How Data Visualization is Helping COVID-19 Research
The COVID-19 outbreak has caused an event of distress around the world. Science communicators have been working with enthusiasm in revealing innovative ways to explain the complex issues in response of the Covid-19 pandemic to the vast variety of spectators. With new updates progressing among the developing Covid-19 pandemic, the struggle of achieving the most relevant information can be overwhelming.
Data visualization is proved to be an efficient way to arrange this intricate phenomena and shape the timeline of the Coronavirus pandemic that has caused such chaotic situations in our daily lives. Data visualization is the process of graphical representation of unstructured or structured data to display information hidden in the data directly to the public. The approach not merely used the visualization tools to present data in the form of graphs but also looking at the world from a data point of view. Visualization has helped to communicate data to the general population and also having quite a real-world influence in the face of this crisis. This approach has led the public to access miscellaneous sets of visualizations to acquire information relevant to their circumstances.
The critical component of communicating research is to compress a massive amount of complicated data into an explicit visualization. The data visualization technique has never been more overriding and challenging than it is in the ongoing situation of Covid-19 across the world.
Covid-19 has been a case study in the effective data visualization from the representative ‘Flatten the curve’ image to the real-time tracking of the virus spread and many other highly innovative ways of presenting the data.
Flatten the Curve
In recent weeks, no graphics has become more popular and recognizable than the ‘flatten the curve’ twin peaked model, prognosticating the number of Covid-19 cases over time. Flatten the curve image has an absorbing history, with its development a typical instance of how data visualization is continuously tweaked and clarified to better illustrate the point. Particularly, the data visualization charts have been crucial in illustrating the conception of ‘flattening the curve’ so that the public can better appreciate the need to practice social distancing to prevent the spread of the Coronavirus.
Viewers can analyze the image highlighting two main characters and see the graph transform to reflect the result:
A steep curve for the dismissive character.
A gentle curve for the cautious character.
Researchers refer to the ‘curve’ to the projected number of people who will contract Covid-19 over a specific period of time. To be clear, this visualization is about the theoretical number that’s used to model the virus spread and not the solid prediction of how many people will be infected.
Depending upon the infection rate of the virus, the curve takes on different shapes. The curve could be steep which represents that the virus spreads exponentially (case counts keep doubling at the constant rate), hence the total number of cases increase to its peak within a few weeks. The infection curves also have a steep fall with the steep rise; the case counts begin to decrease exponentially, once the virus infect everyone who could be infected.
The faster the infection curve rises, the quicker the load will increase on the local health care system far off its capacity to treat people. As we’re seeing worldwide, more and more new patients may be enforced to go without ICU beds, more hospitals may run out of the essential supplies required to respond to the outbreak. On the other hand, the flatter curve represents the same number of people who eventually get infected, but over a long period of time. The slower rate of infection indicates the less pressurized health care system, less hospital visits on any specific day and fewer infected people being ward off.
That is why this animated and illustrated version included the title ‘flatten the curve’. This approach proved to be efficient in the motto of worldwide efforts to combat Covid-19.
Tracking the Spread
Outside the ‘flatten the curve’, the next most noteworthy example of the data visualization for the Covid-19 pandemic has been the number of attempts to summarize the geographic spread of the virus in real time. With a very few notable stand-outs, many websites and countries have attempted to track the virus.
An interactive map was created by the data visualization team that provides various features like ‘zoom in’ to a specific country or even county to analyze the number of cases or deaths and its immense amount of visualization data is free to download. The free access to these data visualization sources made it possible for various organizations to visualize the same data in different innovative ways. For instance, a more complex Coronavirus tracker geared explicitly towards epidemiologists and many other disease researchers.
Maps have also been utilized as a visual representation of data, as people like to see where and how things are occurring. These types of visualizations are utilized to analyze information such as virus’ case counts, infection and fatality rates, seriousness of symptoms and percentage of deceased.
The site “Information is beautiful” provides daily updates on current data related to COVID-19 in the form of a series of infographics, including current case numbers by country, who is most at risk, deaths per million people, etc.
Nevertheless, maps are not really useful to convey the entire story. As it is not possible for a map to convey both the time and magnitude and both of these factors are crucial for understanding a pandemic.
Another widely shared data visualization tool are trajectory charts. Trajectories charts are useful to convey information like each country’s daily death count aligned to their first cases of Covid-19 to compare the fact that how each country is doing at ‘flattening the curve’ and how many countries are having relatively more success at administrating the virus.
Many data visualization organizations are providing such charts featuring an extensive variety of tables and charts that provide various aspects of the pandemic - from the total number of deaths, to the virus’ current mortality rate, to the demographics of those who get ill and more.
Other creative ways of data visualization
There are more interesting ways of visualizing data than the trajectory charts or maps. Data visualization has also been utilized to inventively illustrate many other components of this pandemic.
In early attempts to reach the members of the public who may still be doubtful about the need to practice social distancing, a modern way was created to run simulations of the disease in front of the viewer’s eyes. The animated model unfolds live with colored dots in motion representing the people catching and spreading the virus. The most dots rejecting to move in an identical simulation, representing the people practicing social distancing and resulting in much slower spread of the virus. The arbitrary features of the model represent that it calculates a moderately distinct example each time, however, it always authenticates that social distancing flattens the curve.
Animation by Katapult Magazin https://en.wikipedia.org/wiki/File:Katapult_importance_social_distancing.gif
Both animations show 200 people. Many more people die in the left simulation (without social distancing) than the right (with social distancing). Both animations simulate how contact frequency affects the spread of the virus and are not based on real-world data.
Later on, the same task was tackled mathematically illustrating the significance of social distancing and how it substantially lessens the virus’ harms. In general public, both of the visualizations are feasibly more applicable than the daily death toll trackers, as these visualizations distinctly illustrate that the practice of social distancing does truly work and therefore can more directly influence behaviour.
Other innovative ways of data visualization include IHME’s (Institute for Health Metrics and Evolution) closer attention at healthcare capacity, comparing the growth rate of virus to the number hospital beds and ventilators each state has and is projected to need. Data visualizations have also appeared on a moderately more practical and diverting note like ‘TPfinder.co’, an interactive map. Thus data visualization has been utilized both for the extensive worldwide snapshots and for very confined and hyper-focused concerns.
In the time of Covid-19, data visualization has proven to be an indispensable tool to help study the disease as well as to actively encourage changes in behaviour. Different forms of data visualization such as animated and interactive forms are becoming progressively more frequent and probably represent the future of data visualization. Static graphs for data visualization can be indelible and effective as well as shown by the pervasiveness of ‘flatten the curve’.
Visualizing the data from a worldwide, rapidly changing crisis that interrelate with various other factors is harder than visualizing the data resulting from a single study so that the most crucial information can be conveyed. Yet even so, many people and different data visualization organizations have done astonishing work in order to visualize different facets of this crisis and data visualization techniques and efficacy will likely continue to apprise research communication for many years to come.