Mean daily temperatures predict the thermal limits of malaria transmission better than hourly rate summation
Bibliographic record
Abstract
Temperature shapes the geographic distribution, seasonality, and magnitude of mosquito-borne disease outbreaks. Models predicting transmission often use mosquito and pathogen thermal responses measured at constant temperatures. However, mosquitoes live in fluctuating temperatures. Rate summation––non-linear averaging of trait values measured at constant temperatures—is commonly used to infer performance in fluctuating environments, but its accuracy is rarely validated. We measured three traits that impact transmission—bite rate, survival, fecundity—in a malaria mosquito (Anopheles stephensi) across three diurnal temperature ranges (0, 9, and 12 °C). We compared transmission thermal suitability models with temperature-trait relationships observed under constant temperatures, fluctuating temperatures, and those predicted by rate summation. We mapped results across An. stephenesi’s native Asian and invasive African ranges. We found: 1) daily temperature fluctuation trait values substantially differ from both constant temperature experiments and rate summation; 2) rate summation partially captured decreases in performance near thermal optima, yet incorrectly predicted increases near thermal limits; and 3) while thermal suitability across constant temperatures did not perfectly capture fluctuating environments, it was better than rate summation for estimating and mapping thermal limits. Our study provides insight into methods for predicting mosquito-borne disease risk and emphasizes the need to improve understanding of organismal performance under fluctuating conditions. Malaria transmission is sensitive to temperature and models of malaria typically account for daily fluctuations in temperature through a ‘rate summation’ approach. Here, the authors conduct experimental and modeling work to investigate the accuracy of rate summation in predicting thermal suitability of malaria transmission by Anopheles stephensi.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".