Faster Findings: qualitative analysis of pig disease drivers

Evidano is an AI-powered qualitative data analysis platform that ingests transcripts, maps, and notes to auto-code themes and produce visual reports.

AI-enabled qualitative analysis converts participatory SGMB maps and narratives into quantified, comparable insights you can act on, as demonstrated by the Gwembe Valley case (fieldwork October 2023, study published July 9, 2026).

• The Gwembe SGMB with 12 purposively selected pig value-chain actors identified non-biological drivers of ASF and PCC and produced five spatial layers, behaviour-over-time graphs, and causal loop diagrams.
• Transporter reports (150–200 pigs/trip to Lusaka; 250–350 to Kasumbalesa) flagged potentially underreported movements versus District Veterinary Office records.
• Use the provided 7-step checklist to reproduce these insights in a 2-week pilot and convert maps and notes into prioritized, low-cost interventions (housing, WASH, participatory surveillance).

Fast take: Researchers ran an SGMB session with 12 purposively selected pig value-chain actors in Gwembe District (October 2023) to co-produce spatial layers, behaviour-over-time graphs and causal loop diagrams that identify non-biological disease drivers for ASF and PCC; the study was published July 9, 2026 in PLOS NTDs.

• Payoff: Learn how to operationalize participatory maps into quantified themes, detect underreported transport flows, and generate visual evidence for low-cost interventions.
• Context: practical for One Health teams, surveillance managers, and qualitative researchers working on livestock or neglected tropical diseases.

The SGMB produced five spatial layers, behaviour-over-time graphs, and causal loop diagrams that were synthesized for thematic analysis.

The team used a LayerStack over a regional map to co-produce five spatial layers (settlements, production zones, markets, disease occurrence, service points), behaviour-over-time graphs, and causal loop diagrams (CLDs). Data were synthesized using inductive thematic analysis and triangulated with District Veterinary Office records. Ethics approvals and anonymization were applied; no identifying audio or photos were retained.

• Core qualitative inputs: facilitator notes, LayerStack acetate layers, stakeholder narratives, and district documents.
• Analytic outputs: thematic codes, CLDs for ASF and PCC, and identification of leverage points (low-cost housing, participatory surveillance, WASH).
• Limitations: small purposive sample (n=12); CLDs are qualitative models for hypothesis and intervention design rather than parameterized simulations.

This checklist provides a 7-step workflow to go from SGMB outputs to an evidence brief in about two weeks.

• 1) Collect imports: scan LayerStack acetate layers, upload facilitator notes, typed transcripts, and DVO records into Evidano.
• 2) Auto-transcribe any short audio (or upload typed transcripts); apply custom dictionary for local pig/husbandry terms and PII redaction.
• 3) Auto-suggest codebook, then run a quick manual pass to align codes to participant language (2–3 hours).
• 4) Run thematic + frequency analysis across actor segments (farmers, traders, transporters, vets).
• 5) Generate co-occurrence networks and map-theme overlays to locate hot spots (for example, markets near shallow wells).
• 6) Produce a concise stakeholder brief with visuals and a prioritized list of low-cost interventions (housing, WASH, participatory surveillance).
• 7) Share interactive outputs with community stakeholders and iterate (export CSV/PowerPoint).

The conclusion is that contagious pig disease risk is produced by interacting socio-economic, environmental and trade systems rather than single causes.

The Gwembe Valley SGMB demonstrates that contagious pig disease risk is produced by interacting socio-economic, environmental and trade systems rather than single causes. AI-enabled qualitative analysis converts participatory maps and narratives into quantified, comparable insights you can act on: prioritize WASH plus biosecurity hotspots, surface underreported trade flows, and co-design locally feasible interventions.

• Ethics note: this is research-focused guidance, not clinical advice; follow local ethical approvals for community data work.
• Next step: try the 2-week pilot workflow with your SGMB outputs, start at Try Evidano for free and bring your transcripts, notes, and LayerStack images to a single, secure workspace.