Modelling the effect of non-pharmaceutical interventions on COVID-19 transmission from mobility maps
作者机构：Emirates Centre for Mobility ResearchUnited Arab Emirates UniversityAl Ain15551United Arab Emirates Department of Civil and Environmental EngineeringUnited Arab Emirates UniversityAl Ain15551United Arab Emirates Mathematical Sciences DepartmentCollege of ScienceUnited Arab Emirates UniversityAl Ain15551United Arab Emirates Department of HealthAbu DhabiUnited Arab Emirates Abu Dhabi Public Health CenterAbu DhabiUnited Arab Emirates
出 版 物：《Infectious Disease Modelling》 (传染病建模（英文）)
年 卷 期：2022年第7卷第3期
页 面：400-418页
学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学]
主 题：Non-pharmaceutical interventions COVID-19 Epidemiological modelling Mobility maps
摘 要：The world has faced the COVID-19 pandemic for over two years now,and it is time to revisit the lessons learned from lockdown measures for theoretical and practical epidemiological *** interlink between these measures and the resulting change in mobility(a predictor of the disease transmission contact rate)is *** thus propose a new method for assessing the efficacy of various non-pharmaceutical interventions(NPI)and examine the aptness of incorporating mobility data for epidemiological *** mobility maps for the United Arab Emirates are used as input datasets from the first infection in the country to mid-Oct *** was limited to the pre-vaccination period as this paper focuses on assessing the different NPIs at an early epidemic stage when no vaccines are available and NPIs are the only way to reduce the reproduction number(R_(0)).We developed a travel network density parameterβ_(t)to provide an estimate of NPI impact on mobility *** the infection-fatality ratio and time lag(onset-to-death),a Bayesian probabilistic model is adapted to calculate the change in epidemic development withβ*** showed that the change inβ_(t)clearly impacted R_(0).The three lockdowns strongly affected the growth of transmission rate and collectively reduced R_(0)by 78%before the restrictions were *** model forecasted daily infections and deaths by 2%and 3%fractional *** also projected what-if scenarios for different implementation protocols of each *** developed model can be applied to identify the most efficient NPIs for confronting new COVID-19 waves and the spread of variants,as well as for future pandemics.
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