Related Experiment Video
Updated: Sep 1, 2025
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
10.8K
Predicting past and future SARS-CoV-2-related sick leave using discrete time Markov modelling
Jiayao Lei1, Mark Clements2, Miriam Elfström1
1Karolinska University Laboratory, Karolinska University Hospital, Stockholm, Sweden.
Plos One
|August 12, 2022
Summary
Predicting sick leave for healthcare workers (HCWs) due to SARS-CoV-2 is crucial for epidemic response. A Markov model using testing data quantified COVID-19 related sick leave in HCWs, aiding future outbreak planning.
Area of Science:
- Epidemiology
- Public Health
- Infectious Disease Modeling
Background:
- Healthcare worker (HCW) sick leave prediction is vital for managing epidemics.
- SARS-CoV-2 (COVID-19) significantly impacted healthcare systems globally.
Purpose of the Study:
- To develop a predictive model for SARS-CoV-2-induced sick leave among HCWs.
- To quantify COVID-19 related sick leave using testing data.
Main Methods:
- A discrete-time Markov model was developed.
- Utilized data from 9449 HCWs in Stockholm, Sweden, including SARS-CoV-2 RNA, antibody, and sick leave data for 2020.
- Compared transition probabilities of sick leave during and after the outbreak relative to PCR and serology results.
Main Results:
- Healthy HCWs testing positive for SARS-CoV-2 showed significantly higher probabilities of transitioning to sick leave.
- The proportion of all sick leaves attributed to COVID-19 during the outbreak was up to 55%.
Conclusions:
- A robust Markov model effectively uses SARS-CoV-2 testing data to quantify sick leave.
- This model provides a basis for healthcare planning during outbreaks.
Related Concept Videos
01:18Steps in Outbreak Investigation
176
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
176
01:25Statistical Methods for Analyzing Epidemiological Data
507
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
507
01:21Causality in Epidemiology
726
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
726
01:17Classification of Illness
7.9K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.9K
01:18Introduction To Survival Analysis
362
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
362
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K