Seroprevalence of chikungunya virus in Colombo, Sri Lanka before the 2025 outbreak and implications for population susceptibility

A 2024 seroprevalence study in Colombo, Sri Lanka, revealed that minimal chikungunya transmission occurred over the preceding 16 years, leaving a largely susceptible population with significantly higher immunity in urban areas driven by density and overcrowding, thereby highlighting the critical need for targeted vaccine strategies.

The Gist

Imagine Sri Lanka as a giant, bustling city that had a massive party with a very annoying guest called the Chikungunya Virus back in 2006. This virus is like a mosquito-borne "party crasher" that causes terrible joint pain and fever. After that big party, the city went quiet for 16 years. No one saw the virus again.

Scientists wondered: "Did the virus just leave town, or did the people build a secret shield (immunity) that kept it out?"

To find out, a team of researchers went into two different neighborhoods in Colombo right before a new outbreak started in late 2024. They wanted to see who had that "secret shield" (antibodies) from a past infection.

Here is the story of what they found, explained simply:

1. The Two Neighborhoods: The "Crowded Apartment" vs. The "Spacious Suburb"

The researchers looked at two very different places:

• The Urban Area (Kirulapone): Think of this as a packed apartment building. It's tiny, crowded, and many people live in small spaces. The population density is huge.
• The Semi-Urban Area (Boralesgamuwa): Think of this as a spacious suburb. Houses are bigger, there's more room between neighbors, and it's less crowded.

2. The Big Discovery: The "Shield" Was Missing in the Kids

The researchers tested blood from nearly 1,200 people. They were looking for "Chikungunya antibodies," which act like security badges proving you've fought the virus before.

• The Kids (<16 years old): Almost zero children had these security badges.

  • The Analogy: Imagine a school where no one has ever seen the "Party Crasher" before. This means the virus was completely silent for 16 years. It wasn't hiding in the background; it was simply gone.
  • The Result: Because the virus was gone, the entire generation of children grew up with zero protection. They are like a fresh, open door for the virus to walk right in.

• The Adults (>16 years old): Many adults did have the security badges, but only if they lived in the crowded apartment.

  • In the city (Urban), about 40% of adults had the shield.
  • In the suburbs (Semi-urban), only about 23% had the shield.

3. Why the "Crowded Apartment" Was a Virus Super-Highway

The study found that living in the crowded urban area was the single biggest reason people got infected in the past.

• The Metaphor: Mosquitoes are like tiny, flying delivery drones. In a crowded apartment building, the drones don't have to fly far to find a new person to bite. They can buzz from one room to the next easily. In the spacious suburb, the drones have to fly further, making it harder for the virus to spread.
• The Finding: The study showed that overcrowding, poor housing, and high population density are the fuel that makes the virus spread like wildfire.

4. The "Mosquito Net" Surprise

The researchers asked people if they used mosquito nets. Surprisingly, people who used nets were less likely to have the virus.

• The Twist: Mosquitoes that carry Chikungunya are "day biters" (they bite during the day), while nets are usually used at night. You might think, "Why would a night net help?"
• The Explanation: It turns out, using a net might just mean the person is generally more careful about mosquito protection, or perhaps they use them during the day too. It's like wearing a helmet while riding a bike; even if you aren't falling right now, wearing the helmet shows you are safety-conscious.

5. The "Health Shield" Paradox

The study also looked at people with diabetes, high blood pressure, or who were overweight.

• The Finding: In the city, people with these health conditions were more likely to have the virus.
• The Mystery: This is confusing! Usually, we think sick people stay inside and avoid mosquitoes. But here, it seems the virus found them more often. The scientists aren't 100% sure why yet, but it suggests that if the virus comes back, these people might get hit harder.

The Bottom Line: Why This Matters for the Future

The study was done just before a massive outbreak in late 2024. Here is the takeaway:

1. The City is Vulnerable: Because the virus was gone for 16 years, the children (and many adults) have no immunity. The city is like a dry forest waiting for a spark.
2. The Spark Ignited: As predicted, a huge outbreak happened right after this study.
3. The Lesson: To stop the next outbreak, we can't just rely on vaccines alone. We need to fix the housing. If we can reduce overcrowding and make living conditions better (like giving people more space), we can slow down the "mosquito drones" and stop the virus from spreading so fast.

In short: The virus took a 16-year nap, but when it woke up, it found a city full of unprotected people living in crowded conditions. The study tells us that to win the next battle, we need to build better homes and protect the most vulnerable, not just treat the sickness.

Technical Summary

1. Problem Statement

Following a major Chikungunya virus (CHIKV) outbreak in Sri Lanka between 2006 and 2008, the country experienced a 16-year period (2008–2024) with no reported large-scale outbreaks. This hiatus was hypothesized to be due to high population immunity acquired during the 2006–2008 epidemic. However, with the emergence of a new large outbreak in late 2024 and the recent approval of CHIKV vaccines, there is an urgent need to understand the baseline population immunity prior to this new wave.

Key questions addressed include:

• What is the current age-stratified seroprevalence of CHIKV in Sri Lanka after 16 years of low transmission?
• How does immunity differ between urban and semi-urban settings?
• What are the independent risk factors for seropositivity (e.g., housing, demographics, comorbidities)?
• What are the implications for vaccine deployment strategies?

2. Methodology

Study Design and Population:

• Type: Cross-sectional seroprevalence study.
• Timing: Conducted from September to November 2024, immediately preceding the December 2024 outbreak.
• Location: Colombo, Sri Lanka.
  • Urban Site: Kirulapone (1.23 km², population density ~13,842/km²).
  • Semi-Urban Site: Rattanapitiya and Egodawatta (1.5 km², population density ~5,942/km²).
• Sample Size: 1,196 participants (ages 5–86 years).
  • Urban: $n=816$ (311 households).
  • Semi-urban: $n=380$ (266 households).

