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ASCEND / model.mjs

Launch ASCEND with linked interactive 3D globe

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/**
 * ASCEND illustrative service-flow model, not a fitted clinical or causal model.
 * Units: expected operations and unresolved cases; months; incremental BRL.
 * Fractional counts are deliberate expected flows, not individual patient records.
 *
 * Each month: available = opening unresolved cases + new arrivals;
 * completed = min(available, referral throughput, staffed capacity, funded slots).
 * Referral throughput = arrivals * min(1, baseline referral + intervention gain).
 * Therefore completions cannot exceed arrivals: this model can slow backlog growth
 * but cannot clear an existing backlog or estimate waiting times. It has no exits,
 * mortality, contraindications, repeat operations, or empirical effect estimates.
 *
 * An additional slot costs unitCost, bundling staff, theatre service, and funding.
 * Theatre/combined purchase capacity and fund it; referral only funds use of
 * existing capacity. All intervention budgets are committed expenditure, even
 * when other constraints make some purchased slots unused. Programme fixed costs
 * are paid in full. Current-service costs are excluded from incremental spending.
 */

export const DEFAULTS = Object.freeze({
  budget: 2400000,
  capacity: 90,
  referralGain: 0.15,
  arrivals: 150,
  initialQueue: 240,
  baseReferral: 0.6,
  unitCost: 12000,
  referralFixedCost: 600000,
  baselineFundedSlots: 100,
  months: 12,
});

export const STRATEGIES = Object.freeze([
  Object.freeze({ id: 'current', name: 'Current service', color: '#8493a6' }),
  Object.freeze({ id: 'theatre', name: 'Expand surgical capacity', color: '#61d8bf' }),
  Object.freeze({ id: 'referral', name: 'Strengthen referral', color: '#a99bff' }),
  Object.freeze({ id: 'combined', name: 'Combine both', color: '#f4c56a' }),
]);

const BOUNDS = Object.freeze({
  budget: [0, 6000000], capacity: [40, 180], referralGain: [0, 0.4],
  arrivals: [0, 10000], initialQueue: [0, 1000000], baseReferral: [0, 1],
  unitCost: [1, 10000000], referralFixedCost: [0, 6000000],
  baselineFundedSlots: [0, 10000], months: [1, 120],
});
const TOLERANCE = 1e-7;

/** Only actual finite numbers are accepted; missing fields use defaults. */
export function normalizeState(input = {}) {
  if (input === null || typeof input !== 'object' || Array.isArray(input)) {
    throw new TypeError('Model state must be an object of finite numbers.');
  }
  const state = {};
  for (const [key, fallback] of Object.entries(DEFAULTS)) {
    const value = Object.hasOwn(input, key) ? input[key] : fallback;
    if (typeof value !== 'number' || !Number.isFinite(value)) {
      throw new TypeError(`${key} must be an actual finite number.`);
    }
    const [lower, upper] = BOUNDS[key];
    state[key] = Math.max(lower, Math.min(upper, value));
  }
  state.months = Math.floor(state.months);
  return state;
}

function simulate(state, { additionalSlots = 0, referralGain = 0, expand = false } = {}) {
  const capacity = state.capacity + (expand ? additionalSlots : 0);
  const fundedSlots = state.baselineFundedSlots + additionalSlots;
  const referralThroughput = state.arrivals * Math.min(1, state.baseReferral + referralGain);
  const monthly = [];
  let queue = state.initialQueue;
  let total = 0;
  for (let index = 0; index < state.months; index += 1) {
    const openingQueue = queue;
    const available = openingQueue + state.arrivals;
    const completed = Math.min(available, referralThroughput, capacity, fundedSlots);
    queue = Math.max(0, available - completed);
    total += completed;
    monthly.push({
      month: index + 1, openingQueue, arrivals: state.arrivals, available,
      referralThroughput: Math.min(available, referralThroughput), capacity,
      fundedSlots, completed, closingQueue: queue, queue,
    });
  }
  const final = monthly.at(-1);
  const limits = [
    ['Demand', final.available], ['Referral flow', final.referralThroughput],
    ['Staffed capacity', final.capacity], ['Operating funds', final.fundedSlots],
  ];
  const bindingConstraints = limits.filter(([, limit]) => Math.abs(limit - final.completed) <= TOLERANCE).map(([label]) => label);
  const bottleneck = bindingConstraints.includes('Demand') ? 'Demand' : bindingConstraints.join(' + ');
  return { completed: total, queue, monthly, bottleneck, bindingConstraints };
}

