Homophily in risk and behavior complicate understanding the COVID-19 epidemic curve
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Abstract
New COVID-19 diagnoses have dropped faster than expected in the United States. Interpretations of the decrease have focused on changing factors (e.g. mask-wearing, vaccines, etc.), but predictive models largely ignore heterogeneity in behaviorally-driven exposure risks among distinct groups. We present a simplified compartmental model with differential mixing in two behaviorally distinct groups. We show how homophily in behavior, risk, and exposure can lead to early peaks and rapid declines that critically do not signal the end of the outbreak. Instead, higher exposure risk groups may more rapidly exhaust available susceptibles while the lower risk group are still in a (slower) growth phase of their outbreak curve. This simplified model demonstrates that complex incidence curves, such as those currently seen in the US, can be generated without changes to fundamental drivers of disease dynamics. Correct interpretation of incidence curves will be critical for policy decisions to effectively manage the pandemic.
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SciScore for 10.1101/2021.03.16.21253708: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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:Limitations of the study: Our goal is to demonstrate the potential impact of homophily on observed dynamics, rather than to make concrete quantitative predictions about actual reported incidence curves. Consequently, the model …
SciScore for 10.1101/2021.03.16.21253708: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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:Limitations of the study: Our goal is to demonstrate the potential impact of homophily on observed dynamics, rather than to make concrete quantitative predictions about actual reported incidence curves. Consequently, the model presented is only meant as a simplified example, and many potentially critical details are omitted, such as demographic or socioeconomic correlates with group behaviors. We also do not explicitly model the alternative explanations for the current trends. We do not mean to suggest that these factors are not playing a (large) role in the US COVID-19 pandemic, but aim to highlight a largely un-discussed, and potentially very important, additional influence of homophily among groups. We have here demonstrated the impact of homophily in only two behaviorally distinct groups, though of course the reality is likely the composite contribution of many distinct groups that may vary in both behavior and/or physiological susceptibility (37). Sociocultural factors may also play a critical role in the rates at which different groups interact, since even beyond the percentage of households with economic or “essential workforce” constraints against protective behaviors, crowded neighborhoods and multigenerational homes also complicate the ability to minimize exposure risks (38). While heterogeneous mixing among such distinct etiological and behavioral cohorts is certainly not the only factor influencing currently observed trends, it would be a mistake to ignore the pot...
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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