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What is the influence of individual infectiousness variation on COVID-19 spread across households?

In a recent article posted to the medRxiv* preprint server, scientists characterized the effect of individual infectiousness variance on the transmission heterogeneity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among households.

Study: The effect of variation of individual infectiousness on SARS-CoV-2 transmission in households. Image Credit: Alano Design/Shutterstock


Controlling the spread of a developing infectious illness requires an understanding of transmission. The most commonly used indicator of infectiousness is the reproductive number. Further, computing individual infectiousness variance is crucial for guiding disease control.

Prior reports suggested that the spread of many infectious illnesses is highly heterogeneous (including SARS-CoV-2). Nevertheless, as the contact numbers are accounted rarely in such techniques, those results are challenging to interpret. Moreover, household coronavirus disease 2019 (COVID-19) transmission analyses offer the perfect environment to measure individual infectiousness variance.

About the study

In the present study, the researchers sought to determine the heterogeneity of individual SARS-CoV-2 infectiousness by analyzing information from household COVID-19 transmission investigations. This research aimed to create a statistical model that would measure the diversity in individual infectiousness among households using publicly available data.

The team defined an index SARS-CoV-2 case as the initially discovered infection in a household, while secondary COVID-19 cases were described as the recognized virus-infected household contacts of the index patient. They performed a systematic review to gather household experiments with a minimum of 30 households, providing the number of secondary patients and household COVID-19 contacts for each dwelling by household number with X cases across Y-size households.

Additionally, for each analysis, the investigators retrieved the research period, household contact testing coverage, case ascertainment techniques, circulating SARS-CoV-2 strain, and social and public health measures during the study term. Indeed, input for the modeling examinations used in this work came from this data.

The scientists evaluated information from 17 COVID-19 household transmission experiments with known contact numbers performed during SARS-CoV-2 ancestor strain dominance periods. By adjusting for the contact numbers and baseline transmission chances in individual-based household transmission models fitted to this data, they derived the pooled estimate. The model explained the likelihood of COVID-19 among household contacts depending on how long it has been since other household members contracted SARS-CoV-2 infection. In addition, COVID-19 instances from community infections (i.e., beyond the household) or tertiary infections (infections through other household contacts instead of index cases) were allowed.


The authors demonstrated that the impact of diversity of individual infectiousness on the heterogeneity of COVID-19 transmission in households could be estimated using household data utilizing a modeling strategy. They noted that the pooled infectiousness variance estimate from 14 analyses showed that 20% of most SARS-CoV-2 contagious patients had 3.1 times greater infectiousness than the average cases. This observation was in line with the findings of viral shedding variability.

Furthermore, according to the study inferences, there was a significant variance in the infectiousness of each SARS-CoV-2 patient in households. This variance might be attributable to both host behaviors and biological factors. Considering host behaviors, various contact trends, mainly by age, might be a factor in the infectiousness variations of cases. Moreover, contact pattern assessments indicated that young adults and school-age children tended to socialize with others their age. 

The researchers found that COVID-19 infectiousness fluctuation was correlated substantially with the percentage of cases attributable to 80% transmission (p80), comparable to earlier analyses on superspreading. This inference suggests that it might be a gauge of household infectiousness variance. The secondary attack rate (SAR) and the percentage of households with no infected contacts (p0) also impacted infectiousness fluctuation.

The scientists discovered that relying solely on the polymerase chain reaction (PCR) to verify secondary infections was associated with increased infectiousness variability. In addition to these relationships, they could not discover any links between these statistics and the deployment of lockdown, the method used to identify index and secondary patients, or the SARS-CoV-2 strain that was in circulation during the period of study.

Collectively, the team mentioned that household information might guide the evaluation of COVID-19 transmission variance, which was relevant for epidemic control.


In the current work, the authors quantified individual SARS-CoV-2 infectiousness heterogeneity. In addition, they discussed possible causes for these variances, chiefly viral shedding variation.

In summary, the investigators created a modeling strategy to calculate individual SARS-CoV-2 infectiousness variance from household information. Besides, the study findings show that individual infectiousness varies significantly, which is crucial for managing epidemics.

*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.

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