Estimating risk of long COVID using a Bayesian network-based decision support tool Article Swipe
Jane E. Sinclair
,
Helen J. Mayfield
,
Hongen Lu
,
S. J. Brown
,
Tina Moghaddam
,
Michael Waller
,
Carissa Bonner
,
Olivia Williams
,
John Litt
,
Kirsty R. Short
,
Colleen L. Lau
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5861767/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5861767/v1
Background: Long COVID causes substantial health burden globally, affecting ~30.6% of adults who have ever had symptomatic COVID-19. Despite this, long COVID remains overlooked in public health decision-making. We built a model and easy-to-access online tool for exploring six-month long COVID risk factors. Methods: A Bayesian network model was developed to estimate long-term COVID-19 adverse outcome probability using data from published studies and government reports. The model calculates probabilities of hospitalization, ICU admission, and death, under different scenarios of vaccine coverage, sex, age, comorbidities, previous infection number, and drug treatments. The model also estimates six-month long COVID symptom risk including cardiovascular, gastrointestinal, musculoskeletal, pulmonary, or neurologic symptoms, kidney issues, metabolic problems, coagulation disorders, fatigue, and mental health problems. Results: Model estimates show incomplete vaccination, missed drug treatment during acute infection, and repeated infections to be the greatest controllable influences of increased long COVID risk. The model can be updated to include emerging best evidence, data pertinent to specific countries, vaccines, and outcomes. The interactive user-friendly web-based risk-assessment tool (part of the COVID-19 Risk Calculator (CoRiCal) suite), enables easy access to model outputs. Conclusions: This model and online tool can be used by individuals or in conjunction with clinicians for shared decision making on vaccination, pursuing early drug treatment during acute infection, and continuing protective behaviors such as masking and social distancing. It may also assist public health decision-makers to assess such effects at a population level, contributing to better-informed public health policies.
Related Topics
Concepts
Coronavirus disease 2019 (COVID-19)
Bayesian network
Bayesian probability
2019-20 coronavirus outbreak
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Computer science
Econometrics
Artificial intelligence
Medicine
Mathematics
Virology
Internal medicine
Outbreak
Infectious disease (medical specialty)
Disease
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-5861767/v1
- OA Status
- gold
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406778864
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4406778864Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-5861767/v1Digital Object Identifier
- Title
-
Estimating risk of long COVID using a Bayesian network-based decision support toolWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-24Full publication date if available
- Authors
-
Jane E. Sinclair, Helen J. Mayfield, Hongen Lu, S. J. Brown, Tina Moghaddam, Michael Waller, Carissa Bonner, Olivia Williams, John Litt, Kirsty R. Short, Colleen L. LauList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-5861767/v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.21203/rs.3.rs-5861767/v1Direct OA link when available
- Concepts
-
Coronavirus disease 2019 (COVID-19), Bayesian network, Bayesian probability, 2019-20 coronavirus outbreak, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Computer science, Econometrics, Artificial intelligence, Medicine, Mathematics, Virology, Internal medicine, Outbreak, Infectious disease (medical specialty), DiseaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4406778864 |
|---|---|
| doi | https://doi.org/10.21203/rs.3.rs-5861767/v1 |
| ids.doi | https://doi.org/10.21203/rs.3.rs-5861767/v1 |
| ids.openalex | https://openalex.org/W4406778864 |
| fwci | 0.0 |
| type | preprint |
| title | Estimating risk of long COVID using a Bayesian network-based decision support tool |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11368 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2728 |
| topics[0].subfield.display_name | Neurology |
| topics[0].display_name | Long-Term Effects of COVID-19 |
| topics[1].id | https://openalex.org/T10745 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9961000084877014 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2705 |
| topics[1].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[1].display_name | Heart Rate Variability and Autonomic Control |
| topics[2].id | https://openalex.org/T10041 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.994700014591217 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2725 |
| topics[2].subfield.display_name | Infectious Diseases |
