KibaSeq

At Kibaseq, we’re redefining how outbreaks are anticipated by shifting from reaction to prediction. We turn mosquito genomics into actionable foresight, building a system that signals outbreaks before they strike.From data to detection to decision, it’s early warning reimagined.

Mosquito-borne outbreaks like malaria and dengue are often caught too late, only after patients arrive at clinics. By then, the spread is already in motion.

At Kibaseq, we believe outbreak response should be predictive, not reactive.

We're building Africa’s most advanced geo-tagged mosquito genomics database, the foundation for a realtime early warning system against vector-borne diseases.

In the future, Kibaseq will combine:

High-resolution genomic data on mosquito populations

Pathogen screening results (e.g., Plasmodium, flaviviruses)

Environmental metadata (temperature, rainfall, land use)

Machine learning models trained to detect risk signals

Our goal is to create a predictive platform that alerts health officials to emerging threats, weeks before symptoms appear at scale.

Questions we would like to answer:

Is Dengue likely to appear in this city next month?

Are we seeing an increase in insecticide resistance in this zone?

Has a known malaria vector species shifted its range due to climate change?

What Makes Kibaseq Different?

Unlike traditional disease surveillance, which focuses on patients, Kibaseq’s approach starts at the vector level, where outbreaks begin. By analysing the genetic changes in mosquitoes, we can spot early warning signs that would otherwise go unnoticed.

Because our platform is built around low-cost, field-based collection and centralised sequencing, it is designed to scale across under-resourced regions, including areas that have historically been off the map for disease forecasting.

Right now, we are laying the groundwork:

Building partnerships with field teams across sub-Saharan Africa

Collecting and sequencing mosquito samples

Tagging every genome with location, date, and context

Training initial machine learning models to detect genomic trends

We are not yet predicting outbreaks — but every sample we collecting moves us closer to that goal.

What’s Next for Kibaseq:

As our dataset grows, our models improve. Over the next 12–24 months, Kibaseq will begin piloting region-specific forecasting tools in collaboration with academic partners and public health agencies.

If your organisation is interested in working with us on outbreak prediction, we'd love to hear from you.

Together, we can shift from reaction to prevention — and give communities the head start they need.

If your organisation is interested in working with us on outbreak prediction, we'd love to hear from you.

Together, we can shift from reaction to prevention — and give communities the head start they need.