Categories: usingViral

Using viral load to model disease dynamics

Assays for detecting pathogens are used primarily to diagnose infections. Epidemiologists accumulate results from these tests in time series of case reports to conduct disease surveillance, a cornerstone of public health. During the COVID-19 pandemic, these data have been presented on dashboards of health agencies and media outlets all over the world. The shortcomings of these data have also become apparent: Trends can be misleading when demand for testing changes, when testing becomes more available, or when more (or less) accurate tests are rolled out. Time series of case counts are also a major simplification of the raw data used to generate them; modern diagnostics offer more than binary (positive or negative) results—they also estimate viral load, which can indicate the stage of infection. On page 299 of this issue, Hay et al. (1) develop an approach that uses aggregated viral load data to monitor epidemics more accurately than simple case series.

For most viruses, the contemporary standard assay for detection is quantitative (or real-time) polymerase chain reaction (qPCR). The number of cycles of the reaction at which an amplicon is at sufficient levels to produce a detectable signal is the cycle threshold (Ct) value. Because higher viral loads produce a signal at a lower number of reaction cycles, the Ct is inversely proportional to the amount of virus in the sample. Because acute viral infections follow a pattern whereby viral load peaks days to weeks after exposure and then declines, Ct values from qPCR can give an indication of the stage of an individual’s infection. A low Ct indicates high viral load and therefore the acute phase of illness; high Ct values (i.e., lower viral loads) occur during convalescence. But because viral loads are also low when an infection is just starting and are heterogeneous across individuals, a Ct value is typically not useful for informing an individual’s treatment.

However, more can be learned with Ct values at the population level. To understand the approach, consider an endemic infection where, on average, each case infects exactly one more person. Any snapshot in time would show a stable average viral load because some people are at the beginning of their illness and some are toward the end. It follows that, in a growing epidemic, more cases will be at the acute phase of illness and in a declining epidemic, more will be at a later phase, giving high and low average viral loads, respectively, at the population level (see the figure). This is the premise on which Hay et al. calculate the time-varying reproductive number (Rt) for COVID-19.

Outbreak monitoring with viral load

Viral load can be estimated from quantitative polymerase chain reaction (qPCR) testing for viral genomes. Aggregating viral load for a population can more reliably measure outbreak dynamics than case counts.

GRAPHIC: H. BISHOP/SCIENCE

” data-hide-link-title=”0″ data-icon-position=”” href=”https://science.sciencemag.org/content/sci/373/6552/280/F1.large.jpg?width=800&height=600&carousel=1″ rel=”gallery-fragment-images-1363776216″ title=”Outbreak monitoring with viral load Viral load can be estimated from quantitative polymerase chain reaction (qPCR) testing for viral genomes. Aggregating viral load for a population can more reliably measure outbreak dynamics than case counts.”>

Outbreak monitoring with viral load

Viral load can be estimated from quantitative polymerase chain reaction (qPCR) testing for viral genomes. Aggregating viral load for a population can more reliably measure outbreak dynamics than case counts.

GRAPHIC: H. BISHOP/SCIENCE

Pathogen quantification by qPCR has been leveraged for various aspects of infectious disease epidemiology. Incorporating pathogen quantities has improved the ability to attribute infectious etiologies. This is not trivial for diseases that can be caused by more than one pathogen, especially when they often cause asymptomatic infections. This is most challenging in high-incidence settings where multiple pathogens are frequently detected in clinical samples. For example, because the more than 20 pathogens that cause diarrhea among children in low-resource settings are also frequently carried in the absence of diarrhea, detection of a pathogen in a diarrheal stool is not sufficient to assign etiology. But because the association with diarrhea increases with pathogen quantity for many enteric pathogens, statistical models that compare the quantity of pathogen detected by qPCR between diarrhea cases and controls can be used to estimate the population-level proportion of

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