models/epidemiology/org.eclipse.stem.diseasemodels/src/org/eclipse/stem/diseasemodels/standard/impl/StochasticPoissonSEIRDiseaseModelImpl.java

/*******************************************************************************
 * Copyright (c) 2010 IBM Corporation and others.
 * All rights reserved. This program and the accompanying materials
 * are made available under the terms of the Eclipse Public License v1.0
 * which accompanies this distribution, and is available at
 * http://www.eclipse.org/legal/epl-v10.html
 *
 * Contributors:
 *     IBM Corporation - initial API and implementation
 *******************************************************************************/
package org.eclipse.stem.diseasemodels.standard.impl;

import java.util.Random;

import org.eclipse.emf.common.util.EList;
import org.eclipse.emf.ecore.EClass;
import org.eclipse.stem.core.graph.DynamicLabel;
import org.eclipse.stem.core.graph.IntegrationLabel;
import org.eclipse.stem.core.math.BinomialDistributionUtil;
import org.eclipse.stem.core.model.STEMTime;
import org.eclipse.stem.diseasemodels.standard.SEIRLabelValue;
import org.eclipse.stem.diseasemodels.standard.StandardDiseaseModelLabel;
import org.eclipse.stem.diseasemodels.standard.StandardDiseaseModelLabelValue;
import org.eclipse.stem.diseasemodels.standard.StandardPackage;
import org.eclipse.stem.diseasemodels.standard.StochasticPoissonSEIRDiseaseModel;

/**
 * <!-- begin-user-doc -->
 * An implementation of the model object '<em><b>Stochastic Poisson SEIR Disease Model</b></em>'.
 * <!-- end-user-doc -->
 *
 * @generated
 */
public class StochasticPoissonSEIRDiseaseModelImpl extends SEIRImpl implements StochasticPoissonSEIRDiseaseModel {

	private Random rand = new Random();
	/**
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @generated NOT
	 */
	public StochasticPoissonSEIRDiseaseModelImpl() {
		super();
	}

	/**
	 * @see org.eclipse.stem.diseasemodels.standard.impl.SIImpl#computeDiseaseDeltas(StandardDiseaseModelLabelValue,
	 *      StandardDiseaseModelLabel, long)
	 */
	@Override
	public void calculateDeltas(STEMTime time, double t, long timeDelta, EList<DynamicLabel> labels)  {		
		for(int _i=0;_i<labels.size();++_i) {
			final DynamicLabel label =  labels.get(_i);
			IntegrationLabel ilabel = (IntegrationLabel)label;
			StandardDiseaseModelLabel diseaseLabel = (StandardDiseaseModelLabel)ilabel;
			StandardDiseaseModelLabelValue currentState = (StandardDiseaseModelLabelValue)ilabel.getProbeValue();
			StandardDiseaseModelLabelValue deltaValue = (StandardDiseaseModelLabelValue)ilabel.getDeltaValue();

			deltaValue.reset();
			
			final SEIRLabelValue currentSEIR = (SEIRLabelValue) currentState;

			// This is beta*
			double transmissionRate = getAdjustedTransmissionRate(timeDelta);

			if(!this.isFrequencyDependent()) transmissionRate *= getTransmissionRateScaleFactor(diseaseLabel);


			// The effective Infectious population  is a dimensionles number normalize by total
			// population used in teh computation of bets*S*i where i = Ieffective/Pop.
			// This includes a correction to the current
			// infectious population (Ieffective) based on the conserved exchange of people (circulation)
			// between regions. Note that this is no the "arrivals" and "departures" which are
			// a different process.
			final double effectiveInfectious = getNormalizedEffectiveInfectious(diseaseLabel.getNode(), diseaseLabel, currentSEIR.getI(), StandardPackage.Literals.SI_LABEL_VALUE__I, StandardPackage.Literals.STANDARD_DISEASE_MODEL__CHARACTERISTIC_MIXING_DISTANCE, StandardPackage.Literals.STANDARD_DISEASE_MODEL__ROAD_NETWORK_INFECTIOUS_PROPORTION);

			/*
			 * Compute state transitions
			 * 
			 * Regarding computing the number of transitions from Susceptible to Exposed:
			 * In a linear model the "effective" number of infectious people is just
			 * the number of infectious people In a nonlinear model we have a
			 * nonLinearity exponent that is > 1 this models the effect of immune
			 * system saturation when Susceptible people are exposed to large
			 * numbers of infectious people. then the "effective" number of
			 * infectious people is I^nonLinearity exponent to allow for either
			 * linear or nonlinear models we always calculate I^nonLinearity
			 * exponent and allow nonLinearity exponent >= 1.0
			 */
			double numberOfInfectedToRecovered = getAdjustedRecoveryRate(timeDelta)
					* currentSEIR.getI();
			double numberOfRecoveredToSusceptible = getAdjustedImmunityLossRate(timeDelta)
					* currentSEIR.getR();

			int S = (int)currentSEIR.getS();
			double prob = 0.0;
			if(getNonLinearityCoefficient() != 1.0 && effectiveInfectious >= 0.0)
				prob = transmissionRate * Math.pow(effectiveInfectious, getNonLinearityCoefficient());
			else
				prob = transmissionRate * effectiveInfectious;
			double rndVar = rand.nextDouble();
			int pickN = 0;
			pickN = new BinomialDistributionUtil(1).fastPickFromBinomialDist(prob, S);

			// Move pickK from S to I;

			double numberOfSusceptibleToExposed = pickN;



			double numberOfExposedToInfectious = getAdjustedIncubationRate(timeDelta)
					* currentSEIR.getE();


			// Determine delta S
			final double deltaS = numberOfRecoveredToSusceptible - numberOfSusceptibleToExposed;
			// Determine delta E
			final double deltaE = numberOfSusceptibleToExposed - numberOfExposedToInfectious;		
			// Determine delta I
			final double deltaI = numberOfExposedToInfectious - numberOfInfectedToRecovered;
			// Determine delta R
			final double deltaR = numberOfInfectedToRecovered - numberOfRecoveredToSusceptible;

			SEIRLabelValueImpl ret = (SEIRLabelValueImpl)deltaValue;
			ret.setS(deltaS);
			ret.setE(deltaE);
			ret.setI(deltaI);
			ret.setIncidence(numberOfSusceptibleToExposed);
			ret.setR(deltaR);
			ret.setDiseaseDeaths(0);

			computeAdditionalDeltasAndExchanges(ilabel, time, t, timeDelta);
		}

	} // computeDiseaseDeltas

	/**
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @generated
	 */
	@Override
	protected EClass eStaticClass() {
		return StandardPackage.Literals.STOCHASTIC_POISSON_SEIR_DISEASE_MODEL;
	}


} //StochasticPoissonSEIRDiseaseModelImpl