Extending the range of COVID-19 risk factors in a Bayesian network model for personalised risk assessment
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Abstract
A need is emerging for individuals to gauge their own risks of coronavirus infection as it becomes apparent that contact tracing to contain the spread of the virus is not working in many societies. This paper presents an extension of an existing Bayesian network model for an application in which people can add their own personal risk factors to calculate their probability of exposure to the virus and likely severity if they do catch the illness. The data need not be shared with any central authority. In this way, people can become more aware of their individual risks and adjust their behaviour accordingly, as many countries prepare for a second wave of infections or a prolonged pandemic. This has the advantage not only of preserving privacy but also of containing the virus more effectively by allowing users to act without the time lag of waiting to be informed that a contact has been tested and confirmed COVID-19 positive. Through a nuanced assessment of individual risk, it could also release many people from isolation who are judged highly vulnerable using cruder measures, helping to boost economic activity and decrease social isolation without unduly increasing transmission risk. Although much has been written and reported about single risk factors, little has been done to bring these factors together in a user-friendly way to give an overall risk rating. The causal probabilistic model presented here shows the power of Bayesian networks to represent the interplay of multiple, dependent variables and to predict outcomes. The network, designed for use in the UK, is built using detailed data from government and health authorities and the latest research, and is capable of dynamic updates as new information becomes available. The focus of the paper is on the extended set of risk factors.
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SciScore for 10.1101/2020.10.20.20215814: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Observational data cannot be randomly sampled and controlled experiments cannot be done so almost all the data available come from hospital populations, individuals who have been tested or people voluntarily taking part in studies. Blinding not detected. Power Analysis not detected. Sex as a biological variable The network is initially populated with prior probabilities for the general population – e.g., a person in the UK has a 49.4% chance of being male, a 23.4% chance of being obese and a 16% chance of being a smoker. Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Even after it said it had adjusted for age, … SciScore for 10.1101/2020.10.20.20215814: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Observational data cannot be randomly sampled and controlled experiments cannot be done so almost all the data available come from hospital populations, individuals who have been tested or people voluntarily taking part in studies. Blinding not detected. Power Analysis not detected. Sex as a biological variable The network is initially populated with prior probabilities for the general population – e.g., a person in the UK has a 49.4% chance of being male, a 23.4% chance of being obese and a 16% chance of being a smoker. Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Even after it said it had adjusted for age, region, population density, area deprivation, household composition, socio-economic position, education, household tenure, multigenerational households and occupation, the ONS still found that Black males had twice the risk of White males, which it found to be “unexplained”. Whitesuggested: RRID:MMRRC_037613-MU)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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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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