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Factors shaping the COVID-19 epidemic curve: a multi-country analysis

Overview of attention for article published in BMC Infectious Diseases, October 2021
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3 tweeters

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1 Dimensions

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15 Mendeley
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Title
Factors shaping the COVID-19 epidemic curve: a multi-country analysis
Published in
BMC Infectious Diseases, October 2021
DOI 10.1186/s12879-021-06714-3
Pubmed ID
Authors

Su Yeon Jang, Laith Hussain-Alkhateeb, Tatiana Rivera Ramirez, Ahmed Asa’ad Al-Aghbari, Dhia Joseph Chackalackal, Rocio Cardenas-Sanchez, Maria Angelica Carrillo, In-Hwan Oh, Eduardo Andrés Alfonso-Sierra, Pia Oechsner, Brian Kibiwott Kirui, Martin Anto, Sonia Diaz-Monsalve, Axel Kroeger

Abstract

Lockdown measures are the backbone of containment measures for the COVID-19 pandemic both in high-income countries (HICs) and low- and middle-income countries (LMICs). However, in view of the inevitably-occurring second and third global covid-19 wave, assessing the success and impact of containment measures on the epidemic curve of COVID-19 and people's compliance with such measures is crucial for more effective policies. To determine the containment measures influencing the COVID-19 epidemic curve in nine targeted countries across high-, middle-, and low-income nations. Four HICs (Germany, Sweden, Italy, and South Korea) and five LMICs (Mexico, Colombia, India, Nigeria, and Nepal) were selected to assess the association using interrupted time series analysis of daily case numbers and deaths of COVID-19 considering the following factors: The "stringency index (SI)" indicating how tight the containment measures were implemented in each country; and the level of compliance with the prescribed measures using human mobility data. Additionally, a scoping review was conducted to contextualize the findings. Most countries implemented quite rigorous lockdown measures, particularly the LMICs (India, Nepal, and Colombia) following the model of HICs (Germany and Italy). Exceptions were Sweden and South Korea, which opted for different strategies. The compliance with the restrictions-measured as mobility related to home office, restraining from leisure activities, non-use of local transport and others-was generally good, except in Sweden and South Korea where the restrictions were limited. The endemic curves and time-series analysis showed that the containment measures were successful in HICs but not in LMICs. The imposed lockdown measures are alarming, particularly in resource-constrained settings where such measures are independent of the population segment, which drives the virus transmission. Methods for examining people's movements or hardships that are caused by covid- no work, no food situation are inequitable. Novel and context-adapted approach of dealing with the COVID-19 crisis are therefore crucial.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 20%
Student > Master 2 13%
Student > Bachelor 1 7%
Student > Doctoral Student 1 7%
Professor 1 7%
Other 2 13%
Unknown 5 33%
Readers by discipline Count As %
Social Sciences 2 13%
Philosophy 1 7%
Immunology and Microbiology 1 7%
Medicine and Dentistry 1 7%
Chemistry 1 7%
Other 1 7%
Unknown 8 53%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 December 2021.
All research outputs
#13,176,431
of 19,853,837 outputs
Outputs from BMC Infectious Diseases
#3,835
of 6,851 outputs
Outputs of similar age
#196,172
of 345,648 outputs
Outputs of similar age from BMC Infectious Diseases
#5
of 7 outputs
Altmetric has tracked 19,853,837 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,851 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.7. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 345,648 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.