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10.1016/j.jinf.2020.05.037

http://scihub22266oqcxt.onion/10.1016/j.jinf.2020.05.037
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32461062!7246004!32461062
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suck abstract from ncbi


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pmid32461062      J+Infect 2020 ; 81 (2): 260-265
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  • Modelling SARS-COV2 Spread in London: Approaches to Lift the Lockdown #MMPMID32461062
  • Gosce L; Phillips PA; Spinola P; Gupta DRK; Abubakar PI
  • J Infect 2020[Aug]; 81 (2): 260-265 PMID32461062show ga
  • OBJECTIVE: To use mathematical models to predict the epidemiological impact of lifting the lockdown in London, UK, and alternative strategies to help inform policy in the UK. METHODS: A mathematical model for the transmission of SARS-CoV2 in London. The model was parametrised using data on notified cases, deaths, contacts, and mobility to analyse the epidemic in the UK capital. We investigated the impact of multiple non pharmaceutical interventions (NPIs) and combinations of these measures on future incidence of COVID-19. RESULTS: Immediate action at the early stages of an epidemic in the affected districts would have tackled spread. While an extended lockdown is highly effective, other measures such as shielding older populations, universal testing and facemasks can all potentially contribute to a reduction of infections and deaths. However, based on current evidence it seems unlikely they will be as effective as continued lockdown. In order to achieve elimination and lift lockdown within 5 months, the best strategy seems to be a combination of weekly universal testing, contact tracing and use of facemasks, with concurrent lockdown. This approach could potentially reduce deaths by 48% compared with continued lockdown alone. CONCLUSIONS: A combination of NPIs such as universal testing, contact tracing and mask use while under lockdown would be associated with least deaths and infections. This approach would require high uptake and sustained local effort but it is potentially feasible as may lead to elimination in a relatively short time scale.
  • |*Betacoronavirus[MESH]
  • |*Infection Control/methods[MESH]
  • |Adolescent[MESH]
  • |Adult[MESH]
  • |Age Factors[MESH]
  • |COVID-19[MESH]
  • |Child[MESH]
  • |Child, Preschool[MESH]
  • |Coronavirus Infections/*epidemiology/mortality/prevention & control/transmission[MESH]
  • |Health Policy[MESH]
  • |Humans[MESH]
  • |Infant[MESH]
  • |Infant, Newborn[MESH]
  • |London/epidemiology[MESH]
  • |Middle Aged[MESH]
  • |Models, Statistical[MESH]
  • |Pandemics/prevention & control[MESH]
  • |Pneumonia, Viral/*epidemiology/mortality/prevention & control/transmission[MESH]
  • |SARS-CoV-2[MESH]


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