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Background To manage the public health risk posed by COVID-19 and assess the impact of interventions, policymakers must be able to closely monitor the epidemic's trajectory. Methods Here we present a simple methodology based on basic surveillance metrics for monitoring the spread of COVID-19 and its burden on health services in Scotland. Results We examine how the dynamics of the epidemic have changed over time and assess the similarities and differences between metrics. Discussion We illustrate how our method has been used throughout the epidemic in Scotland, explore potential biases and conclude that our method has proven to be an effective tool for monitoring the epidemic's trajectory.

Funding: BREATHE - The Health Data Research Hub for Respiratory Health, which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK (MC_PC_19004); CSO Rapid Research in Covid-19 Programme (COV/SAN/20/06); HDR UK Measuring and Understanding Multi-morbidity using Routine Data in the UK (MurMuRUK) (HDR-9006-9006; CFC0110); Medical Research Council (MR/R008345/1). Objectives We investigated the association between multimorbidity among patients hospitalised with COVID-19 and their subsequent risk of mortality. We also explored the interaction between the presence of multimorbidity and the requirement for an individual to shield due to the presence of specific conditions and its association with mortality. Design We created a cohort of patients hospitalised in Scotland due to COVID-19 during the first wave (between 28 February 2020 and 22 September 2020) of the pandemic. We identified the level of multimorbidity for the patient on admission and used logistic regression to analyse the association between multimorbidity and risk of mortality among patients hospitalised with COVID-19. Setting Scotland, UK. Participants Patients hospitalised due to COVID-19. Main outcome measures Mortality as recorded on National Records of Scotland death certificate and being coded for COVID-19 on the death certificate or death within 28 days of a positive COVID-19 test. Results Almost 58% of patients admitted to the hospital due to COVID-19 had multimorbidity. Adjusting for confounding factors of age, sex, social class and presence in the shielding group, multimorbidity was significantly associated with mortality (adjusted odds ratio 1.48, 95%CI 1.26–1.75). The presence of multimorbidity and presence in the shielding patients list were independently associated with mortality but there was no multiplicative effect of having both (adjusted odds ratio 0.91, 95%CI 0.64–1.29). Conclusions Multimorbidity is an independent risk factor of mortality among individuals who were hospitalised due to COVID-19. Individuals with multimorbidity could be prioritised when making preventive policies, for example, by expanding shielding advice to this group and prioritising them for vaccination. Publisher PDF

Background Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has challenged public health agencies globally. In order to effectively target government responses, it is critical to identify the individuals most at risk of coronavirus disease-19 (COVID-19), developing severe clinical signs, and mortality. We undertook a systematic review of the literature to present the current status of scientific knowledge in these areas and describe the need for unified global approaches, moving forwards, as well as lessons learnt for future pandemics. Methods Medline, Embase and Global Health were searched to the end of April 2020, as well as the Web of Science. Search terms were specific to the SARS-CoV-2 virus and COVID-19. Comparative studies of risk factors from any setting, population group and in any language were included. Titles, abstracts and full texts were screened by two reviewers and extracted in duplicate into a standardised form. Data were extracted on risk factors for COVID-19 disease, severe disease, or death and were narratively and descriptively synthesised. Results One thousand two hundred and thirty-eight papers were identified post-deduplication. Thirty-three met our inclusion criteria, of which 26 were from China. Six assessed the risk of contracting the disease, 20 the risk of having severe disease and ten the risk of dying. Age, gender and co-morbidities were commonly assessed as risk factors. The weight of evidence showed increasing age to be associated with severe disease and mortality, and general comorbidities with mortality. Only seven studies presented multivariable analyses and power was generally limited. A wide range of definitions were used for disease severity. Conclusions The volume of literature generated in the short time since the appearance of SARS-CoV-2 has been considerable. Many studies have sought to document the risk factors for COVID-19 disease, disease severity and mortality; age was the only risk factor based on robust studies and with a consistent body of evidence. Mechanistic studies are required to understand why age is such an important risk factor. At the start of pandemics, large, standardised, studies that use multivariable analyses are urgently needed so that the populations most at risk can be rapidly protected. Registration This review was registered on PROSPERO as CRD42020177714.

