Scientists develop a model to track viral load in COVID-19 patients

Scientists from the University of Alberta, Canada, have developed a model to determine patient-specific viral load curves for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The data obtained from this model can be used in other higher-level models to evaluate the physiological and pathological impacts of virus infection and determine patient risks. The study is currently available on the medRxiv* preprint server.

Study: Personalized Virus Load Curves of SARS-CoV-2 Infection. Image Credit: ktsdesign / Shutterstock
Study: Personalized Virus Load Curves of SARS-CoV-2 Infection. Image Credit: ktsdesign / Shutterstock

Background

The newly emerged coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has been found to cause a wide variety of symptoms, ranging from mild cough and fever to severe pulmonary and cardiovascular complications. Like other viral infections, the viral load or the amount of virus inside the body is a major determining factor for disease severity in COVID-19 patients. Thus, tracking the viral load in a patient’s body is an essential approach for treating SARS-CoV-2 infection.

Generally, the progression of a virus inside the body is categorized into three phases. In the initial phase just after the viral entry, a rapid and exponential increase in viral load is observed, which is followed by a second phase of slow, gradual decrease and a third phase of fast, exponential decrease in viral load, leading to viral clearance. The duration of each phase typically depends on the infectivity and pathogenicity of a virus and the robustness of the infected person's immune system.  

In the current study, the scientists have developed a simple model that determines patient-specific viral load curves.

Typical virus load curves. The virus load (“titer”) is usually reported as a dilution value, TCID50, that is needed to infect 50% of a given cell culture in (A) absolute scale and (B) logarithmic scale. Shadow areas indicate the three phases in which we divide the virus load progression. (C) Parameters of the standard virus load function (1) corresponding to A, B, D and E. Virus load curve reported in [12] are used in A and B. (D-E) Comparison of the viral load curve from the viral target model (2) with the viral load function (1). Viral target curve showing triphasic and biphasic response are shown in (D) and (E), respectively. Parameter values for the viral load function fitting to the viral target model are in C. (F) Parameters of the target model (2) corresponding to D and E.
Typical virus load curves. The virus load (“titer”) is usually reported as a dilution value, TCID50, that is needed to infect 50% of a given cell culture in (A) absolute scale and (B) logarithmic scale. Shadow areas indicate the three phases in which we divide the virus load progression. (C) Parameters of the standard virus load function (1) corresponding to A, B, D and E. Virus load curve reported in [12] are used in A and B. (D-E) Comparison of the viral load curve from the viral target model (2) with the viral load function (1). Viral target curves showing triphasic and biphasic responses are shown in (D) and (E), respectively. Parameter values for the viral load function fitting to the viral target model are in C. (F) Parameters of the target model (2) corresponding to D and E.

Study design

To develop the model, they have considered each phase of infection progression at two time points. The viral load curve has been developed as a product of three functions, representing three primary phases: the initial growth phase, the intermediate slow decay phase, and the final fast decay phase.

They have fitted the viral load function to the viral titer data obtained from influenza virus-infected mice and SARS-CoV-2-infected humans and monkeys.

Important observations

To determine the viral load function of influenza A virus infection, the time series of viral load titers obtained from 10 influenza virus-infected mice have been employed. By fitting the model to the viral load data, the scientists have estimated that the approximate duration of phase 1, phase 2, and phase 3 are 2.4 days, 3.2 days, and 1.3 days, respectively.

To determine the viral load function of SARS-CoV-2 infection, the scientists have used viral load titers measured in 23 COVID-19 patients daily. They have observed a patient-to-patient variation in viral load over time, with some patients showing a long-term infection of 20 – 25 days and some showing a short-term infection of about ten days. However, the viral load function they developed has successfully described the three phases of viral infection for most patients. This indicates that the viral load model can easily describe the viral load curves for both short-term and long-term viral infections.

The scientists have also used viral load data obtained from 9 monkeys infected with different doses of SARS-CoV-2. By fitting the viral load function to the infected monkeys' data, they have observed that the initial viral growth phase duration is similar among all monkeys, indicating that the initial virus amount is not a crucial determining factor. Moreover, they have observed that there is only one decay phase with larger decay rates in most of the monkeys. However, in some monkeys, a fast, initial decay phase and a second slow decay phase have been observed. This difference between biphasic and triphasic viral load suggests efficient functioning of the immune system in some monkeys.

Study significance

A convenient and straightforward viral load function model has been developed in the current study that can be easily fitted to viral load titer data of influenza A virus infection and SARS-CoV-2 infection. According to the scientists, this model has many important applications. The viral load curves obtained from this model can be used in other higher-level model to analyze pathological features of a viral infection. Moreover, the model can be used to estimate the length of a viral infection and the virus decay rates at the population level, which are vital measures for determining patient risks. Another important function of the model is that it can easily differentiate between fast responders and slow responders, which is vital for identifying high-risk and low-risk individuals.

*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:
Dr. Sanchari Sinha Dutta

Written by

Dr. Sanchari Sinha Dutta

Dr. Sanchari Sinha Dutta is a science communicator who believes in spreading the power of science in every corner of the world. She has a Bachelor of Science (B.Sc.) degree and a Master's of Science (M.Sc.) in biology and human physiology. Following her Master's degree, Sanchari went on to study a Ph.D. in human physiology. She has authored more than 10 original research articles, all of which have been published in world renowned international journals.

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