Stochastic Progression of COVID-19 Symptoms

Source: AIChE
  • Type:
    Conference Presentation
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    AIChE Member Credits 0.5
    AIChE Members $19.00
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    AIChE Undergraduate Student Members Free
    Non-Members $29.00
  • Conference Type:
    AIChE Annual Meeting
  • Presentation Date:
    November 19, 2020
  • Duration:
    17 minutes
  • Skill Level:
    Intermediate
  • PDHs:
    0.30

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COVID-19 is a pandemic viral disease endangering a yet unknowable number of individuals. This disease is two to three times more contagious than influenza, and it is transmissible before symptoms appear. As a result, contact with infected individuals leads to cluster outbreaks. If patients with suspected cases were quickly tested, isolated and instructed to notify their recent contacts, these outbreaks could be more effectively contained. Unfortunately, patients with COVID-19 might have symptoms similar to other common illnesses. Thus, understanding the onset of COVID-19 symptoms might help instruct the public when to seek medical attention. To this end, we apply a Markov Process to a graded partially ordered set based on clinical observations of confirmed cases of COVID-19 to ascertain the most likely order of discernible symptoms (i.e. fever, cough, nausea/vomiting, and diarrhea) in COVID-19 patients. We then compared the progression of these symptoms in COVID-19 to other respiratory diseases, such as influenza, SARS and MERS, to observe if the disease presents differently. Our calculated predictions are consistent with the notion that influenza initiates with cough, whereas COVID-19 is similar to other coronavirus-related diseases, SARS and MERS, in initiating with fever. However, COVID-19 differs from SARS and MERS in the order of gastrointestinal symptoms. Our results support the recommended early checking method for COVID-19 from the CDC, which advises the public to take their temperature at home and when entering facilities. Additionally, our findings suggest that clinicians should consider recording the order of symptoms in COVID-19 patients, and if confirmed with prospective clinical data, this information could reduce the outbreak.
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AIChE Member Credits 0.5
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AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
Non-Members $29.00
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