Summary Background Carbapenemase-producing organisms (CPO) have been largely responsible for the extensive spread of carbapenem resistance, and their prevalence is increasing in many parts of the world. Aim To evaluate clinical and molecular epidemiology and mortality associated with CPO among patients. Methods All CPO from clinical and long-term healthcare surveillance cultures across Scotland in 2003–2017 were reviewed retrospectively. Polymerase chain reaction was used to detect genes coding for carbapenemases. A generalized linear mixed model was used to identify risk factors for mortality. Findings In total, 290 individuals with CPO were identified. The overall incidence increased over time (P60 years [adjusted odds ratio (aOR) 3.36, 95% confidence interval (CI) 1.06–10.63; P=0.033], presence of non-fermenters (aOR 4.88, 95% CI 1.64–14.47; P=0.005), and systemic infection or organ failure (aOR 4.21, 95% CI 1.38–12.81; P=0.032) were independently associated with 30-day mortality. Conclusion The incidence of CPO in Scotland is low but increasing. Awareness is required that inpatients aged >60 years, patients with systemic infection or organ failure, and patients presenting with non-fermenters are at higher risk of death from CPO.

Quantitative evidence for the risk of zoonoses and the spread of antimicrobial resistance remains lacking. Here, as part of the UrbanZoo project, we sampled Escherichia coli from humans, livestock and peri-domestic wildlife in 99 households across Nairobi, Kenya, to investigate its distribution among host species in this rapidly developing urban landscape. We performed whole-genome sequencing of 1,338 E. coli isolates and found that the diversity and sharing patterns of E. coli were heavily structured by household and strongly shaped by host type. We also found evidence for inter-household and inter-host sharing and, importantly, between humans and animals, although this occurs much less frequently. Resistome similarity was differently distributed across host and household, consistent with being driven by shared exposure to antimicrobials. Our results indicate that a large, epidemiologically structured sampling framework combined with WGS is needed to uncover strain-sharing events among different host populations in complex environments and the major contributing pathways that could ultimately drive the emergence of zoonoses and the spread of antimicrobial resistance.

Highlights • First comprehensive description of the relationship between gut mycobiome, fungal sensitisation and seroreactivity in rural pre-school aged African children. • Prevalence of fungal sensitisation in Zimbabwe is discussed. • Gut mycobial abundance and diversity is not associated with SPT reactivity or seroreactivity. • Males have higher antibody responses to P.chrysogenum and E.nigrum., Summary Background The prevalence of allergic diseases has increased over the last few decades, with sensitisation to fungal allergens and gut microbiome dysbiosis implicated in this trend. The fungal community in the gut (mycobiome) has yet to be characterised and related to fungal allergic sensitisation. Thus, we characterised the gut mycobiome and related it to fungal sensitisation and seroreactivity among Zimbabwean children. We further determined the effect of host age, sex, Schistosoma haematobium infection and mycobiome composition on fungal sensitisation and seroreactivity. Methods Using shotgun metagenomic sequencing, we characterised the gut microbiome of stool samples of 116 preschool aged children (PSAC) (≤5 years old, 57(49.1%) male and 59 (50.9%) female). Sensitisation to common fungi in Zimbabwe was assessed using skin prick tests (SPTs). Allergen-specific IgM, IgA, IgG, IgE and IgG4 antibodies were quantified by ELISA. We analysed the relationship between fungal genera and SPT reactivity by ANOVA; fungal genera and IgE antibody reactivity by linear regression; variation in mycobiome abundance with host and environmental factors by PERMANOVA; SPT reactivity and host and environmental factors by logistic regression; seroreactivity and host and environmental factors by ANOVA. Results The mycobiome formed f for, Epicoccum nigrum and Penicillium chrysogenum) and SPT reactivity –related differences in seroreactivity. Conclusion This is the first comprehensive characterisation of gut mycobiome and fungal allergic sensitisation of rural children in Zimbabwe. Although reported allergic disease is low there is a high percentage of sensitisation. Further studies with larger populations are required to understand the role of the mycobiome in allergic diseases., Graphical Abstract Image, graphical abstract

In May this year, the long-awaited final report from the Review on Antimicrobial Resistance was published. 1 The report estimates that the 700 000 annual deaths currently attributable to infections by drug-resistant pathogens will increase, if unchecked, to 10 million by 2050, running up a bill of US$100 trillion in terms of lost global production between now and then. The reported numbers are somewhat apocalyptic, as they are based on crude projections of current trends and presume the failure of anti-malarial drugs and antiretroviral therapy as well as antibiotics. Nor do they account for the possible entry of new drugs onto the market. There is huge uncertainty associated with the human burden of antimicrobial resistance (AMR) in general and antibiotic resistance in particular (for the purpose of this article the widely used term antimicrobial resistance [AMR] is retained, though the emphasis here is on antibiotic resistance). The precision of the estimates does not really matter much: the point is that the problem is already immense and is growing rapidly. We have been squandering our antibiotic resources for far too long and immediate action is needed on a very large scale if we are to reverse current trends., Transactions of the Royal Society of Tropical Medicine and Hygiene, 110 (7), ISSN:0035-9203

