Evaluating the Sensitivity of Event-Based Surveillance and Timeliness of Event and Indicator-Based Surveillance Systems in Kenya Using the 7-1-7 Metrics: A Multi-County Analysis, 2021-2025

Imagine the world's health system as a giant, bustling city. In this city, there are two main ways to spot trouble before it becomes a disaster. The first is like a team of official inspectors who walk around checking specific, pre-defined problems—like counting how many people have a fever or a cough. This is called Indicator-Based Surveillance (IBS). It's reliable and organized, but it only sees what it's looking for, and it might miss trouble happening in the back alleys where people don't go to the inspectors. The second way is like a network of curious neighbors and street vendors who shout out if they see anything weird—a strange smell, a dead animal, or a rumor of a mysterious illness. This is Event-Based Surveillance (EBS). It's messy and unstructured, but it can catch surprises that the inspectors miss.

Why does this matter? Because when a disease outbreak starts, time is the most precious thing we have. If we spot a fire the moment a spark flies, we can put it out with a bucket. If we wait until the whole building is burning, it's too late. Scientists use a global stopwatch called the "7-1-7" rule to see how fast a city reacts. The rule says: find the spark within 7 days, tell the fire department within 1 day, and start putting out the fire within 7 days of that call. If a city can do this, it's a superhero; if not, the fire spreads.

This paper is a report card for four counties in Kenya, where officials tried to mix these two systems together. The researchers wanted to see: Did the "curious neighbors" (EBS) actually help find outbreaks faster than the "official inspectors" (IBS)? And did the whole team manage to beat the 7-1-7 stopwatch?

The Detective Story: What They Found

The researchers looked back at the last four years (from May 2021 to May 2025) and counted 28 confirmed disease outbreaks in the counties of Busia, Meru, Nakuru, and Siaya. They asked a simple question: Who found the trouble first?

The "Curious Neighbors" Win the Race to the Spark
The Event-Based Surveillance system (the neighbors) was the hero of the early game. It managed to spot 16 out of the 28 outbreaks, which is a 57.1% success rate. This means that in more than half the cases, the system caught the problem before the official inspectors did.

• The Speedsters: When EBS found a problem, it was incredibly fast at the first two steps. 100% of the time, they found the outbreak within 7 days of it starting. 81.3% of the time, they told the authorities within just 1 day of finding it.
• The Best at the Job: The system was a superstar at catching Anthrax (a disease often linked to animals), finding 5 out of 7 outbreaks. It also did a decent job with Cholera and Measles.

The "Official Inspectors" Win the Race to the Solution
However, once the fire was spotted, the story changed. The Indicator-Based Surveillance system (the inspectors) was actually better at the final step: putting out the fire.

• When the official system found an outbreak, 75% of the time, they started their response actions within 7 days of being notified.
• In contrast, when the "neighbors" (EBS) found the outbreak, they only managed to start the response within 7 days 68.8% of the time.

The Big Picture: The 7-1-7 Challenge
The ultimate goal was to do everything perfectly: find it in 7 days, tell someone in 1 day, and act in 7 days.

• Only 15 out of the 28 outbreaks (about 53.6%) met the full 7-1-7 target.
• Interestingly, the "neighbors" (EBS) were slightly better at meeting the whole target (56.3%) than the "inspectors" (50%), mostly because they were so good at the very first step.

The Hiccups: Why It Wasn't Perfect

The paper also looked at why the system sometimes stumbled. It's like a relay race where the baton gets dropped.

• The "Silent" Problems: Some diseases, like Monkeypox, were hard to catch. The "neighbors" often missed them because people were scared or embarrassed to talk about them (stigma), or because they didn't know what the symptoms looked like.
• The "Slow" Response: Even when the fire was spotted, the team sometimes took too long to grab the water buckets. This happened because of things like waiting for money to arrive, not having enough trucks to get to the scene, or waiting too long for lab results.
• The Good Stuff: When things did go well, it was because of trained community helpers, hotlines people could call, and digital tools that made reporting easy.

The Takeaway

The paper suggests that Kenya's health system is like a team with two different superpowers. The Event-Based system is the best at hearing the first siren—it's the one that spots the spark early. The Indicator-Based system is the best at running the fire truck—it's the one that knows exactly what to do once the alarm is raised.

The researchers conclude that we shouldn't pick one over the other. Instead, we need to make sure the "neighbors" can shout to the "inspectors" instantly, so the whole team can beat the 7-1-7 clock and keep the city safe. It's not a solved problem yet—only about half the outbreaks met the perfect standard—but the mix of these two systems is clearly working better than either one alone.