Syndromic surveillance with monthly aggregate health systems information data for COVID-19 pandemic response in Neno, Malawi: a monitoring study
Bibliographic record
Abstract
Background In the context of diminutive COVID-19 screening and testing, syndromic surveillance can be used to identify areas with higher-than-expected SARS-CoV-2 symptoms for targeted public health interventions. We used syndromic surveillance to monitor potential SARS-CoV-2 outbreaks in 14 health facilities in the Neno district of rural Malawi. Methods We monitored three indicators identified as potential symptoms of SARS-CoV-2 infection: the proportion of outpatient visits for fast-breathing cases in children under 5 years (FBC<5); the proportion of suspected malaria cases confirmed as non-malaria in children under 5 years (NMC<5); and the same indicator in individuals aged 5 years and older (NMC≥5). We extracted data aggregated by month and at the health facility-level from the District Health Information System. With data from January, 2016, to February, 2020, as a baseline, we used a linear model with a negative binomial distribution to estimate expected proportions for the indicators in absence of the COVID-19 pandemic with 95% prediction intervals (PI) for March, 2020, to July, 2021. We compared the observed proportions to the expected rates, focusing on the first two waves of infections (June to July, 2020, and January to March, 2021). Findings The proportion of FBC<5 was consistently higher than expected, with a peak in May, 2020, when 2·5% of outpatient visits were fast breathing cases in children younger than 5 years of age (compared with the expected rate of 0·8% [95% PI 0·4–1·5]). NMC<5 was as expected throughout the study period. The NMC≥5 indicator remained as expected, except for increases in suspected cases tested negative for malaria, to 31·3% (from the expected 18·6% [95% PI 12·3–28·7]) in November, 2020, and to 32·5% (from the expected 21·7% [95% PI 14·2–32·2]) in July, 2021. Interpretation An increase in FBC<5 and NMC≥5 before observed COVID-19 waves might indicate SARS-CoV-2 infections that were missed before robust testing. This tendency was not seen in NMC<5, which can represent differences in symptomatology leading to decreased health-seeking behaviours for this age group. Syndromic surveillance can allow for real-time responses at facilities, including increased and focused testing and screening to identify potential SARS-CoV-2 infections. Funding Supported by Canadian Institutes of Health Research.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".