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10.1016/j.chaos.2020.109965 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.109965 32863609!7445132!32863609     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Chaos+Solitons+Fractals 2020 ; 139 (ä): 109965 Nephropedia Template TP {{tp|p=32863609|t=2020. Not all interventions are equal for the height of the second peak |pdf=|usr=}}{{32863609}} gab.com Text Not all interventions are equal for the height of the second peak JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32863609 #FOAvip 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(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 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. Twit Text FOAVip Not all interventions are equal for the height of the second peak ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32863609 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32863609 #FOAvip English Wikipedia <ref>{{Cite pmid|32863609}}</ref> Not all interventions are equal for the height of the second peak #MMPMID32863609 Jorritsma J; Hulshof T; Komjathy J Chaos Solitons Fractals 2020[Oct]; 139 (ä): 109965 PMID32863609show ga 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(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 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. äNot all interventions are equal for the height of the second peak (epmc).pdf DeepDyve Pubget Overpricing