Hospital-based and community-based ‘high-risk cohort’ studies investigating humans at risk of zoonotic infection due to occupational or residential exposure to animals were conducted in Vietnam, with diverse viruses identified from faecal samples collected from humans, domestic and wild animals. In this study, we focus on the positive-sense RNA virus family Picornaviridae, investigating the prevalence, diversity, and potential for cross-species transmission. Through metagenomic sequencing, we found picornavirus contigs in 23% of samples, belonging to 15 picornavirus genera. Prevalence was highest in bats (67%) while diversity was highest in rats (nine genera). In addition, 22% of the contigs were derived from novel viruses: Twelve phylogenetically distinct clusters were observed in rats of which seven belong to novel species or types in the genera Hunnivirus, Parechovirus, Cardiovirus, Mosavirus and Mupivirus, four distinct clusters were found in bats, belonging to one novel parechovirus species and one related to an unclassified picornavirus. There was no evidence for zoonotic transmission in our data. Our study provides an improved knowledge of the diversity and prevalence of picornaviruses, including a variety of novel picornaviruses in rats and bats. We highlight the importance of monitoring the human–animal interface for possible spill-over events.

In the first wave of the COVID-19 pandemic (April 2020), SARS-CoV-2 was detected in farmed minks and genomic sequencing was performed on mink farms and farm personnel. Here, we describe the outbreak and use sequence data with Bayesian phylodynamic methods to explore SARS-CoV-2 transmission in minks and related humans on farms. High number of farm infections (68/126) in minks and farm related personnel (>50% of farms) were detected, with limited spread to the general human population. Three of five initial introductions of SARS-CoV-2 lead to subsequent spread between mink farms until November 2020. The largest cluster acquired a mutation in the receptor binding domain of the Spike protein (position 486), evolved faster and spread more widely and longer. Movement of people and distance between farms were statistically significant predictors of virus dispersal between farms. Our study provides novel insights into SARS-CoV-2 transmission between mink farms and highlights the importance of combing genetic information with epidemiological information at the animal-human interface.

Background:The variation in the pathogen type as well as the spatial heterogeneity of predictors make the generality of any associations with pathogen discovery debatable. Our previous work confirmed that the association of a group of predictors differed across different types of RNA viruses, yet there have been no previous comparisons of the specific predictors for RNA virus discovery in different regions. The aim of the current study was to close the gap by investigating whether predictors of discovery rates within three regions—the United States, China, and Africa—differ from one another and from those at the global level.Methods:Based on a comprehensive list of human-infective RNA viruses, we collated published data on first discovery of each species in each region. We used a Poisson boosted regression tree (BRT) model to examine the relationship between virus discovery and 33 predictors representing climate, socio-economics, land use, and biodiversity across each region separately. The discovery probability in three regions in 2010–2019 was mapped using the fitted models and historical predictors.Results:The numbers of human-infective virus species discovered in the United States, China, and Africa up to 2019 were 95, 80, and 107 respectively, with China lagging behind the other two regions. In each region, discoveries were clustered in hotspots. BRT modelling suggested that in all three regions RNA virus discovery was better predicted by land use and socio-economic variables than climatic variables and biodiversity, although the relative importance of these predictors varied by region. Map of virus discovery probability in 2010–2019 indicated several new hotspots outside historical high-risk areas. Most new virus species since 2010 in each region (6/6 in the United States, 19/19 in China, 12/19 in Africa) were discovered in high-risk areas as predicted by our model.Conclusions:The drivers of spatiotemporal variation in virus discovery rates vary in different regions of the world. Within regions virus discovery is driven mainly by land-use and socio-economic variables; climate and biodiversity variables are consistently less important predictors than at a global scale. Potential new discovery hotspots in 2010–2019 are identified. Results from the study could guide active surveillance for new human-infective viruses in local high-risk areas.Funding:FFZ is funded by the Darwin Trust of Edinburgh (https://darwintrust.bio.ed.ac.uk/). MEJW has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 874735 (VEO) (https://www.veo-europe.eu/).