/** Returns current, theatre, referral, combined; infeasible options are explicit. */
export function evaluate(input = {}) {
  const state = normalizeState(input);
  const baseline = simulate(state);
  return STRATEGIES.map(strategy => {
    const isCurrent = strategy.id === 'current';
    const usesReferral = strategy.id === 'referral' || strategy.id === 'combined';
    const expand = strategy.id === 'theatre' || strategy.id === 'combined';
    const feasible = isCurrent || (state.budget > 0 && (!usesReferral || state.budget >= state.referralFixedCost));
    const reason = feasible ? '' : state.budget <= 0
      ? 'No incremental budget is available.'
      : 'Budget is below the fixed referral programme cost.';
    const programmeCost = !isCurrent && feasible && usesReferral ? state.referralFixedCost : 0;
    const cost = !isCurrent && feasible ? state.budget : 0;
    const slotExpenditure = Math.max(0, cost - programmeCost);
    const additionalSlots = slotExpenditure / state.unitCost / state.months;
    const result = isCurrent || !feasible ? baseline : simulate(state, {
      additionalSlots,
      referralGain: usesReferral ? state.referralGain : 0,
      expand,
    });
    return {
      ...strategy, ...result, feasible, reason,
      incremental: Math.max(0, result.completed - baseline.completed),
      cost, programmeCost, slotExpenditure, additionalSlots,
    };
  });
}

/**
 * Maximizes extra completed operations, then minimizes committed cost.
 * Exact outcome/cost ties retain input order; tiedIds makes that convention visible.
 * This is a scenario ranking, not a clinical recommendation or cost-effectiveness test.
 */
export function recommendation(results) {
  if (!Array.isArray(results)) throw new TypeError('Results must be an array.');
  const feasible = results.filter(result => result.feasible !== false);
  if (!feasible.length) throw new RangeError('At least one feasible result is required.');
  for (const result of feasible) {
    if (!Number.isFinite(result.incremental) || !Number.isFinite(result.cost)) {
      throw new TypeError('Feasible results must have finite incremental outcomes and cost.');
    }
  }
  const bestEffect = Math.max(...feasible.map(result => result.incremental));
  const effectTies = feasible.filter(result => bestEffect - result.incremental <= TOLERANCE);
  const lowestCost = Math.min(...effectTies.map(result => result.cost));
  const ties = effectTies.filter(result => result.cost - lowestCost <= TOLERANCE);
  return { ...ties[0], tiedIds: ties.map(result => result.id), effectTiedIds: effectTies.map(result => result.id) };
}

