A meteorological-based forecasting model for predicting minimal infection rates in Culex pipiens-restuans complex using Québec’s West Nile virus integrated surveillance system
Bibliographic record
Abstract
Background: The ministère de la Santé et des Services sociaux (MSSS) du Québec (Québec's health authority) has expressed an interest in the development of an early warning tool to identify seasonal human outbreaks of West Nile virus infection in order to modulate public health interventions.The objective of this study was to determine if a user-friendly meteorological-based forecasting tool could be used to predict minimal infection rates for the Culex pipiens-restuans complex-a proxy of human risk-ahead of mosquito season.Methods: Annual minimal infection rate (number of positive pools/number of mosquitoes) was calculated for 856 mosquito traps set from 2003 to 2006 and 2013 to 2018 throughout the south of Québec's.Coefficient of determination (R 2 ) were estimated using the validation dataset (one third of the database by random selection) with generalized estimation equations, which were prior fitted backwards with polynomial terms using the training dataset (two thirds of the database), in order to minimize the Bayesian information criteria.Mean temperatures and precipitation were grouped at five temporal scales (by month, by season and by 4, 6 and 10-months groupings).Results: Mean temperatures and cumulative precipitation from the previous months of March (R 2 =0.37),May (R 2 =0.36), December (R 2 =0.35) and the autumn season (R 2 =0.38) accounted for ~40% of Cx. pipiens-restuans annual minimal infection rates variations.Including the "year of sampling" variable in all regression models increased the predictive abilities (R 2 between 0.42 and 0.57). Conclusion:All regression models explored have too weak predictive abilities to be useful as a public health tool.Other factors implicated in the epidemiology of the West Nile virus need to be incorporated in a meteorological-based early warning model for it to be useful to the provincial health authorities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".