BACKGROUND: Antimicrobial resistance is one of the great challenges facing global health security in the modern era. Wildlife, particularly those that use urban environments, are an important but understudied component of epidemiology of antimicrobial resistance. We investigated antimicrobial resistance overlap between sympatric wildlife, humans, livestock, and their shared environment across the developing city of Nairobi, Kenya. We use these data to examine the role of urban wildlife in the spread of clinically relevant antimicrobial resistance.METHODS: 99 households across Nairobi were randomly selected on the basis of socioeconomic stratification. A detailed survey was administered to household occupants, and samples (n=2102) were collected from the faeces of 75 wildlife species inhabiting household compounds (ie, the household and its perimeter; n=849), 13 livestock species (n=656), and humans (n=333), and from the external environment (n=288). Escherichia coli, our sentinel organism, was cultured and a single isolate from each sample tested for sensitivity to 13 antibiotics. Diversity of antimicrobial resistant phenotypes was compared between urban wildlife, humans, livestock, and the environment, to investigate whether wildlife are a net source for antimicrobial resistance in Nairobi. Generalised linear mixed models were used to determine whether the prevalence of antimicrobial resistant phenotypes and multidrug-resistant E coli carriage in urban wildlife is linked to variation in ecological traits, such as foraging behaviour, and to determine household-level risk factors for sharing of antimicrobial resistance between humans, wildlife, and livestock.FINDINGS: E coli were isolated from 485 samples collected from wildlife between Sept 6,2015, and Sept 28, 2016. Wildlife carried a low prevalence of E coli isolates susceptible to all antibiotics tested (45 [9%] of 485 samples) and a high prevalence of clinically relevant multidrug resistance (252 [52%] of 485 samples), which varied between taxa and by foraging traits. Multiple isolates were resistant to one agent from at least seven antimicrobial classes tested for, and a single isolate was resistant to all antibiotics tested for in the study. The phenotypic diversity of antimicrobial-resistant E coli in wildlife was lower than in livestock, humans, and the environment. Within household compounds, statistical models identified two interfaces for exchange of antimicrobial resistance: between both rodents, humans and their rubbish, and seed-eating birds, humans and their rubbish; and between seed-eating birds, cattle, and bovine manure.INTERPRETATION: Urban wildlife carry a high burden of clinically relevant antimicrobial-resistant E coli in Nairobi, exhibiting resistance to drugs considered crucial for human medicine by WHO. Identifiable traits of the wildlife contribute to this exposure; however, compared with humans, livestock, and the environment, low phenotypic diversity in wildlife is consistent with the hypothesis that wildlife are a net sink rather than source of clinically relevant resistance. Wildlife that interact closely with humans, livestock, and both human and livestock waste within households, are exposed to more antimicrobial resistant phenotypes, and could therefore act as conduits for the dissemination of clinically relevant antimicrobial resistance to the wider environment. These results provide novel insight into the broader epidemiology of antimicrobial resistance in complex urban environments, characteristic of lower-middle-income countries.FUNDING: UK Medical Research Council and CGIAR Research Program on Agriculture for Nutrition and Health.

Swineinfluenza A virus(SwIV) infection has considerable economic and animal welfare consequences and, because of the zoonotic potential, can also have public health implications. The 2009 pandemicH1N1‘swine-origin’ infection is now endemic in both pigs and humans. In Europe, avian-like H1avN1, human-like H1huN2, human-like swineH3N2and, since 2009, pandemic H1N1 (pH1N1) lineage viruses andreassortants, constitute the dominant subtypes. In this study, we used a swine pH1N1 challenge virus to investigate the efficacy ofwhole inactivated virus vaccineshomologous or heterologous to the challenge virus as well as a commercial vaccine. We found that vaccine-mediated protection was most effective when vaccine antigen and challenge virus were homologous and correlated with the specific production ofneutralising antibodiesand a cellular response to the challenge virus. We conclude that a conventional whole inactivated SwIV vaccine must be antigenically matched to the challenge strain to be an effective control measure.

