By TONY ESTRELLA
And how South Korea and Taiwan’s approach to diagnosis and tracking is leading to positive results
By now, the sight of people wearing surgical masks, flinching at the sights and sounds of someone coughing or sneezing, governments restricting large gatherings, and sports leagues suspending or cancelling matches is familiar across the world.
Even though this newest coronavirus we now call COVID-19 is not the deadliest disease as measured by daily deaths, the concern over the outbreak is forcing urgent actions.
One of the core concerns is limiting how quickly the virus can spread. Having too many people require urgent care can overwhelm healthcare systems. This anxiety has led to a call to ‘flatten the curve’ to avoid this challenge.
The approach to implement protective measures includes a mix of policy decisions, use of technology, proven clinical tools, and rapid responses to conditions that are rapidly changing. If you ask any entrepreneur or innovator, the key to achieving successful solutions is to understand ‘what problem are you trying to solve?’. In this COVID-19 pandemic, three pain points stand out requiring wide-scale attention:
1. How to identify COVID-19 positive carriers within a large group of people?
2. How to manage the health of people either in a home quarantine or in a hospital setting?
3. How can new data quickly be aggregated and shared to develop new insights for prevention, care, and vaccine development?
In the first of three articles, we’ll explore the challenge currently dominating news headlines globally: how to identify positive carriers of the virus. Let’s start by reviewing how COVID-19 actively spreads.
The link between “Peak Viral Load” and transmitting the virus
In 2003, SARS spread across Asia and left an indelible memory on people working in clinical settings, policymakers, and families. SARS also created a baseline virus profile which can compared with COVID-19. Researchers from Johns Hopkins recently published an article comparing the two viruses. The results from both this study and information shared by a panel of experts from a recent webinar by WuXi NextCODE uncovered one crucial difference: the timing of the Peak Viral Load — the maximum concentration of the virus, versus Peak Symptom Strength — theperiod where a person experiences the most substantial effects of the virus.
In SARS, the peak Viral Load occurred at about the same time as the peak period of symptom strength.
In COVID-19, the peak Viral Load occurs days before any symptoms may show up.
Current data is showing that individuals can have COVID-19 without showing symptoms during the first 5 days, AND this may also be when individuals are most infectious.
How to identify COVID-19 positive carriers within a large group of people?
Thousands of people trapped on cruise ships. People stepping off a flight. Workers seeking to enter their office building. Hospital staff caring for COVID-19 patients.
These are only a few examples where there is a burning need for tools to identify carriers of coronavirus. To better understand this group of pain points, let’s differentiate between the two cases:
1) Case 1: Screenings are tests given to individuals to search for visible symptoms
2) Case 2: Diagnostics are tests given to individuals to identify the presence or absence of a disease definitively
Temperature checks are the current global standard for screenings. Whether these are by handheld thermometers administered person-by-person, or by industrial-grade thermal cameras scanning larger groups at one time such as at offices entrances and airports, the objective of this approach is to find people who have a fever. Unfortunately, a high temperature is not an exclusive symptom for COVID-19. Other types of coronaviruses, such as the common cold or the seasonal flu, can also cause a fever.
Therefore, the more exact pain point for Screening is how to better identify people who could spread COVID-19. Given the information from the Johns Hopkins study, this is currently an overwhelming challenge since people can be carriers of COVID-19 without showing symptoms.
The need for Diagnostics is how to deploy tests at large scale to assess whether large groups of people currently have COVID-19. This pain point is operationally complex because it requires three dependencies. First, a country must have an approved and standardised test which is easily accessible and returns results quickly. Second, each country’s regulatory bodies for healthcare must support the approach. And finally, there must be sufficient infrastructure to process the samples and return the results in a time-efficient manner.
The following questions can help shape the solution design for both screenings and diagnostics to fix this critical pain point:
1) Can an assessment be deployed to a large number of people in a cost-efficient way?
2) Can people be tested both once and if necessary, repeatedly?
3) How can data be used to identify clusters quickly?
4) How are regulators within each country fast-tracking approvals to tap into new solutions?
5) Can the waiting time be reduced from testing to receive an accurate result?
6) How can data privacy be protected?
7) How can results be analysed at scale with a rapid turn-around?
Let’s look at some real-life examples, starting with expanding the tools for diagnosing COVID-19.
In Singapore, the Home Team Science and Technology Agency (HTX) and Veredus Laboratories jointly developed and received regulatory approval for a new diagnostic kit in less than two months. The tests, which examine two swabs taken from a person’s nose, return a result with 99% accuracy in three hours. These kits can be deployed in various settings, including passengers getting off cruise ships. The speed for creating this solution is possible because it only took 1 month to identify the genomic profile for COVID-19 versus 1 year to accomplish the same for SARS. Currently, the volume of these tests is relatively low and is only available in Singapore. Regardless, the product is already making a significant impact on the island-state.
