Computational modelling of COVID-19: A study of compliance and superspreaders
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Abstract
Background
The success of social distancing implementations of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) depends heavily on population compliance. Mathematical modelling has been used extensively to assess the rate of viral transmission from behavioural responses. Previous epidemics of SARS-Cov-2 have been characterised by superspreaders, a small number of individuals who transmit a disease to a large group of individuals, who contribute to the stochasticity (or randomness) of transmission compared to other pathogens such as Influenza. This growing evidence proves an urgent matter to understand transmission routes in order to target and combat outbreaks.
Objective
To investigate the role of superspreaders in the rate of viral transmission with various levels of compliance.
Method
A SEIRS inspired social network model is adapted and calibrated to observe the infected links of a general population with and without superspreaders on four compliance levels. Local and global connection parameters are adjusted to simulate close contact networks and travel restrictions respectively and each performance assessed. The mean and standard deviation of infections with superspreaders and non-superspreaders were calculated for each compliance level.
Results
Increased levels of compliance of superspreaders proves a significant reduction in infections. Assuming long-lasting immunity, superspreaders could potentially slow down the spread due to their high connectivity.
Discussion
The main advantage of applying the network model is to capture the heterogeneity and locality of social networks, including the role of superspreaders in epidemic dynamics. The main challenge is the immediate attention on social settings with targeted interventions to tackle superspreaders in future empirical work.
Conclusion
Superspreaders play a central role in slowing down infection spread following compliance guidelines. It is crucial to adjust social distancing measures to prevent future outbreaks accompanied by population-wide testing and effective tracing.
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SciScore for 10.1101/2021.05.12.21257079: (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:Limitations: Although the network model indicates a close relationship between compliance and the value of social distancing measures, an extended critique is needed in future work to understand the reasons behind various compliance levels [21]. Management strategies would need to be considered thoroughly in order to address social …
SciScore for 10.1101/2021.05.12.21257079: (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:Limitations: Although the network model indicates a close relationship between compliance and the value of social distancing measures, an extended critique is needed in future work to understand the reasons behind various compliance levels [21]. Management strategies would need to be considered thoroughly in order to address social settings and ensure the balance between compliance and livelihood [22]. This simulation assumes long-lasting herd immunity in which further work is needed to establish detailed transmission routes providing vaccination options. The model also assumes the basic reproductive number R0 of 2.2 for superspreaders, which may vary as previous literature suggests it can range from 1.5-4% [23]. Therefore, further studies can be done through modelling compliances levels with varying R0 to explore the detailed consequences on the rate of infection spread. This also suggests infections will persist without strong effective control measures. This model does not solely reflect the population in the UK, but rather in simulated homogeneous and heterogeneous populations. However, the main finding is to focus on illustrating the effects of compliances in a population where superspreaders exist [24]. The model reflects our understanding of the importance of compliance and adherence to government guidelines for a quicker uplift of lockdown measures [25]. Another assumption of the model framework is that we assumed a proportion of infected individuals to be symptomatic...
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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