Simple quantitative framework for understanding the causes of COVID-19 that is severe.

Los Alamos National Laboratory in Los Angeles, California developed an explanation for the reason why severe acute respiratory syndrome coronavirus 2 infections (SARS-CoV-2) can cause high levels of inflammation.

Their model concentrates on cells that trigger an inflammatory response, by creating a feedback loop between infected and damaged cells. The constant hyperinflammatory state makes it difficult to eliminate damaged cells. Based on their findings the antiviral treatment has the greatest impact if it is administered early enough to prevent inflammation.

A preprint version of the study is available on the medRxiv* server, while the article is subject to peer review.

Study: A simple model for COVID-19 that describes the severity of the disease as well as the effects of treatment. Image Credit: NIAID

Information about the proposed model

The researchers created an understanding of the nature of the severe coronavirus disease 2019 (COVID-19) disease and the mechanisms that cause symptoms like high inflammation. The model was based upon cells which trigger inflammation in different ways.

One group of cells may cause inflammation when they are infected with SARS-CoV-2. They also exhibit a pathogen-associated molecular pattern (PAMPs). This could indicate hypoxia, or a deficiency in nutrients for cells.

Another group of cells is damaged which exhibit damage-associated molecular pattern (DAMPs). This could indicate the presence or potential spread of pathogens.

They speculated that a feedback loop can cause severe infection. SARS-CoV-2 triggers high levels of inflammation in cells that are not infected that cause massive damage and signals DAMPs which trigger the inflammation response.


The model predicted that severe infections would result from the high levels of inflammation. However in a simulation of COVID-19 cases with hyper-inflammation, the peak of inflammation was not evident until later in the progression of the disease.

The results of the modeling are similar to autopsy results for people who died from COVID-19 with hyperinflammation and high inflammatory biomarkers levels. The simulation predicts that 13.5 percent of patients with COVID-19 will suffer from high inflammation, which is similar to the 14% seen in patients with severe COVID-19 disease.

Analysis revealed the root cause of hyperinflammation. The results reveal that hyper-inflammation is caused by SARS-CoV-2 that causes the immune system to be in a constant state if there is a high level of inflammation. This is the case even after the infection has passed.

Viral load and characteristics of inflammation by groups of inflammation. (A) Viral loads during the course of infection. The median is the median. The shaded area represents the 10th and the 90th percentiles of viral load. (B) The distribution of peak viral loads, (C) the VL decay from peak infection to 5 days following the peak infection, and (D) the time of occurrence of peak VL after infection. (E) The pathogenesis of inflammation based on the trajectory of inflammation groups. The shaded area represents the 10th and 90th percentiles, respectively, of D1, while curves represent the median. (F) the distribution of D1 at its peak, (G) the distribution of D1 at 60 days post-infection and (H) the date of onset of peak D1 after infection. Resolved inflammation is represented in orange by the symbol R. In pink, the symbol H is Hyperinflammation.

The severity of COVID-19-related disease was determined by the amount of virus in the body. Patients who shed the virus more quickly were more likely to have mild infections. Simulation results showed that peak viral load did not correlate with the severity of the disease.

The model clarifies that severe COVID-19 caused by viral load. This is because the body takes a long time to eliminate infected cells, which increases the possibility of hyperinflammation. The model also demonstrated that slow clearance increased PAMP and DAMP signaling leading to an ongoing inflammatory response. A rise in inflammation could result in new damaged cells, which could increase DAMP signaling.

A low viral clearance was a predictor of hyperinflammation.

The distribution of parameters of the model is determined by the groups of inflammation. Multivariate models might not allow overlap in distribution. Inflammation trajectory groups R: Resolved inflammation; H Hyperinflammation.

Research has demonstrated that severe COVID-19 could be caused by an overactive immune system, particularly the uncontrolled interferon response. Type-I IFN has one function that is to trigger an antiviral response on non-immune cells.

The model developed by the research team shows that hyper-inflammation is associated with a weak IFN response. When the type-I IFN response was strong and strong, the model suggests there is a limit on cells with PAMPs and, consequently an inflammatory state that is less severe.

A weak interferon response could hinder the activation of natural killer cells (NK cells). A delayed NK response can make it difficult for cells to be cleared of cells that have taken longer to release DAMP and PAMP signals. As a result, the body is still experiencing an increase in inflammation and severity of disease.

The model also showed that a large number of immune cells in the innate system were associated with severe illness and hyper-inflammation.

Simulations show that corticosteroids may aid in reducing the damage caused by bystander cells in patients suffering from mild infections. Corticosteroids can make it harder for the body to eliminate PAMP-carrying cells which can increase disease severity.

Researchers suggest that doctors prescribe antivirals to patients as quickly as possible to prevent further inflammation and to prevent the progression of the disease.

*Important notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:

Content Source:

Gemma Wilson

Gemma is a journalism graduate with keen interest in covering business news – specifically startups. She has as a keen eye for technologies and has predicted quite a few successful startups over the last couple of years.

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