mdsi_analysis.py

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from typing import Dict, List

def capacity_projection(clients_per_day: int = 18, clinic_days_per_team: int = 48, teams: int = 6) -> int:
    return clients_per_day * clinic_days_per_team * teams

def cost_estimate(n_clients: int, variable_per_client: float = 74.0, fixed_total: float = 75000.0) -> Dict:
    total_variable = variable_per_client * n_clients
    total = total_variable + fixed_total
    return {
        "n_clients": n_clients,
        "variable_per_client": variable_per_client,
        "fixed_total": fixed_total,
        "total_variable": total_variable,
        "total_cost": total,
        "cost_per_client": total / max(1, n_clients)
    }

def prioritize_settlements(records: List[Dict]) -> List[Dict]:
    # records: [{name, population, risk_index, access_burden, repeat_opportunity}]
    if not records:
        return []
    def scale(vals):
        mn, mx = min(vals), max(vals)
        return [0.0 if mx==mn else (v-mn)/(mx-mn) for v in vals]
    pop_s = scale([r.get("population", 0) for r in records])
    risk_s = scale([r.get("risk_index", 0) for r in records])
    acc_s  = scale([r.get("access_burden", 0) for r in records])
    rep_s  = scale([r.get("repeat_opportunity", 0) for r in records])
    out = []
    for i, r in enumerate(records):
        score = 0.35*pop_s[i] + 0.35*risk_s[i] + 0.15*acc_s[i] + 0.15*rep_s[i]
        rr = dict(r); rr["priority_score"] = round(score, 3)
        out.append(rr)
    out.sort(key=lambda x: x["priority_score"], reverse=True)
    return out

def outcomes_summary(delta_a1c=-0.4, delta_sbp=-5, delta_bmi=-0.8, delta_ldl=-12):
    return {
        "median_delta_a1c_pct": delta_a1c,
        "median_delta_systolic_bp_mmHg": delta_sbp,
        "median_delta_bmi_kg_m2": delta_bmi,
        "median_delta_ldl_mg_dl": delta_ldl
    }