The coronavirus pandemic has resulted in an explosion of statistics and data being presented to both the general public and scientific community. But how do we make sense of what the data is telling us, and how can we use statistical data to make practical decisions? In these videos, experts in this area explain how statistical models are used to improve outcomes - either identifying treatments for COVID patients, designing experiments to understand the vectors of infection, designing clinical trials or exploring public health policy.
The experts in these videos - from both academic research institutions and the pharmaceutical industry - discuss these issues at a personal (patient-centred) and societal level. While healthcare outcomes and the treatment of people who have contracted COVID are covered, so too are wider issues for society - how do we use statistical information to communicate to the public? How do we teach it in classrooms? How do we use this information to make policy decisions? What are the concerns for privacy and personal information? Lastly, how can we critically engage with the data we're presented and ensure the public are being given correct, unbiased and unadulterated information?
Lessons Learned in Designing Clinical Trials
Peter Mesenbrink of Novartis describes the challenges of designing clinical trials for effective COVID-19 treatment, and talks about the lessons learned in the process.
On COVID-19 Outbreak Predictions
Milan Stehlik of the University of Valparaiso discusses issues affecting the estimation and prediction of COVID-19 outbreaks from statistical and mathematical perspectives, including stability, parameter sensitivity, precision, and the stochastic nature of the problem, such as random perturbation in parameters causing instability in predicting and estimating.
Lessons from the 1918 Influenza Pandemic
Ronald Fricker from Virginia Tech University explores what lessons we can learn from the 1918 influenza pandemic, or Spanish Flu, and how looking back at history can help us navigate the current COVID-19 pandemic.
Digital Medical Statistics: Enabling Innovation During the COVID-19 Pandemic
Charmaine Demanuele, Director of Biostatistics at Pfizer, discusses how mobile and wearable technology, coupled with statistical modelling and advances in AI, are being used to mitigate the COVID-19 pandemic, and allow clinical trials during the pandemic.
The Importance of Accurate Data Visualization in COVID-19
Dr Heather Mattie of Harvard University talks about the importance of data visualization when communicating information about the COVID-19 pandemic, using an example of a misleading graph used by health officials to make health policy decisions.
Creating Informative and Accurate Data Visualizations
Dr Heather Mattie of Harvard University demonstrates how to correct misleading data visualizations in order to see a more accurate picture of health data. The example covered in the video was used to justify health policy changes in relation to COVID-19; Dr Mattie shows how the correct visualization produces different, but more accurate, conclusions.
Florence Nightingale: Master of Data
Dr Noel-Anne Bradshaw of London Metropolitan University shows the parallels between Florence Nightingale's work in the 19th century and the issues we face during the Coronavirus pandemic. Not many people know that Nightingale worked to investigate the high rates of mortality in the British Army, and used the resulting data to improve the health of the British Armed Forces and to transform government statistics.
Privacy Risk and Preservation for COVID-19 Contact Tracing
Professor Fang Liu of Notre Dame University provides a brief overview of the GPS- and Bluetooth-based apps used in contact tracing, examining privacy risks and discussing the effectiveness of privacy preservation measures present in these apps.
Monitoring the Health of Populations by Tracking Disease Outbreaks: Yellow Fever
Steve Rigdon from St. Louis University describes the process of designing experiments to determine the cause of yellow fever infections during the outbreak in Cuba in the early 1900s. The process of designing experiments in order to understand how people are infected is directly relevant to how we better understand the spread of COVID-19.
Cure Models Can Help COVID-19 Research
Yingwei Peng of Queen's University, Ontario, describes the potentials of cure models (a type of survival analysis model) that can contribute to research in COVID-19 prevention.
Engaging Students During the COVID-19 Health Crisis
In this short video Laura Le, Kari Lock Morgan and Lucy D'Agostino McGowan discuss how to engage students with a sensitive topic like COVID-19. They include strategies for determining whether to engage in these topics as well as strategies for how to engage, should the decision be made to do so.