Social Heterogeneity Drives Complex Patterns of the COVID-19 Pandemic: Insights From a Novel Stochastic Heterogeneous Epidemic Model (SHEM)
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Abstract
In addition to vaccine and impactful treatments, mitigation strategies represent an effective way to combat the COVID-19 virus and an invaluable resource in this task is numerical modeling that can reveal key factors in COVID-19 pandemic development. On the other hand, it has become evident that regional infection curves of COVID-19 exhibit complex patterns which often differ from curves predicted by forecasting models. The wide variations in attack rate observed among different social strata suggest that this may be due to social heterogeneity not accounted for by regional models. We investigated this hypothesis by developing and using a new Stochastic Heterogeneous Epidemic Model that focuses on subpopulations that are vulnerable in the sense of having an increased likelihood of spreading infection among themselves. We found that the isolation or embedding of vulnerable sub-clusters in a major population hub generated complex stochastic infection patterns which included multiple peaks and growth periods, an extended plateau, a prolonged tail, or a delayed second wave of infection. Embedded vulnerable groups became hotspots that drove infection despite efforts of the main population to socially distance, while isolated groups suffered delayed but intense infection. Amplification of infection by these hotspots facilitated transmission from one urban area to another, causing the epidemic to hopscotch in a stochastic manner to places it would not otherwise reach; whereas vaccination only in hotspot populations stopped geographic spread of infection. Our results suggest that social heterogeneity is a key factor in the formation of complex infection propagation patterns. Thus, the mitigation and vaccination of vulnerable groups is essential to control the COVID-19 pandemic worldwide. The design of our new model allows it to be applied in future studies of real-world scenarios on any scale, limited only by computing memory and the ability to determine the underlying topology and parameters.
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SciScore for 10.1101/2020.07.10.20150813: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Model features, limitations, and future studies: An epidemic can be likened to a forest fire, which spreads by diffusion along a front, but can also jump by embers that may or may not start a new blaze. Such spread to virgin areas, with a virus as with a fire, is intrinsically stochastic and such stochasticity, which is not explicitly …
SciScore for 10.1101/2020.07.10.20150813: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Model features, limitations, and future studies: An epidemic can be likened to a forest fire, which spreads by diffusion along a front, but can also jump by embers that may or may not start a new blaze. Such spread to virgin areas, with a virus as with a fire, is intrinsically stochastic and such stochasticity, which is not explicitly included in mean-field models, may contribute to the remarkable patchiness of the COVID-19 epidemic. This has caused the epidemic to appear entirely different to observers in different locations, leading to politicization of the response, which is, itself, a form of social heterogeneity. For rare spread to small, isolated subgroups (embers) this stochasticity is crucial. Patchiness is aggravated by the over-dispersion (super-spreading) of secondary cases of COVID-19, where the majority of infected individuals do not spread the virus, but some can cause up to a hundred secondary infections [14]. Our model is explicitly stochastic, with a mechanism to account for over-dispersion, by keeping a partial history of individual infections. Furthermore, the design of our new model allows it to be applied in future studies of real-world scenarios on any scale, limited only by memory and the ability to determine the underlying topology and parameters. However in our model, we make no attempt to distinguish between symptomatic and asymptomatic cases, despite recent findings by Chao et al. [24] in their agent-based model (dubbed Corvid) that demonstrated tha...
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.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a protocol registration statement.
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