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AI machine learning program discovers how to prevent COVID-19 from ever returning

In an unprecedented machine learning study, the most effective drug combinations to prevent COVID-19 from returning, following initial infection, have been discovered. However, these combinations are not universally effective, but vary between patients.

The research was led by experts at the University of California, Riverside, utilizing real-world data from a Chinese hospital. 

Artificial Intelligence model examined multiple factors

The study revealed that individual factors, such as age, weight, and the presence of other diseases, heavily influence which drug combinations most effectively stifle the virus and prevent COVID-19 return. This cutting-edge research has been published in the esteemed journal, Frontiers in Artificial Intelligence.

China’s data holds a unique significance in this context. In the United States, treatment for COVID-19 typically consists of one or two drugs. In contrast, Chinese physicians were prescribing up to eight distinct drugs during the early phase of the pandemic. This approach facilitated a more comprehensive analysis of various drug combinations.

Additionally, post-hospital discharge, Chinese COVID-19 patients must quarantine in government-managed hotels. This system allowed researchers to garner insights about reinfection rates and how to prevent COVID-19 return in a more systematic fashion. 

“That makes this study unique and interesting. You can’t get this kind of data anywhere else in the world,” stated Xinping Cui, a statistics professor at UCR and one of the authors of the study.

Attempting to prevent COVID-19 using AI

The study kicked off in April 2020, a mere month into the pandemic. During this time, most research was honing in on mortality rates. However, doctors in Shenzhen, near Hong Kong, were more apprehensive about reinfection rates since fewer individuals there were succumbing to the disease. 

Jiayu Liao, an associate professor of bioengineering and co-author of the study, stated, “Surprisingly, nearly 30% of patients became positive again within 28 days of being released from the hospital.”

The analysis included data from over 400 COVID patients, with an average age of 45. The majority of these patients had moderate cases of the virus. The group had equal representation from both genders, and most patients were treated with diverse combinations of an antiviral, an anti-inflammatory, and an immune-modulating drug, such as interferon or hydroxychloroquine.

How the researchers learned to prevent COVID-19 return

The fact that different demographic groups responded better to different combinations can be attributed to how the virus functions. As Liao elucidated, “COVID-19 suppresses interferon, a protein cells make to inhibit invading viruses. With defenses lowered, COVID can replicate until the immune system explodes in the body, and destroys tissues.”

Patients with weaker immune systems before COVID infection needed an immune-boosting drug for effective defense against the virus. On the other hand, the immune systems of younger individuals tend to overreact upon infection, which could lead to severe tissue inflammation and even death. Therefore, an immune suppressant is required in their treatment regimen to help them prevent COVID-19 return.

“When we get treatment for diseases, many doctors tend to offer one solution for people 18 and up. We should now reconsider age differences, as well as other disease conditions, such as diabetes and obesity,” said Liao.

How the AI model discovered how to prevent COVID-19 return

When testing drug efficacy, scientists typically conduct a clinical trial where patients with the same disease and baseline characteristics are randomly assigned to treatment or control groups. This standard approach, however, doesn’t account for other medical conditions that could influence the drug’s performance for specific subgroups.

This study’s real-world data brought confounding factors into play, which the researchers had to account for. They innovatively tackled this issue by virtually matching COVID-19 patients with similar characteristics undergoing different treatment combinations. 

This approach allowed them to infer the effectiveness of various treatment combinations for different subgroups. Cui explained this novel technique, saying, “In this way, we could generalize the efficacy of treatment combinations in different subgroups.”

Although we have a better understanding of COVID-19 today, and vaccines have significantly lowered death rates, there’s still a great deal to uncover regarding treatments and the prevention of reinfections. Cui expressed hope that their findings could be a valuable resource in addressing these concerns.

One giant leap for AI machine learning

Machine learning’s applications in COVID-related research extend beyond this investigation into multi-drug combinations. Artifical intelligence has been instrumental in disease diagnosis, vaccine development, and drug design. Liao envisions an expanding role for this technology in the future.

“In medicine, machine learning and artificial intelligence have not yet had as much impact as I believe they will in the future,” said Liao. “This project is a great example of how we can move toward truly personalized medicine.”

In summary, the groundbreaking research sheds light on the promise of using machine learning to analyze real-world data and advance our understanding of effective treatments. Personalized drug combinations, tailored to individual characteristics, could be a significant step forward in managing and preventing COVID-19 reinfections. 

The innovative use of data in this study provides a clear illustration of the potential for machine learning in healthcare, signaling a future in which medical treatments are increasingly personalized and effective.

