AI-Driven Qualitative Analysis of Cholera Outbreak

Evidano is an AI-powered qualitative data analysis platform that accelerates secure transcription, coding, and cross-segment analysis for qualitative health research. The PLOS qualitative study of Malawi’s 2022–2024 cholera outbreak (published Jul 10, 2026) shows that climate shocks, health-system fragility, and social and economic vulnerability converged to produce 57, 639 cases and 1, 727 deaths, based on 24 in-depth interviews.

• The PLOS study used 24 semi-structured interviews with clinicians and environmental health staff in Neno and Chikwawa conducted in Aug–Sep 2024.
• The Malawi Ministry of Health reported 57, 639 cases and 1, 727 deaths in the 2022–2024 outbreak, cited in the PLOS paper.
• Qualitative provider narratives in the study revealed pathway convergence across climate events, health-system strain, and social vulnerability that routine surveillance did not capture.

The PLOS qualitative study demonstrates why provider interviews reveal pathway convergence that quantitative surveillance missed. The PLOS team reports that climate shocks, health-system fragility, and social and economic vulnerability created feedback loops that turned seasonal cholera into Malawi’s deadliest outbreak, and the original paper is available at PLOS Neglected Tropical Diseases.

• Direct evidence: 24 in-depth interviews with clinicians and environmental health staff in Neno and Chikwawa (Aug–Sep 2024).
• Key numeric anchors: 57, 639 cases and 1, 727 deaths reported to the Ministry of Health.
• Study value: provider narratives reveal temporal compression of disasters, IDP camp conditions, and cross-border transmission pathways that quantitative surveillance missed.

The PLOS authors used a qualitative descriptive design with 24 semi-structured interviews, field visits, and thematic coding in Dedoose. The authors reported themes by frequency, extensiveness, and intensity across transcripts, producing three pathway themes labeled climate, health system, and social and economic determinants.

• Sampling: purposive selection of first responders across facility types and roles, including clinicians, environmental health staff, and managers.
• Coding: iterative codebook development with multiple coders and reconciliation meetings following standard practices for qualitative reliability.
• Analytic outcome: the study mapped feedback loops such as damaged roads leading to supply disruption, overwhelmed clinics, and prolonged transmission.

The following 7-step workflow reproduces the PLOS study logic in Evidano and can produce policy-ready outputs in 1–2 weeks for a pilot, scaling with corpus size:

• 1) Collect and upload transcripts, field notes, and audio into Evidano and enable PII redaction.
• 2) Auto-transcribe and apply a custom dictionary for local terms; translate as needed.
• 3) Import or draft an initial codebook and run AI-assisted pre-coding across all documents.
• 4) Manually review a stratified sample, reconcile codes, and lock the codebook.
• 5) Run thematic frequency, sentiment, and cross-segment comparisons by district and pre/post-cyclone timepoints.
• 6) Generate co-occurrence networks to visualize pathway convergence and export top quotes per theme.
• 7) Produce a one-page decision brief and slide deck for operational partners.

The Malawi qualitative study (published Jul 10, 2026) shows that interviews reveal pathway convergence that surveillance counts alone cannot. If a team analyzes transcripts, field notes, or mixed corpora and needs reproducible thematic and cross-segment insight, Evidano accelerates that work while protecting sensitive data.

• Try a pilot: upload a batch of transcripts, run an AI-assisted code pass, and generate a co-occurrence map in days not weeks.
• Try Evidano for free to run a pilot or Evidano to learn more or book a demo.