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Malaria begins with a mosquito’s decision to land.

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A global scientific effort to turn mosquito behavior data into training data. Our models learn which kinds of compounds are most likely to change a mosquito’s decision to land on human skin.

Proudly partnering with leaders in global health, crop protection, and machine learning.

  • University of Toronto, Chemical Engineering & Applied Chemistry
  • United States Department of Agriculture
  • Rutgers University, Philip E. Marucci Center for Blueberry & Cranberry Research, New Jersey Agricultural Experiment Station
  • NVIDIA Inception Program
  • Ifakara Health Institute
  • Kenya Medical Research Institute (KEMRI)
  • Spatial Repellents, University of Notre Dame
  • Dweck Lab
  • Lambda
  • Google Cloud
  • Spectrum Impact

Mosquito behavior data to malaria prevention.

Goal 01 ·

Run 50,000 behavior-rich assays a month and share results within 24 hours of each run.

Goal 02 ·

Predict how at least 75% of new compounds change mosquito landing behavior.

Goal 03 ·

Scale-up at three formulations that reduce malaria transmission by at least 50% compared with current options.

Mosquito behavior data → training data → compounds that change a mosquito’s decision

CONTROL
COMPOUND 143

Ask questions across every assay, compound, and result.

Get an Ask Monarch setup code from your lab portal, then connect it to Claude, Codex, or your terminal.

What we’ve done so far.

2,073 Mosquito assay runs to date
10 Independent lines tested
13 Network labs
50,000 assays Target · not yet reached