Mathematical Modelling for Community Based Intervention for Managing Diabetes: A Systematic Literature Review
Bibliographic record
Abstract
Aalbrecht A Irawan,1 Nursanti Anggriani,2 Yudhie Andriyana,3 Rizky Abdulah4 1Doctoral Program in Mathematics, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Jatinangor, West Java, Indonesia; 2Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Jatinangor, West Java, Indonesia; 3Department of Statistics, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Jatinangor, West Java, Indonesia; 4Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Jatinangor, West Java, IndonesiaCorrespondence: Nursanti Anggriani, Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Jalan Raya Bandung Sumedang KM 21, Jatinangor, West Java, 45363, Indonesia, Email nursanti.anggriani@unpad.ac.idAbstract: Diabetes mellitus (DM) poses a significant global health and economic challenge. Effective diabetes management requires a multifaceted approach that combines clinical and community-based interventions. Community-based interventions are critical to address the growing burden of diabetes. Despite numerous independent studies on community-based interventions for T2DM management and mathematical models, there has been no comprehensive review integrating these two domains. This systematic literature review aimed to fill this gap by examining mathematical modelling in the context of community-based interventions for T2DM management. Following the PRISMA guidelines, relevant articles were identified, screened, and assessed for eligibility using the Scopus, ScienceDirect, and PubMed databases. The inclusion criterion was English-language research articles published between 2014 and 2024 that focused on T2DM interventions using mathematical models. Seven articles met the final inclusion criteria and were analysed to answer research questions related to the geographical origin of the data, nature of the intervention, specific mathematical model used, and the main findings of the primary study. This review highlights that mathematical models are critical for optimising community-based interventions, by identifying key risk factors, predicting disease progression, and evaluating the effectiveness of various treatments. By synthesising findings from different geographical and economic contexts, this review highlights the importance of culturally and contextually relevant strategies for diabetes management. The integration of robust mathematical models with community-based approaches promises to develop more effective evidence-based strategies for diabetes management, particularly in resource-limited settings.Keywords: mathematical model, diabetes mellitus type 2, community-based intervention
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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 | 0.020 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".