Rapporter / Institutionen för medicin

Permanent URI for this collectionhttps://gupea-staging.ub.gu.se/handle/2077/70982

Browse

Recent Submissions

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Self-reported symptoms and their relation to COVID-19 infection and its severity: A Swedish pilot study
    (School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, 2023) Grimby-Ekman, Anna; Grönkvist, Rode; Gomez, Maria F.; Sudre, Carole
    On March 11th, 2020, the World Health Organization officially recognized COVID-19 as a global pandemic. Health systems worldwide were overwhelmed, in part due to the variety of new variants that began to emerge, presenting diverse symptom profiles and levels of infectiousness. This study aimed to develop a predictive scale based on Rasch analysis, using symptoms reported by individuals with positive PCR tests. Additionally, the project sought to evaluate the scale's effectiveness in screening for positive PCR tests among those tested and in predicting hospitalization among those who tested positive. Data were obtained from the COVID Symptom Study smartphone application in the United Kingdom, United States of America, and Sweden, with a focus on Swedish data from the COVID Symptom Study Sweden. Early symptoms, within the first 5 days, were of particular interest for early prediction of COVID-19 infection severity. A Rasch analysis was used to investigate whether the symptoms could form a measurement scale related to COVID-19 infection. This could indicate the importance of the symptoms in regard to severity of the infection and regarding predictability. A low location of the symptoms on the the scale indicates that they are common and could indicate low severity. A high scale location would then indicate more rare symptoms that could be connected to more severe infection. Logistic regression was used to predict positive PCR tests among individuals who underwent PCR testing, as well as hospitalization among those with positive PCR tests. The scale's fit to the Rasch model was moderate, its predictive ability for hospitalization among individuals with positive PCR tests was acceptable, as indicated by an area under the curve (AUC) of 0.7 (Mandrekar, 2010), but maybe not clinically useful (Fan et al., 2006). However, the representation of COVID-19 cases in the Swedish data during the first half of 2020 was limited to more severe cases, primarily reflecting individuals with severe symptoms. 5 (38) In conclusion, symptom clustering holds promise in understanding patterns in COVID-19 symptoms and could serve as a valuable screening tool for identifying severe cases. Further research, particularly focusing on predictive models and comparative analyses, is necessary to fully understand these symptom patterns and their practical applications. Our findings indicate that predicting COVID-19 severity is feasible, making continued research in this field imperative.
  • No Thumbnail Available
    Item
  • No Thumbnail Available
    Item
    Prediction models for planning health care resources. During the first wave of the Covid-19 pandemic 2020
    (University of Gothenburg, 2022) Grimby-Ekman, Anna; Cronie, Ottmar; Bock, David; Palaszewski, Bo; Norman Kjellström, Anna; School of Public Health and Community Medicine, Institute of Medicine
    Due to the Covid-19 pandemic, has emphasized a need for planning health care resources based on only a few aggregated data points and little knowledge of the data-generating process. In the first part of the report, we present the process of our work in spring 2020 during the first wave and especially during the first part with the high demands on health care resources. In the second part of the report, we discuss the logistic growth model (LGM), one of our models used to predict the peak height and the peak timing. We present some different approaches to use the LGM, and compare these to a different data set, Belgium data. For the Swedish regional data, the LGM on raw observations gave a good estimate on the peak height. The adjusted LGM, using cumulative new inpatient beds, fitted the Swedish regional data to a satisfying degree. For the Belgium data, the LGM on raw observations gave a good estimate on peak height and timing. The adjusted LGM, using cumulative new inpatient beds, did not work for the Belgium data as it gave a too early peak time and a too low peak height. The experience from our work, in combination with now existing literature, the process in a similar future situation would include better knowledge on how to find and combine data to get as reliable forecasts as possible and to use creativity in combination with theoretical competence.