| // DeterministicSEIRScenarioTest.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.Collection; |
| 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.Model; |
| import org.eclipse.stem.core.model.NodeDecorator; |
| import org.eclipse.stem.core.scenario.Scenario; |
| import org.eclipse.stem.diseasemodels.standard.SEIRLabelValue; |
| import org.eclipse.stem.diseasemodels.standard.impl.SEIRLabelValueImpl; |
| |
| /** |
| * This class is a JUnit test case for a Deterministic SEIR disease model |
| * scenario. |
| * |
| * <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>γ = {@link SIRTest#IMMUNITY_LOSS_RATE} = 0.1</li> |
| * <li>φ = {@link SEIRTest#INCUBATION_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 SIR Scenario</h2> |
| * <h3>Initial State</h3> |
| * <p> |
| * S= 99.0, I<sup>R</sup>=1.0, I<sup>F</sup>=0.0, R=0.0, B=0.0, D=0.0, |
| * DD=0.0 |
| * </p> |
| * <h3>SEIR 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 + +E +I<sup>R</sup> + I<sup>F</sup> +R)</em></li> |
| * <li><em>ΔB = 0.01 * (99+0+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 = μS + μE +(μ + μ<sub>i</sub> )I<sup>F</sup> + μ I<sup>R</sup> + μR </em> |
| * </li> |
| * <li><em>ΔD = 0.01 * 99 + 0.01 * 0 + (0.01 + 0.1 ) 0 + 0.01 * 1 + 0.01 * 0</em> |
| * </li> |
| * <li><em>ΔD= 1</em> </li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔS = μ (S + E +I + R) - β<sup>*</sup> S I + γR - μ S</em></li> |
| * <li><em>ΔS = 0.01 (99+(1+0+0)+0) - 0.001 * 99 * (1+0+0) + 0.01 * 0 - 0.01 * 99</em></li> |
| * <li><em>ΔS = 1.0 - 0.099 + 0.01 - 0.99</em></li> |
| * <li><em>ΔS = -0.089</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔE = β<sup>*</sup>SI - φE - μE</sup></em></li> |
| * <li><em>ΔE = 0.001 * 99 * 1 - 0.1 * 0 - 0.01 * 0</em></li> |
| * <li><em>ΔE = 0.099</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔI<sup>R</sup> = (1-x)φE - σ I<sup>R</sup> - μ I<sup>R</sup></em></li> |
| * <li><em>ΔI<sup>R</sup> = 0.9 * 0 - 0.01 * 1 - 0.01 * 1</em></li> |
| * <li><em>ΔI<sup>R</sup> = - 0.01 - 0.01</em></li> |
| * <li><em>ΔI<sup>R</sup> = -0.02</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔI<sup>F</sup> = xφE - (μ + μ<sub>i</sub>) I<sup>F</sup></em></li> |
| * <li><em>ΔI<sup>F</sup> = 0.1 * 0 - (0.01 + 0.1) * 0</em></li> |
| * <li><em>ΔI<sup>F</sup> = 0.0</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔR = σ I<sup>R</sup> - γR - μ R</em></li> |
| * <li><em>ΔR = 0.01 * 1 - 0.01 * 0 - 0.01 * 0</em></li> |
| * <li><em>ΔR = .01</em></li> |
| * </ul> |
| * <p> |
| * S= 98.911, E=0.099 , I<sup>R</sup>=0.98, I<sup>F</sup>=0.0, R=0.01, |
| * B=1.0, D=1.0, DD=0.0 |
| * </p> |
| * |
| * <h3>SEIR 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 + E + I<sup>R</sup> + I<sup>F</sup> +R)</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</em> </li> |
| * <li><em>ΔDD= 0.0</em> </li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔD = μS + μE + (μ + μ<sub>i</sub> )I<sup>F</sup> + μ I<sup>R</sup> + μ R </em> |
| * </li> |
| * <li><em>ΔD = 0.01 * 98.911 + 0.01 * 0.099 + (0.01 + 0.1) * 0 + 0.01 * 0.98 + 0.01 * 0.01</em> |
| * </li> |
| * <li><em>ΔD= 1.0</em> </li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔS = μ (S + E +I + R) - β<sup>*</sup> S I + γR - μ S</em></li> |
| * <li><em>ΔS = 0.01 (99.911+ 0.988 (0.980+0)+0.01) - 0.001 * 98.911 * 0.98 + 0.1 * 0.01 - 0.01 * 98.911</em></li> |
| * <li><em>ΔS = 1.0 - 0.09693278 + 0.001 - 0.98911</em></li> |
| * <li><em>ΔS = -0.085704278</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔE = β<sup>*</sup>SI - φE - μE</sup></em></li> |
| * <li><em>ΔE = 0.001 * 98.911 * 0.98 - 0.1 * 0.099 - 0.01 * 0.099</em></li> |
