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This article points to the untapped potential for meaningful and mutually beneficial exchange between development research and practice, by presenting an example of an iterative process of knowledge formation, whereby project staff’s collective experiential insights and inductive learning are used to obtain an enriched Socio-ecological Model (SEM), which is attuned to the lived experiences in the field and is reinforced by the available research evidence. Using Catholic Relief Services, one of the largest humanitarian and development organizations worldwide, as case study, interviews were conducted with project staff from nine livelihood and food security projects and gathered staff’s perceptions and experiences with promoting gender-equitable outcomes through improved intra-household gender dynamics and men’s involvement. The qualitative analysis of the interviews shows that, while projects tried to integrate activities across the four levels of the SEM (individual, family, community and societal), staff perceived that the stickiness of social norms, women’s time poverty and limited buy-in from local organizations affected progress and presented new challenges that required constant adaptation. Our proposed method shows how an SEM can be enriched by incorporating these additional elements and by using existing research to confirm the significance of the exercise. An enriched SEM, by explicitly pointing to cross-cutting challenges that emerge from the field, is better reflective of the realities in which the staff works than a simple SEM. A process of SEM’s validation through incorporating insights from field staff and collaboratively involving researchers has the potential to deepen how projects or organizations think about the way they can foster gender transformative change; as well as to lead to more informed research and enhance researchers’ appreciations of the practical nature of development project challenges.

Motivation The ability to distinguish imported cases from locally acquired cases has important consequences for the selection of public health control strategies. Genomic data can be useful for this, for example, using a phylogeographic analysis in which genomic data from multiple locations are compared to determine likely migration events between locations. However, these methods typically require good samples of genomes from all locations, which is rarely available. Results Here, we propose an alternative approach that only uses genomic data from a location of interest. By comparing each new case with previous cases from the same location, we are able to detect imported cases, as they have a different genealogical distribution than that of locally acquired cases. We show that, when variations in the size of the local population are accounted for, our method has good sensitivity and excellent specificity for the detection of imports. We applied our method to data simulated under the structured coalescent model and demonstrate relatively good performance even when the local population has the same size as the external population. Finally, we applied our method to several recent genomic datasets from both bacterial and viral pathogens, and show that it can, in a matter of seconds or minutes, deliver important insights on the number of imports to a geographically limited sample of a pathogen population. Availability and implementation The R package DetectImports is freely available from https://github.com/xavierdidelot/DetectImports. Supplementary information Supplementary data are available at Bioinformatics online.

Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches to reconstruct outbreaks exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is stable among repeated serial samples from the same host, is transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.

Ovarian cancer (OC) is the most lethal gynecologic malignancy. Cannabis sativa is being used to treat different medical conditions. We sought to examine the effectiveness of combinations of cannabis compounds against OC. Cytotoxic activity was determined by XTT assay on HTB75 and HTB161 cell lines. Apoptosis and cell cycle were determined by fluorescence-activated cell sorting (FACS). Gene expression was determined by quantitative PCR. The two most active fractions, F5 and F7, from a high Δ9–tetrahydrocannabinol (THC) cannabis strain extract and their standard mix (SM) showed cytotoxic activity against OC cells. The most effective phytocannabinoid combination was THC+cannabichromene (CBC)+cannabigerol (CBG). F5, F7 and SM affected cell cycle, led to cell apoptosis and to a marked reduction in cell migration. Moreover, these fractions act in synergy with niraparib, and were ~50 fold more cytotoxic to OC cells than to normal keratenocytes. Niraparib+F7 treatment was effective on OC patient's cells. F7 and the niraparin+fraction (F5 and F7) treatments reduced Mitogen-Activated Protein Kinase 4 (MAPK4) gene expression; this reduction may act in synergy with the niraparib inhibition of Poly (ADP-ribose) polymerase 1 (PARP1) activity. Combinations of cannabis compounds and niraparib should be examined for efficacy in pre-clinical studies and clinical trials.

