scholarly journals Standardization and Age-Distribution of COVID-19: Implications for Variability in Case Fatality and Outbreak Identification

2020 ◽  
Author(s):  
David N. Fisman ◽  
Amy L. Greer ◽  
Ashleigh R. Tuite
Keyword(s):  
Data Analysis ◽  
Early Stage ◽  
Age Distribution ◽  
Case Fatality ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Apparent Size ◽  
Older Individuals ◽  
Indirect Standardization ◽  
The Impact

AbstractBackgroundEpidemiological data from the COVID-19 pandemic has demonstrated variability in attack rates by age, and country-to-country variability in case fatality ratio (CFR).ObjectiveTo use direct and indirect standardization for insights into the impact of age-specific under-reporting on between-country variability in CFR, and apparent size of COVID-19 epidemics.DesignPost-hoc secondary data analysis (“case studies”), and mathematical modeling.SettingChina, global.InterventionsNone.MeasurementsData were extracted from a sentinel epidemiological study by the Chinese Center for Disease Control (CCDC) that describes attack rates and CFR for COVID-19 in China prior to February 12, 2020. Standardized morbidity ratios (SMR) were used to impute missing cases and adjust CFR. Age-specific attack rates and CFR were applied to different countries with differing age structures (Italy, Japan, Indonesia, and Egypt), in order to generate estimates for CFR, apparent epidemic size, and time to outbreak recognition for identical age-specific attack rates.ResultsSMR demonstrated that 50-70% of cases were likely missed during the Chinese epidemic. Adjustment for under-recognition of younger cases decreased CFR from 2.4% to 0.8% (assuming 50% case ascertainment in older individuals). Standardizing the Chinese epidemic to countries with older populations (Italy, and Japan) resulted in larger apparent epidemic sizes, higher CFR and earlier outbreak recognition. The opposite effect was demonstrated for countries with younger populations (Indonesia, and Egypt).LimitationsSecondary data analysis based on a single country at an early stage of the COVID-19 pandemic, with no attempt to incorporate second order effects (ICU saturation) on CFR.ConclusionDirect and indirect standardization are simple tools that provide key insights into between-country variation in the apparent size and severity of COVID-19 epidemics.FundingThe research was supported by a grant to DNF from the Canadian Institutes for Health Research (2019 COVID-19 rapid researching funding OV4-170360).

scholarly journals Factors influencing COVID-19 case burden and fatality rates findings from secondary data analysis of major urban agglomerations in India

2020 ◽  
Vol 7 (8) ◽  
pp. 3284
Author(s):  
Deodatt M. Suryawanshi ◽  
Raghuram Venugopal ◽  
Ramchandra Goyal
Keyword(s):  
Data Analysis ◽  
Negative Correlation ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Secondary Data ◽  
Health Financing ◽  
Secondary Data Analysis ◽  
Fatality Rate ◽  
Health Index ◽  
Significant Difference

In December 2019, SARS COV-2 which originated in the Chinese city of Wuhan achieved pandemic proportions and spread rapidly to countries through International air traffic causing acute respiratory infection and deaths. Presence of International airports, demography, health financing and human developments factors were assumed to influence COVID-19 cases burden and case fatality rate (CFR). So, this study was undertaken to find a association between these factors and COVID-19 cases and deaths. The study used 48 districts using purposive sampling as proxy for cities and used secondary data analysis. Data was obtained for various variables like demographic, Health Financing, Indices and Testing infrastructure, COVID cases burden and case fatality from trusted sources. Descriptive statistics correlational statistics using Pearsons coefficient students T was used to describe, correlate and find significant difference in the data. The analysis found a significant difference between COVID cases burden in districts with International Airports (p<0.039) and those without it. Positive correlation of population density (r=0.65) with COVID-19 case burden and negative correlation of case fatality rate with NITI Aayogs health index (r=-0.12), human development index (HDI) (r=-0.18), per-capita expenditure on health (r=-0.072) and a correlation of r=0.16 was observed for gross state domestic product. Decongestion of cities through perspective urban planning is the need of the hour. Stricter quarantine measures in those districts with international airports can help reduce the transmission. Negative correlation of HDI and NITI Aayogs health index with CFR emphasizes the importance of improvements in social determinants of health.

scholarly journals THE COVID-19 PANDEMIC: AN ACCELERATOR OF DIGITALISATION IN TOURISM

2021 ◽  
Vol 14(63) (2) ◽  
pp. 19-26
Author(s):  
Alina Petronela Pricope (Vancia) ◽  
◽  
Codruța Adina Băltescu ◽  
Keyword(s):  
Data Analysis ◽  
Digital Technologies ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
The Impact ◽  
Tour Operator

The aim of this paper is to analyse the effects of the COVID-19 pandemic on leveraging technology to generate new opportunities for travel companies. Tourism is one of the sectors most affected by the Covid-19 pandemic, companies in this sector have never faced such a dramatic situation and their challenge has been to survive and find new ways to generate profit. In this context, the pandemic has become an accelerator of digitalisation and has made digital technologies vital. The study is based on secondary data analysis and the research objectives are to examine the impact of COVID - 19 on travel companies and to highlight the importance of developing digitalisation during the pandemic. To achieve these goals, the authors analysed the actions during the pandemic of the world's largest tour operator, Tui Group

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