uau7egx0

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?:abstract	As of November 2020, the number of COVID-19 cases was increasing rapidly in many countries In Europe, the virus spread slowed considerably in the late spring due to strict lockdown, but a second wave of the pandemic grew throughout the fall In this study, we first reconstruct the time evolution of the effective reproduction numbers R(t) for each country by integrating the equations of the classic Susceptible-Infectious-Recovered (SIR) model We cluster countries based on the estimated R(t) through a suitable time series dissimilarity The clustering result suggests that simple dynamical mechanisms determine how countries respond to changes in COVID-19 case counts Inspired by these results, we extend the simple SIR model for disease spread to include a social response to explain the number X(t) of new confirmed daily cases In particular, we characterize the social response with a first-order model that depends on three parameters ν1,ν2,ν3 The parameter ν1 describes the effect of relaxed intervention when the incidence rate is low;ν2 models the impact of interventions when incidence rate is high;ν3 represents the fatigue, i e , the weakening of interventions as time passes The proposed model reproduces typical evolving patterns of COVID-19 epidemic waves observed in many countries Estimating the parameters ν1,ν2,ν3 and initial conditions, such as R0, for different countries helps to identify important dynamics in their social responses One conclusion is that the leading cause of the strong second wave in Europe in the fall of 2020 was not the relaxation of interventions during the summer, but rather the failure to enforce interventions in the fall As of November 2020, the number of COVID-19 cases was increasing rapidly in many countries. In Europe, the virus spread slowed considerably in the late spring due to strict lockdown, but a second wave of the pandemic grew throughout the fall. In this study, we first reconstruct the time evolution of the effective reproduction numbers R(t) for each country by integrating the equations of the classic Susceptible-Infectious-Recovered (SIR) model. We cluster countries based on the estimated R(t) through a suitable time series dissimilarity. The clustering result suggests that simple dynamical mechanisms determine how countries respond to changes in COVID-19 case counts. Inspired by these results, we extend the simple SIR model for disease spread to include a social response to explain the number X(t) of new confirmed daily cases. In particular, we characterize the social response with a first-order model that depends on three parameters ν1,ν2,ν3. The parameter ν1 describes the effect of relaxed intervention when the incidence rate is low; ν2 models the impact of interventions when incidence rate is high; ν3 represents the fatigue, i.e., the weakening of interventions as time passes. The proposed model reproduces typical evolving patterns of COVID-19 epidemic waves observed in many countries. Estimating the parameters ν1,ν2,ν3 and initial conditions, such as R0, for different countries helps to identify important dynamics in their social responses. One conclusion is that the leading cause of the strong second wave in Europe in the fall of 2020 was not the relaxation of interventions during the summer, but rather the failure to enforce interventions in the fall.
?:creator	<https://research.tib.eu/covid-19/entity/Rypdal%2C_Kristoffer%3B_Bianchi%2C_Filippo_Maria%3B_Rypdal%2C_Martin> <https://research.tib.eu/covid-19/entity/Rypdal%2C_Kristoffer_Bianchi_Filippo_Maria_Rypdal_Martin>
?:journal	Int._j._environ._res._public_health_(Online) International_Journal_of_Environmental_Research_and_Public_Health
?:license	unk
?:publication_isRelatedTo_Disease	<https://research.tib.eu/covid-19/entity/COVID-19>
?:source	WHO
?:title	Intervention Fatigue is the Primary Cause of Strong Secondary Waves in the COVID-19 Pandemic
?:type	<https://research.tib.eu/covid-19/vocab/Publication>
?:who_covidence_id	#1011506 #984769
?:year	2020

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?:abstract

As of November 2020, the number of COVID-19 cases was increasing rapidly in many countries In Europe, the virus spread slowed considerably in the late spring due to strict lockdown, but a second wave of the pandemic grew throughout the fall In this study, we first reconstruct the time evolution of the effective reproduction numbers R(t) for each country by integrating the equations of the classic Susceptible-Infectious-Recovered (SIR) model We cluster countries based on the estimated R(t) through a suitable time series dissimilarity The clustering result suggests that simple dynamical mechanisms determine how countries respond to changes in COVID-19 case counts Inspired by these results, we extend the simple SIR model for disease spread to include a social response to explain the number X(t) of new confirmed daily cases In particular, we characterize the social response with a first-order model that depends on three parameters ν1,ν2,ν3 The parameter ν1 describes the effect of relaxed intervention when the incidence rate is low;ν2 models the impact of interventions when incidence rate is high;ν3 represents the fatigue, i e , the weakening of interventions as time passes The proposed model reproduces typical evolving patterns of COVID-19 epidemic waves observed in many countries Estimating the parameters ν1,ν2,ν3 and initial conditions, such as R0, for different countries helps to identify important dynamics in their social responses One conclusion is that the leading cause of the strong second wave in Europe in the fall of 2020 was not the relaxation of interventions during the summer, but rather the failure to enforce interventions in the fall
As of November 2020, the number of COVID-19 cases was increasing rapidly in many countries. In Europe, the virus spread slowed considerably in the late spring due to strict lockdown, but a second wave of the pandemic grew throughout the fall. In this study, we first reconstruct the time evolution of the effective reproduction numbers R(t) for each country by integrating the equations of the classic Susceptible-Infectious-Recovered (SIR) model. We cluster countries based on the estimated R(t) through a suitable time series dissimilarity. The clustering result suggests that simple dynamical mechanisms determine how countries respond to changes in COVID-19 case counts. Inspired by these results, we extend the simple SIR model for disease spread to include a social response to explain the number X(t) of new confirmed daily cases. In particular, we characterize the social response with a first-order model that depends on three parameters ν1,ν2,ν3. The parameter ν1 describes the effect of relaxed intervention when the incidence rate is low; ν2 models the impact of interventions when incidence rate is high; ν3 represents the fatigue, i.e., the weakening of interventions as time passes. The proposed model reproduces typical evolving patterns of COVID-19 epidemic waves observed in many countries. Estimating the parameters ν1,ν2,ν3 and initial conditions, such as R0, for different countries helps to identify important dynamics in their social responses. One conclusion is that the leading cause of the strong second wave in Europe in the fall of 2020 was not the relaxation of interventions during the summer, but rather the failure to enforce interventions in the fall.

?:creator