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BMC Infect Dis . Factors shaping the COVID-19 epidemic curve: a multi-country analysis

tetano

Editor, Senior Moderator
BMC Infect Dis


. 2021 Oct 2;21(1):1032.
doi: 10.1186/s12879-021-06714-3.
Factors shaping the COVID-19 epidemic curve: a multi-country analysis


Su Yeon Jang[SUP] 1 [/SUP], Laith Hussain-Alkhateeb[SUP] 2 [/SUP], Tatiana Rivera Ramirez[SUP] 3 [/SUP], Ahmed Asa'ad Al-Aghbari[SUP] 3 [/SUP], Dhia Joseph Chackalackal[SUP] 3 [/SUP], Rocio Cardenas-Sanchez[SUP] 3 4 [/SUP], Maria Angelica Carrillo[SUP] 3 5 [/SUP], In-Hwan Oh[SUP] 1 [/SUP], Eduardo Andrés Alfonso-Sierra[SUP] 3 [/SUP], Pia Oechsner[SUP] 6 [/SUP], Brian Kibiwott Kirui[SUP] 7 [/SUP], Martin Anto[SUP] 8 [/SUP], Sonia Diaz-Monsalve[SUP] 3 [/SUP], Axel Kroeger[SUP] 9 [/SUP]



Affiliations

Abstract

Background: 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.
Methods: 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.
Results: 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.
Conclusion: 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.

Keywords: COVID-19; Coronavirus disease 2019; Human mobility; Lockdown; SARS-CoV-2; Segemented Time-series; Stringency index.
 
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