| * <li><em>ΔE = 0.09693278 - 0.0099 - 0.00099</em></li> |
| * <li><em>ΔE = 0.08604278</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔI<sup>R</sup> = (1-x)φE - σ I<sup>R</sup> - μ I<sup>R</sup></em></li> |
| * <li><em>ΔI<sup>R</sup> = 0.9 * 0.0099 - 0.01 * 0.98 - 0.01 * 0.98</em></li> |
| * <li><em>ΔI<sup>R</sup> = 0.00891 - 0.0098 - 0.0098</em></li> |
| * <li><em>ΔI<sup>R</sup> = -0.01069</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔI<sup>F</sup> = xφE - (μ + μ<sub>i</sub>) I<sup>F</sup></em></li> |
| * <li><em>ΔI<sup>F</sup> = 0.1 * 0.099 - (0.01 + 0.1) * 0.0</em></li> |
| * <li><em>ΔI<sup>F</sup> = 0.0099</em></li> |
| * </ul> |
| * <ul> |
| * <li><em>ΔR = σ I<sup>R</sup> - γR - μ R</em></li> |
| * <li><em>ΔR = 0.01 * 0.98 - 0.1 * 0.01 - 0.01 * 0.01</em></li> |
| * <li><em>ΔR = .0087</em></li> |
| * </ul> |
| * S= 98.911, E=0.099 , I<sup>R</sup>=0.98, I<sup>F</sup>=0.0, R=0.01, |
| * B=1.0, D=1.0, DD=0.0 |
| * |
| * <p> |
| * S= 98.82505722, E=0.18504278, I<sup>R</sup>=0.96931, I<sup>F</sup>=0.0099, |
| * R=0.0187, B=2.0, D=2.0, DD=0.0 |
| * </p> |
| * |
| */ |
| public class DeterministicSEIRScenarioTest extends SEIRDiseaseModelScenarioTest { |
| |
| private static final String DISEASE_URI_PREFIX = "DeterministicSEIR"; |
| |
| private static Map<String, Integer> expectedNumberOfLabelsToUpdate = new HashMap<String, Integer>(); |
| |
| private static Map<String, SEIRLabelValue[][][]> expectedDiseaseModelStates = new HashMap<String, SEIRLabelValue[][][]>(); |
| |
| static { |
| |
| // 1x1 |
| expectedDiseaseModelStates.put(TEST_SCENARIO1x1_KEY, |
| new SEIRLabelValue[][][] { |
| // Step 0 |
| { { |
| // N[0,0] |
| new SEIRLabelValueImpl(99.01, 0.0, 0.98, 0, 0.01, |
| 0.0) } }, |
| |
| // Step 1 |
| { { |
| // N[0,0] |
| new SEIRLabelValueImpl(99.02, 0.0, 0.96, 0.0, 0.02, 0) } } } // new |
| // SEIRLabelValue |
| |
| ); // put(TEST_SCENARIO1x1_KEY) |
| |
| // 1x2 |
| expectedDiseaseModelStates |
| .put(TEST_SCENARIO1x2_KEY, |
| new SEIRLabelValue[][][] { |
| // Step 0 |
| { { |
| // N[0,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) } }, |
| |
| // Step 1 |
| { { |
| // N[0,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) } }, |
| // Step 2 |
| { { |
| // N[0,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) } } } // new |
| // SEIRLabelValue |
| ); // put(TEST_SCENARIO1x2_KEY) |
| |
| // 1x3 |
| expectedDiseaseModelStates |
| .put(TEST_SCENARIO1x3_KEY, |
| new SEIRLabelValue[][][] { |
| // Step 0 |
| { { |
| |
| // N[0,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,2] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| } }, |
| |
| // Step 1 |
| { { |
| // N[0,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,2] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| } }, |
| |
| // Step 2 |
| { { |
| // N[0,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,2] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| } } } // new SEIRLabelValue |
| |
| ); // put(TEST_SCENARIO1x3_KEY) |
| |
| // 2x2 |
| expectedDiseaseModelStates.put(TEST_SCENARIO2x2_KEY, |
| new SEIRLabelValue[][][] { |
| // Step 0 |
| { |
| { |
| |
| // N[0,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| }, |
| { |
| |
| // N[1,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[1,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| } }, |
| |
| // Step 1 |
| { |
| { |
| // N[0,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,1z |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| }, |
| { |
| |
| // N[1,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[1,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| } }, |
| |
| // Step 2 |
| { |
| { |
