Authors |
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xvii | |
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PART I Origin and Background of COVID-19 |
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Chapter 1 Introduction to Emerging Respiratory Viruses with Coronavirus Disease (COVID-19) |
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3 | (44) |
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3 | (1) |
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New and Newly Recognized Respiratory Viruses |
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4 | (7) |
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4 | (1) |
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5 | (1) |
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5 | (1) |
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5 | (1) |
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6 | (1) |
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6 | (1) |
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6 | (2) |
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8 | (1) |
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Human Metapneumovirus (HMPV) |
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8 | (1) |
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9 | (1) |
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9 | (1) |
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10 | (1) |
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Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) |
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10 | (1) |
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Middle East Respiratory Syndrome Coronavirus (MERS-CoV) |
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11 | (1) |
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11 | (1) |
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Timeline of the Emerging Viruses |
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11 | (3) |
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14 | (8) |
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Current Worldwide Scenario of SARS-CoV-2 |
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14 | (2) |
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Time Line of the Outbreak |
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16 | (3) |
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Emergence of Coronavirus (SARS-CoV-2) |
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19 | (3) |
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Compression of Coronaviruses in Humans--SARS-CoV, MERS-CoV and COVID-19 |
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22 | (3) |
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Prevention, Control, and Management Strategies from SARS-CoV-2 |
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25 | (13) |
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Containment Strategies for SARS-CoV-2: Isolation, Quarantine |
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25 | (1) |
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Principles of Modern Quarantine |
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26 | (2) |
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Computational Technique of Analysis Effect of Containment Strategies |
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28 | (1) |
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28 | (3) |
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31 | (2) |
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33 | (2) |
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35 | (1) |
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Additional Preventions Tips for Community |
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36 | (1) |
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Case and Contact Management |
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37 | (1) |
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37 | (1) |
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38 | (1) |
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39 | (8) |
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Chapter 2 The Origin Molecular Structure, Function, and Evolution Insights of COVID-19: Morphogenesis and Spike Proteins |
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47 | (64) |
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47 | (2) |
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Emergence of SARS-CoV and SARS-CoV-2 |
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49 | (2) |
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Classification of Coronaviruses |
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51 | (2) |
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Key Features and Entry Mechanism of Human Coronaviruses |
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53 | (2) |
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Morphology, Genomic Structure, and Its Variation of SARS-CoV-2 |
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55 | (7) |
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55 | (1) |
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55 | (2) |
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57 | (1) |
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Structural Proteins of Viral |
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58 | (1) |
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58 | (2) |
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60 | (1) |
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60 | (1) |
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61 | (1) |
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Structure, Function, Antigenicity, and ACE2 Recognition by the SARS-CoV-2 Spike Glycoprotein |
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62 | (4) |
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SARS-CoV-2 S Protein CTD Interactions with Human ACE2 Receptor |
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63 | (1) |
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Correlation of the SARS-CoV-2-RBD and SARS-CoV-RBD Interaction with Human ACE2 Receptor |
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63 | (1) |
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Exhibits Distinct Epitope Features of SARS-CoV-2 on the RBD from SARS-CoV |
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64 | (2) |
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66 | (33) |
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66 | (1) |
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Theory Behind the General Strategy |
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66 | (1) |
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Fundamental Principles of Epitope Prediction for Design of Synthetic Immunizations |
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66 | (1) |
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Q-UEL: A Knowledge Representation Toolkit |
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67 | (1) |
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Sources Data and Material |
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67 | (1) |
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68 | (1) |
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69 | (26) |
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Machine Learning Clustering Technique |
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95 | (1) |
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95 | (2) |
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K-Means Cluster Algorithm |
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97 | (2) |
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99 | (1) |
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99 | (2) |
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101 | (1) |
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101 | (10) |
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PART II COVID-19 Screening, Testing and Detection Systems: Different Paths to the Same Destination |
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Chapter 3 Real Time-Polymerase Chain Reaction (RT-PCR) and Antibody Test |
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111 | (54) |
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111 | (1) |
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112 | (9) |
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113 | (1) |
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Principle Behind RT-PCR Testing |
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114 | (1) |
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How Does RT-PCR Work in Coronavirus Case? |
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115 | (1) |
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115 | (2) |
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Nucleic Acid Testing for SARS-CoV-2 |
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117 | (1) |
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Integrating Nucleic Acid Detection with Clinical Management |
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118 | (1) |
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Device Description and Test Principle |
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118 | (1) |
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119 | (2) |
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Computational Technique of RT-PCR Test Diagnostic Sensitivity and Specificity Reconstruction for COVID-19 |
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121 | (8) |
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122 | (1) |
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122 | (1) |
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123 | (2) |
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125 | (4) |
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Digital Polymerase Chain Reaction |
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129 | (15) |
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Statistical Foundations of dPCR |
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130 | (1) |
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Binomial Probability and Poisson Approximation |
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130 | (1) |
