| // DeterministicSIScenarioTest.java |
| package org.eclipse.stem.diseasemodels.standard.tests; |
| |
| /******************************************************************************* |
| * Copyright (c) 2006 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 |
| *******************************************************************************/ |
| |
| import java.util.ArrayList; |
| import java.util.HashMap; |
| import java.util.List; |
| import java.util.Map; |
| |
| import org.eclipse.stem.core.graph.LabelValue; |
| import org.eclipse.stem.core.model.NodeDecorator; |
| import org.eclipse.stem.diseasemodels.standard.SILabelValue; |
| import org.eclipse.stem.diseasemodels.standard.impl.SILabelValueImpl; |
| |
| /** |
| * This class is a JUnit test case for a Deterministic SI disease model |
| * scenario. |
| * |
| * <ul> |
| * <li>S - The number of <code>Susceptible</code> population members. Members |
| * enter this state by being born or by "recovering" from being |
| * <code>Infectious</code>. They leave this state either by death or by |
| * entering the <code>Infectious</code> state. |
| * {@link SITest#TRANSMISSION_RATE} = 0.1, {@link SITest#RECOVERY_RATE} = 0.1 |
| * |
| * Initialized to {@link DiseaseModelTestUtil#TEST_POPULATION_COUNT} = 100 </li> |
| * <li>I - The number of <code>Infectious</code> population members. |
| * Initialized to {@link SIDiseaseModelScenarioTest#NUMBER_TO_INFECT} = 1 </li> |
| * |
| * <li>B - The number of <code>Births</code> of new (Susceptible) population |
| * members. |
| * |
| * |
| * Computed as a function of the "background mortality rate" |
| * {@link DiseaseModel#getBackgroundMortalityRate()}. We use the death rate |
| * because we assume that the population was in equilibrium before the onset of |
| * the disease (i.e., neither naturally growing or shrinking much over the time |
| * period of the simulation). The value used for the test is . Initialized to 0. |
| * </li> |
| * |
| * |
| * <li>D - The total number of <code>Deaths</code> of all types of population |
| * members. Just like the births, this is computed as a function of the |
| * "background mortality rate" {@link DiseaseModel#getBackgroundMortalityRate()}. |
| * The rate used for the test is {@link DiseaseModelTest#MORTALITY_RATE} = 0.01. |
| * However, it also includes the additional deaths of <code>Infectious</code> |
| * population members (i.e., DD below) due to the disease. Initialized to 0.</li> |
| * <li>DD - The total number of <code>Disease Deaths</code> of |
| * <code>Infectious</code> population members. It is a function of the which |
| * is not a rate, but rather the fraction of <code>Infectious</code> |
| * population members that eventually die from the disease (i.e., it's the |
| * mortality of the disease, how many who get it die from it). |
| * {@link SIImpl#computeInfectiousMortalityRate(double, double)}. Initialized |
| * to 0. </li> |
| * </ul> |
| * <ul> |
| * <li>μ = {@link DiseaseModelTest#MORTALITY_RATE} = 0.01</li> |
| * <li>β = {@link SITest#TRANSMISSION_RATE} = 0.1</li> |
| * <li>σ = {@link SITest#RECOVERY_RATE} = 0.01</li> |
| * <li>x = {@link SITest#INFECTIOUS_MORTALITY} = 0.1 </li> |
| * <li>μ<sub>i</sub> = {@link SITest#INFECTIOUS_MORTAILY_RATE} = 0.1</li> |
| * <li>Area<sub>l</sub> = 1.0</li> |
| * <li>Area = 1.0</li> |
| * <li>P = S + I = {@link DiseaseModelTestUtil#TEST_POPULATION_COUNT} = 100</li> |
| * </ul> |
| * <h2>1x1 Deterministic SI Scenario</h2> |
| * <h3>Initial State</h3> |
| * <p> |
| * S= 99.0, I<sup>R</sup>=1.0, I<sup>F</sup>=0.0, B=0.0, D=0.0, DD=0.0 |
| * </p> |
| * <h3>SI 1x1 Step 0</h3> |
| * |
| * <ul> |
| * <li><em>TSF<sub>l</sub> = (1/P)*(Area/Area<sub>l</sub>)</em></li> |
