A causal Bayesian network approach for consumer product safety and risk assessment
• Describes a novel causal Bayesian network approach for systematic product risk assessment.
• The approach resolves some limitations with current commonly used product risk assessment methods.
• The approach complements product risk assessment methods like RAPEX.
• The approach can produce quantified, auditable product risk assessment with limited or no testing data.
Ivermectin Prophylaxis Used for COVID-19 Reduces COVID-19 Infection and Mortality Rates: A City-Wide, Prospective Observational Study of 220,517 Subjects Using Propensity Score Matching
Background: Ivermectin has demonstrated different mechanisms of action that potentially protect from both COVID-19 infection and COVID-19-related comorbidities. Based on the studies suggesting efficacy in prophylaxis combined with the known safety profile of ivermectin, a citywide prevention program using ivermectin for COVID-19 was implemented in Itajai, a Southern city in Brazil in the state of Santa Catarina. The objective of this study was to evaluate the impact of regular ivermectin use on subsequent COVID-19 infection and mortality rates.
mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review
Latest statistics on England mortality data suggest systematic mis-categorisation of vaccine status and uncertain effectiveness of Covid-19 vaccination
How bad data quality can turn a simulation into a dissimulation that shapes the future
DSA ADS Course, 2022
Data Quality, Black Box Models, Bad Models, Origins of SARS-CoV-2, Epidemiology, Non-pharmaceutical Interventions, Mitigation Strategies, COVID19, Health Policy, Public Policy
Review data quality evaluations, bad model design, policy decision-making based on data science, black box models, unwarranted assumptions in models, and evidence based policy making with near real-time data.