In this study we demonstrate that the adoption of a segmenting and shielding (S&S) strategy could increase scope to partially exit COVID-19 lockdown while limiting the risk of an overwhelming second wave of infection.The S&S strategy has an antecedent in the “cocooning” of infants by immunisation of close family members (Forsyth et al., 2015), and forms a pillar of infection, prevention and control (IPC) strategies (RCN, 2017). We are unaware of it being proposed as a major public health initiative previously.We illustrate the S&S strategy using a mathematical model that segments the vulnerable population and their closest contacts, the “shielders”. We explore the effects on the epidemic curve of a gradual ramping up of protection for the vulnerable population and a gradual ramping down of restrictions on the non-vulnerable population over a period of 12 weeks after lockdown.The most important determinants of outcome are: i) post-lockdown transmission rates within the general population segment and between the general and vulnerable segments; ii) the fraction of the population in the vulnerable and shielder segments; iii) adherence with need to be protected; and iv) the extent to which population immunity builds up in all segments.We explored the effects of extending the duration of lockdown and faster or slower transition to post-lockdown conditions and, most importantly, the trade-off between increased protection of the vulnerable segment and fewer restrictions on the general population.We illustrate how the potential for the relaxation of restrictions interacts with specific policy objectives. We show that the range of options for relaxation in the general population can be increased by maintaining restrictions on the shielder segment and by intensive routine screening of shielders.We find that the outcome of any future policy is strongly influenced by the contact matrix between segments and the relationships between physical distancing measures and transmission rates. These relationships are difficult to quantify so close monitoring of the epidemic would be essential during and after the exit from lockdown.More generally, S&S has potential applications for any infectious disease for which there are defined proportions of the population who cannot be treated or who are at risk of severe outcomes.

Countries of the World Health Organization (WHO) African Region have experienced a wide range of coronavirus disease 2019 (COVID-19) epidemics. This study aimed to identify predictors of the timing of the first COVID-19 case and the per capita mortality in WHO African Region countries during the first and second pandemic waves and to test for associations with the preparedness of health systems and government pandemic responses. Using a region-wide, country-based observational study, we found that the first case was detected earlier in countries with more urban populations, higher international connectivity and greater COVID-19 test capacity but later in island nations. Predictors of a high first wave per capita mortality rate included a more urban population, higher pre-pandemic international connectivity and a higher prevalence of HIV. Countries rated as better prepared and having more resilient health systems were worst affected by the disease, the imposition of restrictions or both, making any benefit of more stringent countermeasures difficult to detect. Predictors for the second wave were similar to the first. Second wave per capita mortality could be predicted from that of the first wave. The COVID-19 pandemic highlights unanticipated vulnerabilities to infectious disease in Africa that should be taken into account in future pandemic preparedness planning., A new study from the WHO African Region identifies features of countries that predict timing of the first case and the per capita mortality rate for the first and second waves of the COVID-19 epidemics.

Retrospective analyses of the non-pharmaceutical interventions (NPIs) used to combat the ongoing COVID-19 outbreak have highlighted the potential of optimizing interventions. These optimal interventions allow policymakers to manage NPIs to minimize the epidemiological and human health impacts of both COVID-19 and the intervention itself. Here, we use a susceptible–infectious–recovered (SIR) mathematical model to explore the feasibility of optimizing the duration, magnitude and trigger point of five different NPI scenarios to minimize the peak prevalence or the attack rate of a simulated UK COVID-19 outbreak. An optimal parameter space to minimize the peak prevalence or the attack rate was identified for each intervention scenario, with each scenario differing with regard to how reductions to transmission were modelled. However, we show that these optimal interventions are fragile, sensitive to epidemiological uncertainty and prone to implementation error. We highlight the use of robust, but suboptimal interventions as an alternative, with these interventions capable of mitigating the peak prevalence or the attack rate over a broader, more achievable parameter space, but being less efficacious than theoretically optimal interventions. This work provides an illustrative example of the concept of intervention optimization across a range of different NPI strategies. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

In December 2019, a novel coronavirus COVID-19 emerged from Wuhan, China, and was soon declared as pandemic by the World Health Organization (WHO) on the 11 March 2020.1 The UK soon followed suit and implemented a national lockdown on the 23 March 2020. As of 9 December 2020, according to WHO, this highly infectious virus has infected more than 67 million people and led to over 1.5 million deaths across the world.2 There is a growing body of evidence on the epidemiology of the condition, risk factors for poor outcomes and effects of interventions.3–9 The rapid generation of robust data is crucial to monitor, understand and mitigate the effects of COVID-19. The Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) database creates a national, real-time prospective cohort using Scotland’s health data infrastructure, to describe the epidemiology of COVID-19 infection, patterns of healthcare use and outcomes, and insights into the effectiveness of and safety of vaccines and treatments for COVID-19.10 This work builds on an established cohort for seasonal and pandemic influenza vaccine and anti-viral assessment in Scotland EAVE (Early Estimation of Vaccine and Anti-Viral Effectiveness).11,12 EAVE is a dormant pandemic protocol that is part of the National Institute for Health Research (NIHR) Pandemic Preparedness Research Portfolio and a platform for previous studies on influenza vaccine and antiviral assessment.

