Literature DB >> 33709641

The lag between daily reported Covid-19 cases and deaths and its relationship to age.

Raymond Jin1.   

Abstract

BACKGROUND: The Coronavirus disease 2019 (Covid-19) has infected millions and killed tens of thousands of people. Public health measures put in place by governments are essential to the success of controlling this disease. However, governments may not feel as incentivized to implement these measures when deaths are not rising along with cases. However, it is known that a delay exists between the time of infection and the time of death. This study attempted to find how long that lag is and how the age of people infected may affect that lag. DESIGN AND METHODS: A descriptive and correlational study was carried out to investigate the length of the lag and the relationship between lag and age.
RESULTS: The average lag between daily Covid-19 cases and deaths was 8.053 days with a standard deviation of 4.116 days for nineteen regions. After excluding data from three more regions due to unavailable age data, the regression yielded an equation of lag = 14.015 - 0.153 (% cases above 60) with a p-value of 0.066. Because the p-value of 0.066 is lower than the 0.10 significance level, there is evidence that a relationship exists between the lag and the age of cases.
CONCLUSIONS: The results show that regions must remain vigilant when Covid-19 cases rapidly increase without similar increases in deaths since there exists a significant lag between the two. Additionally, a younger demographic of cases may lead to an increased lag, further pushing regions into a false sense of security that should be avoided.

Entities:  

Year:  2021        PMID: 33709641     DOI: 10.4081/jphr.2021.2049

Source DB:  PubMed          Journal:  J Public Health Res        ISSN: 2279-9028


  6 in total

1.  Racial and ethnic inequities in the early distribution of U.S. COVID-19 testing sites and mortality.

Authors:  Nathan P Dalva-Baird; Wilson M Alobuia; Eran Bendavid; Jay Bhattacharya
Journal:  Eur J Clin Invest       Date:  2021-08-27       Impact factor: 5.722

2.  Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic.

Authors:  Ming Guan
Journal:  J Epidemiol Glob Health       Date:  2021-12-11

3.  A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations and deaths.

Authors:  Eamon B O'Dea; John M Drake
Journal:  J R Soc Interface       Date:  2022-02-16       Impact factor: 4.118

4.  Deathdaily: A Python Package Index for predicting the number of daily COVID-19 deaths.

Authors:  Yoshiyasu Takefuji
Journal:  Netw Model Anal Health Inform Bioinform       Date:  2022-03-20

5.  Spatial Syndromic Surveillance and COVID-19 in the U.S.: Local Cluster Mapping for Pandemic Preparedness.

Authors:  Andrew J Curtis; Jayakrishnan Ajayakumar; Jacqueline Curtis; Sam Brown
Journal:  Int J Environ Res Public Health       Date:  2022-07-22       Impact factor: 4.614

6.  Methods for early characterisation of the severity and dynamics of SARS-CoV-2 variants: a population-based time series analysis in South Africa.

Authors:  Emily Reichert; Beau Schaeffer; Shae Gantt; Eva Rumpler; Nevashan Govender; Richard Welch; Andronica Moipone Shonhiwa; Chidozie Declan Iwu; Teresa Mashudu Lamola; Itumeleng Moema-Matiea; Darren Muganhiri; William Hanage; Mauricio Santillana; Waasila Jassat; Cheryl Cohen; David Swerdlow
Journal:  Lancet Microbe       Date:  2022-08-31
  6 in total

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