| // N[0,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| }, |
| { |
| |
| // N[1,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[1,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| } } } // new SEIRLabelValue |
| |
| ); // put(TEST_SCENARIO2x2_KEY) |
| |
| // 3x3 |
| expectedDiseaseModelStates.put(TEST_SCENARIO3x3_KEY, |
| new SEIRLabelValue[][][] { |
| // Step 0 |
| { |
| { |
| |
| // N[0,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,2] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| }, |
| { |
| |
| // N[1,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[1,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[1,2] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| }, |
| { |
| |
| // N[2,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[2,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[2,2] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| } }, |
| |
| // Step 1 |
| { |
| { |
| // N[0,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,2] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| }, |
| { |
| |
| // N[1,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[1,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[1,2] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| }, |
| { |
| |
| // N[2,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[2,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[2,2] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| } }, |
| |
| // Step 2 |
| { |
| { |
| // N[0,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[0,2] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| }, |
| { |
| |
| // N[1,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[1,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[1,2] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| }, |
| { |
| |
| // N[2,0] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[2,1] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0), |
| // N[2,2] |
| new SEIRLabelValueImpl(100, 0, 0, 0, 0, |
| 0) |
| |
| } } } // new SEIRLabelValue |
| |
| ); // 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(DeterministicSEIRDiseaseModelTest.createFixture()); |
| return retValue; |
| } // getDiseaseModelsToTest |
| |
| @Override |
| protected int getNumberOfSteps(final String diseaseScenarioKey) { |
| SEIRLabelValue[][][] 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 SEIRLabelValue[][][] seirLabelValue = expectedDiseaseModelStates |
| .get(diseaseScenarioKey); |
| return seirLabelValue[step]; |
| } // getExpectedDiseaseModelState |
| |
| protected String getDiseaseURIPrefix() { |
| return DISEASE_URI_PREFIX; |
| } // getDiseaseURIPrefix |
| |
| /** |
| * @param numRows |
| * the number of rows in the lattice |
| * @param numColumns |
| * the number of columns in the lattice |
| * @return a scenario with nodes in a lattice with a deterministic SEIR |
| * disease model and one population member infected in Node [0][0] |
| */ |
| public static Scenario createScenario(final int numRows, |
| final int numColumns) { |
| final Collection<NodeDecorator> diseaseModels = new ArrayList<NodeDecorator>(); |
| |
| diseaseModels.add(DeterministicSEIRDiseaseModelTest.createFixture()); |
| |
| final Model model = DiseaseModelTestUtil.createLatticeModel( |
| diseaseModels, numRows, numColumns, |
| DiseaseModelTestUtil.TEST_POPULATION_COUNT, |
| DiseaseModelTestUtil.TEST_AREA); |
| |
| final Scenario retValue = DiseaseModelTestUtil.createLatticeScenario( |
| SIDiseaseModelScenarioTest.createInfectors(model), model); |
| |
| assert retValue.sane(); |
| |
| return retValue; |
| } // createFixture |
| |
| } // DeterministicSEIRScenarioTest |