Data Collection:

• Biological: Venous blood samples collected; serum separated and stored at -80°C.
• Clinical/Demographic: Interviewer-administered questionnaires collected socio-demographic data, housing conditions (overcrowding, materials), socio-economic status, and comorbidities (diabetes, hypertension, obesity).
• Comorbidity Definitions:
  • Diabetes: On treatment or Fasting Blood Sugar (FBS) $\ge$ 100 mg/dL.
  • Hypertension: Diagnosed or BP $\ge$ 130/85 mm Hg.
  • Central Obesity: Waist circumference $\ge$ 80cm (F) / $\ge$ 90cm (M).
  • Overweight: BMI $\ge$ 23.9 kg/m² (South Asian cutoff).

Laboratory Assay:

• Target: CHIKV-specific IgG antibodies.
• Method: SERION ELISA classic Chikungunya Virus IgG kit (Institut Virion\Serion GmbH, Germany).
• Exclusion: Borderline results were excluded from the primary analysis.

Statistical Analysis:

• Tools: Python (pandas, NumPy, SciPy, statsmodels) and ArcGIS Pro (for spatial analysis).
• Tests: Mann–Whitney U test (continuous), Pearson Chi-square (categorical), Spearman's rank correlation (age vs. seroprevalence).
• Multivariable Modeling: Logistic regression to identify independent risk factors (adjusted for age, gender, area, comorbidities, and household variables).
• Spatial Analysis: Kernel Density Estimation (KDE) to visualize household-level clustering.

3. Key Results

Overall Seroprevalence:

• Total seropositivity: 34.3% (410/1196).
• Urban vs. Semi-Urban: Significantly higher in urban areas (39.6%) compared to semi-urban areas (22.9%; $p < 0.001$).

Age Stratification:

• Children (<16 years): Extremely low seropositivity.
  • Semi-urban: 0%.
  • Urban: 0.55% (2 individuals).
  • Implication: Minimal CHIKV transmission occurred in the preceding 16 years, leaving a large cohort of children and young adults susceptible.
• Adults (>16 years):
  • Urban: Seroprevalence increased significantly with age (Spearman's $r=0.93$, $p=0.003$). Rates exceeded 50% in those aged >45 years.
  • Semi-urban: No significant increase with age ($p=0.148$).
  • Comparison: Urban seropositivity was significantly higher than semi-urban in all age groups >25 years.

Risk Factors (Multivariable Analysis):

• Strongest Predictor: Living in an urban area (Adjusted Odds Ratio [aOR] 7.48; 95% CI 4.05–13.81). This correlates with higher population density, smaller house sizes, and overcrowding.
• Age: Older age groups (55–64, 65–74, $\ge$75) had significantly higher odds of seropositivity compared to the 18–34 age group.
• Protective Factor: Use of mosquito nets was independently associated with reduced risk (aOR 0.50; $p=0.029$), despite Aedes mosquitoes being day-biters.
• Comorbidities:
  • In the urban cohort, seropositivity was significantly associated with diabetes, central obesity, overweight status, and hypertension in univariable analysis.
  • In multivariable analysis, only overweight status (BMI $\ge$ 23.9) remained an independent risk factor (aOR 2.10).

Spatial Clustering:

• Kernel Density Estimation confirmed spatial clustering of seropositive households, particularly in the high-density urban setting.

4. Key Contributions

1. Baseline Immunity Mapping: Provided the first age-stratified seroprevalence data for Sri Lanka prior to the 2024/2025 outbreak, revealing a "susceptible gap" in the population under 16 years old.
2. Urbanization as a Driver: Quantified the impact of urbanization, identifying living in a high-density urban environment as the single strongest risk factor for past CHIKV infection, surpassing individual demographic factors.
3. Vaccine Strategy Implications: The data suggests that a large proportion of the population (especially children and young adults) lacks herd immunity. This informs the critical need for targeted vaccination strategies to prevent future outbreaks, as natural immunity is insufficient.
4. Comorbidity Link: Highlighted a potential association between metabolic risk factors (specifically overweight status) and CHIKV exposure in urban settings, warranting further investigation into whether these factors influence vector contact or susceptibility.

5. Significance and Conclusion

• Susceptibility: The study confirms that after 16 years of low transmission, Sri Lanka has a highly susceptible population, particularly those born after the 2006–2008 outbreak. The low seroprevalence in children (<16 years) indicates that the virus did not circulate silently during this period.
• Transmission Dynamics: The stark contrast between urban (39.6%) and semi-urban (22.9%) seroprevalence underscores that overcrowding, poor housing conditions, and high population density are primary drivers of CHIKV transmission.
• Public Health Policy:
  • Vaccination: With ~60% of the urban and ~77% of the semi-urban population lacking immunity, vaccine coverage strategies must be aggressive to achieve herd immunity thresholds.
  • Vector Control: The efficacy of mosquito nets (usually for nocturnal vectors) in reducing CHIKV risk suggests that physical barriers may offer protection against Aedes biting during the day or in specific micro-environments, challenging standard assumptions about vector control.
  • Urban Planning: Long-term control of arboviral diseases requires addressing unplanned urbanization and improving housing conditions to reduce overcrowding.

Limitations: The study is a preprint and has not yet undergone peer review. The sample was limited to two specific areas in Colombo, though they represent distinct urban and semi-urban profiles.

Funding: Supported by the NIH, USA (Grant 5U01AI151788-02).