Estimating the seroincidence of scrub typhus using antibody dynamics following infection
Bibliographic record
Abstract
Abstract Introduction Scrub typhus is an acute febrile illness caused by the bacterium Orientia tsutsugamushi . Characterizing the population-level burden of scrub typhus is challenging due to the lack of accessible and accurate diagnostics. In this study, we present a novel approach that utilizes information on antibody dynamics after infection to generate population-level scrub typhus seroincidence estimates from cross-sectional serosurveys. Methods We use data from three cohorts of scrub typhus patients enrolled in Chiang Rai, Thailand, and Vellore, India, and representative population data from two serosurveys in and around the Kathmandu valley, Nepal, and Vellore, India. The samples were tested for IgM and IgG responses to Orientia tsutsugamushi -derived recombinant 56-kDa antigen using commercial ELISA kits. We used Bayesian hierarchical models to fit two-phase models to the antibody responses from scrub typhus cases and used the joint distributions of the peak antibody titers and decay rates to estimate population-level incidence rates in the cross-sectional serosurveys. We compared this new method to a traditional cut-off-based approach for estimating seroincidence. Results Median IgG antibodies persisted above OD 1.7 for 22 months, while IgM displayed longer persistence than expected, with 50% of participants having an OD >1 for 5 months. We estimated an overall seroincidence of 18 per 1000 person-years (95% CI: 16-21) in India and 4 per 1000 person-years (95% CI: 3-6) in Nepal. Among 18 to 29-year-olds, the seroincidence was 8 per 1000 person-years (95% CI 4 -16) in India and 9 per 1000 person-years (95% CI: 6-14) in Nepal. In both India and Nepal, seroincidence was higher in urban and periurban settings compared to rural areas. Compared to our method, seroincidence estimates derived from age-dependent IgG-seroprevalence without accounting for antibody decay underestimated the disease burden by 50%. By incorporating antibody dynamics, the approach described here provides more accurate age-specific infection risk estimates, emphasizing the importance of considering both IgG and IgM decay patterns in scrub typhus seroepidemiology. Conclusion The sero-surveillance approach developed in this study efficiently generates population-level scrub typhus seroincidence estimates from cross-sectional serosurveys. This methodology offers a valuable new tool for informing targeted prevention and control strategies, ultimately contributing to a more effective response to scrub typhus in endemic regions worldwide.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".