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131 | (1) |
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Most Probable Number (MPN) |
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131 | (1) |
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Copy Number Variant (CNV) Applications |
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132 | (1) |
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Absolute Limit of Quantification Due to Specimen Sampling |
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133 | (1) |
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Hypothesis and Technological Implications |
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133 | (1) |
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Conclusion of the Statistical Foundations of dPCR |
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134 | (1) |
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134 | (1) |
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134 | (1) |
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Dynamic Range of Detection |
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135 | (1) |
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Practical Considerations in the Reliability of dPCR Measurements-False-Negative/Positive Signals |
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135 | (1) |
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Miniaturization and Hyper-Compartmentalization |
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136 | (1) |
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137 | (7) |
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Computational Technique of ddPCR Test for Sensitivity Assessment of COVID-19 |
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144 | (4) |
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145 | (1) |
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Specimens Collection, Storage, and Pooling |
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145 | (1) |
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Preparation of Groups of 16 and 32 Individuals |
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146 | (1) |
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Detection of SARS-CoV-2 by Grouped, DPCR Testing |
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146 | (1) |
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Detection of SARS-CoV-2 by Routine Individual RT-PCR Testing |
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146 | (2) |
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Individual Confirmatory Testing for SARS-CoV-2 By RT-PCR and DPCR |
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148 | (1) |
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LoB/LoD Evaluation for SARS-CoV-2 Detection Using DPCR |
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148 | (1) |
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148 | (10) |
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Cohort Description from Routine RT-PCR Testing |
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148 | (1) |
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Results from Grouped DPCR Testing |
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149 | (1) |
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Detailed Results for DPCR in Groups of 8 |
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150 | (2) |
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Detailed Results for DPCR in Groups of 16 |
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152 | (1) |
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Detailed Results for DPCR in Groups of 32 |
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153 | (1) |
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Investigation of RT-PCR-/dPCR+ Discordances |
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153 | (1) |
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Investigation of the Sample RT-PCR+/dPCR |
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153 | (2) |
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Correlation between DPCR Measurements and Ct Values |
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155 | (3) |
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158 | (7) |
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Chapter 4 Antigen-Antibody Reaction-Based Immunodiagnostics Method |
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165 | (38) |
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165 | (1) |
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Definition of Basic Terms of Immunoassays for Disease |
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166 | (5) |
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166 | (1) |
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167 | (1) |
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168 | (1) |
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169 | (1) |
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169 | (1) |
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170 | (1) |
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Affinity, Avidity, and Cross Reactivity |
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170 | (1) |
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Emerged Rapid Immunodiagnostic (Serology Immunoassays) Tests |
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171 | (2) |
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171 | (1) |
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Immunoenzymatic and Immunofluorimetric Assays |
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172 | (1) |
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SARS-CoV-2 Infectivity and Immune Response |
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173 | (2) |
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174 | (1) |
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Immune Response to COVID-19 Disease |
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174 | (1) |
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COVID-19 Antibody Response: Pathogenic or Protective? |
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175 | (1) |
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175 | (11) |
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Immunoinformatics-Based Analysis |
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175 | (1) |
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175 | (1) |
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Predicting Potential Linear B-Cell Epitopes in SARS-CoV-2 |
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176 | (1) |
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Prediction of Potential T-Cell Epitopes in SARS-CoV-2 |
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176 | (1) |
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Prediction of Protective Antigens |
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176 | (1) |
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Analysis of Epitope Conservation and Population Coverage of T-Cell Epitopes |
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177 | (1) |
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Prediction of Allergenicity, Toxicity, and Possibilities of Autoimmune Reactions |
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177 | (1) |
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177 | (9) |
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Support Vector Machine to Predict B-Cell |
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186 | (11) |
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186 | (1) |
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186 | (4) |
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190 | (1) |
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190 | (7) |
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197 | (6) |
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PART III COVID-19 Detection: Advanced Image Processing with Artificial Intelligence Techniques |
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Chapter 5 Lung Function Testing (LFT) with Normal CT Scans and AI Algorithm |
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203 | (38) |
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203 | (1) |
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General Consideration of PFT for COVID-19 |
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204 | (2) |
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205 | (1) |
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206 | (1) |
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Review of Chest CT Findings in Early COVID-19 Studies |
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206 | (2) |
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Monitoring the Severity and Progression of COVID-19 with Chest CT |
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208 | (1) |
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Correlation of Testing with rRT-PCR and Chest CT |
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208 | (1) |
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The Ability to Differentiate Between COVID-19 Pneumonia and Other Pneumonias |
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209 | (1) |
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Deep Learning Architectures for CT SCAN |
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209 | (27) |
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Detection of COVID-19 Using UNet ConvNet |
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210 | (1) |
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210 | (1) |
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211 | (1) |
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212 | (1) |
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213 | (1) |
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214 | (1) |
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Ensemble of Convolutional Autoencoder and Random Forest |
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215 | (1) |
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215 | (1) |
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215 | (2) |
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217 | (4) |
