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Record W4409383224 · doi:10.1038/s41467-025-58612-w

Mean daily temperatures predict the thermal limits of malaria transmission better than hourly rate summation

2025· article· en· W4409383224 on OpenAlexaff
Marta S. Shocket, Joey R. Bernhardt, Kerri Miazgowicz, Alyzeh Orakzai, Van M. Savage, Richard J. Hall, Sadie J. Ryan, Courtney C. Murdock

Bibliographic record

VenueNature Communications · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Guelph
FundersNational Institute of Allergy and Infectious DiseasesU.S. Department of Health and Human ServicesNational Institutes of HealthNational Science Foundation
KeywordsAnopheles stephensiTraitTransmission (telecommunications)Constant (computer programming)Environmental scienceStatisticsThermalBiologyEcologyAtmospheric sciencesMathematicsMeteorologyAedes aegyptiPhysicsComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.255
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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