The predictability of mosquito abundance from daily to monthly timescales
Bibliographic record
Abstract
The prediction of mosquito abundance is of central interest in addressing mosquito population dynamics and in forecasting the associated emerging and re-emerging diseases. However, little work has focused on the systematic evaluation of how well adult mosquito abundance can be predicted as a function of observational resolutions, aggregation scales, and prediction lead time. We use a state space reconstruction (SSR) approach to compare the predictability of mosquito population dynamics at weekly, biweekly, and monthly scales. We focus on the analysis of Aedes vexans and Culiseta melanura populations monitored in Brunswick County (North Carolina, USA) and find that prediction over a 7-d lead time is improved when daily observations are used, compared to the commonly used once-per-week sample. Our results demonstrate that daily observations of mosquito abundance contribute to improving mosquito predictability in two ways: (1) daily observations better capture fluctuations over short timescales, which are missed when sampling at coarser resolutions, and (2) the aggregation of daily abundance observations reduces the impact of noise, thereby increasing the predictability of mosquito population dynamics as the aggregation scale is increased. We show that the evaluation of population dynamical models based on observed and predicted abundance can lead to a spuriously high apparent performance, due to the high autocorrelation in the observations used to update the model state at each successive time step. We show that the comparison of predicted and observed population change, expressed through per capita growth rates, leads to a more informative performance measure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".