/** Row-major scenario grid. y=0 is no assumed referral improvement. */
export function landscape(input = {}, cols = 35, rows = 25) {
  if (![cols, rows].every(value => Number.isInteger(value) && value >= 2 && value <= 200)) {
    throw new RangeError('Landscape dimensions must be integers between 2 and 200.');
  }
  const state = normalizeState(input);
  // Referral throughput never exceeds arrivals, so available demand cannot bind
  // below it, even with an empty opening queue. All remaining limits are fixed:
  // monthly completions are constant, and no monthly history is needed here.
  // Keep evaluate()'s detailed simulation for monthly inspection and conservation.
  const options = STRATEGIES.map(strategy => {
    const isCurrent = strategy.id === 'current';
    const usesReferral = strategy.id === 'referral' || strategy.id === 'combined';
    const feasible = isCurrent || (state.budget > 0 && (!usesReferral || state.budget >= state.referralFixedCost));
    const cost = !isCurrent && feasible ? state.budget : 0;
    const programmeCost = !isCurrent && feasible && usesReferral ? state.referralFixedCost : 0;
    const additionalSlots = (cost - programmeCost) / state.unitCost / state.months;
    return {
      ...strategy, feasible, cost, additionalSlots,
      usesReferral: feasible && usesReferral,
      expand: feasible && (strategy.id === 'theatre' || strategy.id === 'combined'),
    };
  });
  const baseThroughput = state.arrivals * state.baseReferral;
  const cells = [];
  for (let row = 0; row < rows; row += 1) {
    const y = 0.4 * row / (rows - 1);
    const improvedThroughput = state.arrivals * Math.min(1, state.baseReferral + y);
    for (let col = 0; col < cols; col += 1) {
      const x = 40 + 140 * col / (cols - 1);
      const baselineCompleted = Math.min(baseThroughput, x, state.baselineFundedSlots) * state.months;
      const results = options.map(option => {
        const monthlyCompleted = Math.min(
          option.usesReferral ? improvedThroughput : baseThroughput,
          x + (option.expand ? option.additionalSlots : 0),
          state.baselineFundedSlots + option.additionalSlots,
        );
        const completed = monthlyCompleted * state.months;
        return {
          ...option, completed,
          incremental: Math.max(0, completed - baselineCompleted),
          queue: Math.max(0, state.initialQueue + state.months * (state.arrivals - monthlyCompleted)),
        };
      });
      const winner = recommendation(results);
      cells.push({
        index: cells.length, col, row, xIndex: col, yIndex: row, x, y,
        winnerId: winner.id, winnerName: winner.name, color: winner.color,
        incremental: winner.incremental, completed: winner.completed,
        cost: winner.cost, queue: winner.queue, tiedIds: winner.tiedIds,
        effectTiedIds: winner.effectTiedIds,
      });
    }
  }
  return cells;
}

	/**
	* ASCEND illustrative service-flow model, not a fitted clinical or causal model.
	* Units: expected operations and unresolved cases; months; incremental BRL.
	* Fractional counts are deliberate expected flows, not individual patient records.
	*
	* Each month: available = opening unresolved cases + new arrivals;
	* completed = min(available, referral throughput, staffed capacity, funded slots).
	* Referral throughput = arrivals * min(1, baseline referral + intervention gain).
	* Therefore completions cannot exceed arrivals: this model can slow backlog growth
	* but cannot clear an existing backlog or estimate waiting times. It has no exits,
	* mortality, contraindications, repeat operations, or empirical effect estimates.
	*
	* An additional slot costs unitCost, bundling staff, theatre service, and funding.
	* Theatre/combined purchase capacity and fund it; referral only funds use of
	* existing capacity. All intervention budgets are committed expenditure, even
	* when other constraints make some purchased slots unused. Programme fixed costs
	* are paid in full. Current-service costs are excluded from incremental spending.
	*/

	export const DEFAULTS = Object.freeze({
	budget: 2400000,
	capacity: 90,
	referralGain: 0.15,
	arrivals: 150,
	initialQueue: 240,
	baseReferral: 0.6,
	unitCost: 12000,
	referralFixedCost: 600000,
	baselineFundedSlots: 100,
	months: 12,
	});

	export const STRATEGIES = Object.freeze([
	Object.freeze({ id: 'current', name: 'Current service', color: '#8493a6' }),
	Object.freeze({ id: 'theatre', name: 'Expand surgical capacity', color: '#61d8bf' }),
	Object.freeze({ id: 'referral', name: 'Strengthen referral', color: '#a99bff' }),
	Object.freeze({ id: 'combined', name: 'Combine both', color: '#f4c56a' }),
	]);

	const BOUNDS = Object.freeze({
	budget: [0, 6000000], capacity: [40, 180], referralGain: [0, 0.4],
	arrivals: [0, 10000], initialQueue: [0, 1000000], baseReferral: [0, 1],
	unitCost: [1, 10000000], referralFixedCost: [0, 6000000],
	baselineFundedSlots: [0, 10000], months: [1, 120],
	});
	const TOLERANCE = 1e-7;

