From Swab to Server: Testing, Sequencing, and Sharing During a Pandemic

Por: FutureLearn . en: , ,

Explore the journey from swabbing to processing data

Sampling and testing are key to understanding where a disease is spreading. In the COVID-19 pandemic, the number of samples taken, analysed, tracked, and shared have far surpassed any previous pandemic.

On this three-week course from COG-Train, you will learn about the process that samples undergo to become datasets. You’ll also gain the knowledge of how this data is handled in order to satisfy public health concerns during a pandemic.

Discover how sample strategies can affect scientific research and results

Sampling is a critical part of research in any field. In studying a disease such as Covid-19, it’s imperative that your sample and sampling methods are best-suited to reflect accurate results.

Supported by expert educators, you will explore different sample methods to examine their efficacy. With this knowledge, you’ll be equipped to discuss sampling strategies and their correct applications.

Delve into the ethics of data sharing on a pandemic scale

Data from samples provides scientists with the essential information they need to analyse and track a disease.

However, this sample data can contain highly personal information about the population. You’ll discuss the ethical implications of using and sharing data and consider methods of protecting personal information.

Recognise the importance of data sharing and linkage in public health concerns

Linking genomic data sets to clinical and epidemiological data sets can help us understand the effects of different variants, which is especially prevalent for Covid-19.

With the help of COG-Train, you’ll learn to create holistic datasets through linkage. With this knowledge, you’ll further your understanding of Covid-19 and use your findings to address concerns related to public health.

This course is designed for diagnostic and healthcare professionals, as well as anyone involved in the testing and analysis of disease samples.

It will also be advantageous to researchers specialising in the fields of pandemics, diseases, and diagnostics.