Regardless of the coronavirus disease (COVID-19) consequences in society and our workplaces, we are all working in extraordinary times. The sheer fluidity of transition has forced us to deal with this alone in March seems unreal. It is bewildering to think that a relatively isolated number of cases announced to the WHO on 31 December in Wuhan, China, meteorically increased to nearly 330k confirmed cases and 14.4k deaths in over 180 countries as of 22 March 2020.
While society struggled with the public health and economic problems manifesting in the aftermath of COVID-19, corporations scrambling to realign themselves to this new paradigm are finding technologies to help. In particular, data analytics proves to be an ally for epidemiologists as they join forces with data scientists to address the severity of the crisis.
The spread of COVID-19 and the public’s desire for information has sparked the creation of open-source data sets and visualizations, paving the way for a pandemic analytics discipline introduced. Analytics is aggregating and analyzing data from multiple sources to gain information. When used to research and global counter diseases, pandemic analytics is a new way of combating an issue as old as civilization itself: disease proliferation.
In the early 1850s, London fought a widespread rise in the number of cholera cases, John Snow – the father of modern epidemiology – discovered cluster clusters of cholera cases around water pumping. For the first time, the discovery allowed scientists to exploit data to counter pandemics, drive their efforts to measure the danger, identify the enemy, and formulate a suitable response strategy.
That first flash of genius has since advanced, and 170 years of cumulative intelligence have demonstrated that early interventions are disrupting disease spread. However, analysis, decision-making, and subsequent intervention can only be useful if it takes all the information into account first.
Healthcare managers at Sheba Medical Center in Israel use data-driven forecasting to improve staff and resources distribution in anticipation of possible local outbreaks. These solutions are powered by machine learning algorithms that provide predictive insights based on all available disease spread data, such as reported cases, deaths, test results, contact tracing, population density, demographics, migration movement, medical resource availability, and pharmaceutical stockpiles.
Viral propagation has a small silver lining: the exponential development of new data from which we can learn and act. With the right analytics tools, healthcare professionals can address questions such as when the next cluster will most likely appear, which population is most susceptible, and how the virus mutates over time.
Ohn Snow, the founder of modern epidemiology, noticed cluster patterns of cholera cases around water pumps in the early 1850s, as London battled a rampant rise in the number of cholera cases. For the first time, this discovery enabled scientists to leverage data to combat pandemics, drive their efforts to quantify the risk, identify the enemy, and devise a suitable response strategy.
That first flash of genius has since advanced, and 170 years of cumulative intelligence have demonstrated that early interventions are disrupting disease spread. However, analysis, decision-making, and subsequent intervention can only be useful if it is taken into account first.
Accessibility of trusted sources of data has resulted in an unprecedented sharing of visualizations and messages to educate the general public. Take, for example, the dynamic world map created by the Center for Systems Science and Engineering at Johns Hopkins, and these brilliantly simple yet enlightening Washington Post animations. These visualizations quickly inform the public how viruses spread, and which human behavior can support or hinder the spread of viruses.
The democratization of data and analytics software, combined with the vast capacity to exchange information over the internet, has allowed us to see the incredible power of data being used for good.
Accessibility of reliable sources of data has resulted in an unparalleled exchange of visualizations and messages to inform the general public. For example, take the interactive world map created by the Center for Systems Science and Engineering at Johns Hopkins, and these beautifully simple but enlightening Washington Post animations.
These visualizations quickly show the public how viruses spread, and which human behavior can support or hinder the spread of viruses. The democratization of data and analytics software, combined with the vast capacity to exchange information over the internet, has allowed us to see the incredible power of data being used for good.
In recent months, companies have taken an in-house collection of pandemic data to develop their proprietary intelligence. Some of the more enterprising enterprises have even set up internal Track & Respond Command Centers to guide their employees, customers, and broader partner ecosystems through the current crisis.
Early on in the outbreak, HCL realized that it would need its COVID-19 response control center. Coordinated by senior management, it gives HCL data scientists autonomy to develop innovative and strategic perspectives for more informed decision-making. For example, the creation of predictive analytics on potential impacts for HCL customers and the markets where HCL services are provided.
We employed techniques such as statistics, control theory, simulation modeling, and Natural Language Processing ( NLP) to allow leadership to respond quickly during the development of the COVID-19 situation. For simplicity, we are going to categorize our approach under the umbrella of Track & Respond:
TRACK the condition to grasp its significance, both quantitatively and qualitatively.
Perform real-time topic modeling across thousands of international health agency publications and credible news outlets; automate the extraction of quantifiable trends (alerts) as well as actionable information relevant to the role & responsibility.
Policymakers, public agencies, and other institutions worldwide have used AI systems, Big Data analytics, and data analysis software. All of these are used to forecast where the virus may go next, monitor the virus spreading in real-time, recognize drugs that could be helpful against COVID-19, and more.
