# Impact of insecticide control measures and temperature on Dengue Virus transmission by Aedes aegypti mosquitoes

> **NIH NIH F31** · UNIVERSITY OF NOTRE DAME · 2024 · $48,974

## Abstract

Project Summary
Dengue is a human disease caused by the dengue virus (DENV) and transmitted by Aedes aegypti and Aedes
albopictus mosquitoes that afflicts hundreds of millions of humans, causes 20,000 confirmed human deaths
annually, and puts >3.6 billion people at risk. Temperature is well-documented to influence the traits of both
mosquitoes and DENV, exhibiting unimodal responses known as thermal performance curves (TPCs), thus
also altering R0 an important proxy of the population growth rate of pathogens and thus transmission.
Consequently, global climate change is expected to alter incidence and seasonal dynamics of dengue, and
ecologically relevant predictive models are essential to mitigate these changes to disease risk. In recent years,
my sponsor, co-sponsor, and colleagues have developed temperature-dependent, trait-based transmission
models for mosquito-borne diseases (including dengue), using a generalized R0 equation derived from the
classic Ross-Macdonald model. This predictive equation, however, is limited by the fact that temperature is the
sole abiotic factor considered, despite other widespread abiotic factors, such as insecticides, being well known
to impact traits of mosquitoes that affect transmission. My objective for this application is to develop and
parameterize this model for DENV transmission by A. aegypti with commonly deployed insecticides with an
overall goal of reducing dengue. My central hypothesis is that insecticides and temperature interact
synergistically or antagonistically, rather than additively, to affect the R0 of DENV. To test this hypothesis, I will
conduct response surface experiments crossing 5 insecticide doses of both the larvicide temephos and the
adulticide deltamethrin and 7 temperatures on juvenile and adult A. aegypti and DENV, and I will measure all
eight temperature-dependent parameters in the generalized R0 equation. Using Bayesian inference, I will fit
thermal performance curves to each trait across insecticide doses and implement these into the R0 equation.
Once these aims have been completed, I will have developed the first fully parameterized insecticide- and
temperature-dependent R0 model for dengue. The aim of this R0 model is to more accurately predict disease
incidence, identify the extent to which temperature impacts the efficacy of common insecticides, determine the
ideal climatic and seasonal conditions to deploy insecticides in, determine concentration levels required for
insecticides to control dengue in different regions, and determine how to respond to and leverage climate
change in the face of range shifts and expansions.

## Key facts

- **NIH application ID:** 10900909
- **Project number:** 1F31AI183638-01
- **Recipient organization:** UNIVERSITY OF NOTRE DAME
- **Principal Investigator:** Patrick Heffernan
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $48,974
- **Award type:** 1
- **Project period:** 2024-08-15 → 2026-08-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10900909

## Citation

> US National Institutes of Health, RePORTER application 10900909, Impact of insecticide control measures and temperature on Dengue Virus transmission by Aedes aegypti mosquitoes (1F31AI183638-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10900909. Licensed CC0.

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