Background The antihelminthic drug praziquantel has been used as the drug of choice for treating schistosome infection for more than 40 years. Although some epidemiological studies have reported low praziquantel efficacy in cure rate (CR) and/or egg reduction rate (ERR), there is no consistent robust evidence of the development of schistosome resistance to praziquantel (PZQ). There is need to determine factors that lead to variable treatment CR and/or ERR. Therefore, we conducted a systematic review and meta-analysis to review CR and ERR as well as identify their predictors. Methodology/Principal findings In this systematic review and meta-analysis, a literature review was conducted using Biosis Citation Index, Data Citation Index, MEDLINE, and Web of Science Core Collection all of which were provided through Web of Science. Alongside these, EMBASE, and CAB abstracts were searched to identify relevant articles. Random effect meta-regression models were used to identify the factors that influence CR and/or ERR by considering differences in host characteristics and drug dose. In total, 12,127 potential articles were screened and 146 eligible articles (published from 1979 to 2020) were identified and included for the meta-analysis. We found that there has been no significant reduction in CR or ERR over the study period. The results showed more variability in CR, compared with ERR which was more consistent and remained high. The results showed a positive effect of “PZQ treatment dose” with the current recommended dose of 40 mg/kg body weight achieving 57% to 88% CR depending on schistosome species, age of participants, and number of parasitological samples used for diagnosis, and ERR of 95%. Conclusions/Significance Based on a review of over 40 years of research there is no evidence to support concerns about schistosomes developing resistance to PZQ. These results indicate that PZQ remains effective in treating schistosomiasis., Author summary Schistosomiasis is a water-borne disease caused by a group of flatworm parasites known as schistosomes. The drug praziquantel has been used since the 1970s to treat schistosomiasis with some variable measures of efficacy reported. Reasons for the variation can be related to host attributes or parasite attributes. We conducted this study to determine factors affecting drug efficacy and if the efficacy has altered in the past four decades. We identified 146 scientific articles that reported efficacy levels of praziquantel treatment for schistosomiasis as determined by cure rate (CR) and/or egg reduction rate (ERR). We conclude that praziquantel treatment is still effective for schistosomiasis even after four decades of usage. Praziquantel efficacy levels increased with drug dose. Our study showed the majority of studies reported high ERR (>80%) even among studies that reported low CR (

The keeping of livestock has been posited as a risk factor for the emergence of zoonoses and the spread of antimicrobial resistance. However, quantitative evidence regarding the major sources of pathogenic and drug-resistant bacteria and transmission routes between hosts remains lacking. In the largest epidemiological study of this nature to date, we sampled Escherichia coli from humans, livestock, food, wildlife and the environment of 99 households across Nairobi, Kenya to gain a deeper understanding of sharing of bacteria among hosts and potential reservoirs. By analysing whole genome sequencing data from 1,338 E. coli isolates, we reconstruct sharing patterns for the sampled E. coli and its antimicrobial resistance determinants. We find that the diversity and sharing patterns of E. coli is heavily structured by household, which is the primary epidemiological interface for bacterial strain sharing. Strain sharing within households was strongly shaped by host type. We also find evidence for inter-household and inter-host sharing, and importantly, between humans and animals, although this occurs much less frequently. We find similar strain sharing patterns for the E. coli accessory genome, suggesting that it is shaped by recent evolutionary history and is strongly associated with the core genome. Resistome similarity, however, were quite differently distributed across host and household, consistent with their being driven by shared exposure to antimicrobials. Our results indicate that there is potential for the exchange of bacteria between humans, livestock and wildlife in the same household in a tropical urban setting, with wider mixing occurring over a period of months or years, but this does not drive the distribution of antimicrobial resistance.

