Updating Herd Immunity Models for the U.S. in 2020: Implications for the COVID-19 Response
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Abstract
Objectives
To understand what levels of herd immunity are required in the COVID-19 pandemic, given spatial population heterogeneity, to best inform policy and action.
Methods
Using a network of counties in the United States connected by transit data we considered a set of coupled differential equations for susceptible-infectious-removed populations. We calculated the classical herd immunity level plus a version reflecting the heterogeneity of connections in the network by running the model forward in time until the epidemic completed.
Results
Necessary levels of herd immunity vary greatly from county to county. A population weighted average for the United States is 47.5% compared to a classically estimated level of 77.1%.
Conclusions
Common thinking argues that the nation needs to achieve at least 60% herd immunity to emerge from the COVID-19 pandemic. Heterogeneity in contact structure and individual variation in infectivity, susceptibility, and resistance are key factors that reduce the disease-induced herd immunity levels to 34.2–47.5% in our models. Looking forward toward vaccination strategies, these results suggest we should consider not just who is vaccinated but where those vaccinations will do the most good.
Article activity feed
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SciScore for 10.1101/2020.10.05.20207100: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are a number of limitations in this work. The model is, of course, an approximation, and the uniform, multiplicative imposition of nonpharmaceutical interventions (e.g., social distancing, use of face masks, etc.) across all geographies is artificial. Our implementation of partial immunity is also simplistic with some arbitrariness in the parameter choices. The mathematical model confers permanent immunity on recovered …
SciScore for 10.1101/2020.10.05.20207100: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are a number of limitations in this work. The model is, of course, an approximation, and the uniform, multiplicative imposition of nonpharmaceutical interventions (e.g., social distancing, use of face masks, etc.) across all geographies is artificial. Our implementation of partial immunity is also simplistic with some arbitrariness in the parameter choices. The mathematical model confers permanent immunity on recovered individuals, and no one knows how long actual immunity lasts. Reinfections are beginning to be reported and, among other possibilities, the virus could mutate. Thus, there are a number of reasons to be cautious. Social distancing and other mitigation strategies have been imposed in a highly nonuniform way (in time, space, implementation, compliance) across the nation. As these behaviors and strategies are relaxed, the possibility of exposing previously-shielded-but-central-to-transmission nodes and generating new outbreaks is very real. Despite these limitations, the estimates presented here, in combination with other efforts (13– 17), have identified heterogeneity in contact structures (i.e., mixing between age groups or spatial distribution) and individual variation (in terms of infectivity, susceptibility, partial immunity, resistance) as key factors that reduce the disease-induced herd immunity level. It is likely that models featuring more realistic combinations of these factors would suggest even lower levels of disease-induced herd immunity. Natura...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
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