Author: Azizi, Asma; Montalvo, Cesar; Espinoza, Baltazar; Kang, Yun; Castillo-Chavez, Carlos
Title: Epidemics on networks: Reducing disease transmission using health emergency declarations and peer communication Document date: 2019_12_11
ID: 4uy1w3oj_4
Snippet: Ruled-Based Models (Epstein, Parker, Cummings, & Hammond, 2008; Hyman & Li, 2007; Kiss, Cassell, Recker, & Simon, 2010; Misra et al., 2011; Poletti et al., 2009 Poletti et al., , 2011 Poletti et al., , 2012 Sahneh, Chowdhury, & Scoglio, 2012) vary from compartmental ODE models (Hyman & Li, 2007; Misra et al., 2011; Tracht et al., 2010) to individual-based network models (Funk et al., 2009; Granell, G omez, & Arenas, 2013; Meloni et al., 2011; Wu,.....
Document: Ruled-Based Models (Epstein, Parker, Cummings, & Hammond, 2008; Hyman & Li, 2007; Kiss, Cassell, Recker, & Simon, 2010; Misra et al., 2011; Poletti et al., 2009 Poletti et al., , 2011 Poletti et al., , 2012 Sahneh, Chowdhury, & Scoglio, 2012) vary from compartmental ODE models (Hyman & Li, 2007; Misra et al., 2011; Tracht et al., 2010) to individual-based network models (Funk et al., 2009; Granell, G omez, & Arenas, 2013; Meloni et al., 2011; Wu, Fu, Small, & Xu, 2012) . These models have been used to study the dynamics of highly diverse diseases including, for example, influenza and HIV (Fraser, Riley, Anderson, & Ferguson, 2004; Poletti et al., 2011) , or in the study of generic infections (Kiss et al., 2010; Misra et al., 2011; Poletti et al., 2009 Poletti et al., , 2012 . Compartmental models (often using a phenomenological approach) categories designed to capture levels of awareness of infection. Such approach that can be used to incorporate 'awareness' in network models, is the objective of this manuscript. Some models assume that "awareness" spreads along with the invading disease, that is, through identical contact networks. Here, it is assumed that the disease and information spread over the same social network (a drastic simplification). The possibility that awareness and responses to the presence of a new infection among a subset of the population at risk, may significantly alter regular temporal patterns of disease prevalence (lower highs) have been studied. Studies have also shown that epidemic thresholds can be altered Poletti et al., 2009; Sahneh et al., 2012) in response to the effectiveness of non-pharmaceutical intervention. Here, the focus is on the role of policy decisions/ recommendations in altering disease dynamics, possibly the final epidemic size, within a model where awareness (generated by official actions) spreads among those susceptible to infection and their 'friends'.
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