Qualitative analysis of NTD risk communication

Evidano is an AI-powered qualitative data analysis platform that accelerates transcription, translation, secure coding, and cross-segment theme comparison.

The PLOS NTD study (July 14, 2026; data May-July 2024; n=57) shows communities explain NTD patterns by dry versus rainy seasons, and current messaging is fragmented and uneven.

• A July 14, 2026 PLOS NTD study (data May-July 2024, n=57) found communities emphasize seasonal experience (dry vs rainy) over abstract 'climate change' when explaining disease patterns.
• Communities prefer trusted interpersonal networks, radio, and school outreach, but relying on children to relay messages creates equity and fidelity problems.
• Practitioners rely on national MDA and school-based education but face resource, training, and data disaggregation gaps; the study recommends strengthened surveillance, WaSH, trusted messengers, and practitioner training.
• Evidano workflows can reduce transcription overhead, preserve translation fidelity, and speed coding, synthesis, and cross-segment comparison for policy-ready outputs.

The study used an environmental scan, five focus groups, and 13 semi-structured interviews with doctors, nurses, veterinarians, health administrators, and community members to compare practitioner risk communication with community preferences.

The authors ran an environmental scan, five focus groups, and 13 semi-structured interviews with doctors, nurses, veterinarians and health administrators to compare practitioner risk communication with community preferences.

• Communities emphasize seasonal experience (dry vs rainy) over abstract 'climate change' when explaining disease patterns.
• Preferred channels: trusted interpersonal networks (village leaders, churches), radio, and school outreach, though relying on children to relay messages creates equity and fidelity problems.
• Practitioners rely on national MDA and school-based education but face resource, training, and data disaggregation gaps.
• Five tactical recommendations emerged: boost surveillance/data use, counter vaccine hesitancy, invest in practitioner training and WaSH, reduce the communicative burden on children, and integrate traditional knowledge and leaders.

This two-week plan converts raw field audio into policy-ready recommendations using the study's methods as a template.

• Day 1-3: Upload audio and documents to Evidano; set custom dictionary for local terms (Maasai names, local disease terms).
• Day 3-5: Auto-transcribe and auto-translate; run transcript QA with bilingual team members; redact PII.
• Day 6-8: Import initial codebook (deductive codes: seasonality, channels, vaccine hesitancy, WaSH) and run AI-assisted coding across segments (Arusha vs Monduli).
• Day 9-10: Generate cross-segment frequency tables, co-occurrence maps, and sample quotes exports for stakeholder validation.
• Day 11-14: Package a 2-page policy brief and a 10-slide visual deck; schedule a village meeting and a practitioner workshop using the visuals to test message resonance.

This PLOS study (July 14, 2026) is a practical reminder that communities think in seasons, not abstract climate models, and that risk communication must be timely, trusted, and adapted to literacy and technology constraints.

• Start small: pilot targeted, seasonal messaging informed by disaggregated surveillance and qualitative insights.
• Use tools that preserve semantics and security: transcription, translation, and codebook workflows with encrypted storage.
• Try Evidano for free to move from field audio to thematic and cross-segment analysis quickly and securely.