| topics[2].display_name | COVID-19 Clinical Research Studies |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C3008058167 |
| concepts[0].level | 4 |
| concepts[0].score | 0.8054598569869995 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q84263196 |
| concepts[0].display_name | Coronavirus disease 2019 (COVID-19) |
| concepts[1].id | https://openalex.org/C33724603 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6636350750923157 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q812540 |
| concepts[1].display_name | Bayesian network |
| concepts[2].id | https://openalex.org/C107673813 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6238129138946533 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q812534 |
| concepts[2].display_name | Bayesian probability |
| concepts[3].id | https://openalex.org/C3006700255 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5059837698936462 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q81068910 |
| concepts[3].display_name | 2019-20 coronavirus outbreak |
| concepts[4].id | https://openalex.org/C3007834351 |
| concepts[4].level | 5 |
| concepts[4].score | 0.5041362047195435 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q82069695 |
| concepts[4].display_name | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.4646883010864258 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C149782125 |
| concepts[6].level | 1 |
| concepts[6].score | 0.33258217573165894 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[6].display_name | Econometrics |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.30531027913093567 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C71924100 |
| concepts[8].level | 0 |
| concepts[8].score | 0.2588064968585968 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[8].display_name | Medicine |
| concepts[9].id | https://openalex.org/C33923547 |
| concepts[9].level | 0 |
| concepts[9].score | 0.20136258006095886 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[9].display_name | Mathematics |
| concepts[10].id | https://openalex.org/C159047783 |
| concepts[10].level | 1 |
| concepts[10].score | 0.19122162461280823 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7215 |
| concepts[10].display_name | Virology |
| concepts[11].id | https://openalex.org/C126322002 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0825662612915039 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[11].display_name | Internal medicine |
| concepts[12].id | https://openalex.org/C116675565 |
| concepts[12].level | 2 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q3241045 |
| concepts[12].display_name | Outbreak |
| concepts[13].id | https://openalex.org/C524204448 |
| concepts[13].level | 3 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q788926 |
| concepts[13].display_name | Infectious disease (medical specialty) |
| concepts[14].id | https://openalex.org/C2779134260 |
| concepts[14].level | 2 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q12136 |
| concepts[14].display_name | Disease |
| keywords[0].id | https://openalex.org/keywords/coronavirus-disease-2019 |
| keywords[0].score | 0.8054598569869995 |
| keywords[0].display_name | Coronavirus disease 2019 (COVID-19) |
| keywords[1].id | https://openalex.org/keywords/bayesian-network |
| keywords[1].score | 0.6636350750923157 |
| keywords[1].display_name | Bayesian network |
| keywords[2].id | https://openalex.org/keywords/bayesian-probability |
| keywords[2].score | 0.6238129138946533 |
| keywords[2].display_name | Bayesian probability |
| keywords[3].id | https://openalex.org/keywords/2019-20-coronavirus-outbreak |
| keywords[3].score | 0.5059837698936462 |
| keywords[3].display_name | 2019-20 coronavirus outbreak |
| keywords[4].id | https://openalex.org/keywords/severe-acute-respiratory-syndrome-coronavirus-2 |
| keywords[4].score | 0.5041362047195435 |
| keywords[4].display_name | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.4646883010864258 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/econometrics |
| keywords[6].score | 0.33258217573165894 |
| keywords[6].display_name | Econometrics |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.30531027913093567 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/medicine |
| keywords[8].score | 0.2588064968585968 |
| keywords[8].display_name | Medicine |
| keywords[9].id | https://openalex.org/keywords/mathematics |
| keywords[9].score | 0.20136258006095886 |
| keywords[9].display_name | Mathematics |
| keywords[10].id | https://openalex.org/keywords/virology |
| keywords[10].score | 0.19122162461280823 |
| keywords[10].display_name | Virology |
| keywords[11].id | https://openalex.org/keywords/internal-medicine |
| keywords[11].score | 0.0825662612915039 |
| keywords[11].display_name | Internal medicine |
| language | en |
