Public health trends in neurologically relevant infections: a global perspective
Bibliographic record
Abstract
Neuroinfectious diseases represent a growing threat to public health globally. Infections of the central nervous system remain challenging to diagnose and treat, partially driven by the fact that a high proportion of emerging pathogens are capable of causing neurological disease. Many of the trends driving the emergence of novel pathogens, including climate change, ecological degradation, urbanization, and global travel, have accelerated in recent years. These circumstances raise concern for the potential emergence of additional pathogens of pandemic potential in the coming years, necessitating a stronger understanding of the forces that give rise to the emergence and spread of neuroinvasive pathogens and a commitment to public health infrastructure to identify and treat these diseases. In this review, we discuss the clinical and epidemiological features of three types of emerging neuroinvasive pathogens of significant public health consequences that are emblematic of key ongoing trends in global health. We first discuss dengue viruses in the context of climate change, considering the environmental factors that allow for the expansion of the geographic range and seasonal population of the viruses' vector. We then review the rising prevalence of fungal meningitis secondary to medical tourism, a trend representative of the highly globalized nature of modern healthcare. Lastly, we discuss the increasing prevalence of antibiotic-resistant neurological infections driven by the intersection of antibiotic overuse in medical and agricultural settings. Taken together, the rising prevalence of these conditions necessitates a recommitment to investment in public health infrastructure focused on local and global infectious disease surveillance coupled with ongoing development of novel therapeutics and vaccines for emerging pathogens. Such emerging threats also obviate the need to address the root causes driving the emergence of novel infectious diseases, including a sustained effort to address anthropogenic climate change and environmental degradation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".