Thursday, June 17 at 06:45am (PDT)Thursday, June 17 at 02:45pm (BST)Thursday, June 17 10:45pm (KST)
SMB2021 FollowWednesday (Thursday) during the "CT09" time block.
Eastern Washington University
"A New Model for Rat-Fleaâ€“Driven Plague Transmission"
Rats have long been thought to drive plague epidemics, specifically bubonic plague. However an alternative theory for plague transmission has been posited by Dean et al. (PNAS 2018) where ectoparasites living on human hosts drive spread. This talk will present a new mathematical model for the spread of the plague based on rat-flea interactions with the human population and compare our results to existing models. Our results suggest that rat-flea transmission of the plague is still a plausible explanation.
Universidad Adolfo Ibáñez
"An SIR-type model incorporating social distancing dynamics based on point prevalence and socio-behavioral factors"
Modeling human behavior within mathematical modeling of infectious diseases is key to understand and control disease spread. We present a mathematical compartmental model of Susceptible - Infectious - Removed to compare the infected curves given by four different functional forms describing the transmission rate. These depend on social-distance, which varies according to the balance between two opposite thrives: the self-protecting reaction of individuals upon the presence of disease to increase social distancing and their necessity to return to a culturally dependent natural social distance that occurs in the absence of disease. We present results for different society types on point prevalence, the peak size of a first epidemic outbreak and the time of occurrence of that peak, for four different transmission rate functional forms and parameters of interest related to distancing behavior. We observe the vulnerability to disease spread of close contact societies, and also that certain social distancing behavior may provoke an early occurring small peak of a first epidemic outbreak, observing differences in this regard between society types. We discuss the appearance of oscillations of the transmission rates and how these oscillations are impacted through social distancing; breaking the unimodality of the actives-curve produced by the classical SIR-model.
University of Warwick
"A network modelling approach to assess non-pharmaceutical disease controls against SARS-CoV-2 in a worker population"
As part of a concerted pandemic response to protect public health, businesses can enact non-pharmaceutical controls to minimise exposure to pathogens in workplaces and premises open to the public. Amendments to working practices can lead to the amount, duration and/or proximity of interactions being changed, ultimately altering the dynamics of disease spread.We use a data-driven approach to parameterise an individual-based network model for transmission of SARS-CoV-2 amongst the working population, stratified into work sectors. The network comprises layered contacts to consider risk of spread in multiple encounter settings (workplaces, households, social and other). We analyse several interventions targeted towards working practices: mandating a fraction of the population to work from home; using temporally asynchronous work patterns; and introducing 'COVID-secure' workplaces. We also assess the general role of adherence to (or effectiveness of) isolation and test and trace measures and demonstrate the impact of these interventions on epidemiological metrics.Given the heterogeneity of demographic attributes across worker roles, in addition to the individual nature of controls such as contact tracing, we demonstrate the utility of a network model approach to investigate workplace-targeted intervention strategies and the role of test, trace and isolation in tackling disease spread.
University of Maryland
"Optimal allocation of limited testing resources for flattening the COVID-19 curve"
Insufficient testing capacity has proven to be a critical bottleneck in the fight against COVID-19, especially during the early stages of the pandemic. Prioritizing allocation of limited testing resources based on symptom severity (among other factors) has therefore emerged has a key component of public policy ripe for mathematical analysis and optimization, but the typical testing rate expressions utilized in compartmental disease models are inadequate for describing severely constrained resource scenarios. Here, we propose a testing model which flexibly accounts for both limited and plentiful resources, and we use a modified SEIR model with quarantine to find optimal allocations of testing capacity for flattening the epidemic curve. We balance resources between two testing strategies: clinical testing focused only on severely symptomatic individuals and non-clinical testing focused on mild and asymptomatic individuals, where contact tracing and case monitoring are incorporated by an information parameter. We find that purely clinical testing is optimal at very low testing capacities, supporting early guidance to ration tests for the sickest patients. Additionally, we find that a mix of clinical and non-clinical testing becomes optimal as testing capacity increases. Further, we find that reduction of our model's R0 is an unreliable metric for epidemic peak reduction.