| locations[0].id | doi:10.21203/rs.3.rs-5861767/v1 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.21203/rs.3.rs-5861767/v1 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5021875949 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9178-3869 |
| authorships[0].author.display_name | Jane E. Sinclair |
| authorships[0].countries | AU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I165143802 |
| authorships[0].affiliations[0].raw_affiliation_string | The University of Queensland |
| authorships[0].institutions[0].id | https://openalex.org/I165143802 |
| authorships[0].institutions[0].ror | https://ror.org/00rqy9422 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I165143802 |
| authorships[0].institutions[0].country_code | AU |
| authorships[0].institutions[0].display_name | The University of Queensland |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jane E. Sinclair |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | The University of Queensland |
| authorships[1].author.id | https://openalex.org/A5031352764 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3462-4324 |
| authorships[1].author.display_name | Helen J. Mayfield |
| authorships[1].countries | AU |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I165143802 |
| authorships[1].affiliations[0].raw_affiliation_string | The University of Queensland |
| authorships[1].institutions[0].id | https://openalex.org/I165143802 |
| authorships[1].institutions[0].ror | https://ror.org/00rqy9422 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I165143802 |
| authorships[1].institutions[0].country_code | AU |
| authorships[1].institutions[0].display_name | The University of Queensland |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Helen J. Mayfield |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | The University of Queensland |
| authorships[2].author.id | https://openalex.org/A5111027326 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Hongen Lu |
| authorships[2].countries | AU |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I165143802 |
| authorships[2].affiliations[0].raw_affiliation_string | The University of Queensland |
| authorships[2].institutions[0].id | https://openalex.org/I165143802 |
| authorships[2].institutions[0].ror | https://ror.org/00rqy9422 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I165143802 |
| authorships[2].institutions[0].country_code | AU |
| authorships[2].institutions[0].display_name | The University of Queensland |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Hongen Lu |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | The University of Queensland |
| authorships[3].author.id | https://openalex.org/A5061649927 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-7542-8664 |
| authorships[3].author.display_name | S. J. Brown |
| authorships[3].countries | AU |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I165143802 |
| authorships[3].affiliations[0].raw_affiliation_string | The University of Queensland |
| authorships[3].institutions[0].id | https://openalex.org/I165143802 |
| authorships[3].institutions[0].ror | https://ror.org/00rqy9422 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I165143802 |
| authorships[3].institutions[0].country_code | AU |
| authorships[3].institutions[0].display_name | The University of Queensland |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Samuel J. Brown |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | The University of Queensland |
| authorships[4].author.id | https://openalex.org/A5089839780 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-7234-9127 |
| authorships[4].author.display_name | Tina Moghaddam |
| authorships[4].countries | AU |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I165143802 |
| authorships[4].affiliations[0].raw_affiliation_string | The University of Queensland |
| authorships[4].institutions[0].id | https://openalex.org/I165143802 |
| authorships[4].institutions[0].ror | https://ror.org/00rqy9422 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I165143802 |
| authorships[4].institutions[0].country_code | AU |
| authorships[4].institutions[0].display_name | The University of Queensland |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Tina Moghaddam |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | The University of Queensland |
| authorships[5].author.id | https://openalex.org/A5081062241 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-1050-4574 |
| authorships[5].author.display_name | Michael Waller |
| authorships[5].countries | AU |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I165143802 |
| authorships[5].affiliations[0].raw_affiliation_string | The University of Queensland |
| authorships[5].institutions[0].id | https://openalex.org/I165143802 |
| authorships[5].institutions[0].ror | https://ror.org/00rqy9422 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I165143802 |