	/** Only actual finite numbers are accepted; missing fields use defaults. */
	export function normalizeState(input = {}) {
	if (input === null \|\| typeof input !== 'object' \|\| Array.isArray(input)) {
	throw new TypeError('Model state must be an object of finite numbers.');
	}
	const state = {};
	for (const [key, fallback] of Object.entries(DEFAULTS)) {
	const value = Object.hasOwn(input, key) ? input[key] : fallback;
	if (typeof value !== 'number' \|\| !Number.isFinite(value)) {
	throw new TypeError(`${key} must be an actual finite number.`);
	}
	const [lower, upper] = BOUNDS[key];
	state[key] = Math.max(lower, Math.min(upper, value));
	}
	state.months = Math.floor(state.months);
	return state;
	}

	function simulate(state, { additionalSlots = 0, referralGain = 0, expand = false } = {}) {
	const capacity = state.capacity + (expand ? additionalSlots : 0);
	const fundedSlots = state.baselineFundedSlots + additionalSlots;
	const referralThroughput = state.arrivals * Math.min(1, state.baseReferral + referralGain);
	const monthly = [];
	let queue = state.initialQueue;
	let total = 0;
	for (let index = 0; index < state.months; index += 1) {
	const openingQueue = queue;
	const available = openingQueue + state.arrivals;
	const completed = Math.min(available, referralThroughput, capacity, fundedSlots);
	queue = Math.max(0, available - completed);
	total += completed;
	monthly.push({
	month: index + 1, openingQueue, arrivals: state.arrivals, available,
	referralThroughput: Math.min(available, referralThroughput), capacity,
	fundedSlots, completed, closingQueue: queue, queue,
	});
	}
	const final = monthly.at(-1);
	const limits = [
	['Demand', final.available], ['Referral flow', final.referralThroughput],
	['Staffed capacity', final.capacity], ['Operating funds', final.fundedSlots],
	];
	const bindingConstraints = limits.filter(([, limit]) => Math.abs(limit - final.completed) <= TOLERANCE).map(([label]) => label);
	const bottleneck = bindingConstraints.includes('Demand') ? 'Demand' : bindingConstraints.join(' + ');
	return { completed: total, queue, monthly, bottleneck, bindingConstraints };
	}

	/** Returns current, theatre, referral, combined; infeasible options are explicit. */
	export function evaluate(input = {}) {
	const state = normalizeState(input);
	const baseline = simulate(state);
	return STRATEGIES.map(strategy => {
	const isCurrent = strategy.id === 'current';
	const usesReferral = strategy.id === 'referral' \|\| strategy.id === 'combined';
	const expand = strategy.id === 'theatre' \|\| strategy.id === 'combined';
	const feasible = isCurrent \|\| (state.budget > 0 && (!usesReferral \|\| state.budget >= state.referralFixedCost));
	const reason = feasible ? '' : state.budget <= 0
	? 'No incremental budget is available.'
	: 'Budget is below the fixed referral programme cost.';
	const programmeCost = !isCurrent && feasible && usesReferral ? state.referralFixedCost : 0;
	const cost = !isCurrent && feasible ? state.budget : 0;
	const slotExpenditure = Math.max(0, cost - programmeCost);
	const additionalSlots = slotExpenditure / state.unitCost / state.months;
	const result = isCurrent \|\| !feasible ? baseline : simulate(state, {
	additionalSlots,
	referralGain: usesReferral ? state.referralGain : 0,
	expand,
	});
	return {
	...strategy, ...result, feasible, reason,
	incremental: Math.max(0, result.completed - baseline.completed),
	cost, programmeCost, slotExpenditure, additionalSlots,
	};
	});
	}

