Effectiveness of a COVID-19 contact tracing app in a simulation model with indirect and informal contact tracing

During the COVID-19 pandemic, contact tracing was used to identify individuals who had been in contact with a confirmed case so that these contacted individuals could be tested and quarantined to prevent further spread of the SARS-CoV-2 virus. Many countries developed mobile apps to find these contacted individuals faster. We evaluate the epidemiological effectiveness of the Dutch app CoronaMelder, where we measure effectiveness as the reduction of the reproduction number R. To this end, we use a simulation model of SARS-CoV-2 spread and contact tracing, informed by data collected during the study period (December 2020 - March 2021) in the Netherlands. We show that the tracing app caused a clear but small reduction of the reproduction number, and the magnitude of the effect was found to be robust in sensitivity analyses. The app could have been more effective if more people had used it, and if notification of contacts could have been done directly by the user and thus reducing the time intervals between symptom onset and reporting of contacts. The model has two innovative aspects: i) it accounts for the clustered nature of social networks and ii) cases can alert their contacts informally without involvement of health authorities or the tracing app.</p

Elevated risk of infection with SARS-CoV-2 Beta, Gamma, and Delta variants compared with Alpha variant in vaccinated individuals

The extent to which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) break through infection- or vaccine-induced immunity is not well understood. We analyzed 28,578 sequenced SARS-CoV-2 samples from individuals with known immune status obtained through national community testing in the Netherlands from March to August 2021. We found evidence of an increased risk of infection by the Beta (B.1.351), Gamma (P.1), or Delta (B.1.617.2) variants compared with the Alpha (B.1.1.7) variant after vaccination. No clear differences were found between vaccines. However, the effect was larger in the first 14 to 59 days after complete vaccination compared with ≥60 days. In contrast to vaccine-induced immunity, there was no increased risk for reinfection with Beta, Gamma, or Delta variants relative to the Alpha variant in individuals with infection-induced immunity.</p

Quantifying The Impact Of Human Leukocyte Antigen On The Human Gut Microbiota

The composition of the gut microbiota is affected by a number of factors, including the innate and adaptive immune system. The major histocompatibility complex (MHC), or the human leukocyte antigen (HLA) in humans, performs an essential role in vertebrate immunity and is very polymorphic in different populations. HLA determines the specificity of T lymphocyte and natural killer (NK) cell responses, includingthose against the commensal bacteria present in the human gut. Thus, it is likely that our HLA molecules, and thereby the adaptive immune response, can shape the composition of our microbiota. Here, we investigated the effect of HLA haplotype on the microbiota composition. We performed HLA typing and microbiota composition analyses on 3,002 public human gut microbiome data sets. We found that individuals with functionally similar HLA molecules are also similar in their microbiota composition. Our results show a statistical association between host HLA haplotype and gut microbiota composition. Because the HLA haplotype is a readily measurable parameter of the human immune system, these results open the door to incorporating the genetics of the immune system into predictive microbiome models. IMPORTANCE The microorganisms that live in the digestive tracts of humans, known as the gut microbiota, are essential for hosts’ survival, as they support crucial functions. For example, they support the host in facilitating the uptake of nutrients and give colonization resistance against pathogens. The composition of the gut microbiota varies among humans. Studies have proposed multiple factors driving the observed variation, including diet, lifestyle, and health condition. Another major influence on the microbiota is the host’s genetic background. We hypothesized the immune system to be one of the most important genetic factors driving the differences observed between gut microbiotas. Therefore, we searched for a link between the polymorphic molecules that shape human immune responses and the composition of the microbiota. HLA molecules are the most polymorphic molecules in our genome and therefore makes an excellent candidate to test such an association. To our knowledge for the first time, our results indicate a significant impact of the HLA on the human gut microbiota

