Community participatory approaches in infectious disease dynamic transmission modelling: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Community participatory modelling merges participatory research approaches with mathematical modelling. Participatory approaches are grounded in the engagement of people with lived experience (eg, who are affected by the health condition under study) throughout the research process. Mathematical modelling of infectious disease (ID) dynamic transmissions is increasingly used as a tool for public health decision-making, generating predictions, inferring mechanisms and estimating the impact of potential interventions-all of which guide policies, strategies and resource allocation as part of the preparation and response to ID epidemics. However, little is known about the engagement of people with lived experience and affected communities in the ID modelling process. We will map the literature to explore participatory approaches undertaken in ID modelling (practical aspects of formalising participatory modelling), levels of participation and the potential influence from the perspective of communities engaged. METHODS AND ANALYSIS: The scoping review will follow the Joanna Briggs Institute Manual for Evidence Synthesis. The search strategy includes three electronic bibliographic databases (MEDLINE, Scopus and Embase), no language restrictions and sources published from 2000 to present. We will implement the search with and without the participatory keyword, as we recognise that some studies do not explicitly term community engagement as participatory modelling. After deduplication, two authors will independently screen the titles, abstracts and full texts, with discrepancies resolved with a third team member. We will extract the relevant information from the main text, parameter tables, supplemental files, bibliography, acknowledgment and author affiliation sections. The data extraction will follow a deductive content analysis where we draw from community-based participatory research approaches and established mathematical modelling steps. We will also extract data to assess whether there was equitable engagement of knowledge users by checking for indicators of three equitable engagement domains as outlined by the Ward framework (equity within partnership (eg, whether knowledge user influenced modelling decisions or remuneration), capacity to engage in future partnerships and shift in power/influence (eg, coauthorship). We will supplement our narrative analyses with summaries in tabular format and using appropriate data visualisations. ETHICS AND DISSEMINATION: No ethics approval will be required for this scoping review because we will map evidence from publicly available literature sources. We will develop multilingual abstracts or one-page lay summaries of the findings (English, French and Swahili), a policy brief and will coauthor an open-access journal article. A summary of the findings will be shared via knowledge user-led presentations at the Maisha HIV and AIDS Conference and with other community-based organisations at the quarterly peer-to-peer support meetings. REGISTRATION: The protocol has been registered in Open Science Framework, DOI: https://doi.org/10.17605/OSF.IO/XQ2WP (December 2024).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.201 | 0.176 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.087 | 0.026 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".