| authorships[5].institutions[0].country_code | AU |
| authorships[5].institutions[0].display_name | The University of Queensland |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Michael Waller |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | The University of Queensland |
| authorships[6].author.id | https://openalex.org/A5087990860 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-4797-6460 |
| authorships[6].author.display_name | Carissa Bonner |
| authorships[6].countries | AU |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I129604602 |
| authorships[6].affiliations[0].raw_affiliation_string | The University of Sydney |
| authorships[6].institutions[0].id | https://openalex.org/I129604602 |
| authorships[6].institutions[0].ror | https://ror.org/0384j8v12 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I129604602 |
| authorships[6].institutions[0].country_code | AU |
| authorships[6].institutions[0].display_name | The University of Sydney |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Carissa Bonner |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | The University of Sydney |
| authorships[7].author.id | https://openalex.org/A5072208828 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-6515-6917 |
| authorships[7].author.display_name | Olivia Williams |
| authorships[7].countries | AU |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I165143802 |
| authorships[7].affiliations[0].raw_affiliation_string | The University of Queensland |
| authorships[7].institutions[0].id | https://openalex.org/I165143802 |
| authorships[7].institutions[0].ror | https://ror.org/00rqy9422 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I165143802 |
| authorships[7].institutions[0].country_code | AU |
| authorships[7].institutions[0].display_name | The University of Queensland |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Olivia Williams |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | The University of Queensland |
| authorships[8].author.id | https://openalex.org/A5087255880 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-3103-0064 |
| authorships[8].author.display_name | John Litt |
| authorships[8].countries | AU |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I169541294 |
| authorships[8].affiliations[0].raw_affiliation_string | Flinders University |
| authorships[8].institutions[0].id | https://openalex.org/I169541294 |
| authorships[8].institutions[0].ror | https://ror.org/01kpzv902 |
| authorships[8].institutions[0].type | education |
| authorships[8].institutions[0].lineage | https://openalex.org/I169541294 |
| authorships[8].institutions[0].country_code | AU |
| authorships[8].institutions[0].display_name | Flinders University |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | John C.B. Litt |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Flinders University |
| authorships[9].author.id | https://openalex.org/A5033404457 |
| authorships[9].author.orcid | https://orcid.org/0000-0003-4963-6184 |
| authorships[9].author.display_name | Kirsty R. Short |
| authorships[9].countries | AU |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I165143802 |
| authorships[9].affiliations[0].raw_affiliation_string | The University of Queensland |
| authorships[9].institutions[0].id | https://openalex.org/I165143802 |
| authorships[9].institutions[0].ror | https://ror.org/00rqy9422 |
| authorships[9].institutions[0].type | education |
| authorships[9].institutions[0].lineage | https://openalex.org/I165143802 |
| authorships[9].institutions[0].country_code | AU |
| authorships[9].institutions[0].display_name | The University of Queensland |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Kirsty R. Short |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | The University of Queensland |
| authorships[10].author.id | https://openalex.org/A5034599786 |
| authorships[10].author.orcid | https://orcid.org/0000-0001-8288-4169 |
| authorships[10].author.display_name | Colleen L. Lau |
| authorships[10].countries | AU |
| authorships[10].affiliations[0].institution_ids | https://openalex.org/I165143802 |
| authorships[10].affiliations[0].raw_affiliation_string | The University of Queensland |
| authorships[10].institutions[0].id | https://openalex.org/I165143802 |
| authorships[10].institutions[0].ror | https://ror.org/00rqy9422 |
| authorships[10].institutions[0].type | education |
| authorships[10].institutions[0].lineage | https://openalex.org/I165143802 |
| authorships[10].institutions[0].country_code | AU |
| authorships[10].institutions[0].display_name | The University of Queensland |
| authorships[10].author_position | last |
| authorships[10].raw_author_name | Colleen L. Lau |
| authorships[10].is_corresponding | False |