Quarantining close contacts of individuals infected with SARS-CoV-2 for 10 to 14 days is a key strategy in reducing transmission. However, quarantine requirements are often unpopular, with low adherence, especially when a large fraction of the population has been vaccinated. Daily contact testing (DCT), in which contacts are required to isolate only if they test positive, is an alternative to quarantine for mitigating the risk of transmission from traced contacts. In this study, we developed an integrated model of COVID-19 transmission dynamics and compared the strategies of quarantine and DCT with regard to reduction in transmission and social/economic costs (days of quarantine/self-isolation). Specifically, we compared 10-day quarantine to 7 days of self-testing using rapid lateral flow antigen tests, starting 3 days after exposure to a case. We modelled both incomplete adherence to quarantine and incomplete adherence to DCT. We found that DCT reduces transmission from contacts with similar effectiveness, at much lower social/economic costs, especially for highly vaccinated populations. The findings were robust across a spectrum of scenarios with varying assumptions on the speed of contact tracing, sensitivity of lateral flow antigen tests, adherence to quarantine and uptake of testing. Daily tests would also allow rapid initiation of a new round of tracing from infected contacts.

International audience

The COVID-19 pandemic has seen the emergence of digital contact tracing to help to prevent the spread of the disease. A mobile phone app records proximity events between app users, and when a user tests positive for COVID-19, their recent contacts can be notified instantly. Theoretical evidence has supported this new public health intervention

Comparing the pathogen genomes from several cases of an infectious disease has the potential to help us understand and control outbreaks. Many methods exist to reconstruct a phylogeny from such genomes, which represents how the genomes are related to one another. However, such a phylogeny is not directly informative about transmission events between individuals. TransPhylo is a software tool implemented as an R package designed to bridge the gap between pathogen phylogenies and transmission trees. TransPhylo is based on a combined model of transmission between hosts and pathogen evolution within each host. It can simulate both phylogenies and transmission trees jointly under this combined model. TransPhylo can also reconstruct a transmission tree based on a dated phylogeny, by exploring the space of transmission trees compatible with the phylogeny. A transmission tree can be represented as a coloring of a phylogeny where each color represents a different host of the pathogen, and TransPhylo provides convenient ways to plot these colorings and explore the results. This article presents the basic protocols that can be used to make the most of TransPhylo. © 2021 The Authors. Basic Protocol 1: First steps with TransPhylo Basic Protocol 2: Simulation of outbreak data Basic Protocol 3: Inference of transmission Basic Protocol 4: Exploring the results of inference

Background In May 2020, the UK National Health Service (NHS) Test and Trace programme was launched in England in response to the COVID-19 pandemic. The programme was first rolled out on the Isle of Wight and included version 1 of the NHS contact tracing app. The aim of the study was to make a preliminary assessment of the epidemiological impact of the Test and Trace programme using publicly available data. Methods We used COVID-19 daily case data from Public Health England to infer incidence of new infections and estimate the reproduction number (R) for each of the 150 Upper-Tier Local Authorities (UTLAs) in England and nationally, before and after the launch of the Test and Trace programme on the Isle of Wight. We used Bayesian and maximum-likelihood methods to estimate R and compared the Isle of Wight with other UTLAs using a synthetic control method. Findings We observed significant decreases in incidence and R on the Isle of Wight immediately after the launch of the Test and Trace programme. The Isle of Wight had a marked reduction in R, from 1·3 before the Test and Trace programme to 0·5 after by one of our measures, and went from having the third highest R before the Test and Trace programme, to the twelfth lowest afterwards compared with other UTLAs. Interpretation Our results show that the epidemic on the Isle of Wight was controlled quickly and effectively after the launch of Test and Trace. Our findings highlight the need for further research to determine the causes of the reduction in the spread of the disease, as these could be translated into local and national non-pharmaceutical intervention strategies in the period before a treatment or vaccination for COVID-19 becomes available. Funding Li Ka Shing Foundation and UK Economic and Social Research Council.