In Germany, Qiagen is a biotech company who already sells a test that analyses nose and throat swabs for numerous viruses in 30 countries around the world. Their analysis platform searches for 20 different virus variations concurrently in a cartridge-and-scan system that is already deployed widely, but only when their product has regulatory approval. After the outbreak of COVID-19, the company added a new test to identify this new coronavirus into its existing cartridge-based system. They then validated the accuracy of the platform to identify COVID-19 with Chinese patients known to have the virus. The company had success in detecting COVID-19 with results available in less than 1 hour. Qiagen has submitted for regulatory approval including with the FDA in the US. After approval, the hope is to distribute these diagnostic tests across the relevant portion of their 500,000 customers globally within the next month.
A discussion on technical innovation wouldn’t be complete without a mention of how Artificial Intelligence is being used to aid in the fight for COVID-19 detection. Inside the hospitals, China’s inferVISION used 2,000 CT images to train its AI algorithm to identify visual signs of COVID-19. It is helping alleviate tired healthcare workers by providing a tool which can objectively and quickly identify patients from radiological scans of people’s lungs from admitted patients. The system had been deployed in at least 34 hospitals and was used to review 32,000 cases of COVID-19. While this is an interesting approach, it may require more exploration to determine whether additional inputs are needed for it to be an independent and accurate diagnostic.
Case study: How South Korea rapidly ramped up diagnostic testing
As of 13 March, every country is battling to test its population to identify carriers of COVID-19 using some form of diagnostic technology. South Korea has been the most effective at testing large numbers of their society — testing over 20,000 people per day. The BBC covers South Korea’s approach well in this article.
This data becomes more meaningful when accounting for the population size of the country — tests per million people:
The results from these efforts is drastically affecting the number of new daily cases, with more details covered here in this article.
South Korea learned from their experiences with MERS in 2015 to approach diagnosing people differently in future outbreaks. Other countries can potentially adopt two key lessons from their revised approach:
1) Make diagnostic tests widely available quickly: From the BBC article, they state that “there is no shortage of testing kits in South Korea. Four companies have been given approval to make them. It means the country has the capacity to test 140,000 samples a week.” Compare those number of tests per week with those of other countries, and it’s easy to see how the policy and procedures of decision-makers helped meet this urgent demand.
2) Make it easy for people to get tested: It may not always be sensible for people to go to a doctor or a hospital to get tested. South Korean officials borrowed an approach commonly used for other purposes: the drive-thru. Other countries should consider the natural movements of people while practising ‘social distancing’ to make testing both available and deliver on quick turn-around times to limit the potential spread of the virus.
Case study: How Taiwan learned from SARS to limit the fast-spreading of a new outbreak
Taiwan shares many commonalities with Mainland China. A large number of people fly between Taipei and numerous Mainland cities. During SARS, Taiwan learned first-hand that a viral exposure could spread with equal speed because of these travel patterns. They suffered the most number of deaths outside of the mainland and Hong Kong.
After SARS, Taiwan revamped many policies and infrastructure decisions. This Stanford article covers the details well.
As a result of their actions, the spread of COVID-19 has been largely contained, especially compared to the rapid increases seen across the mainland in January and February. As of 13 March, the number of confirmed cases remains below 100. Some lessons from the actions taken by Taiwan include:
1) Keep a strong central command structure. National Health Command Center (NHCC) plays a critical role in to evaluate data and guide centralised decisions. The centralised approach provides clarity for information sharing to all other agencies and parties assisting with limiting the effects from the outbreak, including private industry.
2) Keep enough stocks of crucial medical equipment. Whereas there have been global shortages of masks when COVID-19 reaches a new country, in Taiwan, they carefully managed inventory of protective equipment. For masks, they ensured that there were sufficient stocks available, and the country is capable of manufacturing 10 million new masks per day. And last, the use of a price cap made it possible to limit unnecessary gouging.
3) Data, data, data. From using mobile phone tracking data to track quarantined people to monitoring where each case took place, connecting data points plays a critical role in managing the population risk. The most visible example of data integration came from combining data sets for health from the National Health Insurance Administration and travel from the National Immigration Agency. This combination drove actionable decisions for where to take focus resources, including diagnostic testing, to prevent uncontrolled outbreaks.
A wish for everyone to stay safe and informed
According to the Johns Hopkins study mentioned earlier, there are two practical points we should all follow in lieu of having comprehensive technology tools for screening: 1) “Social distance is the most effective tool we have right now”, and 2) “data generally backs the 2-week quarantine”.
As more extensive and clinical studies are conducted, we’ll eventually have more refined guidance. But for now, we are living with a lot of uncertainty. With each new day, we can hope for positive information and ultimately significant changes — e.g., regulatory approvals for new tools and eventually, treatments.
I hope that the information in this article helps provide context for you. I welcome any comments and suggestions on updates for this article to make it more relevant.
Lastly, a reminder that in two future posts, I’ll cover the other pain points I mentioned earlier. Please share this article with others if you found it enlightening.
Tony Estrella is a global digital health expert and author. This post originally appeared on Medium here.