Management strategies in a SEIR-type model of COVID 19 community spread

• Published in: Scientific reports Vol. 10; no. 1; p. 21256
• Main Authors: Rǎdulescu, Anca, Williams, Cassandra, Cavanagh, Kieran
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.12.2020; Nature Publishing Group
• Subjects: 639/705/1041; 692/699/255/2514; Age; Age groups; Asymptomatic; COVID-19; COVID-19 - epidemiology; COVID-19 - prevention & control; COVID-19 - transmission; Disease transmission; Epidemic models; Epidemics; Epidemiology; Health care; Humanities and Social Sciences; Humans; Infections; Mathematical models; Medical prognosis; Models, Theoretical; Mortality; multidisciplinary; New York; Older people; Pandemics; Pandemics - statistics & numerical data; Physical Distancing; Prediction models; Public health; Quarantine; Risk Factors; School closures; Science; Science (multidisciplinary); Severe acute respiratory syndrome coronavirus 2; Shutdowns; Social distancing; Social isolation; Young adults; New York
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-77628-4

The 2019 Novel Corona virus infection (COVID 19) is an ongoing public health emergency of international focus. Significant gaps persist in our knowledge of COVID 19 epidemiology, transmission dynamics, investigation tools and management, despite (or possibly because of) the fact that the outbreak is an unprecedented global threat. On the positive side, enough is currently known about the epidemic process to permit the construction of mathematical predictive models. In our work, we adapt a traditional SEIR epidemic model to the specific dynamic compartments and epidemic parameters of COVID 19, as it spreads in an age-heterogeneous community. We analyze management strategies of the epidemic course (as they were implemented through lockdown and reopening procedures in many of the US states and countries worldwide); however, to more clearly illustrate ideas, we focus on the example of a small scale college town community, with the timeline of control measures introduced in the state of New York. We generate predictions, and assess the efficiency of these control measures (closures, mobility restrictions, social distancing), in a sustainability context.