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10.1371/journal.pone.0250050 http://scihub22266oqcxt.onion/10.1371/journal.pone.0250050 34283842!8291658!34283842     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2021 ; 16 (7): e0250050 Nephropedia Template TP {{tp|p=34283842|t=2021. Heterogeneity matters: Contact structure and individual variation shape epidemic dynamics |pdf=|usr=}}{{34283842}} gab.com Text Heterogeneity matters: Contact structure and individual variation shape epidemic dynamics JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=34283842 #FOAvip In the recent COVID-19 pandemic, mathematical modeling constitutes an important tool to evaluate the prospective effectiveness of non-pharmaceutical interventions (NPIs) and to guide policy-making. Most research is, however, centered around characterizing the epidemic based on point estimates like the average infectiousness or the average number of contacts. In this work, we use stochastic simulations to investigate the consequences of a population's heterogeneity regarding connectivity and individual viral load levels. Therefore, we translate a COVID-19 ODE model to a stochastic multi-agent system. We use contact networks to model complex interaction structures and a probabilistic infection rate to model individual viral load variation. We observe a large dependency of the dispersion and dynamical evolution on the population's heterogeneity that is not adequately captured by point estimates, for instance, used in ODE models. In particular, models that assume the same clinical and transmission parameters may lead to different conclusions, depending on different types of heterogeneity in the population. For instance, the existence of hubs in the contact network leads to an initial increase of dispersion and the effective reproduction number, but to a lower herd immunity threshold (HIT) compared to homogeneous populations or a population where the heterogeneity stems solely from individual infectivity variations. Twit Text FOAVip Heterogeneity matters: Contact structure and individual variation shape epidemic dynamics ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=34283842 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34283842 #FOAvip English Wikipedia <ref>{{Cite pmid|34283842}}</ref> Heterogeneity matters: Contact structure and individual variation shape epidemic dynamics #MMPMID34283842 Grossmann G; Backenkohler M; Wolf V PLoS One 2021[]; 16 (7): e0250050 PMID34283842show ga In the recent COVID-19 pandemic, mathematical modeling constitutes an important tool to evaluate the prospective effectiveness of non-pharmaceutical interventions (NPIs) and to guide policy-making. Most research is, however, centered around characterizing the epidemic based on point estimates like the average infectiousness or the average number of contacts. In this work, we use stochastic simulations to investigate the consequences of a population's heterogeneity regarding connectivity and individual viral load levels. Therefore, we translate a COVID-19 ODE model to a stochastic multi-agent system. We use contact networks to model complex interaction structures and a probabilistic infection rate to model individual viral load variation. We observe a large dependency of the dispersion and dynamical evolution on the population's heterogeneity that is not adequately captured by point estimates, for instance, used in ODE models. In particular, models that assume the same clinical and transmission parameters may lead to different conclusions, depending on different types of heterogeneity in the population. For instance, the existence of hubs in the contact network leads to an initial increase of dispersion and the effective reproduction number, but to a lower herd immunity threshold (HIT) compared to homogeneous populations or a population where the heterogeneity stems solely from individual infectivity variations. |*Models, Theoretical[MESH] |COVID-19/*epidemiology[MESH] |Humans[MESH] |Immunity, Herd[MESH] |Pandemics[MESH] |Policy Making[MESH]Heterogeneity matters: Contact structure and individual variation shap(epmc).pdf DeepDyve Pubget Overpricing