Journal article
Modeling the effect of lockdown timing as a COVID-19 control measure in countries with differing social contacts
Scientific reports, Vol.11(1), pp.3354-3354
08 Feb 2021
PMID: 33558571
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The application, timing, and duration of lockdown strategies during a pandemic remain poorly quantified with regards to expected public health outcomes. Previous projection models have reached conflicting conclusions about the effect of complete lockdowns on COVID-19 outcomes. We developed a stochastic continuous-time Markov chain (CTMC) model with eight states including the environment (SEAMHQRD-V), and derived a formula for the basic reproduction number, R
, for that model. Applying the [Formula: see text] formula as a function in previously-published social contact matrices from 152 countries, we produced the distribution and four categories of possible [Formula: see text] for the 152 countries and chose one country from each quarter as a representative for four social contact categories (Canada, China, Mexico, and Niger). The model was then used to predict the effects of lockdown timing in those four categories through the representative countries. The analysis for the effect of a lockdown was performed without the influence of the other control measures, like social distancing and mask wearing, to quantify its absolute effect. Hypothetical lockdown timing was shown to be the critical parameter in ameliorating pandemic peak incidence. More importantly, we found that well-timed lockdowns can split the peak of hospitalizations into two smaller distant peaks while extending the overall pandemic duration. The timing of lockdowns reveals that a "tunneling" effect on incidence can be achieved to bypass the peak and prevent pandemic caseloads from exceeding hospital capacity.
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Details
- Title
- Modeling the effect of lockdown timing as a COVID-19 control measure in countries with differing social contacts
- Creators
- Tamer Oraby - The University of Texas Rio Grande ValleyMichael G Tyshenko - University of OttawaJose Campo Maldonado - The University of Texas Rio Grande ValleyKristina Vatcheva - The University of Texas Rio Grande ValleySusie Elsaadany - University of OttawaWalid Q Alali - Kuwait UniversityJoseph C Longenecker - Kuwait UniversityMustafa Al-Zoughool - Kuwait University
- Publication Details
- Scientific reports, Vol.11(1), pp.3354-3354
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000617647000021
- Scopus ID
- 2-s2.0-85101150161
- Other Identifier
- 991022051416104721
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- Domestic collaboration
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- Web of Science research areas
- Medicine, General & Internal