A deductive approach to modeling the spread of COVID-19
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Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), previously known as 2019-nCoV, is responsible for the atypical pneumonia pandemic designated as Coronavirus Disease 2019 (COVID-19). The number of cases continues to grow exponentially reaching 492,000 people in 175 countries as of March 25, 2020. 22,169 people (∼4.5%) infected with SARS-COV-2 virus have died. We have developed an exponential regression model using the COVID-19 case data (Jan 22 – Mar 22, 2020). Our primary model uses designated Phase 1 countries, who exceed 2500 cases on Mar 22. The model is then applied to Phase 2 countries : those that escaped the initial Phase 1 global expansion of COVID-19. With the exception of stabilizing countries (South Korea, Japan, and Iran) all Phase 1 countries are growing exponentially, as per I 2500 ( t ) = 120.4 × e 0.238 t , with a rate, r = 0.238 ± 0.068. Excluding China, the BRICS developing nations and Australia are in Phase 2 . Case data from Phase 2 countries are following the model derived from Phase 1 countries . In the absence of measures employed to flatten the curve including social distancing, quarantine, and healthcare expansion, our model projects over 274,000 cases and 12,300 deaths in the US by Mar 31. India can expect 123,000 cases by April 16. By flattening the curve to the growth rate of stabilizing countries ( r = 0.044 ± 0.062), the US would prevent 8,500 deaths by Mar 31, and India would prevent 5,500 deaths by April 16.
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SciScore for 10.1101/2020.03.26.20044651: (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:Limitations: It is important to consider the significant limitations of this preliminary study. First, our model assumes continuous, regular exponential growth. Disease epidemics ultimately follow a sigmoidal shape, as they approach the carrying capacity of the disease. SEIR models account for a decreasing susceptible population and increasing recovered population with immunity. These models prove to be superior in long term analysis …
SciScore for 10.1101/2020.03.26.20044651: (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:Limitations: It is important to consider the significant limitations of this preliminary study. First, our model assumes continuous, regular exponential growth. Disease epidemics ultimately follow a sigmoidal shape, as they approach the carrying capacity of the disease. SEIR models account for a decreasing susceptible population and increasing recovered population with immunity. These models prove to be superior in long term analysis of a disease’s expansion. It is most likely that our model is only applicable for the initial exponential expansion of a disease. For COVID-19, we caution its usage beyond 60 days after initial seeding of 100 cases. The data collected by the JHU CSSE is impacted by the reporting capabilities of each country. Subclinical cases are improbable to detect through surveillance screening due to lack of laboratory resources in most countries. Such cases would be entirely missed as presumptive cases, as these patients would not report to a healthcare facility. As mentioned earlier, the presumptive asymptomatic SARS-COV-2 positive population is 17.9%23. Beyond sub-clinical cases, governments are instructing mildly symptomatic patients to quarantine at home rather than seek hospital care. These cases would also avoid detection and registration in the data set. The model equations serve to average the differences between healthcare systems, hospital bed per capita, laboratory testing capabilities, population dynamics, etc. When applying the model to any give...
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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