What is the impact of individual differences in infectivity on the spread of COVID-19 within households?

In a recent article posted on medrex sib*Preprint server, Scientists characterized the impact of individual infectivity differentials on heterogeneity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission within households.

Background

To control the ongoing spread of infectious diseases, we need to understand contagion. The most commonly used indicator of infectivity is the reproductive number. Furthermore, calculating the variance of individual infectivity is important for guiding disease control.

Previous reports suggested that the spread of many infectious diseases is highly heterogeneous (including SARS-CoV-2). Nevertheless, these results are difficult to interpret because contact numbers are rarely accounted for by such techniques. It provides a perfect environment for measuring infectious dispersal.

About research

In this study, researchers sought to determine the heterogeneity of individual SARS-CoV-2 infectivity by analyzing information from households COVID-19 infection Investigation. This study aimed to use publicly available data to create a statistical model to measure the diversity of individual infectivity among households.

The team defined a primary case of SARS-CoV-2 as the first found infection in a household, and a secondary COVID-19 case as a recognized viral household contact of the initial patient. explained. They conducted a systematic review to collect a minimum of 30 household experiments, and each household number of X cases across a Y-sized household with secondary patients in each dwelling and her COVID-19 contacts in the household. provided a number of

In addition, for each analysis, researchers obtained the study period, the extent of household contact testing, case confirmation techniques, prevalent SARS-CoV-2 strains, and social and public health measures during the study period. Indeed, the input for the modeling trials used in this work was derived from this data.

Scientists evaluated information from 17 COVID-19 household transmission experiments using known contact numbers conducted during the prevalence of SARS-CoV-2 ancestral strains. By adjusting the contact numbers and baseline transmission probabilities of the individual-based household transmission model fitted to this data, they derived pooled estimates. This model described the likelihood of a household contact becoming infected with her COVID-19 depending on how long it has been since other household members became infected with SARS-CoV-2. Additionally, COVID-19 instances from community transmission (i.e., outside the home) or tertiary transmission (infection from other household contacts, not the original case) were allowed.

result

The authors demonstrated that household data utilizing modeling strategies could be used to estimate the impact of individual infectivity diversity on COVID-19 transmission heterogeneity within households. They showed that pooled variance estimates of infectivity from 14 analyzes showed that 20% of most SARS-CoV-2-infected patients were 3.1 times more infectious than the average case. I noticed that This observation viral shedding variability.

Moreover, the study inferred that there was great variability in the infectivity of each SARS-CoV-2 patient within a household. This difference can be attributed to both host behavior and biological factors. Considering host behavior, different contact tendencies, mainly by age, may be a factor in the variability of case infectivity. In addition, contact pattern assessments showed that young adults and school-age children tended to interact with others of their age.

Researchers found that the variation in COVID-19 infectivity was substantially correlated with the proportion of cases attributable to 80% infection (p80). This is comparable to our previous analysis on superspreads. This reasoning suggests that it may be a measure of household infectious dispersal. The secondary attack rate (SAR) and the proportion of households without contact with an infected person (p0) also influenced the variation in transmissibility.

Scientists have found that relying solely on the polymerase chain reaction (PCR) to validate secondary infections is associated with increased variability in infectivity. In addition to these relationships, they also used these statistics and lockdown developments, indicators and methods used to identify secondary patients, or the SARS-CoV-2 strains circulating during the study period. Could not find any relationship between

Collectively, the team said household information could guide assessments of the spread of COVID-19 transmission. This was related to epidemic control.

Conclusion

In the current study, the authors quantified the heterogeneity of individual SARS-CoV-2 infectivity. Furthermore, they discussed the possible causes of these differences, mainly differences in viral shedding.

In summary, researchers developed a modeling strategy to calculate individual SARS-CoV-2 infectivity variance from household information. Furthermore, research results show that individuals vary widely in their susceptibility to infection, which is important for managing epidemics.

*Important Notices

medRxiv publishes non-peer-reviewed, preliminary scientific reports and should not be considered conclusive, to guide clinical practice/health-related actions, or to be treated as established information .