| * <li><em>TSF<sub>l</sub> = (1/100)*(1/1)</em></li> |
| * <li><em>TSF<sub>l</sub> = 0.01</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>β<sup>*</sup> = β TSF<sub>l</sub></em></li> |
| * <li><em>β<sup>*</sup> = 0.1 * 0.01</em></li> |
| * <li><em>β<sup>*</sup> = 0.001</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔB = μ * (S + I<sup>R</sup> + I<sup>F</sup>)</em></li> |
| * <li><em>ΔB = 0.01 * (99+1+0) </em> </li> |
| * <li><em>ΔB = 1.0</em> </li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔDD = μ<sub>i</sub> I<sup>F</sup> </em> </li> |
| * <li><em>ΔDD= 0.1 * 0</em> </li> |
| * <li><em>ΔDD= 0.0</em> </li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔD = μ S + (μ + μ<sub>i</sub> )I<sup>F</sup> + μ I<sup>R</sup> </em> |
| * </li> |
| * <li><em>ΔD = 0.01 * 99 + (0.01 + 0.1 ) 0 + 0.01 * 1 </em> </li> |
| * <li><em>ΔD= 1</em> </li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔS = μ (S + I) - β<sup>*</sup> S I + σ I<sup>R</sup> - μ S</em></li> |
| * <li><em>ΔS = 0.01 (99+(1+0)) - 0.001 * 99 * (1+0) + 0.01 * 1 - 0.01 * 99</em></li> |
| * <li><em>ΔS = 1.0 - 0.099 + 0.01 - 0.99</em></li> |
| * <li><em>ΔS = -0.079</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔI<sup>R</sup> = (1-x)β<sup>*</sup> S I - σ I<sup>R</sup> - μ I<sup>R</sup></em></li> |
| * <li><em>ΔI<sup>R</sup> = 0.9 * 0.001 * 99 * 1 - 0.01 * 1 - 0.01 * 1</em></li> |
| * <li><em>ΔI<sup>R</sup> = 0.0891 - .01 - .01</em></li> |
| * <li><em>ΔI<sup>R</sup> = 0.0691</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔI<sup>F</sup> = xβ<sup>*</sup> S I - (μ + μ<sub>i</sub>) I<sup>F</sup></em></li> |
| * <li><em>ΔI<sup>F</sup> = 0.1 * 0.001 * 99 * 1 - (0.01 + 0.1) * 0</em></li> |
| * <li><em>ΔI<sup>F</sup> = .0099</em></li> |
| * </ul> |
| * <p> |
| * S= 98.921, I<sup>R</sup>=1.0691, I<sup>F</sup>=0.0099, B=1.0, D=1.0, |
| * DD=0.0 |
| * </p> |
| * |
| * <h3>SI 1x1 Step 1</h3> |
| * <ul> |
| * <li><em>TSF<sub>l</sub> = (1/P)*(Area/Area<sub>l</sub>)</em></li> |
| * <li><em>TSF<sub>l</sub> = (1/100)*(1/1)</em></li> |
| * <li><em>TSF<sub>l</sub> = 0.01</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>β<sup>*</sup> = β TSF<sub>l</sub></em></li> |
| * <li><em>β<sup>*</sup> = 0.1 * 0.01</em></li> |
| * <li><em>β<sup>*</sup> = 0.001</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔB = μ * (S + I<sup>R</sup> + I<sup>F</sup>)</em></li> |
| * <li><em>ΔB = 0.01 * (100) </em> </li> |
| * <li><em>ΔB = 1.0</em> </li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔDD = μ<sub>i</sub> I<sup>F</sup> </em> </li> |
| * <li><em>ΔDD= 0.1 * 0.01</em> </li> |
| * <li><em>ΔDD= 0.001</em> </li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔD = μ S + (μ + μ<sub>i</sub> )I<sup>F</sup> + μ I<sup>R</sup> </em> |
| * </li> |
| * <li><em>ΔD = 0.01 * 98.921 + (0.01 + 0.1 )0.0099 + 0.01 * 1.0691 </em> |
| * </li> |
| * <li><em>ΔD= 1.00099</em> </li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔS = μ (S + I) - β<sup>*</sup> S I + σ I<sup>R</sup> - μ S</em></li> |
| * <li><em>ΔS = 0.01 * 100 - 0.001 * 98.921 * (1.0691+0.0099) + 0.01 * 1.0691 - 0.01 * 98.921</em></li> |
| * <li><em>ΔS = 1.0 - 0.10596859 + 0.010691 - 0.98921</em></li> |
| * <li><em>ΔS = -0.08448759</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔI<sup>R</sup> = (1-x)β<sup>*</sup> S I - σ I<sup>R</sup> - μ I<sup>R</sup></em></li> |
| * <li><em>ΔI<sup>R</sup> = 0.9 * 0.001 * 98.921 * (1.0691+0.0099) - 0.01 * 1.0691 - 0.01 * 1.0691</em></li> |
| * <li><em>ΔI<sup>R</sup> = 0.095371731 - .010691 - .010691</em></li> |
| * <li><em>ΔI<sup>R</sup> = 0.073989731</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔI<sup>F</sup> = xβ<sup>*</sup> S I - (μ + μ<sub>i</sub>) I<sup>F</sup></em></li> |
