Expanding a Global Database of Arboviral Diagnostic Studies

Join the Perkins Lab and learn about mathematical modeling of infectious diseases with our group. 

Accurate diagnosis of dengue, chikungunya, and Zika remains a major challenge because these viruses often produce similar clinical symptoms and are detected using a wide variety of laboratory and clinical diagnostic methods. This project supports the development of a global evidence base to improve understanding of diagnostic test performance across arboviral diseases.

The student researcher will assist in expanding a systematic database of published studies evaluating diagnostic methods for arboviral infections. Previous work focused on studies comparing multiple diseases (e.g., dengue, chikungunya, and Zika) using multiple diagnostic methods. The next phase of the project expands the search to include studies evaluating a single arboviral disease, provided that two or more diagnostic methods are included within the same study.

Responsibilities will include:

• Performing structured literature searches in scientific databases using predefined search strategies.
• Screening abstracts and full-text articles according to inclusion and exclusion criteria.
• Extracting study characteristics, diagnostic methods, sample sizes, and testing outcomes into standardized data collection forms.
• Participating in weekly meetings to discuss progress and learn principles of systematic evidence synthesis.

The curated dataset will contribute to ongoing Bayesian modeling research aimed at estimating the sensitivity and specificity of diagnostic methods in the absence of a perfect reference standard. Results from this work are expected to support peer-reviewed publications and improve evidence-based surveillance of emerging arboviral diseases.