Analyzing and modeling spatial and temporal dynamics of infectious diseases
Bibliographic record
Abstract
Foreword ix Nicholas Chrisman Acknowledgements xi Editors xiii Contributors xv PART I OVERVIEW 1 Introduction to Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases 3 Dongmei Chen, Bernard Moulin, and Jianhong Wu 2 Modeling the Spread of Infectious Diseases: A Review 19 Dongmei Chen PART II MATHEMATICAL MODELING OF INFECTIOUS DISEASES 3 West Nile Virus: A Narrative from Bioinformatics and Mathematical Modeling Studies 45 U.S.N. Murty, Amit K. Banerjee, and Jianhong Wu 4 West Nile Virus Risk Assessment and Forecasting Using Statistical and Dynamical Models 77 Ahmed Abdelrazec, Yurong Cao, Xin Gao, Paul Proctor, Hui Zheng, and Huaiping Zhu 5 Using Mathematical Modeling to Integrate Disease Surveillance and Global Air Transportation Data 97 Julien Arino and Kamran Khan 6 Malaria Models with Spatial Effects 109 Daozhou Gao and Shigui Ruan 7 Avian Influenza Spread and Transmission Dynamics 137 Lydia Bourouiba, Stephen Gourley, Rongsong Liu, John Takekawa, and Jianhong Wu PART III SPATIAL ANALYSIS AND STATISTICAL MODELING OF INFECTIOUS DISEASES 8 Analyzing the Potential Impact of Bird Migration on the Global Spread of H5N1 Avian Influenza (2007 2011) Using Spatiotemporal Mapping Methods 163 Heather Richardson and Dongmei Chen 9 Cloud Computing Enabled Cluster Detection Using a Flexibly Shaped Scan Statistic for Real-Time Syndromic Surveillance 177 Paul Belanger and Kieran Moore 10 Mapping the Distribution of Malaria: Current Approaches and Future Directions 189 Leah R. Johnson, Kevin D. Lafferty, Amy McNally, Erin Mordecai, Krijn P. Paaijmans, Samraat Pawar, and Sadie J. Ryan 11 Statistical Modeling of Spatiotemporal Infectious Disease Transmission 211 Rob Deardon, Xuan Fang, and Grace P.S. Kwong 12 Spatiotemporal Dynamics of Schistosomiasis in China: Bayesian-Based Geostatistical Analysis 233 Zhi-Jie Zhang 13 Spatial Analysis and Statistical Modeling of 2009 H1N1 Pandemic in the Greater Toronto Area 247 Frank Wen, Dongmei Chen, and Anna Majury 14 West Nile Virus Mosquito Abundance Modeling Using Nonstationary Spatiotemporal Geostatistics 263 Eun-Hye Yoo, Dongmei Chen, and Curtis Russel 15 Spatial Pattern Analysis of Multivariate Disease Data 283 Cindy X. Feng and Charmaine B. Dean PART IV GEOSIMULATION AND TOOLS FOR ANALYZING AND SIMULATING SPREADS OF INFECTIOUS DISEASES 16 The ZoonosisMAGS Project (Part 1): Population-Based Geosimulation of Zoonoses in an Informed Virtual Geographic Environment 299 Bernard Moulin, Mondher Bouden, and Daniel Navarro 17 ZoonosisMAGS Project (Part 2): Complementarity of a Rapid-Prototyping Tool and of a Full-Scale Geosimulator for Population-Based Geosimulation of Zoonoses 341 Bernard Moulin, Daniel Navarro, Dominic Marcotte, Said Sedrati, and Mondher Bouden 18 Web Mapping and Behavior Pattern Extraction Tools to Assess Lyme Disease Risk for Humans in Peri-urban Forests 371 Hedi Haddad, Bernard Moulin, Franck Manirakiza, Christelle M'eha, Vincent Godard, and Samuel Mermet 19 An Integrated Approach for Communicable Disease Geosimulation Based on Epidemiological, Human Mobility and Public Intervention Models 403 Hedi Haddad, Bernard Moulin, and Marius Theriault 20 Smartphone Trajectories as Data Sources for Agent-based Infection-spread Modeling 443 Marcia R. Friesen and Robert D. McLeod Index 473
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".