| * <li><em>ΔI<sup>F</sup> = 0.1 * 0.001 * 98.921 * (1.0691+0.0099) - (0.01 + 0.1) * 0.0099</em></li> |
| * <li><em>ΔI<sup>F</sup> = .009507859</em></li> |
| * </ul> |
| * <p> |
| * S= 98.83651241, I<sup>R</sup>=1.143089731, I<sup>F</sup>=0.019407859 , |
| * B=2.0, D=2.00099, DD=0.001 |
| * </p> < |
| * |
| * @see DiseaseModelTestUtil#TEST_POPULATION_COUNT |
| * @see SIDiseaseModelScenarioTest#NUMBER_TO_INFECT |
| * @see DiseaseModelTestUtil#TEST_AREA |
| * @see SITest#INFECTIOUS_MORTALITY |
| * @see SITest#TRANSMISSION_RATE |
| * @see SITest#NON_LINEARITY_COEFFICIENT |
| * @see SITest#RECOVERY_RATE |
| * @see SIImpl |
| */ |
| public class DeterministicSIScenarioTest extends SIDiseaseModelScenarioTest { |
| |
| private static final String DISEASE_URI_PREFIX = "DeterministicSI"; |
| |
| private static Map<String, Integer> expectedNumberOfLabelsToUpdate = new HashMap<String, Integer>(); |
| |
| private static Map<String, SILabelValue[][][]> expectedDiseaseModelStates = new HashMap<String, SILabelValue[][][]>(); |
| |
| static { |
| |
| // 1x1 |
| expectedDiseaseModelStates.put(TEST_SCENARIO1x1_KEY, |
| new SILabelValue[][][] { |
| // Step 0 |
| { { |
| // N[0,0] |
| new SILabelValueImpl(99.02, 0.98, 0.0, |
| 0.0) } }, |
| |
| // Step 1 |
| { { |
| // N[0,0] |
| new SILabelValueImpl(99.04, 0.96, |
| 0.0, 0.0) } } } // new |
| // SILabelValue |
| |
| ); // put(TEST_SCENARIO1x1_KEY) |
| |
| // 1x2 |
| expectedDiseaseModelStates.put(TEST_SCENARIO1x2_KEY, |
| new SILabelValue[][][] { |
| // Step 0 |
| { { |
| // N[0,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,1] |
| new SILabelValueImpl(100, 0, 0, 0) } }, |
| |
| // Step 1 |
| { { |
| // N[0,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,1] |
| new SILabelValueImpl(100, 0, 0, 0) } }, |
| // Step 2 |
| { { |
| // N[0,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,1] |
| new SILabelValueImpl(100, 0, 0, 0) } } } // new |
| // SILabelValue |
| ); // put(TEST_SCENARIO1x2_KEY) |
| |
| // 1x3 |
| expectedDiseaseModelStates.put(TEST_SCENARIO1x3_KEY, |
| new SILabelValue[][][] { |
| // Step 0 |
| { { |
| |
| // N[0,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,1] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,2] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| } }, |
| |
| // Step 1 |
| { { |
| // N[0,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,1] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,2] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| } }, |
| |
| // Step 2 |
| { { |
| // N[0,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,1] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,2] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| } } } // new SILabelValue |
| |
| ); // put(TEST_SCENARIO1x3_KEY) |
| |
| // 2x2 |
| expectedDiseaseModelStates.put(TEST_SCENARIO2x2_KEY, |
| new SILabelValue[][][] { |
| // Step 0 |
| { { |
| |
| // N[0,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,1] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| }, { |
| |
| // N[1,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[1,1] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| } }, |
| |
| // Step 1 |
| { { |
| // N[0,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,1] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| }, { |
| |
| // N[1,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[1,1] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| } }, |
| |
| // Step 2 |
| { { |
| // N[0,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,1] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| }, { |
| |
| // N[1,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[1,1] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| } } } // new SILabelValue |