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 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. 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 is its Python interface, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

In May 2020 the UK introduced a Test, Trace, Isolate programme in response to the COVID-19 pandemic. The programme was first rolled out on the Isle of Wight and included Version 1 of the NHS contact tracing app. We used COVID-19 daily case data to infer incidence of new infections and estimate the reproduction number R for each of 150 Upper Tier Local Authorities in England, and at the National level, before and after the launch of the programme on the Isle of Wight. We used Bayesian and Maximum-Likelihood methods to estimate R, and compared the Isle of Wight to other areas using a synthetic control method. We observed significant decreases in incidence and R on the Isle of Wight immediately after the launch. These results are robust across each of our approaches. Our results show that the sub-epidemic on the Isle of Wight was controlled significantly more effectively than the sub-epidemics of most other Upper Tier Local Authorities, changing from having the third highest reproduction number R (of 150) before the intervention to the tenth lowest afterwards. The data is not yet available to establish a causal link. However, the findings highlight the need for further research to determine the causes of this reduction, as these might translate into local and national non-pharmaceutical intervention strategies in the period before a treatment or vaccination becomes available.

Instantaneous contact tracing New analyses indicate that severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) is more infectious and less virulent than the earlier SARS-CoV-1, which emerged in China in 2002. Unfortunately, the current virus has greater epidemic potential because it is difficult to trace mild or presymptomatic infections. As no treatment is currently available, the only tools that we can currently deploy to stop the epidemic are contact tracing, social distancing, and quarantine, all of which are slow to implement. However imperfect the data, the current global emergency requires more timely interventions. Ferretti et al. explored the feasibility of protecting the population (that is, achieving transmission below the basic reproduction number) using isolation coupled with classical contact tracing by questionnaires versus algorithmic instantaneous contact tracing assisted by a mobile phone application. For prevention, the crucial information is understanding the relative contributions of different routes of transmission. A phone app could show how finite resources must be divided between different intervention strategies for the most effective control. Science , this issue p. eabb6936

Background: The timing of SARS-CoV-2 transmission is a critical factor to understand the epidemic trajectory and the impact of isolation, contact tracing and other non-pharmaceutical interventions on the spread of COVID-19 epidemics. Methods: We examined the distribution of transmission event times with respect to exposure and onset of symptoms. We analysed 119 transmission pairs with known date of onset of symptoms for both index and secondary cases and partial information on their intervals of exposure. We inferred the distribution for generation time and time from onset of symptoms to transmission by maximum likelihood. We modelled different relations between time of infection, onset of symptoms and transmission, inferring the most appropriate one according to the Akaike Information Criterion. Finally, we estimated the fraction of pre-symptomatic and early symptomatic transmissions among all pairs using a Bayesian approach. Findings: For symptomatic individuals, the timing of transmission of SARS-CoV-2 was more directly linked to the onset of clinical symptoms of COVID-19 than to the time since infection. The time of transmission was approximately centered and symmetric around the onset of symptoms, with three quarters of events occurring in the window from 2-3 days before to 2-3 days after. The pre-symptomatic infectious period extended further back in time for individuals with longer incubation periods. Overall, the fraction of transmission from strictly pre-symptomatic infections was high (41%; 95%CI 31-50%), but a comparably large fraction of transmissions occurred on the same day as the onset of symptoms or the next day (35%; 95%CI 26-45%). We caution against overinterpretation of the fraction and timing of late symptomatic transmissions, due to their dependence on behavioural factors and interventions. Interpretation: Infectiousness is causally driven by the onset of symptoms. Public health authorities should reassess their policies on the contact tracing window in the light of individual variability in presymptomatic infectious period. Information about when a case was infected should be collected where possible, in order to assess how far into the past their contacts should be traced. The large fraction of transmission from strictly pre-symptomatic infections limits the efficacy of symptom-based interventions, while the large fraction of early symptomatic transmissions underlines the critical importance of individuals distancing themselves from others as soon as they notice any symptoms, even if mild. Rapid or at-home testing and contextual risk information could greatly facilitate efficient early isolation. Funding Statement: The study was funded by an award from the Li Ka Shing Foundation to CF. Declaration of Interests: None of the authors have competing financial or non-financial interests.

