Epidemiological consequences of immune sensitisation by pre-exposure to vector saliva
Bibliographic record
Abstract
Blood-feeding arthropods-like mosquitoes, sand flies, and ticks-transmit many diseases that impose serious public health and economic burdens. When a blood-feeding arthropod bites a mammal, it injects saliva containing immunogenic compounds that facilitate feeding. Evidence from Leishmania, Plasmodium and arboviral infections suggests that the immune responses elicited by pre-exposure to arthropod saliva can alter disease progression if the host later becomes infected. Such pre-sensitisation of host immunity has been reported to both exacerbate and limit infection symptoms, depending on the system in question, with potential implications for recovery. To explore if and how immune pre-sensitisation alters the effects of vector control, we develop a general model of vector-borne disease. We show that the abundance of pre-sensitised infected hosts should increase when control efforts moderately increase vector mortality rates. If immune pre-sensitisation leads to more rapid clearance of infection, increasing vector mortality rates may achieve greater than expected disease control. However, when immune pre-sensitisation prolongs the duration of infection, e.g., through mildly symptomatic cases for which treatment is unlikely to be sought, vector control can actually increase the total number of infected hosts. The rising infections may go unnoticed unless active surveillance methods are used to detect such sub-clinical individuals, who could provide long-lasting reservoirs for transmission and suffer long-term health consequences of those sub-clinical infections. Sensitivity analysis suggests that these negative consequences could be mitigated through integrated vector management. While the effect of saliva pre-exposure on acute symptoms is well-studied for leishmaniasis, the immunological and clinical consequences are largely uncharted for other vector-parasite-host combinations. We find a large range of plausible epidemiological outcomes, positive and negative for public health, underscoring the need to quantify how immune pre-sensitisation modulates recovery and transmission rates in vector-borne 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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".