Zika epidemic in Colombia and environmental and sociodemographic contributors: an application of a space-time Markov switching model
Bibliographic record
Abstract
ABSTRACT Zika, a viral disease transmitted to humans by the bite of infected Aedes mosquitoes, emerged in the Americas in 2015, causing large-scale epidemics. Colombia alone reported 72,031 Zika cases between 31/May/2015 and 01/October/2016. We used national surveillance data from 1,121 municipalities over 70 epidemiological weeks to identify sociodemographic and environmental factors associated with Zika’s emergence, re-emergence, persistence, and transmission intensity in Colombia. We fitted a zero-state Markov-switching spatio-temporal model under the Bayesian framework, assuming Zika switched between periods of presence and absence according to spatially and temporally varying probabilities of emergence/re-emergence (from absence to presence) and persistence (from presence to presence). These probabilities were assumed to follow a series of mixed multiple logistic regressions. When Zika was present, assuming that the cases follow a negative binomial distribution, we estimated the transmission intensity rate. Our results indicate that Zika emerged/re-emerged sooner and that transmission was more intense in municipalities which were more densely populated, with lower altitude and/or less vegetation cover. Higher weekly temperatures and less weekly-accumulated rain were also associated with Zika emergence. Zika cases persisted for longer in more densely populated areas and with a higher number of cases reported in the previous week. Overall, population density, elevation, and temperature were identified as the main contributors of the first Zika epidemic in Colombia. The estimated probability of Zika presence increased weeks before case reporting, suggesting undetected circulation in the early stages. These results offer insights into priority areas for public health interventions against emerging and re-emerging Aedes -borne diseases.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".