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Long COVID does not behave like a typical illness. For many people, the infection ends but the disease does not. Weeks pass, then months, and symptoms still shape daily life.
Fatigue limits movement and breathlessness interrupts routine tasks. Memory slips without warning. Patients often move between clinics without clear answers, while doctors face a condition that refuses simple labels.
This gap between infection and recovery has pushed scientists to rethink how long COVID works. Instead of asking why symptoms last, researchers now ask what changes inside the body allow those symptoms to persist.
A major study from Australia brings new clarity to that question and shifts the focus toward genes, molecular signals, and large scale data.
COVID symptoms that do not end
Long COVID refers to symptoms that remain more than four weeks after infection. Many patients report exhaustion that rest does not fix. Others struggle with shortness of breath, chest discomfort, or cognitive slowing that affects focus and memory.
Some develop heart complications or neurological issues that appear months later. These effects often disrupt work, family life, and independence. Recovery rarely follows a straight line, and improvement may stall without warning.
Since 2020, about 400 million people worldwide have experienced long COVID. The condition now carries an estimated global cost of one trillion dollars each year, reflecting lost productivity, medical care, and long term support needs.
Genes shape long COVID
A research team led by scientists at the University of South Australia set out to identify the biological drivers behind this condition.
Instead of relying on small clinical studies, the team gathered genetic and molecular data from more than 100 international studies.
This integrated analysis revealed 32 genes that increase the likelihood of developing long COVID. Thirteen of these genes had no previous link to the disease.
Many of the identified genes influence immune activity, inflammation, and tissue repair, processes that shape how the body responds long after infection clears.
One key discovery involved a variant in the FOX P4 gene. This gene plays a role in immune regulation and lung function.
The variant appears to raise susceptibility to long COVID, offering a possible explanation for lingering respiratory symptoms in some patients.
Decoding body data
The researchers relied on large biological datasets known as omics data. These datasets capture activity across genes, proteins, metabolites, and gene expression.
On their own, such datasets overwhelm traditional analysis methods. Advanced bioinformatics and artificial intelligence made sense of this complexity.
Computational models allowed researchers to combine data layers and identify shared molecular patterns across populations and studies.
“These findings mark a major step towards a more precise way of diagnosing and treating the condition,” said Sindy Pinero from the University of South Australia, Adelaide.
“Long COVID is incredibly complex. It affects multiple organs, shows highly variable symptoms, and has no single final diagnostic marker.”
“However, by using computational models to integrate data from across the world, we can begin to uncover consistent molecular signatures of disease and identify biomarkers that point to new treatment targets.”
This approach shifts long COVID research away from isolated symptoms and toward underlying biological processes that connect them.
Long COVID causes lasting symptoms
The review identified dozens of biomarkers linked to immune dysfunction and persistent inflammation. It also highlighted mitochondrial and metabolic abnormalities that suggest ongoing cellular stress.
The researchers found 71 molecular switches that remain active one year after infection. These switches control whether certain genes turn on or off.
More than 1,500 altered gene expression profiles also appeared, many tied to immune balance and neurological function.
Such changes help explain why symptoms persist even after the virus disappears. The body continues to operate in an altered state, driven by signals that fail to reset after infection.
Spotting risk early
Machine learning models integrated these biological layers to predict long term risk. The models estimate which patients face lasting complications and how symptoms may evolve over time.
This predictive ability could change clinical care. Earlier identification of high risk patients may allow targeted monitoring and treatment before symptoms become entrenched.
“This computational framework not only improves our understanding of long COVID but could also accelerate the search for treatments for other post viral symptoms such as chronic fatigue and fibromyalgia,” noted Professor Thuc Duy Le.
The implications extend beyond a single disease and point toward broader applications in post viral research.
Implications beyond long COVID
Professor Le emphasized the limits of traditional biomedical research. Long COVID involves interacting systems that small studies struggle to capture.
“Traditional biomedical research can’t keep pace with the complexity of this condition,” said Professor Le.
“By applying artificial intelligence to global datasets, we can identify causal relationships that are invisible in small clinical trials – for example, how specific genes interact with immune pathways to drive persistent inflammation.”
Beyond long COVID, this work offers a model for responding to future pandemics and chronic diseases.
The research shows how global data sharing, computational science, and molecular biology can move faster than traditional approaches when complexity demands new tools.
The study is published in the journals PLOS Computational Biology and Critical Reviews in Clinical Laboratory Sciences.
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