Project Details
Description
Scientific aims of the project:
- Using machine learning, identify specific combinations of COVID-19 symptoms that
vary depending on diabetes type and severity (defined by self-report diagnosis, HbA1c, and use of medications), diabetes medications and/or degree of obesity (defined using BMI).
- Determine whether persistence of COVID-19 disease (i.e., “long Covid”), occurs more or less frequently depending on diabetes type and severity (defined by self-report diagnosis, HbA1c and use of medications), diabetes medications and/or degree of obesity (defined using BMI).
- Assess temporal and geographical trends in the community-spread of SARS-CoV-2, and how changes in national containment strategies and vaccination impact community spread.
- Compare temporal and geographic trends of the spread of SARS-CoV-2 across countries varying in population demographics, vaccination coverage, and containment strategies.
- Using machine learning, identify specific combinations of COVID-19 symptoms that
vary depending on diabetes type and severity (defined by self-report diagnosis, HbA1c, and use of medications), diabetes medications and/or degree of obesity (defined using BMI).
- Determine whether persistence of COVID-19 disease (i.e., “long Covid”), occurs more or less frequently depending on diabetes type and severity (defined by self-report diagnosis, HbA1c and use of medications), diabetes medications and/or degree of obesity (defined using BMI).
- Assess temporal and geographical trends in the community-spread of SARS-CoV-2, and how changes in national containment strategies and vaccination impact community spread.
- Compare temporal and geographic trends of the spread of SARS-CoV-2 across countries varying in population demographics, vaccination coverage, and containment strategies.
| Short title | eSSENCE@LU 8:8 |
|---|---|
| Status | Active |
| Effective start/end date | 2022/01/01 → 2023/12/31 |