The second SARS virus, SARS-CoV-2, emerged in December 2019, and within a month was globally distributed. It was first introduced into Scotland in February 2020 associated with returning travellers and visitors. By March it was circulating in communities across the UK, and to control COVID-19 cases, and prevent overwhelming of the National Health Service (NHS), a ‘lockdown’ was introduced on 23rd March 2020 with a restriction of people’s movements. To augment the public health efforts a large-scale genome epidemiology effort (as part of the COVID-19 Genomics UK (COG-UK) consortium) resulted in the sequencing of over 5000 SARS-CoV-2 genomes by 18th August 2020 from Scottish cases, about a quarter of the estimated number of cases at that time. Here we quantify the geographical origins of the first wave introductions into Scotland from abroad and other UK regions, the spread of these SARS-CoV-2 lineages to different regions within Scotland (defined at the level of NHS Health Board) and the effect of lockdown on virus ‘success’. We estimate that approximately 300 introductions seeded lineages in Scotland, with around 25% of these lineages composed of more than five viruses, but by June circulating lineages were reduced to low levels, in line with low numbers of recorded positive cases. Lockdown was, thus, associated with a dramatic reduction in infection numbers and the extinguishing of most virus lineages. Unfortunately since the summer cases have been rising in Scotland in a second wave, with >1000 people testing positive on a daily basis, and hospitalisation of COVID-19 cases on the rise again. Examining the available Scottish genome data from the second wave, and comparing it to the first wave, we find that while some UK lineages have persisted through the summer, the majority of lineages responsible for the second wave are new introductions from outside of Scotland and many from outside of the UK. This indicates that, while lockdown in Scotland is directly linked with the first wave case numbers being brought under control, travel-associated imports (mostly from Europe or other parts of the UK) following the easing of lockdown are responsible for seeding the current epidemic population. This demonstrates that the impact of stringent public health measures can be compromised if following this, movements from regions of high to low prevalence are not minimised.

Background: WHO African Region countries have experienced very different COVID-19 epidemics. This study aimed to identify predictors for the timing of the first COVID-19 case and the per capita mortality rate during the first and second pandemic wave in the region, and to test for any impact of countermeasures. Methods: We performed a region-wide, country-based observational study. Data on COVID-19 cases and deaths for all 47 countries in the WHO African Region were obtained from the WHO COVID-19 Dashboard. A set of predictors classified to nine categories were collected and used as explanatory variables. We applied Cox proportional hazards regression models, generalized linear mixed models and multinomial logistic regression models as appropriate. Findings: Predictors for an earlier first case were a more urban population, high volume of international air travel and more land borders, and better COVID-19 test capacity. Predictors for a high per capita mortality rate during the first wave were a more urban population, more pre-pandemic international air travel and higher prevalence of HIV. The stringency and timing of government restrictions on behaviour were not associated with a lower per capita mortality rate in the first wave. A more urban population and a higher infectious disease resilience score were associated with more stringent restrictions and/or a higher per capita mortality rate in the first wave. The predictor set for the second wave was similar, and first wave per capita mortality predicted that in the second wave. These results were not altered when measures of national testing effort were included in the models. Interpretation: COVID-19 in Africa arrived earlier and caused greater mortality in countries with more international travel and a more urban population. Mortality was exacerbated by high HIV prevalence; it is not clear whether this is a direct or indirect effect. Countries that were better prepared and judged to have more resilient health systems were worst affected, both by the disease and by the imposition of restrictions. The COVID-19 pandemic highlights unanticipated vulnerabilities to infectious disease in Africa. Funding Statement: National Institute for Health Research, Darwin Trust of Edinburgh Declaration of Interests: The authors disclose no conflicts of interest.

EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and the Scottish Government's director-general of Health and Social Care. FDRH acknowledges part support from the National Institutes of Health Research (NIHR) School for Primary Care Research, the NIHR Collaboration for Leadership in Applied Health Research and Care Oxford, and the NIHR Oxford Biomedical Research Centre. SVK acknowledges funding from an NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and Scottish Government Chief Scientist Office (SPHSU13). Background The BNT162b2 mRNA (Pfizer–BioNTech) and ChAdOx1 nCoV-19 (Oxford–AstraZeneca) COVID-19 vaccines have shown high efficacy against disease in phase 3 clinical trials and are now being used in national vaccination programmes in the UK and several other countries. Studying the real-world effects of these vaccines is an urgent requirement. The aim of our study was to investigate the association between the mass roll-out of the first doses of these COVID-19 vaccines and hospital admissions for COVID-19. Methods We did a prospective cohort study using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19—EAVE II—database comprising linked vaccination, primary care, real-time reverse transcription-PCR testing, and hospital admission patient records for 5·4 million people in Scotland (about 99% of the population) registered at 940 general practices. Individuals who had previously tested positive were excluded from the analysis. A time-dependent Cox model and Poisson regression models with inverse propensity weights were fitted to estimate effectiveness against COVID-19 hospital admission (defined as 1–adjusted rate ratio) following the first dose of vaccine. Findings Between Dec 8, 2020, and Feb 22, 2021, a total of 1 331 993 people were vaccinated over the study period. The mean age of those vaccinated was 65·0 years (SD 16·2). The first dose of the BNT162b2 mRNA vaccine was associated with a vaccine effect of 91% (95% CI 85–94) for reduced COVID-19 hospital admission at 28–34 days post-vaccination. Vaccine effect at the same time interval for the ChAdOx1 vaccine was 88% (95% CI 75–94). Results of combined vaccine effects against hospital admission due to COVID-19 were similar when restricting the analysis to those aged 80 years and older (83%, 95% CI 72–89 at 28–34 days post-vaccination). Interpretation Mass roll-out of the first doses of the BNT162b2 mRNA and ChAdOx1 vaccines was associated with substantial reductions in the risk of hospital admission due to COVID-19 in Scotland. There remains the possibility that some of the observed effects might have been due to residual confounding. Funding UK Research and Innovation (Medical Research Council), Research and Innovation Industrial Strategy Challenge Fund, Health Data Research UK. Publisher PDF