Quantifying The Impact Of Human Leukocyte Antigen On The Human Gut Microbiota

The composition of the gut microbiota is affected by a number of factors, including the innate and adaptive immune system. The major histocompatibility complex (MHC), or the human leukocyte antigen (HLA) in humans, performs an essential role in vertebrate immunity and is very polymorphic in different populations. HLA determines the specificity of T lymphocyte and natural killer (NK) cell responses, includingthose against the commensal bacteria present in the human gut. Thus, it is likely that our HLA molecules, and thereby the adaptive immune response, can shape the composition of our microbiota. Here, we investigated the effect of HLA haplotype on the microbiota composition. We performed HLA typing and microbiota composition analyses on 3,002 public human gut microbiome data sets. We found that individuals with functionally similar HLA molecules are also similar in their microbiota composition. Our results show a statistical association between host HLA haplotype and gut microbiota composition. Because the HLA haplotype is a readily measurable parameter of the human immune system, these results open the door to incorporating the genetics of the immune system into predictive microbiome models. IMPORTANCE The microorganisms that live in the digestive tracts of humans, known as the gut microbiota, are essential for hosts’ survival, as they support crucial functions. For example, they support the host in facilitating the uptake of nutrients and give colonization resistance against pathogens. The composition of the gut microbiota varies among humans. Studies have proposed multiple factors driving the observed variation, including diet, lifestyle, and health condition. Another major influence on the microbiota is the host’s genetic background. We hypothesized the immune system to be one of the most important genetic factors driving the differences observed between gut microbiotas. Therefore, we searched for a link between the polymorphic molecules that shape human immune responses and the composition of the microbiota. HLA molecules are the most polymorphic molecules in our genome and therefore makes an excellent candidate to test such an association. To our knowledge for the first time, our results indicate a significant impact of the HLA on the human gut microbiota

Effectiveness of a COVID-19 contact tracing app in a simulation model with indirect and informal contact tracing

During the COVID-19 pandemic, contact tracing was used to identify individuals who had been in contact with a confirmed case so that these contacted individuals could be tested and quarantined to prevent further spread of the SARS-CoV-2 virus. Many countries developed mobile apps to find these contacted individuals faster. We evaluate the epidemiological effectiveness of the Dutch app CoronaMelder, where we measure effectiveness as the reduction of the reproduction number R. To this end, we use a simulation model of SARS-CoV-2 spread and contact tracing, informed by data collected during the study period (December 2020 - March 2021) in the Netherlands. We show that the tracing app caused a clear but small reduction of the reproduction number, and the magnitude of the effect was found to be robust in sensitivity analyses. The app could have been more effective if more people had used it, and if notification of contacts could have been done directly by the user and thus reducing the time intervals between symptom onset and reporting of contacts. The model has two innovative aspects: i) it accounts for the clustered nature of social networks and ii) cases can alert their contacts informally without involvement of health authorities or the tracing app

Empirical evidence of transmission over a school-household network for SARS-CoV-2; exploration of transmission pairs stratified by primary and secondary school

Background: Children play a key role in the transmission of many infectious diseases. They have many of their close social encounters at home or at school. We hypothesized that most of the transmission of respiratory infections among children occur in these two settings and that transmission patterns can be predicted by a bipartite network of schools and households. Aim and methods: To confirm transmission over a school-household network, SARS-CoV-2 transmission pairs in children aged 4–17 years were analyzed by study year and primary/secondary school. Cases with symptom onset between 1 March 2021 and 4 April 2021 identified by source and contact-tracing in the Netherlands were included. In this period, primary schools were open and secondary school students attended class at least once per week. Within pairs, spatial distance between the postcodes was calculated as the Euclidean distance. Results: A total of 4059 transmission pairs were identified; 51.9% between primary schoolers; 19.6% between primary and secondary schoolers; 28.5% between secondary schoolers. Most (68.5%) of the transmission for children in the same study year occurred at school. In contrast, most of the transmission of children from different study years (64.3%) and most primary-secondary transmission (81.7%) occurred at home. The average spatial distance between infections was 1.2 km (median 0.4) for primary school pairs, 1.6 km (median 0) for primary-secondary school pairs and 4.1 km (median 1.2) for secondary school pairs. Conclusion: The results provide evidence of transmission on a bipartite school-household network. Schools play an important role in transmission within study years, and households play an important role in transmission between study years and between primary and secondary schools. Spatial distance between infections in a transmission pair reflects the smaller school catchment area of primary schools versus secondary schools. Many of these observed patterns likely hold for other respiratory pathogens