Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2022 12:00 AM
End Date
7-1-2022 12:00 AM
Description
Epidemiological models are commonly used to predict and describe the spread of a viral outbreak among a population. Many such models use differential equations and transition rates to predict the growth dynamics of the infectious and exposed groups with a greater population. These methods do not distinguish between infectious individuals. In this paper, we propose a new model that includes asymptomatic carriers while holding constant many of the transition rates and assumptions of the classical models. Seeking to replicate realistic outbreak scenarios, we introduce a way to estimate the reproduction number R0 of epidemics and apply these estimations to our model. Our results replicate those described in similar research papers, showing that a small proportion of asymptomatic carriers can be responsible for a majority of the transmissions. We propose possible extensions to our model, underlining the impactful applications it may have on healthcare management and public safety policymaking.
Estimating the Impact of Asymptomatic Carriers on the spread of Infectious Diseases: An interaction-based Model
Online
Epidemiological models are commonly used to predict and describe the spread of a viral outbreak among a population. Many such models use differential equations and transition rates to predict the growth dynamics of the infectious and exposed groups with a greater population. These methods do not distinguish between infectious individuals. In this paper, we propose a new model that includes asymptomatic carriers while holding constant many of the transition rates and assumptions of the classical models. Seeking to replicate realistic outbreak scenarios, we introduce a way to estimate the reproduction number R0 of epidemics and apply these estimations to our model. Our results replicate those described in similar research papers, showing that a small proportion of asymptomatic carriers can be responsible for a majority of the transmissions. We propose possible extensions to our model, underlining the impactful applications it may have on healthcare management and public safety policymaking.
https://aisel.aisnet.org/hicss-55/os/digital_transformation/5