| |
| ); // put(TEST_SCENARIO2x2_KEY) |
| |
| // 3x3 |
| expectedDiseaseModelStates.put(TEST_SCENARIO3x3_KEY, |
| new SILabelValue[][][] { |
| // Step 0 |
| { { |
| |
| // N[0,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,1] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,2] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| }, { |
| |
| // N[1,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[1,1] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[1,2] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| }, { |
| |
| // N[2,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[2,1] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[2,2] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| } }, |
| |
| // Step 1 |
| { { |
| // N[0,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,1] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,2] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| }, { |
| |
| // N[1,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[1,1] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[1,2] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| }, { |
| |
| // N[2,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[2,1] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[2,2] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| } }, |
| |
| // Step 2 |
| { { |
| // N[0,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,1] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[0,2] |
| new SILabelValueImpl(100, 0, 0,0) |
| |
| }, { |
| |
| // N[1,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[1,1] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[1,2] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| }, { |
| |
| // N[2,0] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[2,1] |
| new SILabelValueImpl(100, 0, 0, 0), |
| // N[2,2] |
| new SILabelValueImpl(100, 0, 0, 0) |
| |
| } } } // new SILabelValue |
| |
| ); // put(TEST_SCENARIO3x3_KEY) |
| |
| // Fill out the map that specifies how many labels should be updated by |
| // a disease model for each test. |
| for (TestSpec testSpec : testSpecifications) { |
| expectedNumberOfLabelsToUpdate |
| .put( |
| testSpec.scenarioDiseaseKey, |
| new Integer( |
| computeExpectedNumberOfLabels(expectedDiseaseModelStates |
| .get(testSpec.scenarioDiseaseKey)))); |
| } // for each test specification |
| |
| } // static |
| |
| /** |
| * @see org.eclipse.stem.diseasemodels.standard.tests.DiseaseModelScenarioTest#getDiseaseModelsToTest() |
| */ |
| @Override |
| public List<NodeDecorator> getDiseaseModelsToTest() { |
| final List<NodeDecorator> retValue = new ArrayList<NodeDecorator>(); |
| retValue.add(DeterministicSIDiseaseModelTest.createFixture()); |
| return retValue; |
| } // getDiseaseModelsToTest |
| |
| @Override |
| protected int getNumberOfSteps(final String diseaseScenarioKey) { |
| SILabelValue[][][] temp = expectedDiseaseModelStates |
| .get(diseaseScenarioKey); |
| return temp.length; |
| } // getNumberOfSteps |
| |
| @Override |
| protected int getExpectedNumberOfLabelsToUpdate( |
| final String diseaseScenarioKey) { |
| Integer temp = expectedNumberOfLabelsToUpdate.get(diseaseScenarioKey); |
| return temp.intValue(); |
| } // getExpectedNumberOfLabelsToUpdate |
| |
| @Override |
| protected LabelValue[][] getExpectedDiseaseModelState( |
| final String diseaseScenarioKey, final int step) { |
| final SILabelValue[][][] siLabelValue = expectedDiseaseModelStates |
| .get(diseaseScenarioKey); |
| return siLabelValue[step]; |
| } // getExpectedDiseaseModelState |
| |
| protected String getDiseaseURIPrefix() { |
| return DISEASE_URI_PREFIX; |
| } // getDiseaseURIPrefix |
| } // DeterministicSIScenarioTest |
