Our Open Source Dashboard to Track Corona Virus
First things first.
This weekend, our team at Locale took up a small project to build a non-apocalyptic, friendlier, minimal and easy-to-use visualization Covid19 dashboard. This dashboard helps you track the outbreak of the coronavirus in real-time as the outbreak unfolds. The dashboard can be accessed here. The data source for this project can be found here and the tech stack we used for this project was Vue.js, MapboxGL, DeckGL, Node.js.
The best part is that we have made the code open-source (so feel free to contribute)
A Brief History of Corona
Coronavirus (CoV) are a large family of viruses that cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV). The WHO recently declared novel coronavirus as a pandemic already spreading to at least 114 countries and killing around 4,600 worldwide.
A brief timeline of the spread of the novel corona-virus is here below
China alerted WHO to several cases of unusual pneumonia in Wuhan, in the port city of 11 million people in the central Hubei province. The virus was unknown.
Positive cases of coronavirus started arising in China. The first death in China was of an old man who had come in contact with the seafood market in Wuhan. By the end of January, China reported 7711 positive cases and 170 death cases. The novel coronavirus also spread to Thailand, Japan, Russia, Spain, Sweden, and the United Kingdom.
The deadly corona-virus (COVID-19) spread to many more countries. The global death toll surpassed 4,600 with the number of positive cases exceeding 126,100 cases. WHO declared the novel coronavirus as a pandemic affecting all continents except the Antarctic. Health experts all around the world are working round the clock to find a cure for this unusual pneumonia-causing coronavirus.
How can we use geospatial analytics for disease tracking?
Maps lead to the birth of epidemiology. In 1854, when cholera broke out in London, everyone considered the cause of this to be particles in the air. Jon Snow, a physician at the time plotted all the cases of cholera on a map of London and found out that the cause wasn’t the foul air, but contaminated water from a street pump!
This first-ever location intelligence exercise conducted also gave the rise to the field of epidemiology- the study of incidence, distribution, and control of diseases!
Geo-referencing disease cases give a spatial dimension to health/environment linkages. This not only helps pinpoint issues but also describes the intensity or extent of the cause or effect. We can do that by identifying actionable and biologically meaningful data patterns, developing predictions about future risk and epidemic trajectories, and characterizing possible losses under a range of intervention scenarios.
Maps can help highlight localized issues (like exposure to diseases from the location of disposal site) as well as more diffuse issues (like exposure to radiation from reductions in atmospheric ozone). They can act as early warning tools.