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  1. Article: Not all interventions are equal for the height of the second peak.

    Jorritsma, Joost / Hulshof, Tim / Komjáthy, Júlia

    Chaos, solitons, and fractals

    2020  Volume 139, Page(s) 109965

    Abstract: In this paper we conduct a simulation study of the spread of an epidemic like COVID-19 with temporary immunity on finite spatial and non-spatial network models. In particular, we assume that an epidemic spreads stochastically on a scale-free network and ... ...

    Abstract In this paper we conduct a simulation study of the spread of an epidemic like COVID-19 with temporary immunity on finite spatial and non-spatial network models. In particular, we assume that an epidemic spreads stochastically on a scale-free network and that each infected individual in the network gains a
    Keywords covid19
    Language English
    Publishing date 2020-08-25
    Publishing country England
    Document type Journal Article
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2020.109965
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Not all interventions are equal for the height of the second peak

    Jorritsma, Joost / Hulshof, Tim / Komjáthy, Júlia

    Chaos Solitons Fractals

    Abstract: ... Our second finding is that all three interventions manage to flatten the first peak (the travel restrictions ... second peak: for limiting the maximal number of contacts, the second peak can be as high as 1/3 ... of the first peak, and twice as high as it would be without intervention. Thirdly, interventions introduce ...

    Abstract In this paper we conduct a simulation study of the spread of an epidemic like COVID-19 with temporary immunity on finite spatial and non-spatial network models. In particular, we assume that an epidemic spreads stochastically on a scale-free network and that each infected individual in the network gains a temporary immunity after its infectious period is over. After the temporary immunity period is over, the individual becomes susceptible to the virus again. When the underlying contact network is embedded in Euclidean geometry, we model three different intervention strategies that aim to control the spread of the epidemic: social distancing, restrictions on travel, and restrictions on maximal number of social contacts per node. Our first finding is that on a finite network, a long enough average immunity period leads to extinction of the pandemic after the first peak, analogous to the concept of “herd immunity”. For each model, there is a critical average immunity duration L cabove which this happens. Our second finding is that all three interventions manage to flatten the first peak (the travel restrictions most efficiently), as well as decrease the critical immunity duration L c, but elongate the epidemic. However, when the average immunity duration L is shorter than L c, the price for the flattened first peak is often a high second peak: for limiting the maximal number of contacts, the second peak can be as high as 1/3 of the first peak, and twice as high as it would be without intervention. Thirdly, interventions introduce oscillations into the system and the time to reach equilibrium is, for almost all scenarios, much longer. We conclude that network-based epidemic models can show a variety of behaviors that are not captured by the continuous compartmental models.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #728469
    Database COVID19

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  3. Article: Not all interventions are equal for the height of the second peak

    Hulshof, Tim / Jorritsma, Joost / Komj'athy, J'ulia

    Abstract: ... Our second finding is that all three interventions manage to flatten the first peak (the travel restrictions ... second peak: for limiting the maximal number of contacts, the second peak can be as high as 1/3 ... of the first peak, and twice as high as it would be without intervention. Thirdly, interventions introduce ...

    Abstract In this paper we conduct a simulation study of the spread of an epidemic like COVID-19 with temporary immunity on finite spatial and non-spatial network models. In particular, we assume that an epidemic spreads stochastically on a scale-free network and that each infected individual in the network gains a temporary immunity after its infectious period is over. After the temporary immunity period is over, the individual becomes susceptible to the virus again. When the underlying contact network is embedded in Euclidean geometry, we model three different intervention strategies that aim to control the spread of the epidemic: social distancing, restrictions on travel, and restrictions on maximal number of social contacts per node. Our first finding is that on a finite network, a long enough average immunity period leads to extinction of the pandemic after the first peak, analogous to the concept of"herd immunity". For each model, there is a critical average immunity length $L_c$ above which this happens. Our second finding is that all three interventions manage to flatten the first peak (the travel restrictions most efficiently), as well as decrease the critical immunity length $L_c$, but elongate the epidemic. However, when the average immunity length $L$ is shorter than $L_c$, the price for the flattened first peak is often a high second peak: for limiting the maximal number of contacts, the second peak can be as high as 1/3 of the first peak, and twice as high as it would be without intervention. Thirdly, interventions introduce oscillations into the system and the time to reach equilibrium is, for almost all scenarios, much longer. We conclude that network-based epidemic models can show a variety of behaviors that are not captured by the continuous compartmental models.
    Keywords covid19
    Publisher ArXiv
    Document type Article
    Database COVID19

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  4. Book ; Online: Not all interventions are equal for the height of the second peak

    Hulshof, Tim / Jorritsma, Joost / Komjáthy, Júlia

    2020  

    Abstract: ... Our second finding is that all three interventions manage to flatten the first peak (the travel restrictions ... second peak: for limiting the maximal number of contacts, the second peak can be as high as 1/3 ... of the first peak, and twice as high as it would be without intervention. Thirdly, interventions introduce ...

    Abstract In this paper we conduct a simulation study of the spread of an epidemic like COVID-19 with temporary immunity on finite spatial and non-spatial network models. In particular, we assume that an epidemic spreads stochastically on a scale-free network and that each infected individual in the network gains a temporary immunity after its infectious period is over. After the temporary immunity period is over, the individual becomes susceptible to the virus again. When the underlying contact network is embedded in Euclidean geometry, we model three different intervention strategies that aim to control the spread of the epidemic: social distancing, restrictions on travel, and restrictions on maximal number of social contacts per node. Our first finding is that on a finite network, a long enough average immunity period leads to extinction of the pandemic after the first peak, analogous to the concept of "herd immunity". For each model, there is a critical average immunity length $L_c$ above which this happens. Our second finding is that all three interventions manage to flatten the first peak (the travel restrictions most efficiently), as well as decrease the critical immunity length $L_c$, but elongate the epidemic. However, when the average immunity length $L$ is shorter than $L_c$, the price for the flattened first peak is often a high second peak: for limiting the maximal number of contacts, the second peak can be as high as 1/3 of the first peak, and twice as high as it would be without intervention. Thirdly, interventions introduce oscillations into the system and the time to reach equilibrium is, for almost all scenarios, much longer. We conclude that network-based epidemic models can show a variety of behaviors that are not captured by the continuous compartmental models.

    Comment: 17 pages text, 27 figures of which 22 in the appendix
    Keywords Physics - Physics and Society ; Physics - Biological Physics ; Quantitative Biology - Populations and Evolution ; covid19
    Subject code 612
    Publishing date 2020-05-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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