Estimation of unconfirmed COVID-19 cases from a cross-sectional survey of >10 000 households and a symptom-based machine learning model in Gilgit-Baltistan, Pakistan
Introduction Robust estimates of COVID-19 prevalence in settings with limited capacity for SARS-CoV-2 molecular and serologic testing are scarce. We aimed to describe the epidemiology of confirmed and probable COVID-19 in Gilgit-Baltistan, and to develop a symptom-based predictive model to identify...
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BMJ Publishing Group
2025-05-01
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| author | Monica Taljaard Zulfiqar A Bhutta Lisa G Pell Diego G Bassani Sajid Soofi Shaun K Morris Daniel S Farrar Muhammad Karim Lauren Erdman Yasin Muhammad Sher Hafiz Khan Zachary Tanner Imran Ahmed Chauhadry Falak Madhani Shariq Paracha Masood Ali Khan Rachel F Spitzer Sarah M Abu Fadaleh |
| author_facet | Monica Taljaard Zulfiqar A Bhutta Lisa G Pell Diego G Bassani Sajid Soofi Shaun K Morris Daniel S Farrar Muhammad Karim Lauren Erdman Yasin Muhammad Sher Hafiz Khan Zachary Tanner Imran Ahmed Chauhadry Falak Madhani Shariq Paracha Masood Ali Khan Rachel F Spitzer Sarah M Abu Fadaleh |
| author_sort | Monica Taljaard |
| collection | DOAJ |
| description | Introduction Robust estimates of COVID-19 prevalence in settings with limited capacity for SARS-CoV-2 molecular and serologic testing are scarce. We aimed to describe the epidemiology of confirmed and probable COVID-19 in Gilgit-Baltistan, and to develop a symptom-based predictive model to identify infected but undiagnosed individuals with COVID-19.Methods We conducted a cross-sectional survey in 10 257 randomly selected households in Gilgit-Baltistan from June to August 2021. Data regarding SARS-CoV-2 testing, healthcare worker (HCW) diagnoses, symptoms and outcomes since March 2020 were self-reported by households. ‘Confirmed/probable’ infection was defined as a positive test, HCW COVID-19 diagnosis or HCW pneumonia diagnosis with COVID-19-positive contact. Robust Poisson regression was conducted to assess differences in symptoms, outcomes and SARS-CoV-2 testing rates. We developed a symptom-based machine learning model to differentiate confirmed/probable infections from those with negative tests. We applied this model to untested respondents to estimate the total prevalence of SARS-CoV-2 infection.Results Data were collected for 77 924 people. Overall, 314 (0.5%) had confirmed/probable infections, 3263 (4.4%) had negative tests and 74 347 (95.1%) were untested. Children were tested less often than adults (adjusted prevalence ratio (aPR) 0.08, 95% CI 0.06 to 0.12 for ages 1–4 years vs 30–39 years), while males were tested more often than females (aPR 1.51, 95% CI 1.40 to 1.63). In the predictive model, area under the receiver operating characteristic curve was 0.92 (95% CI 0.90 to 0.93). We estimate there were 8–17 total SARS-CoV-2 infections for each positive test (8–17:1). The ratio of estimated to confirmed cases was higher for ages 1–4 years (211–480:1), 5–9 years (80–185:1) and for females (13–25:1).Conclusions From March 2020 to August 2021, the majority of SARS-CoV-2 infections in Gilgit-Baltistan went unconfirmed, particularly among women and children. Predictive models which incorporate self-reported symptoms may improve understanding of the burden of disease in settings lacking diagnostic capacity. |
| format | Article |
| id | doaj-art-64c2cebf48514f119dfcf97bcbc7acfc |
| institution | Kabale University |
| issn | 2753-4294 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMJ Publishing Group |
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| spelling | doaj-art-64c2cebf48514f119dfcf97bcbc7acfc2025-08-20T03:29:35ZengBMJ Publishing GroupBMJ Public Health2753-42942025-05-013110.1136/bmjph-2024-001255Estimation of unconfirmed COVID-19 cases from a cross-sectional survey of >10 000 households and a symptom-based machine learning model in Gilgit-Baltistan, PakistanMonica Taljaard0Zulfiqar A Bhutta1Lisa G Pell2Diego G Bassani3Sajid Soofi4Shaun K Morris5Daniel S Farrar6Muhammad Karim7Lauren Erdman8Yasin Muhammad9Sher Hafiz Khan10Zachary Tanner11Imran Ahmed Chauhadry12Falak Madhani13Shariq Paracha14Masood Ali Khan15Rachel F Spitzer16Sarah M Abu Fadaleh1711 School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, CanadaInstitute of Global Health Department, The Aga Khan University, Karachi, Sindh, Pakistan1 Centre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada1 Centre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, CanadaPediatrics and Child Health, The Aga Khan University, Karachi, Pakistan15 Division of Infectious Diseases, The Hospital for Sick Children, Toronto, Ontario, Canada1 Centre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada7 