An Extended COVID-19 Epidemiological Model with Vaccination and Multiple Interventions for Controlling COVID-19 Outbreaks in the UK

  1. Shuhao Zhang
  2. Gaoshan Bi
  3. Xiang Wang
  4. Yun Yang
  5. Jun Qi
  6. Shujun Li
  7. Xuxin Mao
  8. Ruoling Peng
  9. Po Yang

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Abstract

For controlling the first wave of the UK COVID-19 pandemic in 2020, a plethora of hypothetical COVID-19 models has been developed for simulating how diseases spread under different non-pharmaceutical interventions like suppression and mitigation and providing useful guidance to UK policymakers. While many models demonstrate their effectiveness on predicting and controlling the spread of COVID-19, they rarely consider consequence of incorporating the effects of potential SARS-CoV-2 variants and implementing vaccine interventions in large-scale. By December 2020, the second wave in the UK appeared to be much more aggressive with many more cases as one potentially more contagious SARS-CoV-2 variant was detected in the UK since September 2020. Meanwhile, UK has begun their first mass vaccination campaign on 8 December 2020, where three vaccines were in use including Pfizer, BioNTech and Moderna. Thus, these new issues pose an emergent need to build up advanced models for accessing effectiveness of taking both vaccination and multiple interventions for controlling COVID-19 outbreaks and balancing healthcare demands. Targeting at this problem, we conducted a feasibility study by defining a new mathematical model SEMCVRD (Susceptible [S], Exposed [E] (infected but asymptomatic), Mild [M] and Critical [C] (mild cases, severe and critical cases), [V] (vaccinated), Recovered [R] and Deceased [D]), containing two importantly new features: the combined infection of the mutant strain and the original strain and the addition of a new group who have been vaccinated. The model was fitted and evaluated with a public COVID-19 dataset including daily new infections, new deaths and daily vaccination in the UK from February 2020 to February 2021. Based on the simulation results, 1) we find under the assumption that the vaccine is equivalently effective against both the original strain and new variants of COVID-19, if the UK government implements insensitive suppression intervention for 13 weeks, COVID-19 epidemic will be controlled by the first week of April 2021 and nearly ended by the first week of May 2021. It shows that taking both vaccine and suppression interventions can effectively inhibit the spread and infection of the new mutant virus. 2) we suggest implementing a 3-weeks phased and progressive lifting intervention strategy up to a low intensity mitigation level for effectively controlling COVID-19 outbreaks in the UK. By implementing this strategy, the total number of infections in the UK will be limited to 4.2 million and the total number of deaths in the UK is 135 thousand, by the end of June 2021. The epidemic will nearly end in the early of June 2021, and the UK will not experience a shortage of medical resources. 3) On the assumption that UK has a capability of providing 600 thousand vaccinations every day, a 3-weeks phased and progressive lifting intervention strategy up to a moderate intensity mitigation level can end the epidemic by the end of May 2021. This strategy would reduce the overall infections and deaths of COVID-19 outbreaks, and balance healthcare demand in the UK.

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  1. Version published to 10.1101/2021.03.10.21252748v2 on medRxiv
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    SciScore for 10.1101/2021.03.10.21252748: (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:
    On the other hand, there are still limitations in the simulation and analysis of our model. First of all, the prediction of our model depends on the estimation of the intensity of intervention by estimating the average number of daily contacts of infectious individuals in a certain area. Over time, each intervention will have the same or similar effects on the number of reproduction in different regions. On cultural or other issues in certain counties, the actual effect of the intensity of intervention may vary. In the UK or similar countries/regions, how to quantify the intensity of intervention requires accurate measurement of the social distance of the entire society, the isolation of cases in the family, and the family isolation of family members. Second, our model uses a variety of reasonable biological COVID-19 parameters based on the latest evidence shown in Table 1, but these assumed values may vary by population or country. For example, assuming that the average time from mild cases to severe cases is 7 days, the average time from severe cases to death of hospitalized elderly is 14 days, and so on. Changes in these variables may affect our estimates of infection and deaths in the UK. Third, our model simulates the mixed infection of the original virus and the mutant virus, assuming a mixed infection rate parameter, which is determined by the population of the main area infected by the mutant virus and the proportion of infection in the area. As the infection spreads,...

    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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  3. Version published to 10.1101/2021.03.10.21252748v1 on medRxiv