We can prevent the next pandemic with data

Last week, President Biden set out his ambitious, “science-based” six-point action plan to combat Covid-19 in the months ahead.

While countries like the US are still fighting to contain the spread of the virus – through a mixture of vaccination programs and public health measures – scientists are already planning how to prevent the next pandemic.

No-one wants a repeat of the damage and disruption of the last 18 months.

So, the question we need to ask is: How can we prevent the next disease outbreak becoming the next global pandemic?

And the simple answer is: with real-time data.

Quick and early detection is key

To reduce the threat from emerging infectious diseases we need to spot them quickly and collect as much data as we can about them as fast as possible.

The system currently goes like this: A health practitioner identifies an instance of a disease on the WHO’s watchlist and reports it to local health authorities.

The information then gets passed upwards to regional, national, and ultimately international bodies.

If there are lots of similar reports, from individual practitioners, laboratories and public health bodies for example, the WHO’s Global Outbreak Alert and Response Network gathers the data and analyses it to identify if it is an infectious disease outbreak and whether it could become a pandemic.

If the disease spreads, governments have their own emergency systems in place to respond.

Of course, this sequence depends on several things, such as the availability of quality local healthcare provision, the expertise of health practitioners in being able to identify the disease, and the quality of information sharing along the chain.

If we want to prevent future pandemics, these systems need to be strengthened.

A recent piece for the British medical Journal (BMJ) calls for a new global virus surveillance network to prevent pandemics, with data at its heart.

The piece says the network would conduct surveillance to detect viruses moving from wildlife to livestock and humans well before they develop into localized outbreaks.

It calls for global databases to collect genetic data from new zoonotic viruses, which could then be analyzed to help researchers develop improved diagnostics.

Give people the power

Rather than having to rely on experts to identify virus outbreaks, what if we could harness people power instead?

That was the idea of Patipat Susumpow, a Thai national who developed a digital surveillance system to detect diseases in animals that could pass to humans.

Called Participatory One Health Disease Detection (PODD), it collects data from volunteer reporters, automatically identifies disease outbreaks and notifies authorities.

The system essentially turns people into ‘disease detectives’.

During a trial in Thailand, 296 volunteers monitored animal and human diseases, as well as environmental problems, in their communities, and reported them via the PODD mobile phone app.

After 16 months, 1029 abnormal events had been reported, including sick and dead animals and diseases.

For example, a woman riding on her motorbike through a rural part of Thailand saw a cow frothing at the mouth. She pulled over, took some photos, and reported it on the PODD app.

Local authorities identified that the cow had foot and mouth disease and immediately stepped in.

They managed to limit the disease to three cows, saving local farmers millions of dollars of potential losses.

A report into the trial said: “Many potentially devastating animal disease outbreaks were detected and successfully controlled, including 26 chicken high mortality outbreaks, 4 cattle disease outbreaks, 3 pig disease outbreaks, and 3 fish disease outbreaks.”

PODD recently won The Trinity Challenge, an initiative that aims to harness the power of data and analytics to help the world prepare for the next global health emergency.

We must be bold

It’s clear we need to employ innovative ideas and bold thinking to prevent the next pandemic.

We need to develop effective new solutions that include digital technology and machine learning models.

And we need to improve the completeness and availability of data and the speed at which it is captured and shared.

In the Covid pandemic, data collection has often been arbitrary and fragmented, with agencies using different systems and methodologies and failing to share data with each other.

Rapid lateral flow testing can help solve these problems by adopting real-time data capture and management abilities. Several diagnostic tests now come with digital platforms that allow users to record and report test results and associated data.

Test providers whose tests have the ability to easily report data to all stakeholders will have a huge role to play in preventing pandemics.

Public health agencies need the complete and full picture – and more importantly, they need it in real-time so they can make informed evidence-led decisions. These agencies will increasingly be turning to the industry, seeking for innovative and easy-to-use solutions.

It’s great to see this need is already being acknowledged by some forward thinking diagnostic companies, but we all need to do more, and quicker. Digital technology specialists are doing everything in their power to help the industry embrace data and use it to prevent adverse events.

To be effective at preventing the next pandemic, everyone will have to play their part, from global agencies and governments right down to individuals.

At all levels, digital and data must drive our planning and preparation.

Photo: ra2studio, Getty Images



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