Kebba Jobarteh is a Disease Surveillance Officer with the Epidemiology and Disease Control Program at the Ministry of Health in The Gambia, based in Banjul. He spoke with us about moving the Ministry’s surveillance analysis out of spreadsheets and into R—and the role of Epiverse training via MRC-LSHTM The Gambia Data Science Department.
What brought you to this work?
I studied science in high school, and then public health at college, with my focus on biostatistics and epidemiology. So when I was employed and posted to the Epidemiology and Disease Control Program, I said to myself: I’m interested in analyzing data. Data is my ambition. The program is responsible for disease surveillance across the country, and my area of focus was the data to support this. I’m responsible for the analysis.
That moment lit a fire in me because if AI could do that for one Mississippi business, imagine what the impact of the AI Agency could do across communities. I didn’t just see another tool of technology; I saw digital transformation. That’s when I knew I was stepping into something bigger than myself.
What was the problem you were trying to solve?
Healthcare settings generate a lot of data. It needs to be analyzed to make decisions. But we had been generating data without analyzing it that much. That was the gap.
Another challenge we face: We have very few data analysis specialists in the Ministry. And I was working entirely in Excel, where you have to do everything manually. That takes a lot of time.
So when I heard about Epiverse, that is what drew me to it.
We are now able to efficiently analyze and interpret our data, enabling more informed and evidence-based decision-making.
Kebba Jobarteh Epidemiology and Disease Surveillance Officer Ministry of Health, The Gambia
What was the training you received?
I was first introduced to Epiverse at a pre-conference at MRC Unit The Gambia at LSHTM. I fell in love with it. I was then fortunate to have a three-month internship through the Epiverse project at MRC@LSHTM Data Science Department.
That took me on a learning journey through R. While I would say I’m still at the beginner level, I can now do a lot of things using R that were not possible before.
Fortunately, the training is continuing. data.org Epiverse Fellow Lamin Jawara organizes a weekly R training in collaboration with the Data Science department at MRC@LSHTM, which takes place every Thursday from 12 pm to 2 pm and takes the whole Ministry of Health staff in our complex through data analysis in R. Any staff member who has the time is advised to come and attend.
What was the impact?
The training we received from the fellowship program got us up and running. Immediately after I finished my internship, we recorded our first case of mpox in The Gambia. Based on my Epiverse knowledge, I was the one responsible for producing the situational report on a daily basis. I used the Epiverse tools to analyze the data by person, place, and time, then produced the SitRep and shared it. We had one confirmed case. We’ve seen a lot of suspected cases since, but after laboratory confirmation, they’ve all been negative.
We also produce a weekly IDSR bulletin—Integrated Disease Surveillance and Response—and we analyze the data for each week.
The analysis changed things in three ways.
It improved the data itself. When we started analyzing, we saw gaps. There were important variables in our dataset that were simply empty. The data is generated at the facility level, so we’ve gone back to encourage them not to miss any variable when entering data—every variable matters, and it needs to be entered.
It directed our interventions. We analyze to identify hotspots: which areas are recording which disease conditions, and which interventions we can put in place. Through that analysis, we found that schistosomiasis is endemic to two regions in The Gambia—Central River Region and Upper River Region.
It changed how long the work takes. In Excel, everything was manual. With R, the only hard part is preparing the script. Once the script is done, the work is easier—anytime you need the analysis, you just import the data. Within seconds, you get your results.
What’s the potential for these solutions to travel?
Right now, I can say I’m the person doing most of the R analysis for my institution.
But during that weekly R training in collaboration with the Data Science department at MRC@LSHTM many people have fallen in love with R. They want to use it to analyze their data. More people are engaging. I’ve seen a lot of enthusiastic people who want to work with data and use R for analysis.
Our program is the focal point for surveillance, but there are other disease-specific programs across the Ministry of Health—e.g., the Malaria Control Program, Hepatitis, HIV, and cancer control. Each one is targeting a specific disease, and each one has its own data. If those teams familiarize themselves with Epiverse and analyze their own data, they’ll be able to design a lot more interventions, targeted to what they’re seeing.
In closing, my appeal is for open-source material—a lot of it, shared openly, so that anybody who has the chance can go through it and familiarize themselves with it. We need more experts when it comes to data analysis using the Epiverse tools. They are very important to the work.
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