Background: Phylogenetic reconstruction is a necessary first step in many analyses which use whole genome sequence data from bacterial populations. There are many available methods to infer phylogenies, and these have various advantages and disadvantages, but few unbiased comparisons of the range of approaches have been made. Methods: We simulated data from a defined 'true tree' using a realistic evolutionary model. We built phylogenies from this data using a range of methods, and compared reconstructed trees to the true tree using two measures, noting the computational time needed for different phylogenetic reconstructions. We also used real data from Streptococcus pneumoniae alignments to compare individual core gene trees to a core genome tree. Results: We found that, as expected, maximum likelihood trees from good quality alignments were the most accurate, but also the most computationally intensive. Using less accurate phylogenetic reconstruction methods, we were able to obtain results of comparable accuracy; we found that approximate results can rapidly be obtained using genetic distance based methods. In real data we found that highly conserved core genes, such as those involved in translation, gave an inaccurate tree topology, whereas genes involved in recombination events gave inaccurate branch lengths. We also show a tree-of-trees, relating the results of different phylogenetic reconstructions to each other. Conclusions: We recommend three approaches, depending on requirements for accuracy and computational time. For the most accurate tree, use of either RAxML or IQ-TREE with an alignment of variable sites produced by mapping to a reference genome is best. Quicker approaches that do not perform full maximum likelihood optimisation may be useful for many analyses requiring a phylogeny, as generating a high quality input alignment is likely to be the major limiting factor of accurate tree topology. We have publicly released our simulated data and code to enable further comparisons.

The increasing availability of large genomic data sets as well as the advent of Bayesian phylogenetics facilitates the investigation of phylogenetic incongruence, which can result in the impossibility of representing phylogenetic relationships using a single tree. While sometimes considered as a nuisance, phylogenetic incongruence can also reflect meaningful biological processes as well as relevant statistical uncertainty, both of which can yield valuable insights in evolutionary studies. We introduce a new tool for investigating phylogenetic incongruence through the exploration of phylogenetic tree landscapes. Our approach, implemented in the R package treespace, combines tree metrics and multivariate analysis to provide low‐dimensional representations of the topological variability in a set of trees, which can be used for identifying clusters of similar trees and group‐specific consensus phylogenies. treespace also provides a user‐friendly web interface for interactive data analysis and is integrated alongside existing standards for phylogenetics. It fills a gap in the current phylogenetics toolbox in R and will facilitate the investigation of phylogenetic results.

Viral phylogenetic methods contribute to understanding how HIV spreads in populations, and thereby help guide the design of prevention interventions. So far, most analyses have been applied to well-sampled concentrated HIV-1 epidemics in wealthy countries. To direct the use of phylogenetic tools to where the impact of HIV-1 is greatest, the Phylogenetics And Networks for Generalized HIV Epidemics in Africa (PANGEA-HIV) consortium generates full-genome viral sequences from across sub-Saharan Africa. Analyzing these data presents new challenges, since epidemics are principally driven by heterosexual transmission and a smaller fraction of cases is sampled. Here, we show that viral phylogenetic tools can be adapted and used to estimate epidemiological quantities of central importance to HIV-1 prevention in sub-Saharan Africa. We used a community-wide methods comparison exercise on simulated data, where participants were blinded to the true dynamics they were inferring. Two distinct simulations captured generalized HIV-1 epidemics, before and after a large community-level intervention that reduced infection levels. Five research groups participated. Structured coalescent modeling approaches were most successful: phylogenetic estimates of HIV-1 incidence, incidence reductions, and the proportion of transmissions from individuals in their first 3 months of infection correlated with the true values (Pearson correlation > 90%), with small bias. However, on some simulations, true values were markedly outside reported confidence or credibility intervals. The blinded comparison revealed current limits and strengths in using HIV phylogenetics in challenging settings, provided benchmarks for future methods’ development, and supports using the latest generation of phylogenetic tools to advance HIV surveillance and prevention., Molecular Biology and Evolution, 34 (1), ISSN:0737-4038, ISSN:1537-1719

