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herd immunity


A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2

Science

In response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), some politicians have been keen to exploit the idea of achieving herd immunity. Countering this possibility are estimates derived from work on historical vaccination studies, which suggest that herd immunity may only be achieved at an unacceptable cost of lives. Because human populations are far from homogeneous, Britton et al. show that by introducing age and activity heterogeneities into population models for SARS-CoV-2, herd immunity can be achieved at a population-wide infection rate of ∼40%, considerably lower than previous estimates. This shift is because transmission and immunity are concentrated among the most active members of a population, who are often younger and less vulnerable. If nonpharmaceutical interventions are very strict, no herd immunity is achieved, and infections will then resurge if they are eased too quickly. Science , this issue p. [846][1] Despite various levels of preventive measures, in 2020, many countries have suffered severely from the coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Using a model, we show that population heterogeneity can affect disease-induced immunity considerably because the proportion of infected individuals in groups with the highest contact rates is greater than that in groups with low contact rates. We estimate that if R = 2.5 in an age-structured community with mixing rates fitted to social activity, then the disease-induced herd immunity level can be ~43%, which is substantially less than the classical herd immunity level of 60% obtained through homogeneous immunization of the population. Our estimates should be interpreted as an illustration of how population heterogeneity affects herd immunity rather than as an exact value or even a best estimate. [1]: /lookup/doi/10.1126/science.abc6810


Leveraging AI to Battle This Pandemic -- And The Next One

#artificialintelligence

Over the past few months the world has experienced a series of Covid-19 outbreaks that have generally followed the same pathway: an initial phase with few infections and limited response, followed by a take-off of the famous epidemic curve accompanied by a country-wide lockdown to flatten the curve. Then, once the curve peaks, governments have to address what President Trump has called "the biggest decision" of his life: when and how to manage de-confinement. Throughout the pandemic, great emphasis has been placed on the sharing (or lack of it) of critical information across countries -- in particular from China -- about the spread of the disease. By contrast, relatively little has been said about how Covid-19 could have been better managed by leveraging the advanced data technologies that have transformed businesses over the past 20 years. In this article we discuss one way that governments could leverage those technologies in managing a future pandemic -- and perhaps even the closing phases of the current one.


Leveraging AI to Battle This Pandemic -- And The Next One

#artificialintelligence

We've made our coronavirus coverage free for all readers. To get all of HBR's content delivered to your inbox, sign up for the Daily Alert newsletter. Over the past few months the world has experienced a series of Covid-19 outbreaks that have generally followed the same pathway: an initial phase with few infections and limited response, followed by a take-off of the famous epidemic curve accompanied by a country-wide lockdown to flatten the curve. Then, once the curve peaks, governments have to address what President Trump has called "the biggest decision" of his life: when and how to manage de-confinement. Throughout the pandemic, great emphasis has been placed on the sharing (or lack of it) of critical information across countries -- in particular from China -- about the spread of the disease.