1380. Machine Learning-based Estimation of Unconfirmed COVID-19 Cases from a 10,000-Household Survey in Gilgit-Baltistan, Pakistan
Notice bibliographique
Résumé
Abstract Background Robust estimates of COVID-19 prevalence during the pandemic are scarce, particularly in settings with limited SARS-CoV-2 testing. Gilgit-Baltistan (GB) is a remote region of Pakistan where healthcare access is limited by underdeveloped facility and road infrastructure. We leveraged a large household survey to describe the burden of confirmed and unconfirmed COVID-19 in GB. Methods We conducted a cross-sectional survey in GB from June–August 2021 during the baseline phase of a cluster randomized trial. Households were randomly selected using a stratified, two-stage sampling design. Data regarding SARS-CoV-2 testing, healthcare worker (HCW) diagnoses without testing, symptoms, and outcomes since March 2020 were self-reported for all household members. “Confirmed/probable” COVID-19 was defined as a positive test, HCW diagnosis of COVID-19, or HCW diagnosis of pneumonia with COVID-19 positive contact. Using machine learning (ML) and bootstrap validation, we developed a symptom-based diagnostic model to differentiate confirmed/probable infections from those with negative SARS-CoV-2 tests (Fig. 1). We applied this model to untested respondents to estimate the total prevalence of COVID-19.Figure 1.Workflow diagram for machine learning analysis. auROC=Area under the receiver operating characteristic curve; CI=Confidence interval; LR=Logistic regression; RF=Random forest; SVM=Support vector machines; XGB=eXtreme Gradient Boosting Results Data were collected from 77924 people in 10264 households. Overall, 314 had confirmed/probable COVID-19, 3263 had negative tests, and 74347 were untested. SARS-CoV-2 testing was less common in females (vs. males; 38 vs. 58 tests per 1000 people) and children (vs. adults; 17 vs. 76 tests per 1000 people). Using an extreme gradient boosting model, area under the receiver operating characteristic curve was 0.92 (95% confidence interval [CI] 0.90–0.93), sensitivity was 0.81 (CI 0.75–0.85), and specificity was 0.88 (CI 0.85–0.90). With this model, total estimated cases were 8–17 times more than the number of individuals with positive tests (Fig. 2). The ratio of estimated to confirmed cases was higher for children (90–213 times) and females (13–25 times).Figure 2.Estimation of probable and possible COVID-19 cases, overall and by age and sex.Confirmed COVID-19 indicates individuals with positive SARS-CoV-2 tests; probable COVID-19 includes HCW diagnoses of COVID-19 and positive predictions from the machine learning analysis; possible COVID-19 includes individuals with positive close contacts or HCW diagnoses of pneumonia. Ratios are depicted as a plausible range between ‘confirmed : probable’ and ‘confirmed : probable + possible’. Conclusion From March 2020–August 2021, the majority of COVID-19 cases in GB went unconfirmed. Women and children were tested less often, perhaps due to preferences in healthcare seeking and perceptions of lower risk of severe illness. Our approach may be used to estimate COVID-19 prevalence in settings with limited testing capacity. Disclosures Shaun Morris, MD, MPH, DTM&H, FRCPC, FAAP, GlaxoSmithKline: Honoraria|JNJ China: Honoraria|Merck: served on ad hoc advisory board|Pfizer: Grant/Research Support|Pfizer: served on ad-hoc advisory board|Sanofi-Pasteur: served on ad-hoc advisory board
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».