Modeling the Impact of Active HPV Testing Intervention on Cervical Cancer Burden in India
Bibliographic record
Abstract
ABSTRACT The global burden of mortality in women due to cervical cancer is relatively high in low and medium-income countries (LMIC), particularly India. The majority of cervical cancer cases are due to persistent and long-term infection with the Human papillomavirus (HPV). HPV testing plays a crucial role in identifying women with HPV infections at an early stage, before progression to cervical cancer, enabling timely intervention. This helps in reducing the cervical cancer burden in LMIC, in addition to vaccination and screening. However, there are no systematic modelling studies to investigate the role of HPV testing in cervical cancer burden. This work develops a mathematical model to investigate the impact of active HPV testing in reducing HPV infections and cervical cancer burden in India. By introducing an HPV infected compartment using HPV testing, our model captures the dynamics of known infected and unknown infected individuals, tracking infection progression, recovery from HPV infection, and cancer outcomes (cancerous cases, cancer mortality, cancer survival). The parameters in the proposed model are calibrated using the literature and epidemiological data specific to the Indian context. The simulation studies involving different testing population ages and HPV testing coverage rates are performed to understand the impact of active testing. The results from these studies indicate that higher testing rates can reduce the prevalence of cervical cancer and overall mortality. Furthermore, an optimal control approach with the active HPV testing coverage as a decision variable is employed to derive cost-effective strategies for minimizing treatment and HPV testing expenses. These findings highlight the necessity of appropriate resource allocation and policy interventions to curb the prevalence of HPV and mitigate the global burden of cervical cancer in India.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".