People who work at the sites of the disease outbreak gather critical COVID-19 data such as transmissible, risk factors, incubation time, and mortality rate. This data is used for visualization, mathematical model development, and neural network training.
While the COVID-19 pandemic persists, the sociological and economic implications are becoming evident, and so several countries, companies, and individuals are developing data-based approaches for the recovery of Coronavirus.
What needs to happen to get things back on track, and which efforts could pay more than others?
These days people frequently talk about data analytics and COVID-19 as a combined concept. A novel Coronavirus is confronting scientists and public health officials, and the search is on to learn as much about how it affects people as possible. Although these efforts are real, there is also a move towards analyzing data to encourage progress during and after the Coronavirus outbreak.
Some speak about Coronavirus as an invisible enemy. We explain how it needs to act as if the people affected were fighting a war. Since the war, the world had bounced back before, but never struggled with this form of Coronavirus. So earlier efforts to help recovery won’t necessarily apply as people try to find the best ways forward.
Rolls-Royce had this in mind when asking data experts to work together on a project by analyzing knowledge to promote economic recovery. The aim is to identify the top economic recovery cycle measures and identify what sectors, individual firms, and governments can do to reduce economic recessions.
The parties participating in the partnership will also try to define how COVID-19 has changed people’s behaviors and what those changes mean for potential strategies for the recovery of Coronavirus. The group will openly include its conclusions, allowing everyone interested to benefit from them.
Some data experts also realize how the key to active economic recovery is collaboration. They share data with relevant parties and then allow these entities to provide insights to help all involved.
In Australia, government ministers recently met via teleconference to discuss new ways of using data. He acknowledged that as the COVID-19 pandemic persists, conventional ways of providing resources to the individuals or companies who need them are no longer feasible. A question raised during the meeting concerned how parties could engage more frequently in data sharing while providing the necessary help to people in a secure manner.
Instead of looking at content from a single source, collecting information from many jurisdictions around Australia could help officials better assess which places are suffering the most. The data could also illuminate what kind of help officials in particular locations could be offering people.
A more common example of working with data to facilitate coronavirus recovery approaches is in the aviation field. One collaboration between the Civil Air Navigation Services Organization (CANSO) and a firm named Aireon analyses 66 satellite data from Automatic Dependent Surveillance-Broadcast (ADS-B) to assess the pre and post-COVID-19 impacts.
Health officials often rely on geospatial data to learn how some regions and communities are affected by COVID-19. Some public-facing resources, for example, encourage people to enter their symptoms or record their well-being so that medical professionals can know more about the communities or cities at risk. Also, sectors such as telecommunications, retail, and insurance used geospatial data to perform more precise pre-coronavirus assessments. These tests may also accelerate recoveries.
For example, telecommunications companies could determine which areas would benefit most from an infrastructure upgrade that allows more companies to use high-tech options, such as connected equipment and apps, to get their businesses back on track. By knowing the links between data analytics and COVID-19, people in power positions could make more informed choices about stimulating measurable, beneficial recoveries.
QuadMed, a company specializing in employer-sponsored wellness solutions, recently announced a collaboration to learn more about COVID-19 risk factors with a data analytics provider. Examining how the age or health conditions of a patient may make COVID-19 more or less likely to affect them, for example, helps scientists to see which groups of individuals might be at an elevated risk of contracting the illness.
The Coronavirus will endanger society for a while, medical experts say. Curbing the spread of the virus will relieve the pressure on health care but will not cause COVID-19 to go away.
Data analytics was used in different ways by the Italian health system, such as determining conditions, allocating resources, and finding hints in medical records. The idea is that analyzing the data would personalize the efforts made to help each patient heal and predict the duration of their stays in the hospital. Artificial intelligence algorithms could also become more intelligent in making more effective decisions for sick patients with the use and helping health care professionals.
Scientists also look at data mining and COVID-19 when keeping track of what happens with ongoing clinical trials. For example, one project lists more than 600 studies and allows people to filter out the results through administered treatment, the severity of viruses, and more. Such databases provide crucial information to aid decision-makers in learning about the effectiveness of specific interventions versus others.
As society struggles with the public health and economic challenges that are manifested in the wake of COVID-19, companies rushing to realign themselves to this new reality are looking for technology to help. In particular, data analytics proves to be an ally for epidemiologists as they join forces with data scientists to address the severity of the crisis.
The dissemination of COVID-19 and the demand for knowledge among the public has sparked the development of open-source data sets and visualizations, paving the way for a discipline that we will introduce as pandemic analytics. Analytics is aggregating and analyzing data from various sources to provide information, mainly when used to analyze especially counter global outbreaks.
An AI program run by a Toronto-based company named BlueDot detected the first anomalies linked to what was then known to be a mystery pneumonia strain in Wuhan on 21 December 2019. To find a connection with the 2003 SARS epidemic, the AI program read over one million documents in 65 languages. Just nine days later, the WHO alerted the general public to the existence of this new threat.
