Data Preparation for West Nile Virus Agent-Based Modelling: Protocol for Processing Bird Population Estimates and Incorporating ArcMap in AnyLogic
Bibliographic record
Abstract
BACKGROUND: West Nile Virus (WNV) was first isolated in 1937. Since the 1950s, many outbreaks have occurred in various countries. The first appearance of infected birds in Manitoba, Canada was in 2002. OBJECTIVE: This paper describes the data preparation phase of setting up a geographic information system (GIS) simulation environment for WNV Agent-Based Modelling in Manitoba. METHODS: The main technology used in this protocol is based on AnyLogic and ArcGIS software. A diverse variety of topics and techniques regarding the data collection phase are presented, as modelling WNV has many disparate attributes, including landscape and weather impacts on mosquito population dynamics and birds' roosting locations, population count, and movement patterns. RESULTS: Different maps were combined to create a grid land cover map of Manitoba, Canada in a shapefile format compatible with AnyLogic, in order to modulate mosquito parameters. A significant amount of data regarding 152 bird species, along with their population estimates and locations in Manitoba, were gathered and assembled. Municipality shapefile maps were converted to built-in AnyLogic GIS regions for better compatibility with census data and initial placement of human agents. Accessing shapefiles and their databases in AnyLogic are also discussed. CONCLUSIONS: AnyLogic simulation software in combination with Esri ArcGIS provides a powerful toolbox for developers and modellers to simulate almost any GIS-based environment or process. This research should be useful to others working on a variety of mosquito-borne diseases (eg, Zika, dengue, and chikungunya) by demonstrating the importance of data relating to Manitoba and/or introducing procedures to compile such data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 0.027 |
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 source (direct Gemma or distilled Codex), 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".