Accounting for the role of asymptomatic patients in understanding the dynamics of the COVID-19 pandemic: a case study from Singapore
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Abstract
Objectives
To forecast the true growth of COVID-19 cases in Singapore after accounting for asymptomatic infections, we study and make modifications to the SEIR (Susceptible-Exposed-Infected-Recovered) epidemiological model by incorporating hospitalization dynamics and the presence of asymptomatic cases. We then compare the simulation results of our three epidemiological models of interest against the daily reported COVID-19 case counts during the time period from 23rd January to 6th April 2020. Finally, we compare and evaluate on the performance and accuracy of the aforementioned models’ simulations.
Methods
Three epidemiological models are used to forecast the true growth of COVID-19 case counts by accounting for asymptomatic infections in Singapore. They are the exponential model, SEIR model with hospitalization dynamics (SEIHRD), and the SEIHRD model with inclusion of asymptomatic cases (SEAIHRD).
Results
Simulation results of all three models reflect underestimation of COVID-19 cases in Singapore during the early stages of the pandemic. At a 40% asymptomatic proportion, we report basic reproduction number R 0 = 3.28 and 3.74 under the SEIHRD and SEAIHRD models respectively. At a 60% asymptomatic proportion, we report R 0 = 3.48 and 3.96 under the SEIHRD and SEAIHRD models respectively.
Conclusions
Based on the results of different simulation scenarios, we are highly confident that the number of COVID-19 cases in Singapore was underestimated during the early stages of the pandemic. This is supported by the exponential increase of COVID-19 cases in Singapore as the pandemic evolved.
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SciScore for 10.1101/2021.07.21.21260919: (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: 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:4.2 Limitations: Our study has several limitations. First, we can only definitively conclude the underestimation of Singapore’s COVID-19 cases in the early stages of the pandemic based on the exponential increase of confirmed cases in later months, but the degree of underestimation is still unclear due to our lack of understanding of the …
SciScore for 10.1101/2021.07.21.21260919: (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: 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:4.2 Limitations: Our study has several limitations. First, we can only definitively conclude the underestimation of Singapore’s COVID-19 cases in the early stages of the pandemic based on the exponential increase of confirmed cases in later months, but the degree of underestimation is still unclear due to our lack of understanding of the virus. The degree of under-reporting of cases can only be estimated by conducting a serological surveillance, which relies on the detection of COVID-19 antibodies as a result of a past COVID-19 infection (WHO, 2020). Hence, serological tests are much more reliable and accurate for detecting previous infections in individuals with few or no COVID-19 symptoms (CDC, 2020a). However, Singapore has primarily been adopting the use of polymerase chain reaction (PCR) tests, which involves the extraction of a swab sample from the nose and/or throat from the patient (ICA, 2021). Given the lack of Singapore serological testing results and data, we can only make predictions of the true COVID-19 case counts, subject to the pandemic developing in a similar manner to that of our chosen statistical distributions and accuracy of our parameters based on assumptions made during the development of the models. Second, the model assumes individuals infected with COVID-19 either recover or die, with no reinfection taking place. New research suggests this might not be true, with countries such as Mexico, Sweden and South Korea all reporting cases of COVID-19 reinfec...
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.
Results from scite Reference Check: We found no unreliable references.
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