| authorships[10].raw_affiliation_strings | The University of Queensland |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.21203/rs.3.rs-5861767/v1 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Estimating risk of long COVID using a Bayesian network-based decision support tool |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11368 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2728 |
| primary_topic.subfield.display_name | Neurology |
| primary_topic.display_name | Long-Term Effects of COVID-19 |
| related_works | https://openalex.org/W3036314732, https://openalex.org/W3009669391, https://openalex.org/W3176864053, https://openalex.org/W4206669628, https://openalex.org/W3171943759, https://openalex.org/W4292098121, https://openalex.org/W3154141118, https://openalex.org/W4388896133, https://openalex.org/W3031607536, https://openalex.org/W3198183218 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.21203/rs.3.rs-5861767/v1 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-5861767/v1 |
| primary_location.id | doi:10.21203/rs.3.rs-5861767/v1 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-5861767/v1 |
| publication_date | 2025-01-24 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3162692087, https://openalex.org/W3023787531, https://openalex.org/W4381308351, https://openalex.org/W4212914366, https://openalex.org/W4386033475, https://openalex.org/W4294656213, https://openalex.org/W4281476185, https://openalex.org/W4366983740, https://openalex.org/W4360600280, https://openalex.org/W4379932354, https://openalex.org/W4308767470, https://openalex.org/W3212377033, https://openalex.org/W3208822038, https://openalex.org/W4225390123, https://openalex.org/W4293298552, https://openalex.org/W4402209745, https://openalex.org/W2617318877, https://openalex.org/W2167536963, https://openalex.org/W3093750853, https://openalex.org/W4400422756, https://openalex.org/W4386435258, https://openalex.org/W4384405676, https://openalex.org/W4289556003 |
| referenced_works_count | 23 |
| abstract_inverted_index.a | 31, 234 |
| abstract_inverted_index.It | 222 |
| abstract_inverted_index.We | 29 |
| abstract_inverted_index.as | 217 |
| abstract_inverted_index.at | 233 |
| abstract_inverted_index.be | 135, 148, 190 |
| abstract_inverted_index.by | 192 |
| abstract_inverted_index.in | 25, 195 |
| abstract_inverted_index.of | 11, 70, 79, 140, 170 |
| abstract_inverted_index.on | 203 |
| abstract_inverted_index.or | 105, 194 |
| abstract_inverted_index.to | 51, 134, 150, 157, 180, 229, 238 |
| abstract_inverted_index.ICU | 72 |
| abstract_inverted_index.The | 66, 91, 145, 163 |
| abstract_inverted_index.and | 33, 63, 74, 88, 115, 131, 161, 186, 212, 219 |
| abstract_inverted_index.can | 147, 189 |
| abstract_inverted_index.for | 37, 199 |
| abstract_inverted_index.had | 16 |
| abstract_inverted_index.may | 223 |
| abstract_inverted_index.the | 136, 171 |
| abstract_inverted_index.was | 49 |
| abstract_inverted_index.who | 13 |
| abstract_inverted_index.Long | 2 |
| abstract_inverted_index.Risk | 173 |
| abstract_inverted_index.This | 184 |
| abstract_inverted_index.age, | 83 |
| abstract_inverted_index.also | 93, 224 |
| abstract_inverted_index.best | 153 |
| abstract_inverted_index.data | 59, 155 |
| abstract_inverted_index.drug | 89, 126, 207 |
| abstract_inverted_index.easy | 178 |
| abstract_inverted_index.ever | 15 |
| abstract_inverted_index.from | 60 |
| abstract_inverted_index.have | 14 |
| abstract_inverted_index.long | 21, 40, 96, 142 |
| abstract_inverted_index.risk | 42, 99 |
| abstract_inverted_index.sex, | 82 |
| abstract_inverted_index.show | 122 |
| abstract_inverted_index.such | 216, 231 |
| abstract_inverted_index.tool | 36, 168, 188 |
| abstract_inverted_index.used | 191 |
| abstract_inverted_index.with | 197 |
| abstract_inverted_index.(part | 169 |
| abstract_inverted_index.COVID | 3, 22, 41, 97, 143 |
| abstract_inverted_index.Model | 120 |
| abstract_inverted_index.acute | 129, 210 |
| abstract_inverted_index.built | 30 |
| abstract_inverted_index.early | 206 |
| abstract_inverted_index.model | 32, 48, 67, 92, 146, 181, 185 |
| abstract_inverted_index.risk. | 144 |
| abstract_inverted_index.this, | 20 |
| abstract_inverted_index.under | 76 |
| abstract_inverted_index.using | 58 |
| abstract_inverted_index.access | 179 |
| abstract_inverted_index.adults | 12 |
| abstract_inverted_index.assess | 230 |
| abstract_inverted_index.assist | 225 |
| abstract_inverted_index.burden | 7 |
| abstract_inverted_index.causes | 4 |
| abstract_inverted_index.death, | 75 |
| abstract_inverted_index.during | 128, 209 |
| abstract_inverted_index.health | 6, 27, 117, 227, 241 |
| abstract_inverted_index.kidney | 108 |
| abstract_inverted_index.level, | 236 |
| abstract_inverted_index.making | 202 |
| abstract_inverted_index.mental | 116 |
| abstract_inverted_index.missed | 125 |
| abstract_inverted_index.online | 35, 187 |
| abstract_inverted_index.public | 26, 226, 240 |
| abstract_inverted_index.shared | 200 |
| abstract_inverted_index.social | 220 |