Centre of Excellence in Women and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan3 Vector Institute, The Hospital for Sick Children and University of Toronto, Toronto, Ontario, Canada2 Gilgit Regional Office, Aga Khan Health Service Pakistan, Gilgit, Gilgit-Baltistan, Pakistan2 Gilgit Regional Office, Aga Khan Health Service Pakistan, Gilgit, Gilgit-Baltistan, Pakistan1 Centre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada7 Centre of Excellence in Women and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan8 Aga Khan Health Service Pakistan, Karachi, Sindh, Pakistan8 Aga Khan Health Service Pakistan, Karachi, Sindh, Pakistan2 Gilgit Regional Office, Aga Khan Health Service Pakistan, Gilgit, Gilgit-Baltistan, Pakistan12 Department of Obstetrics and Gynaecology, University of Toronto, Toronto, Ontario, Canada1 Centre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, CanadaIntroduction Robust estimates of COVID-19 prevalence in settings with limited capacity for SARS-CoV-2 molecular and serologic testing are scarce. We aimed to describe the epidemiology of confirmed and probable COVID-19 in Gilgit-Baltistan, and to develop a symptom-based predictive model to identify infected but undiagnosed individuals with COVID-19.Methods We conducted a cross-sectional survey in 10 257 randomly selected households in Gilgit-Baltistan from June to August 2021. Data regarding SARS-CoV-2 testing, healthcare worker (HCW) diagnoses, symptoms and outcomes since March 2020 were self-reported by households. ‘Confirmed/probable’ infection was defined as a positive test, HCW COVID-19 diagnosis or HCW pneumonia diagnosis with COVID-19-positive contact. Robust Poisson regression was conducted to assess differences in symptoms, outcomes and SARS-CoV-2 testing rates. We developed a symptom-based machine learning model to differentiate confirmed/probable infections from those with negative tests. We applied this model to untested respondents to estimate the total prevalence of SARS-CoV-2 infection.Results Data were collected for 77 924 people. Overall, 314 (0.5%) had confirmed/probable infections, 3263 (4.4%) had negative tests and 74 347 (95.1%) were untested. Children were tested less often than adults (adjusted prevalence ratio (aPR) 0.08, 95% CI 0.06 to 0.12 for ages 1–4 years vs 30–39 years), while males were tested more often than females (aPR 1.51, 95% CI 1.40 to 1.63). In the predictive model, area under the receiver operating characteristic curve was 0.92 (95% CI 0.90 to 0.93). We estimate there were 8–17 total SARS-CoV-2 infections for each positive test (8–17:1). The ratio of estimated to confirmed cases was higher for ages 1–4 years (211–480:1), 5–9 years (80–185:1) and for females (13–25:1).Conclusions From March 2020 to August 2021, the majority of SARS-CoV-2 infections in Gilgit-Baltistan went unconfirmed, particularly among women and children. Predictive models which incorporate self-reported symptoms may improve understanding of the burden of disease in settings lacking diagnostic capacity.https://bmjpublichealth.bmj.com/content/3/1/e001255.full |
| spellingShingle | Monica Taljaard Zulfiqar A Bhutta Lisa G Pell Diego G Bassani Sajid Soofi Shaun K Morris Daniel S Farrar Muhammad Karim Lauren Erdman Yasin Muhammad Sher Hafiz Khan Zachary Tanner Imran Ahmed Chauhadry Falak Madhani Shariq Paracha Masood Ali Khan Rachel F Spitzer Sarah M Abu Fadaleh Estimation of unconfirmed COVID-19 cases from a cross-sectional survey of >10 000 households and a symptom-based machine learning model in Gilgit-Baltistan, Pakistan BMJ Public Health |
| title | Estimation of unconfirmed COVID-19 cases from a cross-sectional survey of >10 000 households and a symptom-based machine learning model in Gilgit-Baltistan, Pakistan |
| title_full | Estimation of unconfirmed COVID-19 cases from a cross-sectional survey of >10 000 households and a symptom-based machine learning model in Gilgit-Baltistan, Pakistan |
| title_fullStr | Estimation of unconfirmed COVID-19 cases from a cross-sectional survey of >10 000 households and a symptom-based machine learning model in Gilgit-Baltistan, Pakistan |
| title_full_unstemmed | Estimation of unconfirmed COVID-19 cases from a cross-sectional survey of >10 000 households and a symptom-based machine learning model in Gilgit-Baltistan, Pakistan |
| title_short | Estimation of unconfirmed COVID-19 cases from a cross-sectional survey of >10 000 households and a symptom-based machine learning model in Gilgit-Baltistan, Pakistan |
| title_sort | estimation of unconfirmed covid 19 cases from a cross sectional survey of 10 000 households and a symptom based machine learning model in gilgit baltistan pakistan |
| url | https://bmjpublichealth.bmj.com/content/3/1/e001255.full |
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