Bayesian hierarchical modeling of Mpox in the African region (2022–2024): Addressing zero-inflation and spatial autocorrelation
Bibliographic record
Abstract
Mpox remains a signi_cant public health challenge in endemic regions of Africa. Understanding its spatial distribution and identifying key drivers in high-risk countries is critical for guiding e_ective interventions. This study applies a Zero-Inated Poisson (ZIP) model with spatial autocorrelation to estimate the adjusted relative risk (RR) of Mpox incidence across 24 African countries, strati_ed by Human Development Index (HDI) levels. The model accounts for overdispersion and excess zeros by incorporating spatial random e_ects and socio-environmental covariates, and was validated through model diagnostics and sensitivity analysis, demonstrating robustness of results. Spatial analysis revealed substantial heterogeneity in Mpox incidence, with elevated risk in the Democratic Republic of Congo (DRC), Nigeria, and Central African Republic (CAR) persisting after covariate adjustment (p < 0:001). Higher HDI levels were inversely associated with Mpox risk, with HDI quintile Q4 (very high HDI) showing a signi _cant reduction (aRR = 0.431; 95 % CrI: 0.099{0.724). Protective factors in low-risk areas included increased life expectancy at birth (aRR = 0.768; 95 % CrI: 0.688{0.892), higher educational attainment (aRR = 0.774; 95 % CrI: 0.680{0.921), nonlinear increases in gross national income (GNI) per capita, and a greater density of skilled health workers (aRR = 0.788; 95 % CrI: 0.701{0.934). Conversely, higher urban density was associated with increased Mpox risk, underscoring the inuence of population clustering on transmission dynamics. Notably, statistically signi_cant elevated-risk areas persisted in endemic countries of Western and Central Africa after covariate adjustment (p < 0:001). In contrast, previously undetected risk emerged in parts of Southern and Eastern Africa post-adjustment, revealing latent patterns obscured in the crude analysis (p < 0:001). Exceedance probability maps identi_ed countries with P(RR > 1) > 0.9 as priority areas for intensi_ed surveillance and targeted intervention. These patterns were not fully explained by the included covariates, suggesting the inuence of unmeasured factors such as environmental and climate variability, zoonotic reservoirs, or human{animal interactions. Further research is needed to deepen understanding of Mpox epidemiology and support locally tailored interventions.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".