| abstract_inverted_index.~30.6% | 10 |
| abstract_inverted_index.Despite | 19 |
| abstract_inverted_index.adverse | 55 |
| abstract_inverted_index.effects | 232 |
| abstract_inverted_index.enables | 177 |
| abstract_inverted_index.include | 151 |
| abstract_inverted_index.issues, | 109 |
| abstract_inverted_index.masking | 218 |
| abstract_inverted_index.network | 47 |
| abstract_inverted_index.number, | 87 |
| abstract_inverted_index.outcome | 56 |
| abstract_inverted_index.remains | 23 |
| abstract_inverted_index.studies | 62 |
| abstract_inverted_index.suite), | 176 |
| abstract_inverted_index.symptom | 98 |
| abstract_inverted_index.updated | 149 |
| abstract_inverted_index.vaccine | 80 |
| abstract_inverted_index.</bold>A | 45 |
| abstract_inverted_index.Bayesian | 46 |
| abstract_inverted_index.COVID-19 | 54, 172 |
| abstract_inverted_index.decision | 201 |
| abstract_inverted_index.emerging | 152 |
| abstract_inverted_index.estimate | 52 |
| abstract_inverted_index.factors. | 43 |
| abstract_inverted_index.fatigue, | 114 |
| abstract_inverted_index.greatest | 137 |
| abstract_inverted_index.outputs. | 182 |
| abstract_inverted_index.previous | 85 |
| abstract_inverted_index.pursuing | 205 |
| abstract_inverted_index.repeated | 132 |
| abstract_inverted_index.reports. | 65 |
| abstract_inverted_index.specific | 158 |
| abstract_inverted_index.(CoRiCal) | 175 |
| abstract_inverted_index.COVID-19. | 18 |
| abstract_inverted_index.affecting | 9 |
| abstract_inverted_index.behaviors | 215 |
| abstract_inverted_index.coverage, | 81 |
| abstract_inverted_index.developed | 50 |
| abstract_inverted_index.different | 77 |
| abstract_inverted_index.estimates | 94, 121 |
| abstract_inverted_index.evidence, | 154 |
| abstract_inverted_index.exploring | 38 |
| abstract_inverted_index.globally, | 8 |
| abstract_inverted_index.including | 100 |
| abstract_inverted_index.increased | 141 |
| abstract_inverted_index.infection | 86 |
| abstract_inverted_index.long-term | 53 |
| abstract_inverted_index.metabolic | 110 |
| abstract_inverted_index.outcomes. | 162 |
| abstract_inverted_index.pertinent | 156 |
| abstract_inverted_index.policies. | 242 |
| abstract_inverted_index.problems, | 111 |
| abstract_inverted_index.problems. | 118 |
| abstract_inverted_index.published | 61 |
| abstract_inverted_index.scenarios | 78 |
| abstract_inverted_index.six-month | 39, 95 |
| abstract_inverted_index.symptoms, | 107 |
| abstract_inverted_index.treatment | 127, 208 |
| abstract_inverted_index.vaccines, | 160 |
| abstract_inverted_index.web-based | 166 |
| abstract_inverted_index.Calculator | 174 |
| abstract_inverted_index.admission, | 73 |
| abstract_inverted_index.calculates | 68 |
| abstract_inverted_index.clinicians | 198 |
| abstract_inverted_index.continuing | 213 |
| abstract_inverted_index.countries, | 159 |
| abstract_inverted_index.disorders, | 113 |
| abstract_inverted_index.government | 64 |
| abstract_inverted_index.incomplete | 123 |
| abstract_inverted_index.infection, | 130, 211 |
| abstract_inverted_index.infections | 133 |
| abstract_inverted_index.influences | 139 |
| abstract_inverted_index.neurologic | 106 |
| abstract_inverted_index.overlooked | 24 |
| abstract_inverted_index.population | 235 |
| abstract_inverted_index.protective | 214 |
| abstract_inverted_index.pulmonary, | 104 |
| abstract_inverted_index.coagulation | 112 |
| abstract_inverted_index.conjunction | 196 |
| abstract_inverted_index.distancing. | 221 |
| abstract_inverted_index.individuals | 193 |
| abstract_inverted_index.interactive | 164 |
| abstract_inverted_index.probability | 57 |
| abstract_inverted_index.substantial | 5 |
| abstract_inverted_index.symptomatic | 17 |
| abstract_inverted_index.treatments. | 90 |
| abstract_inverted_index.contributing | 237 |
| abstract_inverted_index.controllable | 138 |
| abstract_inverted_index.vaccination, | 124, 204 |
| abstract_inverted_index.probabilities | 69 |
| abstract_inverted_index.user-friendly | 165 |
| abstract_inverted_index.<bold>Methods: | 44 |
| abstract_inverted_index.comorbidities, | 84 |
| abstract_inverted_index.easy-to-access | 34 |
| abstract_inverted_index.better-informed | 239 |
| abstract_inverted_index.cardiovascular, | 101 |
| abstract_inverted_index.decision-makers | 228 |
| abstract_inverted_index.risk-assessment | 167 |
| abstract_inverted_index.decision-making. | 28 |
| abstract_inverted_index.hospitalization, | 71 |
| abstract_inverted_index.musculoskeletal, | 103 |
| abstract_inverted_index.gastrointestinal, | 102 |
| abstract_inverted_index.<bold>Results:</bold> | 119 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| abstract_inverted_index.<bold>Background:</bold> | 1 |
| abstract_inverted_index.<bold>Conclusions:</bold> | 183 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile.value | 0.03101821 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |