Chopping the tail: How preventing superspreading can help to maintain COVID-19 control
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Abstract
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SciScore for 10.1101/2020.06.30.20143115: (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:One limitation on understanding the effect of heterogeneity in transmission in particular locations is the challenge of estimating epidemiological parameters from noisy and imperfect data: necessarily a balancing act between model simplicity and complexity. Here, we rely on metrics of heterogeneity previously estimated for SARS-CoV-1 and SARS-CoV-22,12,45 instead of estimating them directly from data; we focus our parameter estimation …
SciScore for 10.1101/2020.06.30.20143115: (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:One limitation on understanding the effect of heterogeneity in transmission in particular locations is the challenge of estimating epidemiological parameters from noisy and imperfect data: necessarily a balancing act between model simplicity and complexity. Here, we rely on metrics of heterogeneity previously estimated for SARS-CoV-1 and SARS-CoV-22,12,45 instead of estimating them directly from data; we focus our parameter estimation on the mean of the transmission rate distribution. Heterogeneity in contact rates or infectiousness, and the resulting distributional variance and skew, may vary based on local patterns of movement, contact, behavior, and population demography. This heterogeneity can have important consequences: in some cases epidemics with low mean R0 can actually infect a larger proportion of the population than epidemics with higher mean R0—as was the case for the 1918 influenza pandemic as compared to the 2014 Ebola outbreak—due to the heterogeneity in transmission rates, as described by higher moments of the secondary case distribution 7. The true epidemiological parameters in any given location, and the extent of our uncertainty in these parameters, also remain unknown because of the computational challenges of parameter estimation given the limited information contained in noisy case, death, and mobility data. For example, depending on how a particular candidate parameter combination weights the noisiness of cases and deaths and estimates initial conditio...
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.
- No protocol registration statement was detected.
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