Developing healthcare technologies is a problem for data to be solved on a scale, and this is where AI will play a crucial role. AI technology has already been deployed to help diagnose Coronavirus by analyzing the image, reducing the diagnostic time from CT scan results.
AI and ML can also be used to expedite the process of pharmaceutical development. Until now, only one drug developed with AI has reached human clinical trials. Yet even this lonely achievement is highly remarkable as the technology has been able to accelerate a process usually taking years.
AI and medical researchers will likely help reduce the timelines for drug production to months or weeks only. With the world still in desperate need of a COVID-19 vaccine months after the first death recorded, this synergy between humans and machines in the pharmaceutical space is needed.
As the world prepares itself for the effects of the COVID-19 epidemic, it’s important to note that technology is nothing but humanity’s collective progress over time. We have the resources in technology to help us survive and defend ourselves.
We don’t know what’s in store for us in the weeks and months to come, but we’re going to face it together, and our greatest strength is how we communicate, evaluate, and draw wisdom from everyday experience. With the right technologies being implemented in the right way, today and in the future, we can control and mitigate the effects of illness.
Right now, we’re only starting to ramp up our testing in the US and roll out antibody testing. We need to get to the point that we are testing people and checking people for their antibodies to get an idea of who was exposed and who may have a sort of immunity.
It may be easier said than done to reach that point. Although some coronavirus antibody tests are currently available in the US, they may lack acceptable standards or guidelines. A recent staff memo from the Subcommittee on Economic and Consumer Policy found that federal agencies have neglected to evaluate the reliability of antibody testing, allowing for unchecked dissemination of incorrect and potentially fraudulent studies.
Organizations would need to utilize advanced Big Data Analytics tools to develop robust, useful antibody testing, and speed the production of vaccines, Swann said.
Several organizations have begun to use these technologies to speed up drug discovery at COVID-19 and better understand how the immune system fights the virus. In early April, pharmaceutical firms GlaxoSmithKline (GSK) and Vir Biotechnology collaborated to use artificial intelligence and CRISPR to accelerate coronavirus care development.
The Harvard T. Chan School of Public Health has recently joined forces in the public sector with the Human Vaccinations Project to launch the Human Immunomics Initiative, which uses artificial intelligence models to speed up vaccinations for a variety of diseases, including COVID-19.
In addition to facilitating the production of medicines, data analytics tools can play a significant role in preventing the potential spread of the virus. These tools will be essential to prevent a second wave of infections that could overwhelm hospitals and health systems as the US begins to consider reopening the economy.
We will need to deploy data analytics tools in schools, hospitals, and workplaces to ensure that we properly screen people as they come in those settings’ doors, said Swann.
Examples of such applications have arisen across the country from research entities and tech firms. A Southern Illinois University (SIU) team recently created a data visualization tool that leverages GPS information to show users where the identified COVID-19 cases are located. Also, tech giants Google and Apple have collaborated to create a Bluetooth-powered touch tracing device.
However, as effective as these tools can be, they inevitably come with potential risks to privacy. Google and Apple had to revise their original proposal for the contact tracing app two weeks after revealing their collaboration to resolve industry stakeholders’ input and privacy issues.
Australia and the UK had faced similar criticism when they released their apps for coronavirus contact tracing.
Developers and policymakers will have to make data privacy a top priority for these technologies to play any role in the next phase of the pandemic.
With these changes in data and technology, we’ll have to address potential concerns about privacy – whether those concerns are specific to healthcare information or more general apprehensions.
With the nation’s focus starting to turn towards relaxed social distancing, the reopening of non-essential companies, and the tiny light at the end of the tunnel, it is essential to examine what tactics and resources the industry require to keep ahead of future health emergencies.
As Swann pointed out, these approaches would have to rely primarily on effective, granular-level data monitoring.
Many states have seen the importance of models that will allow users to explore various social distancing scenarios and interventions. A lot of those got ramped up in a short amount of time, and that’s something that will continue.
Most of the distancing strategies at the state level, or even a big city or district, is right now. But if we can get to a place where we are more efficiently using data analytics, we may be able to target our policies and interventions based on current conditions.
Ultimately, moving into and beyond the next phase of the COVID-19 pandemic will require partnerships, data exchange, and advanced analytical techniques to benefit the ongoing situation.
With the Coronavirus outbreak or Covid-19, digital teams have been forced to re-adjust their focus and look through this time of crisis to improve their service delivery as a backbone for the company.
Given the confusion as to when the pandemic will subside and how significantly it will continue to affect companies, there is an assumption that brands will continue to come through for their consumers and businesses will continue to be able to support their employees. During this time, data analytics and science leaders change their emphasis to serve the consumer and industry better.
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