Uganda National Acute Febrile Illness Agent Detection Serosurvey 2004-2005
Bibliographic record
Abstract
Background: Due to their non-specific clinical presentation, acute febrile illnesses (AFI) are often diagnosed clinically as diseases known to be endemic to the region in which they are found. Uganda has been the site of multiple emerging-disease outbreaks, and there are several diseases that present with undifferentiated AFI, requiring further laboratory confirmation; however, limited laboratory capacity can impair timely diagnosis and public health interventions. This results in misdiagnosis and underreporting of emerging diseases of public health importance. The 2004-2005 Uganda National AFI Agent Detection Serosurvey (AFI serosurvey)–a retrospective investigation of seroprevalence of exposure to selected infectious agents–involved testing a subset of banked sera from the 2004-2005 Uganda HIV/AIDS Serobehavioural Survey (UHSBS). The AFI serosurvey is part of a multi-phase collaboration between Uganda Ministry of Health, Uganda Virus Research Institute (UVRI) and CDC-Atlanta/CDC-Uganda to investigate AFI in Uganda. Methods & Materials: We selected a random 3097-sample subset from 19,656 UHSBS banked sera for inclusion in the AFI serosurvey; 2705 were ultimately analyzed after applying exclusion criteria. Data from laboratory testing were analyzed and mapped using SAS v9.3 and ArcGIS 10, respectively. Results: Laboratory diagnostic testing results demonstrated: leptospirosis ELISA and microagglutination test (MAT) (10.4% weighted proportion, SE = 1.2%), brucellosis MAT (0.3% weighted proportion, SE = 0.1%), spotted fever group rickettsiae ELISA (56.7% weighted proportion, SE = 1.4%) and typhus group rickettsiae ELISA (41.6% weighted proportion, SE = 1.4%), malaria MSP119 ELISA (88.4% weighted proportion, SE = 0.7%), orthopoxvirus IgG ELISA (13.5% weighted proportion, SE = 0.8%), chikungunya IgM ELISA (31.1% weighted proportion, SE = 1.0%), dengue IgM ELISA (1.0% weighted proportion, SE = 0.2%) and IgG ELISA (0.7% weighted proportion, SE = 0.2%). A specimen subset (n = 198) was tested for melioidosis using indirect hemagglutination (IHA); 4.6% were seropositive. Conclusion: Pre-existing national serosurveys can be a source of information on prevalence of AFI etiologic agents. This AFI serosurvey describes the distribution, regional risk, and interregional variability for selected diseases contributing to AFI across Uganda; it will inform prioritization of infectious disease surveillance and laboratory capacity-building activities. Results from this study combined with similarly obtained results from the ongoing testing from the 2011 Uganda AIDS Indicator Survey-based serosurvey will demonstrate changing seroprevalence patterns, allowing for evaluation of potential ecologic drivers for disease distribution variances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".