Ahmedabad, Gujarat, India
Population health analytics, epidemiology support, evidence review, program evaluation, and applied public-health decision support.
Duration
9 weeks
Program fee
₹10,000
Lab Gallery
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Students learn evidence appraisal, data cleaning, analytic framing, dashboard interpretation, concise briefing writing, and professional communication for health-focused audiences.
Meridian Population Health Evidence Lab gives students a rigorous but accessible introduction to public-health evidence work in a professional setting. The program combines structured analytics assignments with evidence synthesis, health systems discussion, and concise writing practice designed for policy-adjacent environments. Students learn how to move from messy inputs and broad questions toward disciplined interpretation, transparent assumptions, and presentation-ready outputs.
Faculty highlight #1
Applications have not opened yet.
Lead Faculty, Population Health Methods
Oversees analytical rigor, evidence interpretation, and writing quality across cohorts.
Faculty highlight #2
Program Mentor, Evaluation Design
Supports survey-based analysis, briefing structure, and program evaluation exercises.
The mentorship model balances analytical instruction, writing critique, and applied public-health context. Students receive iterative feedback on both method and communication quality.
Techniques
Equipment
5 mentors supporting this program
72 interns trained across previous cohorts
Completion certificate provided
Best suited for students in public health, biostatistics, economics, life sciences, medicine, sociology, or data-focused programs with strong interest in applied evidence work.
Selection is based on academic fit, writing clarity, public-health interest, and readiness to engage with evidence-based analysis under mentor guidance.
Participants are trained in responsible dataset use, de-identification awareness, document handling, remote-work etiquette, and presentation conduct for public-facing evidence work.
Participants are expected to write clearly, cite evidence carefully, respect dataset confidentiality, and maintain a policy-neutral, method-driven approach to interpretation.