| ages: Iterable of ages to calculate coverage for |
| routine_coverage: Routine vaccination coverage rate |
| vaccine_efficacy_before: Efficacy for those not yet sexually active |
| vaccine_efficacy_after: Efficacy for those already sexually active |
| campaigns: Optional list of catch-up campaigns |
| sexual_debut_model: Model for sexual debut probabilities |
| age_at_vaccination: Target age for routine vaccination |
| protection_duration_years: Years of protection (None = lifelong) |
| |
| Returns: |
| DataFrame with 'age' and 'coverage' columns |
| """ |
| ages_arr = np.asarray(list(ages), dtype=int) |
| cov = np.zeros_like(ages_arr, dtype=float) |
|
|
| |
| idx = np.where(ages_arr == age_at_vaccination)[0] |
| if idx.size: |
| p_debut = sexual_debut_model.cdf(age_at_vaccination) |
| base_cov = ( |
| routine_coverage * vaccine_efficacy_before * (1.0 - p_debut) |
| + routine_coverage * vaccine_efficacy_after * p_debut |
| ) |
| cov[idx[0]] = base_cov |
| |
| |
| for i, age in enumerate(ages_arr): |
| if age > age_at_vaccination: |
| years_since_vac = age - age_at_vaccination |
| |
| |
| if protection_duration_years is None: |
| waning_multiplier = 1.0 |
| else: |
| waning_multiplier = calculate_waning_multiplier( |
| years_since_vac, protection_duration_years |
| ) |
| |
| |
| |
| cov[i] = base_cov * waning_multiplier |
|
|
| |
| if campaigns: |
| for camp in campaigns: |
| mask = (ages_arr >= camp.age_from) & (ages_arr <= camp.age_to) |
| if not np.any(mask): |
| continue |
| |
| mid_age = float((camp.age_from + camp.age_to) / 2.0) |
| p_debut = sexual_debut_model.cdf(mid_age) |
| base_camp_cov = ( |
| camp.coverage * vaccine_efficacy_before * (1.0 - p_debut) |
| + camp.coverage * vaccine_efficacy_after * p_debut |
| ) |
| |
| |
| if protection_duration_years is not None: |
| for i, age in enumerate(ages_arr): |
| if mask[i]: |
| years_since_vac = max(0, age - mid_age) |
| waning_multiplier = calculate_waning_multiplier( |
| years_since_vac, protection_duration_years |
| ) |
| cov[i] += base_camp_cov * waning_multiplier |
| else: |
| cov[mask] += base_camp_cov |
|
|
| cov = np.clip(cov, 0.0, 1.0) |
| return pd.DataFrame({"age": ages_arr, "coverage": cov}) |
|
|
|
|
| def calculate_waning_multiplier( |
| years_since_vaccination: float, protection_duration_years: int |
| ) -> float: |
| """ |
| Calculate the waning multiplier based on years since vaccination. |
| |
| Uses a step function model: |
| - Full protection (multiplier = 1.0) for years < protection_duration_years |
| - No protection (multiplier = 0.0) for years >= protection_duration_years |
| |
| Args: |
| years_since_vaccination: Years elapsed since vaccination |
| protection_duration_years: Duration of vaccine protection in years |
| |
| Returns: |
| Waning multiplier (0.0 to 1.0) |
| """ |
| if years_since_vaccination < protection_duration_years: |
| return 1.0 |
| else: |
| return 0.0 |
|
|
|
|
|
|
|
|
