Bibliographic record
Abstract
Emerging and re-emerging infectious diseases pose a major threat to global health, yet little is known about their complex socio-ecological pathways that impact population health disproportionately at fine spatial scales. This is common in low-and middle-income countries with stark inequalities such as Brazil, which faces unprecedented changes in land-use and climate, and the burden of several epidemics, including the Zika virus, yellow fever virus, and SARS-CoV-2 responsible for the COVID-19 pandemic. This thesis presents research conducted on these diseases to advance knowledge on the relationships of ecological-epidemiological factors in natural and socio-institutional settings, which shape differential population risk to infectious diseases in Brazil. Guided by the theoretical frameworks of disease ecology and landscape epidemiology, this thesis uses large open-access datasets, GIS, and statistical modelling to explore the geographic spread of Zika virus, the emergence of yellow fever virus in the natural environment, differential risk to COVID-19 spread in the built environment, and population response to COVID-19 via immunisation. Forest cover and temperature fostered the environmental suitability of yellow fever, while race, income, and healthcare systems contributed to inequalities in access to COVID-19 testing, infection, and death. Mass vaccination campaigns illustrated in the context of COVID-19 showed that vaccine coverage varies by area, age, race, sex, and socioeconomic status. In these settings, population interconnectivity via settlement, mobility, and behaviour significantly influenced disproportionate geographic exposure to disease risk. Findings and implications from this research can be used to inform public health policies and interventions in Brazil. Future research should continue to explore the integration of large open-access datasets for understanding geographical inequalities in disease risk.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".