Temporal Series Analysis of Population Cycle Threshold Counts as a Predictor of Surge in Cases and Hospitalizations during the SARS-CoV-2 Pandemic
Tools to predict surges in cases and hospitalizations during the COVID-19 pandemic may help guide public health decisions. Low cycle threshold (CT) counts may indicate greater SARS-CoV-2 concentrations in the respiratory tract, and thereby may be used as a surrogate marker of enhanced viral transmis...
| Main Authors: | , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Viruses |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4915/15/2/421 |
| _version_ | 1797617781297905664 |
|---|---|
| author | Fernando Cava Jesús San Román Pablo Barreiro Francisco Javier Candel Francisco Javier Álvarez-Timón David Melero Nerea Coya Raquel Guillén David Cantarero-Prieto Javier Lera-Torres Noelia Cobo-Ortiz Jesús Canora Francisco Javier Martínez-Peromingo Raquel Barba María del Mar Carretero Juan Emilio Losa Antonio Zapatero |
| author_facet | Fernando Cava Jesús San Román Pablo Barreiro Francisco Javier Candel Francisco Javier Álvarez-Timón David Melero Nerea Coya Raquel Guillén David Cantarero-Prieto Javier Lera-Torres Noelia Cobo-Ortiz Jesús Canora Francisco Javier Martínez-Peromingo Raquel Barba María del Mar Carretero Juan Emilio Losa Antonio Zapatero |
| author_sort | Fernando Cava |
| collection | DOAJ |
| description | Tools to predict surges in cases and hospitalizations during the COVID-19 pandemic may help guide public health decisions. Low cycle threshold (CT) counts may indicate greater SARS-CoV-2 concentrations in the respiratory tract, and thereby may be used as a surrogate marker of enhanced viral transmission. Several population studies have found an association between the oscillations in the mean CT over time and the evolution of the pandemic. For the first time, we applied temporal series analysis (Granger-type causality) to validate the CT counts as an epidemiological marker of forthcoming pandemic waves using samples and analyzing cases and hospital admissions during the third pandemic wave (October 2020 to May 2021) in Madrid. A total of 22,906 SARS-CoV-2 RT-PCR-positive nasopharyngeal swabs were evaluated; the mean CT value was 27.4 (SD: 2.1) (22.2% below 20 cycles). During this period, 422,110 cases and 36,727 hospital admissions were also recorded. A temporal association was found between the CT counts and the cases of COVID-19 with a lag of 9–10 days (<i>p</i> ≤ 0.01) and hospital admissions by COVID-19 (<i>p</i> < 0.04) with a lag of 2–6 days. According to a validated method to prove associations between variables that change over time, the short-term evolution of average CT counts in the population may forecast the evolution of the COVID-19 pandemic. |
| first_indexed | 2024-03-11T08:00:37Z |
| format | Article |
| id | doaj.art-529a649b491b4009addf67a46ff846bb |
| institution | Directory Open Access Journal |
| issn | 1999-4915 |
| language | English |
| last_indexed | 2024-03-11T08:00:37Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj.art-529a649b491b4009addf67a46ff846bb2023-11-16T23:48:47ZengMDPI AGViruses1999-49152023-02-0115242110.3390/v15020421Temporal Series Analysis of Population Cycle Threshold Counts as a Predictor of Surge in Cases and Hospitalizations during the SARS-CoV-2 PandemicFernando Cava0Jesús San Román1Pablo Barreiro2Francisco Javier Candel3Francisco Javier Álvarez-Timón4David Melero5Nerea Coya6Raquel Guillén7David Cantarero-Prieto8Javier Lera-Torres9Noelia Cobo-Ortiz10Jesús Canora11Francisco Javier Martínez-Peromingo12Raquel Barba13María del Mar Carretero14Juan Emilio Losa15Antonio Zapatero16Department of Clinical Chemistry, Synlab, 28036 Madrid, SpainDepartment of Medical Specialties and Public Health, Universidad Rey Juan Carlos, 28922 Madrid, SpainDepartment of Medical Specialties and Public Health, Universidad Rey Juan Carlos, 28922 Madrid, SpainUnit of Microbiology, Regional Laboratory of Public Health, Hospital Enfermera Isabel Zendal, 28055 Madrid, SpainUnit of Microbiology, Regional Laboratory of Public Health, Hospital Enfermera Isabel Zendal, 28055 Madrid, SpainClinical Laboratory, Infanta Sofia University Hospital, UR Health, 28703 Madrid, SpainClinical Laboratory, Infanta Sofia University Hospital, UR Health, 28703 Madrid, SpainClinical Laboratory, Infanta Sofia University Hospital, UR Health, 28703 Madrid, SpainHealth Economics Research Group, University of Cantabria and IDIVAL, 39005 Santander, SpainHealth Economics Research Group, University of Cantabria and IDIVAL, 39005 Santander, SpainHealth Economics Research Group, University of Cantabria and IDIVAL, 39005 Santander, SpainDepartment of Internal Medicine, Hospital Universitario de Fuenlabrada, Universidad Rey Juan Carlos, 28922 Madrid, SpainDepartment of Geriatrics, Hospital Universitario Rey Juan Carlos, Universidad Rey Juan Carlos, 28922 Madrid, SpainDepartment of Internal Medicine, Hospital