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 .
Using a stochastic Susceptible-Infected-Removed (SIR) meta-population model of disease transmission, we present analytical calculations and numerical simulations dissecting the interplay between stochasticity and the division of a population into mutually independent sub-populations. We show that subdivision activates two stochastic effects---extinction and desynchronization---diminishing the overall impact of the outbreak , even when the total population has already left the stochastic regime and the basic reproduction number is not altered by the subdivision. Both effects are quantitatively captured by our theoretical estimates, allowing us to determine their individual contributions to the observed reduction of the peak of the epidemic .
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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
Using a stochastic Susceptible-Infected-Removed (SIR) meta-population model of disease transmission, we present analytical calculations and numerical simulations dissecting the interplay between stochasticity and the division of a population into mutually independent sub-populations. We show that subdivision activates two stochastic effects---extinction and desynchronization---diminishing the overall impact of the outbreak
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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,
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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
total population has already left the stochastic regime and the basic reproduction number is not altered by the subdivision. Both effects are quantitatively captured by our theoretical estimates, allowing us to determine their individual contributions to the observed reduction of the peak of the epidemic