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Fully Connected Segmentation Neural Network (FCSegNet) |
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221 | (1) |
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222 | (2) |
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224 | (5) |
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229 | (1) |
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229 | (7) |
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236 | (1) |
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236 | (5) |
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Chapter 6 Chest X-Ray Image-Based Testing Using Machine Learning Techniques |
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241 | (38) |
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241 | (1) |
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Chest X-Ray Imaging for COVID-19 |
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242 | (6) |
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Ground Glass Opacity of COVID-19 Pneumonia |
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242 | (2) |
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Usually Affected Part of Lungs with COVID-19 |
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244 | (2) |
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Reliability of Detecting COVID-19 Using Chest X-Ray |
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246 | (1) |
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Features and Limitations of Chest Radiographs in COVID-19 |
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247 | (1) |
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247 | (1) |
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248 | (1) |
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Machine Learning Architectures for Chest X-Ray |
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248 | (26) |
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Ensemble Feature Optimization with KNN Classification |
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248 | (1) |
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Image-Based Classification Method |
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249 | (2) |
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251 | (3) |
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254 | (1) |
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254 | (2) |
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256 | (1) |
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Deep Convolutional Neural Networks |
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257 | (3) |
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260 | (1) |
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260 | (4) |
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264 | (3) |
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ResNet50, InceptionV3, and InceptionResNetV2 Models |
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267 | (1) |
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267 | (1) |
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268 | (1) |
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269 | (5) |
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274 | (5) |
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Chapter 7 Blood Cell Microscope Image-Based Testing Using Deep Learning Techniques |
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279 | (18) |
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279 | (1) |
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COVID-19 and Blood Analysis: A Case Study |
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279 | (1) |
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280 | (13) |
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280 | (1) |
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280 | (1) |
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281 | (1) |
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282 | (3) |
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Parasitemia Evaluation Methods |
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285 | (1) |
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285 | (5) |
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290 | (2) |
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292 | (1) |
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293 | (1) |
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293 | (4) |
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PART IV Analysis of the Pre- and Post-Impact of the COVID-19 Pandemic Crisis |
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Chapter 8 Direct and Indirect Impacts of Environmental Factors on the COVID-19 Pandemic |
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297 | (50) |
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297 | (2) |
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COVID-19 and Other Large-Scale Epidemic Diseases of the 21st Century |
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299 | (3) |
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COVID-19 Environmental Impacts |
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302 | (18) |
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Impacts on the Physical Systems of the Environment |
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302 | (1) |
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Air Quality and Local Climate |
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303 | (7) |
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Impact on Water Resources |
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310 | (3) |
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Impact on Aquatic Systems and Wildlife |
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313 | (3) |
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Impacts on the Ecological Systems |
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316 | (1) |
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Impacts on Environmental Dimension of the Global Affairs |
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317 | (1) |
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Environmental Monitoring and Climate Services |
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318 | (2) |
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Impacts on the Present Climate and Climate Change |
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320 | (2) |
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Artificial Intelligence Tools and Techniques to Measure and Analysis the Impact of COVID-19 on Environment |
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322 | (17) |
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322 | (1) |
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323 | (1) |
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323 | (4) |
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327 | (12) |
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339 | (1) |
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340 | (1) |
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341 | (6) |
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Chapter 9 Direct and Indirect Impacts of the COVID-19 Pandemic Crisis on Economy |
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347 | (38) |
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347 | (3) |
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Impact Analysis from Past Epidemics as a Statistical Lesson |
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350 | (2) |
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352 | (1) |
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352 | (1) |
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Amplified Global Pandemic Scenario |
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353 | (1) |
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Global Economy Affection and Policies to Competing COVID-19 |
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353 | (2) |
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353 | (1) |
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Non-Government Business Policy |
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354 | (1) |
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Direct and Indirect Costs |
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355 | (8) |
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356 | (1) |
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356 | (1) |
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357 | (2) |
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Demand Shocks and Fluctuation |
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359 | (4) |
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Computational Model for Visual Analysis of COVID-19's Impact on the Global Economy |
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363 | (9) |
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363 | (1) |
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World Supply Shock Capacity Reduction |
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364 | (3) |
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367 | (1) |
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368 | (1) |
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Consumer Confidence and Demand Fluctuation |
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368 | (4) |
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COVID-19 and the Stock Market Uncertainty Analysis Using Time Series Model |
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372 | (8) |
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374 | (1) |
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375 | (1) |
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376 | (3) |
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Coronavirus and Unemployment Rates |
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379 | (1) |
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380 | (1) |
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381 | (4) |
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Chapter 10 Direct and Indirect Impacts of the COVID-19 Pandemic Crisis on Food & Agriculture |
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385 | (42) |
