Analysis: Monitoring disease ‘hot spots’ could prevent the next pandemic

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As more and more people around the world get vaccinated, you can almost hear the collective sigh of relief. But the next pandemic threat is probably already creeping its way through the population.

My research as an infectious disease epidemiologist has revealed that there is a simple strategy to mitigate emerging epidemics: proactive, real-time surveillance in settings where disease spread from animals to humans is greatest. likely to occur.

In other words, don’t wait for sick people to show up to the hospital. Instead, watch for populations where the disease is actually spreading.

The current pandemic prevention strategy

Global health professionals have long known that pandemics fueled by spread of zoonotic diseases, or the transmission of disease from animals to humans, was a problem. In 1947, the World Health Organization established a worldwide network of hospitals for detect pandemic threats by a process called syndromic surveillance. The process relies on standardized symptom checklists to look for signals of emerging or re-emerging diseases with pandemic potential among patient populations with symptoms that are difficult to diagnose.

This clinical strategy is based both on the arrival of infected individuals sentinel hospitals and medical authorities who influential and persistent enough to sound the alarm.

There is only one problem: by the time a sick person comes to the hospital, an epidemic has already occurred. In the case of SARS-CoV-2, the virus that causes COVID-19, it was probably widespread long before it was detected. This time, the clinical strategy alone failed us.

The overflow of zoonotic disease is not a fact

A more proactive approach is now gaining importance in the world of pandemic prevention: the theory of viral evolution. This theory suggests that animal viruses become dangerous human viruses gradually over time through frequent zoonotic fallout.

This is not a one-size-fits-all deal: an “intermediate” animal such as a civet cat, pangolin, or pig can be made to mutate the virus so that it can make initial jumps to humans. But the final host that allows a variant to fully adapt to humans may be humans themselves.

The theory of viral evolution is played out in real time with the rapid development of COVID-19 variants. In fact, an international team of scientists have proposed that undetected human-to-human transmission after a jump from animal to human is likely the cause. origin of SARS-CoV-2.

When new outbreaks of zoonotic viral diseases like Ebola first gained global attention in the 1970s, research into the extent of disease transmission drew on antibody assays, blood tests to identify people who have already been infected. Antibody monitoring, also called serological surveys, test blood samples from target populations to identify the number of people infected. Serological surveys help determine if diseases like Ebola are circulating undetected.

Turns out they were: Ebola antibodies have been found in more than 5% of people tested in Liberia in 1982, decades before the West African epidemic of 2014. These findings support the theory of viral evolution: It takes time – sometimes a long time – to make an animal virus dangerous and transmissible between humans.

It also means that scientists have a chance to step in.

Measuring the impact of zoonoses

One way to take advantage of the delay in adaptation of animal viruses to humans is repeated long-term monitoring. Establishment of a pandemic threat alert system with this strategy in mind could help detect pre-pandemic viruses before they become harmful to humans. And the best place to start is right at the source.

My team worked with virologist Shi Zhengli from the Wuhan Institute of Virology to develop a human antibody test to test a very distant cousin of SARS-CoV-2 found in bats. We established evidence of a zoonotic overflow in a 2015 small serological survey in Yunnan, China: 3% of study participants living near bats carrier of this SARS-type coronavirus tested positive for antibodies. But there was an unexpected result: None of the previously infected study participants reported harmful health effects. Earlier fallout from SARS coronaviruses – like the first SARS outbreak in 2003 and Middle East Respiratory Syndrome (MERS) in 2012 – had caused high levels of illness and death. This one did no such thing.

Researchers conducted a larger study in southern China between 2015 and 2017. It is an area home to bats known to carry SARS-like coronaviruses, including the one that caused the original SARS pandemic of 2003 and that most closely linked to SARS-CoV-2.

Less than 1% of participants in this study tested positive for antibodies, meaning they had previously been infected with the SARS-like coronavirus. Again, none of them reported negative health effects. But syndromic surveillance – the same strategy used by sentinel hospitals – revealed something even more unexpected: a 5% of community participants reported symptoms consistent with SARS in the past year.

This study did more than just provide the biological evidence needed to establish a proof of concept for measuring zoonotic fallout. The Pandemic Threat Alert System also detected a signal of a SARS-like infection that could not yet be detected by blood tests. He may even have detected early variants of SARS-CoV-2.

If surveillance protocols had been in place, these findings would have triggered a search for community members who may have been part of an undetected outbreak. But without a plan in place, the signal was missed.

From prediction to monitoring via genetic sequencing

The lion’s share of funding and pandemic prevention efforts over the past two decades has focused on finding pathogens in wildlife and predicting pandemics before animal viruses can infect humans. But this approach did not predict major zoonotic outbreaks – including H1N1 influenza in 2009, MERS in 2012, the Ebola outbreak in West Africa in 2014, or the current COVID-19 pandemic. .

Predictive modeling has, however, provided robust heat maps of the Global “hot spots” where zoonotic overflow is most likely to occur.

Regular long-term monitoring of these “hot spots” could detect overflow signals, as well as any changes that occur over time. These could include an increase in the number of antibody positive individuals, increased levels of disease, and demographic changes in those infected. As with any proactive disease surveillance, if a signal is detected, an outbreak investigation would follow. People identified with symptoms that cannot be easily diagnosed can then be screened by genetic sequencing to characterize and identify new viruses.

This is exactly what Greg Gray and his team at Duke University did in their search for undiscovered coronavirus in rural Sarawak, Malaysia, a known “hotspot” for zoonotic fallout. Eight of the 301 samples taken from pneumonia patients hospitalized in 2017-18 were found to have a canine coronavirus never before seen in humans. Complete sequencing of the viral genome not only suggested that it had recently jumped out of an animal host, but also harbored the same mutation that made SARS and SARS-CoV-2 so deadly.

Let’s not miss the next pandemic warning signal

The good news is that the surveillance infrastructure in global “hot spots” already exists. the Linking organizations for regional disease surveillance The program links six regional disease surveillance networks in 28 countries. They pioneered “participatory surveillance”, partnering with communities at high risk for both initial zoonotic fallout and most severe health outcomes to aid in prevention efforts.

For example, Cambodia, a country at risk for the spread of pandemic bird flu, has set up a free national hotline for community members to report animal diseases directly to the Ministry of Health in real time. On-the-ground approaches like these are essential to a timely and coordinated public health response to stop epidemics before they turn into pandemics.

Warning signs are easy to miss when global and local priorities are tentative. The same error must not happen again.

This article is republished from The conversation under a Creative Commons license. Read it original article.



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