Publication:
SEAHIR: A Specialized Compartmental Model for COVID-19

dc.contributor.authorSenok, Abiola
dc.contributor.authorAlsheikh-Ali, Alawi
dc.contributor.authorLoney, Tom
dc.date.accessioned2022-03-17T07:46:12Z
dc.date.available2022-03-17T07:46:12Z
dc.date.issued2021
dc.description.abstractAbstract: The SEIR (Susceptible-Exposed-Infected-Removed) model is widely used in epidemiology to mathematically model the spread of infectious diseases with incubation periods. However, the SEIR model prototype is generic and not able to capture the unique nature of a novel viral pandemic such as SARS-CoV-2. We have developed and tested a specialized version of the SEIR model, called SEAHIR (Susceptible-Exposed-Asymptomatic-Hospitalized-Isolated-Removed) model. This proposed model is able to capture the unique dynamics of the COVID-19 outbreak including further dividing the Infected compartment into: (1) “Asymptomatic”, (2) “Isolated” and (3) “Hospitalized” to delineate the transmission specifics of each compartment and forecast healthcare requirements. The model also takes into consideration the impact of non-pharmaceutical interventions such as physical distancing and different testing strategies on the number of confirmed cases. We used a publicly available dataset from the United Arab Emirates (UAE) as a case study to optimize the main parameters of the model and benchmarked it against the historical number of cases. The SEAHIR model was used by decision-makers in Dubai’s COVID-19 Command and Control Center to make timely decisions on developing testing strategies, increasing healthcare capacity, and implementing interventions to contain the spread of the virus. The novel six-compartment SEAHIR model could be utilized by decision-makers and researchers in other countries for current or future pandemics.en_US
dc.identifier.other204-2021.183
dc.identifier.urihttps://repository.mbru.ac.ae/handle/1/929
dc.language.isoenen_US
dc.subjectCompartmental modelsen_US
dc.subjectCOVID-19en_US
dc.subjectErlang distributionen_US
dc.subjectPredictive modellingen_US
dc.subjectSARSCoV-2en_US
dc.subjectSEIRen_US
dc.titleSEAHIR: A Specialized Compartmental Model for COVID-19en_US
dc.typeArticleen_US
dspace.entity.typePublicationen_US

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