More about COVID-19

COVID-19, short for Coronavirus Disease 2019, is an illness caused by the SARS-CoV-2 virus, a novel coronavirus first identified in Wuhan, China, in late 2019. Coronaviruses are a large family of viruses that can cause illnesses in animals and humans. In humans, they can cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS).


Symptoms of COVID-19 can range from mild to severe and can appear 2-14 days after exposure to the virus. They include fever or chills, cough, shortness of breath or difficulty breathing, fatigue, body aches, loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, and diarrhea.


COVID-19 spreads primarily through close contact from person-to-person, generally through respiratory droplets from coughing, sneezing, or talking. It can also spread by touching a surface or object that has the virus on it and then touching your own mouth, nose, or eyes, but this is not the main way the virus spreads. Airborne transmission over longer distances is also possible, particularly in enclosed spaces with poor ventilation.


While some people remain asymptomatic or experience mild symptoms, others can become seriously ill and require hospitalization. Factors such as age and underlying health conditions (like heart or lung disease or diabetes) appear to increase the risk of severe COVID-19 illness.


Measures to prevent the spread of the virus include social distancing (keeping at least 6 feet away from others), wearing masks, frequent handwashing, avoiding close contact with sick individuals, and cleaning and disinfecting frequently touched objects and surfaces.


Several vaccines have been developed and approved for emergency use to prevent COVID-19, including those produced by Pfizer-BioNTech, Moderna, Johnson & Johnson, and AstraZeneca. Vaccination is a crucial tool in mitigating the impact of the pandemic.

COVID-19 treatment

Treatment strategies for COVID-19 have evolved as we’ve learned more about the virus. They range from supportive care for mild cases to antiviral drugs such as remdesivir, corticosteroids such as dexamethasone for severe cases, and monoclonal antibodies. In October 2020, the antiviral drug remdesivir became the first drug to be approved by the U.S. Food and Drug Administration for the treatment of COVID-19.


The COVID-19 pandemic has had a substantial impact on global health, economies, and daily life. It’s also led to widespread social and economic disruption, including the largest global recession since the Great Depression.


Several variants of the virus have been identified globally. These include the B.1.1.7 variant (Alpha, first identified in the UK), B.1.351 (Beta, first identified in South Africa), P.1 (Gamma, first identified in Brazil), and B.1.617.2 (Delta, first identified in India). Some of these variants appear to spread more easily, and there is ongoing research to determine how they may affect disease severity and vaccine effectiveness.

As scientific understanding of COVID-19 continues to evolve, ongoing research is vital in the areas of prevention, testing, treatments, and vaccines. To get the most up-to-date information, it’s important to rely on credible sources such as the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), and other trusted health departments.


An important aspect of the disease that has gained attention is “long COVID,” also known as Post-Acute Sequelae of SARS-CoV-2 infection (PASC). This refers to symptoms and health complications that continue for weeks or even months beyond the acute phase of the illness. These can include fatigue, difficulty thinking or “brain fog,” loss of taste or smell, difficulty breathing or shortness of breath, and organ damage, among others. Research is ongoing to understand why some people experience these longer-term effects and how to treat and manage them.


Various types of tests have been developed to detect current and past infections. Polymerase Chain Reaction (PCR) tests detect the virus’s genetic material and are generally used to diagnose active infections. Antigen tests detect specific proteins on the virus, while antibody tests check your blood by looking for antibodies, which may tell you if you had a past infection.

Public health measures 

To control the spread of the virus, countries around the world implemented a variety of public health measures. These include lockdowns, curfews, travel restrictions, quarantine and isolation procedures, mask mandates, and contact tracing efforts.

Mental health impact

The pandemic has also had a significant impact on mental health worldwide, with increased reports of stress, anxiety, depression, and other mental health disorders. This is due to a combination of factors, including the fear and uncertainty of the disease, the effects of physical distancing measures, and the economic impact of the pandemic.


COVID-19 has sparked an unprecedented global research effort, with scientists around the world studying the virus, how it spreads, and how to combat it. This has led to rapid advancements in vaccines and therapeutics and has revolutionized many aspects of how medical research is conducted, with more collaboration and data sharing than ever before.

Global Inequities

The pandemic has highlighted and exacerbated existing inequities both within and between countries. Access to testing, treatment, and vaccines has been uneven, with wealthier countries having greater access. The economic impact has also been disproportionate, with those in precarious employment or in lower-income countries often hit the hardest.

Image Credit: mikemacmarketing


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