Use of a rapid digital microfluidics-powered immunoassay for assessing measles and rubella infection and immunity in outbreak settings in the Democratic Republic of the Congo
Bibliographic record
Abstract
The Democratic Republic of the Congo (DRC) has a high measles incidence despite elimination efforts and has yet to introduce rubella vaccine. We evaluated the performance of a prototype rapid digital microfluidics powered (DMF) enzyme-linked immunoassay (ELISA) assessing measles and rubella infection, by testing for immunoglobulin M (IgM), and immunity from natural infection or vaccine, by testing immunoglobulin G (IgG), in outbreak settings. Field evaluations were conducted during September 2017, in Kinshasa province, DRC. Blood specimens were collected during an outbreak investigation of suspected measles cases and tested for measles and rubella IgM and IgG using the DMF-ELISA in the field. Simultaneously, a household serosurvey for measles and rubella IgG was conducted in a recently confirmed measles outbreak area. DMF-ELISA results were compared with reference ELISA results tested at DRC's National Public Health Laboratory and the US Centers for Disease Control and Prevention. Of 157 suspected measles cases, rubella IgM was detected in 54% while measles IgM was detected in 13%. Measles IgG-positive cases were higher among vaccinated persons (87%) than unvaccinated persons (72%). In the recent measles outbreak area, measles IgG seroprevalence was 93% overall, while rubella seroprevalence was lower for children (77%) than women (98%). Compared with reference ELISA, DMF-ELISA sensitivity and specificity were 82% and 78% for measles IgG; 88% and 89% for measles IgM; 85% and 85% for rubella IgG; and 81% and 83% for rubella IgM, respectively. Rubella infection was detected in more than half of persons meeting the suspected measles case definition during a presumed measles outbreak, suggesting substantial unrecognized rubella incidence, and highlighting the need for rubella vaccine introduction into the national schedule. The performance of the DMF-ELISA suggested that this technology can be used to develop rapid diagnostic tests for measles and rubella.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".