models/epidemiology/org.eclipse.stem.diseasemodels.experimental/src/org/eclipse/stem/diseasemodels/experimental/impl/CellularDiseaseModelImpl.java

/*******************************************************************************
 * Copyright (c) 2009 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.experimental.impl;

import org.eclipse.emf.ecore.EClass;
import org.eclipse.stem.core.graph.LabelValue;
import org.eclipse.stem.core.model.STEMTime;
import org.eclipse.stem.diseasemodels.experimental.CellularDiseaseModel;
import org.eclipse.stem.diseasemodels.experimental.ExperimentalPackage;
import org.eclipse.stem.diseasemodels.standard.DiseaseModelLabelValue;
import org.eclipse.stem.diseasemodels.standard.SILabelValue;
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.impl.SIImpl;
import org.eclipse.stem.diseasemodels.standard.impl.SILabelValueImpl;

/**
 * <!-- begin-user-doc -->
 * An implementation of the model object '<em><b>Percolation Disease Model</b></em>'.
 * <!-- end-user-doc -->
 * <p>
 * </p>
 *
 * @generated
 */
public class CellularDiseaseModelImpl extends SIImpl implements CellularDiseaseModel {
	/**
	 * <!-- begin-user-doc -->
	 * <!-- end-user-doc -->
	 * @generated
	 */
	public CellularDiseaseModelImpl() {
		super();
	}

	//LogDiseaseState lds = null;
	//int icount = 0;
	
	
	
	/**
	 * 
	 * @see org.eclipse.stem.diseasemodels.standard.impl.SIImpl#computeDiseaseDeltas(org.eclipse.stem.core.model.STEMTime, org.eclipse.stem.diseasemodels.standard.StandardDiseaseModelLabelValue, org.eclipse.stem.diseasemodels.standard.StandardDiseaseModelLabel, long)
	 */
	@Override
	public StandardDiseaseModelLabelValue computeDiseaseDeltas(
			final STEMTime time,
			final StandardDiseaseModelLabelValue currentState,
			final StandardDiseaseModelLabel diseaseLabel, final long timeDelta, DiseaseModelLabelValue returnValue) {
		
		final SILabelValue currentSI = (SILabelValue) currentState;
		
		// This is beta*
		final double transmissionRate = (getAdjustedTransmissionRate(timeDelta)
				* 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, currentSI.getI(), StandardPackage.Literals.SI_LABEL_VALUE__I);

		/*
		 * 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 numberOfSusceptibleToInfected = transmissionRate
		* currentSI.getS()* Math.pow(effectiveInfectious, getNonLinearityCoefficient());
		
		// Compute the number of recovering "infectious" that become "recovered"
		// this is rescaled by the surviving I population  so we don't move people that
		// are already dead.
		double numberOfInfectedToSusceptible = getAdjustedRecoveryRate(timeDelta)
				* currentSI.getI();
		

		
		// Determine delta S
		final double deltaS = - numberOfSusceptibleToInfected + numberOfInfectedToSusceptible;
		// Determine delta I
		final double deltaI = numberOfSusceptibleToInfected - numberOfInfectedToSusceptible;	
		
		SILabelValueImpl ret = (SILabelValueImpl)returnValue;
		ret.setS(deltaS);
		ret.setI(deltaI);
		ret.setIncidence(numberOfSusceptibleToInfected);
		ret.setDiseaseDeaths(0.0);
		return ret;
	} // computeDiseaseDeltas
	
	
	
	
	/**
	 * ModelSpecificAdjustments for a Stochastic model adds noise to or adjusts 
	 * the disease state transition values by multiplying
	 * the additions by a random variable r ~ (1+/-x) with x small.
	 * The requirements that no more individuals can be moved from a state than are
	 * already in that state is still enforced.
	 * 
	 */
	@Override
	public void doModelSpecificAdjustments(
			final StandardDiseaseModelLabelValue currentState) {

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

	public void doModelSpecificAdjustments(LabelValue label) {
		// Nothing
	}
	
	@Override
	public boolean isDeterministic() {
		return true;
	}
} //CellularDiseaseModelImpl