Universitario Rey Juan Carlos, Universidad Rey Juan Carlos, 28922 Madrid, SpainUnit of Microbiology, Regional Laboratory of Public Health, Hospital Enfermera Isabel Zendal, 28055 Madrid, SpainInfectious Diseases Unit, University Hospital Fundación Alcorcón, 28922 Madrid, SpainDepartment of Internal Medicine, Hospital Universitario de Fuenlabrada, Universidad Rey Juan Carlos, 28922 Madrid, SpainTools to predict surges in cases and hospitalizations during the COVID-19 pandemic may help guide public health decisions. Low cycle threshold (CT) counts may indicate greater SARS-CoV-2 concentrations in the respiratory tract, and thereby may be used as a surrogate marker of enhanced viral transmission. Several population studies have found an association between the oscillations in the mean CT over time and the evolution of the pandemic. For the first time, we applied temporal series analysis (Granger-type causality) to validate the CT counts as an epidemiological marker of forthcoming pandemic waves using samples and analyzing cases and hospital admissions during the third pandemic wave (October 2020 to May 2021) in Madrid. A total of 22,906 SARS-CoV-2 RT-PCR-positive nasopharyngeal swabs were evaluated; the mean CT value was 27.4 (SD: 2.1) (22.2% below 20 cycles). During this period, 422,110 cases and 36,727 hospital admissions were also recorded. A temporal association was found between the CT counts and the cases of COVID-19 with a lag of 9–10 days (<i>p</i> ≤ 0.01) and hospital admissions by COVID-19 (<i>p</i> < 0.04) with a lag of 2–6 days. According to a validated method to prove associations between variables that change over time, the short-term evolution of average CT counts in the population may forecast the evolution of the COVID-19 pandemic.https://www.mdpi.com/1999-4915/15/2/421COVID-19SARS-CoV-2hospitalizationscycle thresholdRT-PCRpandemic |
| spellingShingle | Fernando Cava Jesús San Román Pablo Barreiro Francisco Javier Candel Francisco Javier Álvarez-Timón David Melero Nerea Coya Raquel Guillén David Cantarero-Prieto Javier Lera-Torres Noelia Cobo-Ortiz Jesús Canora Francisco Javier Martínez-Peromingo Raquel Barba María del Mar Carretero Juan Emilio Losa Antonio Zapatero Temporal Series Analysis of Population Cycle Threshold Counts as a Predictor of Surge in Cases and Hospitalizations during the SARS-CoV-2 Pandemic Viruses COVID-19 SARS-CoV-2 hospitalizations cycle threshold RT-PCR pandemic |
| title | Temporal Series Analysis of Population Cycle Threshold Counts as a Predictor of Surge in Cases and Hospitalizations during the SARS-CoV-2 Pandemic |
| title_full | Temporal Series Analysis of Population Cycle Threshold Counts as a Predictor of Surge in Cases and Hospitalizations during the SARS-CoV-2 Pandemic |
| title_fullStr | Temporal Series Analysis of Population Cycle Threshold Counts as a Predictor of Surge in Cases and Hospitalizations during the SARS-CoV-2 Pandemic |
| title_full_unstemmed | Temporal Series Analysis of Population Cycle Threshold Counts as a Predictor of Surge in Cases and Hospitalizations during the SARS-CoV-2 Pandemic |
| title_short | Temporal Series Analysis of Population Cycle Threshold Counts as a Predictor of Surge in Cases and Hospitalizations during the SARS-CoV-2 Pandemic |
| title_sort | temporal series analysis of population cycle threshold counts as a predictor of surge in cases and hospitalizations during the sars cov 2 pandemic |
| topic | COVID-19 SARS-CoV-2 hospitalizations cycle threshold RT-PCR pandemic |
| url | https://www.mdpi.com/1999-4915/15/2/421 |
| work_keys_str_mv | AT fernandocava temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT jesussanroman temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT pablobarreiro temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT franciscojaviercandel temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT franciscojavieralvareztimon temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT davidmelero temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT nereacoya temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT raquelguillen temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT davidcantareroprieto temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT javierleratorres temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT noeliacoboortiz temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT jesuscanora temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT franciscojaviermartinezperomingo temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT raquelbarba temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT mariadelmarcarretero temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT juanemiliolosa temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic AT antoniozapatero temporalseriesanalysisofpopulationcyclethresholdcountsasapredictorofsurgeincasesandhospitalizationsduringthesarscov2pandemic |