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SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

The NHS COVID-19 app was launched in England and Wales in September 2020, with a Bluetooth-based contact tracing functionality designed to reduce transmission of SARS-CoV-2. We show that user engagement and the app’s epidemiological impacts varied according to changing social and epidemic characteristics throughout the app’s first year. We describe the interaction and complementarity of manual and digital contact tracing approaches. Results of our statistical analyses of anonymised, aggregated app data include that app users who were recently notified were more likely to test positive than app users who were not recently notified, by a factor that varied considerably over time. We estimate that the app’s contact tracing function alone averted about 1 million cases (sensitivity analysis 450,000–1,400,000) during its first year, corresponding to 44,000 hospital cases (SA 20,000–60,000) and 9,600 deaths (SA 4600–13,000). The NHS COVID-19 digital contact tracing app was designed to notify people of potential exposure to SARS-CoV-2. Here, the authors summarise the uptake and engagement with the app in its first year, and estimate its epidemiological impact in terms of numbers of cases, hospitalisations, and deaths averted.

Cell lines allow studying various biological processes that may not be easily tractable in whole organisms. Here, we have established the first male-specific cell line from the African malaria mosquito, Anopheles gambiae . The cells, named AgMM and derived from the sex-sorted neonate larvae, were able to undergo spontaneous contractions for a number of passages following establishment, indicating their myoblast origin. Comparison of their transcriptome to the transcriptome of an A. gambiae -derived Sua5.1 hemocyte cells revealed distinguishing molecular signatures of each cell line, including numerous muscle-related genes that were highly and uniquely expressed in the AgMM cells. Moreover, the AgMM cells express the primary sex determiner gene Yob and support male sex determination and dosage compensation pathways. Therefore, the AgMM cell line represents a valuable tool for molecular and biochemical in vitro studies of these male-specific processes. In a broader context, a rich transcriptomic data set generated in this study contributes to a better understanding of transcribed regions of the A. gambiae genome and sheds light on the biology of both cell types, facilitating their anticipated use for various cell-based assays.

Plant viruses, viroids, and phytoplasmas are systemic, unculturable pathogens that are increasingly endangering sustainable food security and environmental safety worldwide [...].Plant viruses, viroids, and phytoplasmas are systemic, unculturable pathogens that are increasingly endangering sustainable food security and environmental safety worldwide [...].

The transmission dynamics and burden of SARS-CoV-2 in many regions of the world is still largely unknown due to the scarcity of epidemiological analyses and lack of testing to assess the prevalence of disease. In this work, we develop a quantitative framework based on excess mortality data to reconstruct SARS-CoV-2 transmission dynamics and assess the level of underreporting in infections and deaths. Using weekly all-cause mortality data from Iran, we are able to show a strong agreement between our attack rate estimates and seroprevalence measurements in each province and find significant heterogeneity in the level of exposure across the country with 11 provinces reaching near 100% attack rates. Despite having a young population, our analysis reveals that incorporating limited access to medical services in our model, coupled with undercounting of COVID-19-related deaths, leads to estimates of infection fatality rate in most provinces of Iran that are comparable to high-income countries. Testing capacity continues to limit detection of COVID-19 infections and impacts reliability of mortality estimates. Here, the authors develop a statistical model to estimate COVID-19 attributable deaths using all-cause mortality data from Iran and estimate that around half of these deaths have been reported.

In the last years, several genotypic fitness landscapes—combinations of a small number of mutations—have been experimentally resolved. To learn about the general properties of “real” fitness landscapes, it is key to characterize these experimental landscapes via simple measures of their structure, related to evolutionary features. Some of the most relevant measures are based on the selectively acessible paths and their properties. In this paper, we present some measures of evolutionary constraints based on (i) the similarity between accessible paths and (ii) the abundance and characteristics of “chains” of obligatory mutations, that are paths going through genotypes with a single fitter neighbor. These measures have a clear evolutionary interpretation. Furthermore, we show that chains are only weakly correlated to classical measures of epistasis. In fact, some of these measures of constraint are non-monotonic in the amount of epistatic interactions, but have instead a maximum for intermediate values. Finally, we show how these measures shed light on evolutionary constraints and predictability in experimentally resolved landscapes.

While many countries employed digital contact tracing to contain the spread of SARS-CoV-2, the contribution of cospace-time interaction (i.e., individuals who shared the same space and time) to transmission and to super-spreading in the real world has seldom been systematically studied due to the lack of systematic sampling and testing of contacts. To address this issue, we utilized data from 2230 cases and 220,878 contacts with detailed epidemiological information during the Omicron outbreak in Beijing in 2022. We observed that contact number per day of tracing for individuals in dwelling, workplace, cospace-time interactions, and community settings could be described by gamma distribution with distinct parameters. Our findings revealed that 38% of traced transmissions occurred through cospace-time interactions whilst control measures were in place. However, using a mathematical model to incorporate contacts in different locations, we found that without control measures, cospace-time interactions contributed to only 11% (95%CI: 10%–12%) of transmissions and the super-spreading risk for this setting was 4% (95%CI: 3%–5%), both the lowest among all settings studied. These results suggest that public health measures should be optimized to achieve a balance between the benefits of digital contact tracing for cospace-time interactions and the challenges posed by contact tracing within the same setting. Digital contact tracing was widely adopted during the COVID-19 pandemic to enable identification of incidental ‘cospace-time’ interactions with people who were not close contacts. Here the authors evaluate the effectiveness of digital contact tracing using data from Beijing, China.

We investigate the dependence of the site frequency spectrum on the topological structure of genealogical trees. We show that basic population genetic statistics, for instance, estimators of θ or neutrality tests such as Tajima’s D, can be decomposed into components of waiting times between coalescent events and of tree topology. Our results clarify the relative impact of the two components on these statistics. We provide a rigorous interpretation of positive or negative values of an important class of neutrality tests in terms of the underlying tree shape. In particular, we show that values of Tajima’s D and Fay and Wu’s H depend in a direct way on a peculiar measure of tree balance, which is mostly determined by the root balance of the tree. We present a new test for selection in the same class as Fay and Wu’s H and discuss its interpretation and power. Finally, we determine the trees corresponding to extreme expected values of these neutrality tests and present formulas for these extreme values as a function of sample size and number of segregating sites.

The expression of accessory non-structural proteins V and W in Newcastle disease virus (NDV) infections depends on RNA editing. These proteins are derived from frameshifts of the sequence coding for the P protein via co-transcriptional insertion of one or two guanines in the mRNA. However, a larger number of guanines can be inserted with lower frequencies. We analysed data from deep RNA sequencing of samples from in vitro and in vivo NDV infections to uncover the patterns of mRNA editing in NDV. The distribution of insertions is well described by a simple Markov model of polymerase stuttering, providing strong quantitative confirmation of the molecular process hypothesised by Kolakofsky and collaborators three decades ago. Our results suggest that the probability that the NDV polymerase would stutter is about 0.45 initially, and 0.3 for further subsequent insertions. The latter probability is approximately independent of the number of previous insertions, the host cell, and viral strain. However, in LaSota infections, we also observe deviations from the predicted V/W ratio of about 3:1 according to this model, which could be attributed to deviations from this stuttering model or to further mechanisms downregulating the abundance of W protein.