Adjusting COVID-19 Reports for Countries Age Disparities: A Comparative Framework for Reporting Performances
Eryarsoy, E.; Delen, D.; Davazdahemami, B.
Show abstract
ObjectivesThe COVID-19 outbreak has impacted distinct health care systems differently. While the rate of disease for COVID-19 is highly age-variant, there is no unified and age/gender-inclusive reporting taking place. This renders the comparison of individual countries based on their corresponding metrics, such as CFR difficult. In this paper, we examine cross-country differences, in terms of the age distribution of symptomatic cases, hospitalizations, intensive care unit (ICU) cases, and fatalities. In addition, we propose a new quality measure (called dissonance ratio) to facilitate comparison of countries performance in testing and reporting COVID-19 cases (i.e., their reporting quality). MethodsBy combining population pyramids with estimated COVID-19 age-dependent conditional probabilities, we bridge country-level incidence data gathered from different countries and attribute the variability in data to country demographics. ResultsWe show that age-adjustment can account for as much as a 22-fold difference in the expected number of fatalities across different countries. We provide case, hospitalization, ICU, and fatality breakdown estimates for a comprehensive list of countries. Also, a comparison is conducted between countries in terms of their performance in reporting COVID-19 cases and fatalities. ConclusionsOur research sheds light on the importance of and propose a methodology to use countries population pyramids for obtaining accurate estimates of the healthcare system requirements based on the experience of other, already affected, countries at the time of pandemics.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A flexible method for optimising sharing of healthcare resources and demand in the context of the COVID-19 pandemic 92%
- Data-Driven Study of the COVID-19 Pandemic via Age-Structured Modelling and Prediction of the Health System Failure in Brazil amid Diverse Intervention Strategies 92%
- Sickness Absence Rates in NHS England Staff during the COVID-19 Pandemic: insights from multivariate regression and time series modelling 91%
Similar papers in this journal
- Nowcasting and Forecasting the Spread of COVID-19 and Healthcare Demand In Turkey, A Modelling Study 93%
- A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized with Multidrug-Resistant Organisms 91%
- Predicting hospital demand during the COVID-19 outbreak in Bogota, Colombia 90%
Similar papers in this journal
- Development and validation of an algorithm to estimate the risk of severe complications of COVID-19 to prioritise vaccination 92%
- Modelling Palliative and End of Life resource requirements during COVID-19: implications for quality care 91%
- Cohort Study Protocol of the Brazilian Collaborative Research Network on COVID-19: strengthening WHO global data 91%
Similar papers in this journal
- Strengthening Policy Coding Methodologies to Improve COVID-19 Disease Modeling and Policy Responses: A Proposed Coding Framework and Recommendations 91%
- Comparing methods to predict baseline mortality for excess mortality calculations 90%
- Data-Driven Prediction of COVID-19 Cases in Germany for Decision Making 89%
Similar papers in this journal
- The effect of multiple interventions to balance healthcare demand for controlling COVID-19 outbreaks: a modelling study 92%
- Leveraging Temporal Learning with Dynamic Range (TLDR) for Enhanced Prediction of Outcomes in Recurrent Exposure and Treatment Settings in Electronic Health Records 91%
- A Comprehensive County Level Framework to Identify Factors Affecting Hospital Capacity and Predict Future Hospital Demand 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.