	/**
	* Maximizes extra completed operations, then minimizes committed cost.
	* Exact outcome/cost ties retain input order; tiedIds makes that convention visible.
	* This is a scenario ranking, not a clinical recommendation or cost-effectiveness test.
	*/
	export function recommendation(results) {
	if (!Array.isArray(results)) throw new TypeError('Results must be an array.');
	const feasible = results.filter(result => result.feasible !== false);
	if (!feasible.length) throw new RangeError('At least one feasible result is required.');
	for (const result of feasible) {
	if (!Number.isFinite(result.incremental) \|\| !Number.isFinite(result.cost)) {
	throw new TypeError('Feasible results must have finite incremental outcomes and cost.');
	}
	}
	const bestEffect = Math.max(...feasible.map(result => result.incremental));
	const effectTies = feasible.filter(result => bestEffect - result.incremental <= TOLERANCE);
	const lowestCost = Math.min(...effectTies.map(result => result.cost));
	const ties = effectTies.filter(result => result.cost - lowestCost <= TOLERANCE);
	return { ...ties[0], tiedIds: ties.map(result => result.id), effectTiedIds: effectTies.map(result => result.id) };
	}

	/** Row-major scenario grid. y=0 is no assumed referral improvement. */
	export function landscape(input = {}, cols = 35, rows = 25) {
	if (![cols, rows].every(value => Number.isInteger(value) && value >= 2 && value <= 200)) {
	throw new RangeError('Landscape dimensions must be integers between 2 and 200.');
	}
	const state = normalizeState(input);
	// Referral throughput never exceeds arrivals, so available demand cannot bind
	// below it, even with an empty opening queue. All remaining limits are fixed:
	// monthly completions are constant, and no monthly history is needed here.
	// Keep evaluate()'s detailed simulation for monthly inspection and conservation.
	const options = STRATEGIES.map(strategy => {
	const isCurrent = strategy.id === 'current';
	const usesReferral = strategy.id === 'referral' \|\| strategy.id === 'combined';
	const feasible = isCurrent \|\| (state.budget > 0 && (!usesReferral \|\| state.budget >= state.referralFixedCost));
	const cost = !isCurrent && feasible ? state.budget : 0;
	const programmeCost = !isCurrent && feasible && usesReferral ? state.referralFixedCost : 0;
	const additionalSlots = (cost - programmeCost) / state.unitCost / state.months;
	return {
	...strategy, feasible, cost, additionalSlots,
	usesReferral: feasible && usesReferral,
	expand: feasible && (strategy.id === 'theatre' \|\| strategy.id === 'combined'),
	};
	});
	const baseThroughput = state.arrivals * state.baseReferral;
	const cells = [];
	for (let row = 0; row < rows; row += 1) {
	const y = 0.4 * row / (rows - 1);
	const improvedThroughput = state.arrivals * Math.min(1, state.baseReferral + y);
	for (let col = 0; col < cols; col += 1) {
	const x = 40 + 140 * col / (cols - 1);
	const baselineCompleted = Math.min(baseThroughput, x, state.baselineFundedSlots) * state.months;
	const results = options.map(option => {
	const monthlyCompleted = Math.min(
	option.usesReferral ? improvedThroughput : baseThroughput,
	x + (option.expand ? option.additionalSlots : 0),
	state.baselineFundedSlots + option.additionalSlots,
	);
	const completed = monthlyCompleted * state.months;
	return {
	...option, completed,
	incremental: Math.max(0, completed - baselineCompleted),
	queue: Math.max(0, state.initialQueue + state.months * (state.arrivals - monthlyCompleted)),
	};
	});
	const winner = recommendation(results);
	cells.push({
	index: cells.length, col, row, xIndex: col, yIndex: row, x, y,
	winnerId: winner.id, winnerName: winner.name, color: winner.color,
	incremental: winner.incremental, completed: winner.completed,
	cost: winner.cost, queue: winner.queue, tiedIds: winner.tiedIds,
	effectTiedIds: winner.effectTiedIds,
	});
	}
	}
	return cells;
	}