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1.
Biophys J ; 120(14): 2872-2879, 2021 07 20.
Article in English | MEDLINE | ID: covidwho-1605779

ABSTRACT

We study the transition of an epidemic from growth phase to decay of the active infections in a population when lockdown health measures are introduced to reduce the probability of disease transmission. Although in the case of uniform lockdown, a simple compartmental model would indicate instantaneous transition to decay of the epidemic, this is not the case when partially isolated active clusters remain with the potential to create a series of small outbreaks. We model this using the Gillespie stochastic simulation algorithm based on a connected set of stochastic susceptible-infected-removed/recovered networks representing the locked-down majority population (in which the reproduction number is less than 1) weakly coupled to a large set of small clusters in which the infection may propagate. We find that the presence of such active clusters can lead to slower than expected decay of the epidemic and significantly delayed onset of the decay phase. We study the relative contributions of these changes, caused by the active clusters within the population, to the additional total infected population. We also demonstrate that limiting the size of the inevitable active clusters can be efficient in reducing their impact on the overall size of the epidemic outbreak. The deceleration of the decay phase becomes apparent when the active clusters form at least 5% of the population.


Subject(s)
Disease Outbreaks , Epidemics , Algorithms , Computer Simulation , Humans , Probability
2.
Sci Rep ; 11(1): 1661, 2021 01 18.
Article in English | MEDLINE | ID: covidwho-1035942

ABSTRACT

A better understanding of how the COVID-19 pandemic responds to social distancing efforts is required for the control of future outbreaks and to calibrate partial lock-downs. We present quantitative relationships between key parameters characterizing the COVID-19 epidemiology and social distancing efforts of nine selected European countries. Epidemiological parameters were extracted from the number of daily deaths data, while mitigation efforts are estimated from mobile phone tracking data. The decrease of the basic reproductive number ([Formula: see text]) as well as the duration of the initial exponential expansion phase of the epidemic strongly correlates with the magnitude of mobility reduction. Utilizing these relationships we decipher the relative impact of the timing and the extent of social distancing on the total death burden of the pandemic.


Subject(s)
COVID-19/transmission , Physical Distancing , COVID-19/epidemiology , COVID-19/pathology , COVID-19/virology , Cell Phone , Europe/epidemiology , Geographic Information Systems , Humans , Pandemics , Quarantine , SARS-CoV-2/isolation & purification , Travel/statistics & numerical data
3.
Infect Dis Model ; 5: 357-361, 2020.
Article in English | MEDLINE | ID: covidwho-548995

ABSTRACT

We investigate the effects of social distancing in controlling the impact of the COVID-19 epidemic using a simple susceptible-infected-removed epidemic model. We show that an alternative or complementary approach based on targeted isolation of the vulnerable sub-population may provide a more efficient and robust strategy at a lower economic and social cost within a shorter timeframe resulting in a collectively immune population.

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