Climate-Linked Dengue Risk Modelling | Sri Lanka | Public Health & Climate Resilience

Lead Research Advisor
Prof. Roshini Sooriyarachchi, University of Colombo (Co-author: Shanika L. Wickramasuriya, Department of Statistics)

Problem Statement

Sri Lanka faces recurring dengue outbreaks with increasing severity, influenced by climate variability, population vulnerability, and health system pressures. At the time of the study, there was limited national-scale, statistically validated evidence linking demographic, clinical, and climatological factors to dengue severity.

Intervention Design

A national multilevel statistical study was conducted to identify the drivers of different types of dengue infections by integrating:

• Clinical data

• Demographic data

• Climatological data

• Geographic clustering (district-level effects)

The research applied advanced multilevel modelling techniques to understand both individual-level and district-level risk factors.

Total Program Value

Academic national-scale research study (Data sourced from Sri Lanka national dengue surveillance systems)

Target Population / Coverage

• 5,059 confirmed dengue patients

• 15 districts across Sri Lanka

• Study period: 2006–2008

• National epidemiological coverage

Measured Outputs

• Identification of high-impact demographic predictors: Age, Sex

• Identification of critical clinical indicators:

  • Platelet Count

  • Packed Cell Volume (PCV)

  • Survival Time

• Quantification of climatological influence:

  • Rainfall

  • Temperature

  • Humidity

  • Lag effects

  • Validation of district-level clustering effects

Measured Outcomes

• Statistically proven link between climate variables and dengue severity

• Evidence base for early-warning disease surveillance models

• Strong foundation for predictive public health analytics

• Direct relevance to climate-driven epidemic preparedness

Independent Verification

• Peer-reviewed academic research

• National surveillance data

• University-based statistical validation

Sustainability & Long-Term Impact

• Enables climate-linked early warning systems for dengue outbreaks

• Informs donor-funded epidemic preparedness programs

• Supports health system resilience and climate adaptation strategies

Donor Relevance

Demonstrates how climate intelligence, clinical data, and predictive analytics can be used together to:

• Prevent loss of life

• Improve outbreak response

• Strengthen climate-resilient health systems

Institutional Assurance Statement
“This case study is supported by peer-reviewed research, national surveillance data, and independent academic verification.”