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385 | (1) |
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The Impact of COVID-19 on Agriculture-Food Market |
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386 | (4) |
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388 | (2) |
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390 | (1) |
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390 | (4) |
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391 | (1) |
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392 | (1) |
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393 | (1) |
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Agricultural System Connectivity |
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393 | (1) |
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Other Impacts and Questions |
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394 | (1) |
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Computation Model of Analysis |
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394 | (18) |
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394 | (1) |
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Impact of Pandemic on Food Safety Level |
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395 | (1) |
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396 | (2) |
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398 | (4) |
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402 | (1) |
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Production Function and Growth Accounting Model |
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403 | (1) |
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Data and Summary Statistics |
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404 | (1) |
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405 | (7) |
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412 | (9) |
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Economic Impact on Agriculture: World |
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412 | (5) |
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Economic Impact on Agriculture: India |
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417 | (4) |
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421 | (1) |
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422 | (1) |
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422 | (5) |
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Chapter 11 Direct and Indirect Impacts of the COVID-19 Pandemic Crisis on Hotels, Tour and Travel Sectors |
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427 | (38) |
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427 | (1) |
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Current Situation in the Tourism Sector |
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428 | (1) |
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COVID-19 Circumstances and Tourism |
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429 | (5) |
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COVID-19: Dismantling and Re-Mantling Tourism in Three Stages |
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434 | (8) |
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439 | (1) |
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Tourism Supply--Businesses |
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440 | (1) |
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Destination Management Organizations and Policymakers |
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441 | (1) |
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Impact of the Current Crisis on Tourism Destinations |
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442 | (5) |
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442 | (1) |
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442 | (1) |
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442 | (2) |
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444 | (1) |
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The United Kingdom Tourism |
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445 | (1) |
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445 | (1) |
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446 | (1) |
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446 | (1) |
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Computational Models for Tourism Demand Forecasting |
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447 | (13) |
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447 | (1) |
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447 | (8) |
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Support Vector Regression (SVR) |
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455 | (2) |
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457 | (1) |
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457 | (1) |
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458 | (2) |
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Expected Impactful Sectors Analysis |
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460 | (2) |
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460 | (1) |
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461 | (1) |
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461 | (1) |
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461 | (1) |
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461 | (1) |
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462 | (1) |
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Fashion and Luxury (Shopping Tourism) |
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462 | (1) |
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462 | (1) |
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462 | (3) |
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Chapter 12 Direct and Indirect Impacts of the COVID-19 Pandemic Crisis on Human Physical and Physiological Health |
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465 | (42) |
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465 | (1) |
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Impact of COVID-19 and Physical Inactivity on The Immune System |
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466 | (6) |
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COVID-19, Physical Activity, and the Respiratory System |
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468 | (1) |
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Impact of COVID-19 and Physical Inactivity on Cardiovascular System |
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469 | (1) |
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Impact of COVID-19 and Physical Inactivity on Musculoskeletal System |
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470 | (2) |
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COVID-19 Infection and the Brain Function |
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472 | (1) |
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Does SARS-Cov-2 Infection Threaten and Damage the Brain? |
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472 | (1) |
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Can Physical Fitness Protect or Attenuate the Consequences of Infection? |
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472 | (1) |
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Recommendation to Fight Against COVID-19-Associated Neurological and Mental Disorders |
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473 | (1) |
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Impact of COVID-19 on Older Adults |
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474 | (2) |
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Possible Effects of COVID-19 on Muscle Atrophy and Physical Function |
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474 | (1) |
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Are Frailty and Sarcopenia Possible Outcomes of COVID-19? |
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475 | (1) |
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Computational Approach to Analysis Impact of COVID-19 on Human Physiological Health |
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476 | (22) |
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478 | (2) |
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480 | (1) |
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480 | (2) |
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482 | (2) |
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Methods and Textual Data Analytics |
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484 | (1) |
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Exploratory Textual Analytics |
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485 | (1) |
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Data Acquisition and Preparation |
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486 | (1) |
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Word and Phrase Associations |
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486 | (1) |
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487 | (1) |
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Association with Non-Textual Variables |
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487 | (2) |
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489 | (1) |
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Machine Learning with Classification Methods |
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489 | (1) |
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490 | (2) |
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492 | (3) |
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A Digital Mental Health Revolution |
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495 | (1) |
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495 | (1) |
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Mental Health Smartphone Applications |
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496 | (1) |
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497 | (1) |
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497 | (1) |
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498 | (1) |
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498 | (9) |
Index |
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507 | |