Spatio-temporal analysis of the distribution and co-circulation of dengue, chikungunya, and Zika in Medellín, Colombia, from 2013 to 2021
Bibliographic record
Abstract
BACKGROUND: Dengue, chikungunya, and Zika present significant public health challenges in Colombia. Spatial studies help clarify the distribution and progression of these diseases over time and location. Objective to describe the spatio-temporal distribution and clustering patterns of dengue, chikungunya, and Zika in Medellín, Colombia, between 2013 and 2021, with the aim of providing baseline spatial intelligence to support future epidemiological and policy-oriented analyses. METHODS: We analyzed dengue, chikungunya, and Zika cases in Medellín from 2013 to 2021, using weekly data from 27,459 geocoded cases across 265 neighborhoods. Cases were geocoded by neighborhood based on residential addresses in the national surveillance system (SIVIGILA). Spatio-temporal analysis identified high-risk clusters and examined the co-circulation of the diseases through multivariate analysis. We used scan statistics with a discrete Poisson model to detect high-risk clusters. RESULTS: From 2013 to 2021, 26,350 dengue cases probable and confirmed were reported, with an annual incidence of 137.3 per 100,000 residents. Chikungunya and Zika emerged in 2014 and 2015, with 574 and 515 cases reported, resulting in incidences of 5.1 and 3.8 per 100,000 residents, respectively. We identified five dengue clusters and four clusters each for Zika and chikungunya, mainly in Medellín's northeast. Multivariate analysis revealed six clusters, with four exhibiting high risk for all three diseases. Co-circulation of dengue, chikungunya, and Zika occurred between September 2015 and February 2017. Dengue clusters peaked between 2015 and 2016, while chikungunya and Zika peaks occurred in 2015 and 2016, respectively. CONCLUSIONS: This study advances understanding of spatio-temporal dynamics in arbovirus transmission in Medellín, highlighting high-risk clusters for dengue, chikungunya, and Zika and their collective presence. Our findings support targeted public health interventions to mitigate these diseases.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".