How to prevent the next pandemic: monitoring people with disease spillovers

As more and more people around the world are vaccinated, people can almost hear a collective sigh of relief. But the threat of the next pandemic may now have spread among the population.

My research as an infectious disease epidemiologist found that there is a simple strategy to alleviate emerging epidemics: active, real-time monitoring in an environment where animal-to-human disease is most likely to occur.

In other words, don’t wait for the patient to show up in the hospital. Instead, monitor the population where the disease spillover actually occurs.

Current pandemic prevention strategies

Global health professionals have long known that epidemics are caused by Zoonotic spillover, Or the spread of animal-to-human diseases is a problem. In 1947, the World Health Organization established a global network of hospitals, Detect pandemic threats Through a called Symptom monitoringThe process relies on a standardized list of symptoms to find signs of new or reappearing diseases with pandemic potential in patient populations whose symptoms are not easily diagnosed.

This clinical strategy depends on the infected individual Sentinel hospital And medical institutions Influential and persistent Enough to cause an alarm.

There is only one problem: when someone is sick in the hospital, the epidemic has already the case of SARS-CoV-2, the virus that causes COVID-19, It is likely to have been widely spread long before it was discovered. This time, the clinical strategy alone disappointed us.

Zoonotic disease spillover does not happen overnight

A more proactive approach is now becoming increasingly prominent in the field of pandemic prevention: the theory of virus evolution.This theory shows Animal viruses become dangerous human viruses Through frequent zoonotic spillovers, it gradually increases over time.

This is not a one-time transaction: an “intermediate” animal, such as a civet cat, pangolin, or pig, may be needed to mutate the virus so that it can make an initial leap against humans. But the ultimate host for the mutation to fully adapt to humans may be humans themselves.

With the rapid development of viruses, the theory of virus evolution is being staged in real time COVID-19 variantsIn fact, an international team of scientists proposed that the undetected human-to-human transmission after the animal-to-human jump is likely to be The origin of SARS-CoV-2.

In the 1970s, when outbreaks of new zoonotic diseases such as Ebola first caught the world’s attention, research on the spread of the disease relied on Antibody detection, Blood tests to identify people who have been infected.Antibody monitoring, also known as Serological investigation, Testing blood samples from the target population to determine how many people are infected. Serological investigations help determine whether diseases such as Ebola are spreading undetected.

It turned out they were: Ebola antibodies were found in more than 5% of people were tested in Liberia in 1982, Decades before the West African epidemic in 2014. These results support the theory of virus evolution: it takes time to make animal viruses dangerous and spread from person to person—sometimes a lot of time.

This also means that scientists have the opportunity to intervene.

Measuring the spillover of zoonotic diseases

One way to use animal viruses to fully adapt to the preparation time of humans is long-term, repeated monitoring.Set one Pandemic Threat Early Warning System Considering that this strategy might help Pre-pandemic virus detection Before they were harmful to humans. The best starting point is to start directly from the source.

My team and virologist Shi Zhengli Researchers at the Wuhan Institute of Virology have developed a human antibody assay to test distant relatives of SARS-CoV-2 found in bats. We established evidence of zoonotic spillovers in a small serum survey conducted in Yunnan, China in 2015: 3% of study participants live near bats The SARS-like coronavirus tested positive for antibodies. But there was an unexpected result: none of the previously infected study participants reported any harmful health effects. The early spillover of the SARS coronavirus—such as the first SARS epidemic in 2003 and the Middle East Respiratory Syndrome (MERS) in 2012—has led to high levels of illness and death. This person did not do such a thing.

Researchers conducted a larger study in southern China from 2015 to 2017.This area is home to bats known to carry SARS-like coronaviruses, including those that cause The original 2003 SARS pandemic And that Most closely related to SARS-CoV-2.

In this study, less than 1% of the participants tested positive for antibodies, which means they had previously been infected with a SARS-like coronavirus. Likewise, none of them reported negative health effects.But symptom monitoring-the same strategy used in sentinel hospitals-revealed something more unexpected: an additional 5% of community participants The reported symptoms are consistent with SARS in the past year.

This research provides more than just the biological evidence needed to establish a proof of concept to measure zoonotic spillover. The pandemic threat early warning system also found signs of infection similar to SARS, but it could not be detected by blood tests. It may even detect an early variant of SARS-CoV-2.

If monitoring protocols are in place, these results will trigger searches for community members who may be involved in undetected outbreaks. But without a set plan, the signal was missed.

From prediction to monitoring to gene sequencing

In the past 20 years, most of the pandemic prevention funding and efforts have been focused on discovering wild animal pathogens and predicting pandemics before animal viruses infect humans. But this method does not predict any major zoonotic outbreaks—including H1N1 flu in 2009, MERS in 2012, Ebola in West Africa in 2014, or the current COVID-19 pandemic.

However, predictive modeling provides a powerful heat map Global “hot spots” Where zoonotic spillovers are most likely to occur.

Regular long-term monitoring of these “hot spots” can detect overflow signals and any changes that occur over time. These may include an increase in antibody-positive individuals, disease levels and demographic changes in infected people. As with any active disease surveillance, if a signal is detected, an outbreak investigation will be conducted.People who identify with Symptoms that are not easy to diagnose Gene sequencing can then be used for screening to characterize and identify new viruses.

This is exactly what Greg Gray of Duke University and his team were looking for Undetected coronavirus In rural Sarawak, Malaysia, a well-known zoonotic spillover “hot spot”. Of the 301 samples collected from pneumonia patients hospitalized in 2017-2018, 8 were found to be infected with an unprecedented canine coronavirus. Sequencing of the complete viral genome not only showed that it had recently emerged from an animal host, but also carried the same mutations that made SARS and SARS-CoV-2 so deadly.

Let us not miss the next pandemic warning signal

The good news is that the surveillance infrastructure for global “hot spots” already exists.This Connect with regional disease surveillance organizations The plan connects six regional disease surveillance networks in 28 countries. They pioneered “participant monitoring”, working with high-risk communities with initial zoonotic spillovers and the most serious health outcomes to contribute to prevention efforts.

For example, Cambodia, which is at risk of a pandemic avian flu spillover, has set up a free national hotline for community members to directly report animal diseases to the Ministry of Health in real time. Field initiation methods like this are the key to a timely and coordinated public health response to stop the epidemic before it becomes a pandemic.

When global and local priorities are tentative, it is easy to miss warning signs. The same error does not have to happen again.

Maureen Miller, Adjunct Associate Professor of Epidemiology, Columbia University

This article is reproduced from conversation Under a Creative Commons Source article.

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