A pandemic creates a challenging situation where governing authorities are faced with the complex decision of what the appropriate measures are given the existing information. Building on the key observation that the stochastic evolution of a process such as the spread of infection has a finite extinction probability even when it is expected to grow exponentially on average , we propose a strategy of containment that falls in between the relatively mild social distancing measures and the maximally restrictive lock-down strategies. Our proposed strategy involves partitioning the population into smaller isolated sub-populations within which, while social distancing is practised as much as possible to reduce the contact rate, a relatively normal lifestyle is maintained. As a rule of thumb, the optimal size of the sub-populations can be obtained by dividing the total population size by the best estimate of the number of infected individuals at the time of the implementation of this containment strategy.
 Building on the observation that the spread of an infection is subject to stochastic fluctuations when infection numbers are small , we propose a strategy of containment that falls in between relatively mild social distancing measures and maximally restrictive lockdown strategies. The key innovation of our proposed strategy is its ability to recruit stochastic effects such as spontaneous extinction and fluctuations in timing to contain the epidemic, even when infection numbers in the population as a whole are not small and their spatial distribution is unknown. It involves partitioning the population into smaller isolated sub-populations within which, while social distancing is practiced as much as possible to reduce the contact rate, a relatively normal lifestyle is maintained. As a rule of thumb, the optimal size of the sub-populations can be obtained by dividing the total population size by the best estimate of the number of infected individuals at the time of the implementation of this containment strategy.
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A pandemic creates a challenging situation where governing authorities are faced with the complex decision of what the appropriate measures are given the existing information.
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key
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stochastic evolution of a process such as the spread of infection has a finite extinction probability even when it is expected to grow exponentially on average
spread of an infection is subject to stochastic fluctuations when infection numbers are small
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the
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the maximally restrictive lock-down strategies. Our proposed strategy
maximally restrictive lockdown strategies. The key innovation of our proposed strategy is its ability to recruit stochastic effects such as spontaneous extinction and fluctuations in timing to contain the epidemic, even when infection numbers in the population as a whole are not small and their spatial distribution is unknown. It
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practised
practiced