In the past, it has been common practice for veterinary health experts, public
health professionals and environmental health specialists to work separately.
But now we are moving into a new framework called One Health
that encourages everyone to work together.
For example, if you look at anthrax disease,
when an outbreak occurs and animals die people can get anthrax
when they get exposed to their animals or their products.
And also, when these animals are not buried properly, they contaminate the environment
so this is a classical example of the definition of One Health.
And now we're seeing that 60% of infectious diseases are zoonotic
in nature, meaning they can jump from animals to humans,
and this will most likely increase in the coming years.
My name is Valentina Ndolo.
I specialise in applying machine learning to understand the distribution
of anthrax risk across Uganda and Kenya.
I chose these two locations because the two countries have had several
sporadic outbreaks of anthrax disease, and these outbreaks
are becoming more frequent and the intensity is also increasing.
The outbreaks affect livestock as well as humans.
And this is a really big concern for public health as well as veterinary health.
So one of the challenges facing ecological datasets is sampling bias.
And what this means is that areas that are closer to surveillance sites
are usually more sampled than areas that are farther away.
And this causes a big problem for NICHE modellings
and this is why I've decided to use a new approach that can incorporate
these structural dependencies and design more accurate models
that can actually be more useful to policymakers and stakeholders.
Before my Ph.D., I specialised in laboratory science
as being a biochemist and also doing a lab-based project for my master's degree.
So when I started my Ph.D., I was a complete tech newbie.
So that means I hadn't really applied some of the methods I'm using now before.
And I had to learn them from scratch.
When I started my Ph.D. during my first year,
I attended various online courses as well as the free training courses
offered by the university, and I was able to learn how to use different computer
programing methods, particularly R, and also how to apply machine
learning methods like my MAXENT and INLA, as well as other algorithms
that I used to predict disease hotspots across wider geographical areas.
So one of the biggest challenges in terms of pursuing scientific
careers at advanced levels is really understanding the kind of area
you want to pursue and how to go about making the applications.
And sometimes this is difficult, especially when prospective applicants
don't have an idea of how to write personal statements or how to write the research proposal,
how to approach their supervisors and secure one, generally
just how to go through the application process for admission and scholarships.
And this is the reason why I set up STEMing Africa initiative to provide
talented young women with the skills they need to understand how to write their proposals,
to write their personal statements, to approach supervisors,
and generally how to do their applications
so that they guarantee the success from the onset.
Because when I was applying for my Ph.D.
and when applying for my master's, I did it basically alone.
And I really understood how important it was to have some sort of background
information and some mentorship from someone who has been through the process.
So, yeah, this is what I'm trying to do
with the initiative just to be that gap, be that middle person, to help
these talented young women to get places at leading universities across the world.