Disentangling Increased Testing from Covid-19 Epidemic Spread
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Abstract
To design effective disease control strategies, it is critical to understand the incidence of diseases. In the Covid-19 epidemic in the United States (caused by outbreak of the SARS-CoV-2 virus), testing capacity was initially very limited and has been increasing at the same time as the virus has been spreading. When estimating the incidence, it can be difficult to distinguish whether increased numbers of positive tests stem from increases in the spread of the virus or increases in testing. This has made it very difficult to identify locations in which the epidemic poses the largest public health risks. Here, we use a probabilistic model to quantify beliefs about testing strategies and understand implications regarding incidence. We apply this model to estimate the incidence in each state of the United States, and find that: (1) the Covid-19 epidemic is likely to be more widespread than reported by limited testing, (2) the Covid-19 epidemic growth in the summer months is likely smaller than it was during the spring months, and (3) the regions which are at highest risk of Covid-19 epidemic outbreaks are not always those with the largest number of positive test results.
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SciScore for 10.1101/2020.07.09.20141762: (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:This study has several limitations. First, our results are dependent upon the quality of the external studies. However, we used several seroprevalence studies to provide a range of reasonable parameter values and minimize our dependence upon an individual study. Second, our model does not project future spread, such as could be done with a mechanistic model (e.g., SIR model; [1]). Third, our model uses test results, which are dependent …
SciScore for 10.1101/2020.07.09.20141762: (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:This study has several limitations. First, our results are dependent upon the quality of the external studies. However, we used several seroprevalence studies to provide a range of reasonable parameter values and minimize our dependence upon an individual study. Second, our model does not project future spread, such as could be done with a mechanistic model (e.g., SIR model; [1]). Third, our model uses test results, which are dependent on both test availability and usage, as well as test characteristics (e.g., sensitivity, specificity), for which we cannot correct. This study also has important strengths which can be incorporated into future analyses of the incidence of SARS-CoV-2. First, we used several types of epidemiologic studies, as well as several seroprevalence studies, to estimate the free parameter, thus minimizing reliance upon a single external study or type. Second, this parameter can be re-estimated as results of new external studies become available. In particular, representative seroprevalence studies in localities with reported test counts will improve the estimation of the free parameter in our model. Third, our model has no other external inputs such as symptomatic rates which are difficult to estimate for a novel virus. Fourth, our model can be applied repeatedly and at various geographic levels to help inform public health decisions including re-opening of different localities. In conclusion, we have developed a robust probabilistic model to estimate inci...
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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