Evolutionary relationships are described by phylogenetic trees, but a central barrier in many fields is the difficulty of interpreting data containing conflicting phylogenetic signals. Obtaining credible trees that capture the relationships present in complex data is one of the fundamental challenges in evolution today. We present a way to map trees which extracts distinct alternative evolutionary relationships embedded in data and resolves phylogenetic uncertainty. Our method reveals a remarkably distinct phylogenetic signature in the VP30 gene of Ebolavirus, indicating possible recombination with significant implications for vaccine development. Moving to higher organisms, we use our approach to detect alternative histories of the evolution of anole lizards. Our approach has the capacity to resolve key areas of uncertainty in the study of evolution, and to broaden the credibility and appeal of phylogenetic methods.

Background Multidrug-resistant tuberculosis poses a major threat to the success of tuberculosis control programs worldwide. Understanding how drug-resistant tuberculosis evolves can inform the development of new therapeutic and preventive strategies. Methods Here, we use novel genome-wide analysis techniques to identify polymorphisms that are associated with drug resistance, adaptive evolution and the structure of the phylogenetic tree. A total of 471 samples from different patients collected between 2009 and 2013 in the Lima suburbs of Callao and Lima South were sequenced on the Illumina MiSeq platform with 150bp paired-end reads. After alignment to the reference H37Rv genome, variants were called using standardized methodology. Genome-wide analysis was undertaken using custom written scripts implemented in R software. Results High quality homoplastic single nucleotide polymorphisms were observed in genes known to confer drug resistance as well as genes in the Mycobacterium tuberculosis ESX secreted protein pathway, pks12, and close to toxin/anti-toxin pairs. Correlation of homoplastic variant sites identified that many were significantly correlated, suggestive of epistasis. Variation in genes coding for ESX secreted proteins also significantly disrupted phylogenetic structure. Mutations in ESX genes in key antigenic epitope positions were also found to disrupt tree topology. Conclusions Variation in these genes have a biologically plausible effect on immunogenicity and virulence. This makes functional characterization warranted to determine the effects of these polymorphisms on bacterial fitness and transmission.

We present a formalisation of a category of schemes that we refer to as broadcast-enhanced key predistribution schemes (BEKPSs). These schemes are suitable for networks with access to a trusted base station and an authenticated broadcast channel. We demonstrate that the access to these extra resources allows for the creation of BEKPSs with advantages over key predistribution schemes such as flexibility and more efficient revocation. There are many possible ways to implement BEKPSs, and we propose a framework for describing and analysing them. In their paper “From Key Predistribution to Key Redistribution,” Cichoń et al. [2010] propose a scheme for “redistributing” keys to a wireless sensor network using a broadcast channel after an initial key predistribution. We classify this as a BEKPS and analyse it in that context. We provide simpler proofs of some results from their paper, give a precise analysis of the resilience of their scheme, and discuss possible modifications. We then study two scenarios where BEKPSs may be particularly desirable and propose a suitable family of BEKPSs for each case. We demonstrate that they are practical and efficient to implement, and our analysis shows their effectiveness in achieving suitable trade-offs between the conflicting priorities in resource-constrained networks.

Providing security for a wireless sensor network composed of small sensor nodes with limited battery power and memory can be a non-trivial task. A variety of key predistribution schemes have been proposed which allocate symmetric keys to the sensor nodes before deployment. In this paper we examine the role of expander graphs in key predistribution schemes for wireless sensor networks. Roughly speaking, a graph has good expansion if every 'small' subset of vertices has a 'large' neighbourhood, and intuitively, expansion is a desirable property for graphs of networks. It has been claimed that good expansion in the product graph is necessary for 'optimal' networks. We demonstrate flaws in this claim, argue instead that good expansion is desirable in the intersection graph, and discuss how this can be achieved. We then consider key predistribution schemes based on expander graph constructions and compare them to other schemes in the literature. Finally, we propose the use of expansion and other graph-theoretical techniques as metrics for assessing key predistribution schemes and their resulting wireless sensor networks.