Background: The BNT162b2 mRNA (Pfizer-BioNTech) and ChAdOx1 (Oxford-AstraZeneca) COVID-19 vaccines have demonstrated high efficacy against infection in phase 3 clinical trials and are now being used in national vaccination programmes in the UK and several other countries. There is an urgent need to study the ‘real-world’ effects of these vaccines. The aim of our study was to estimate the effectiveness of the first dose of these COVID-19 vaccines in preventing hospital admissions. Methods: We conducted a prospective cohort study using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) database comprising of linked vaccination, primary care, Real-Time Polymerase Chain Reaction (RT-PCR) testing, hospitalisation and mortality records for 5.4 million people in Scotland (covering ~99% of population). A time-dependent Cox model and Poisson regression models were fitted to estimate effectiveness against COVID-19 related hospitalisation (defined as 1- Adjusted Hazard Ratio) following the first dose of vaccine. Findings: The first dose of the BNT162b2 vaccine was associated with a vaccine effect of 85% (95% confidence interval [CI] 76 to 91) for COVID-19 related hospitalisation at 28-34 days post-vaccination. Vaccine effect at the same time interval for the ChAdOx1 vaccine was 94% (95% CI 73 to 99). Results of combined vaccine effect for prevention of COVID-19 related hospitalisation were comparable when restricting the analysis to those aged ≥80 years (81%; 95% CI 65 to 90 at 28-34 days post-vaccination). Interpretation: A single dose of the BNT162b2 mRNA and ChAdOx1 vaccines resulted in substantial reductions in the risk of COVID-19 related hospitalisation in Scotland. Funding: UK Research and Innovation (Medical Research Council); Research and Innovation Industrial Strategy Challenge Fund; Health Data Research UK. Conflict of Interest: AS is a member of the Scottish Government Chief Medical Officer’s COVID-19Advisory Group and the New and Emerging Respiratory Virus Threats (NERVTAG) Risk Stratification Subgroup. CRS declares funding from the MRC, NIHR, CSO and New Zealand Ministry for Business, Innovation and Employment and Health Research Council during the conduct of this study. SVK is co-chair of the Scottish Government’s Expert Reference Group on COVID-19 and ethnicity, is a member of the Scientific Advisory Group on Emergencies (SAGE) subgroup on ethnicity and acknowledges funding from a NRS Senior Clinical Fellowship, MRC and CSO. All other authors report no conflicts of interest. Ethical Approval: Approvals were obtained from the National Research Ethics Service Committee, Southeast Scotland 02 (reference number: 12/SS/0201) and Public Benefit and Privacy Panel for Health and Social Care (reference number: 1920-0279).

Objective:To determine risk factors for carbapenemase-producing organisms (CPOs) and to determine the prognostic impact of CPOs.Design:A retrospective matched case–control study.Patients:Inpatients across Scotland in 2010–2016 were included. Patients with a CPO were matched with 2 control groups by hospital, admission date, specimen type, and bacteria. One group comprised patients either infected or colonized with a non-CPO and the other group were general inpatients.Methods:Conditional logistic regression models were used to identify risk factors for CPO infection and colonization, respectively. Mortality rates and length of postisolation hospitalization were compared between CPO and non-CPO patients.Results:In total, 70 CPO infection cases (with 210 general inpatient controls and 121 non-CPO controls) and 34 CPO colonization cases (with 102 general inpatient controls and 60 non-CPO controls) were identified. Risk factors for CPO infection versus general inpatients were prior hospital stay (adjusted odds ratio [aOR], 4.05; 95% confidence interval [CI], 1.52–10.78; P = .005), longer hospitalization (aOR, 1.07; 95% CI, 1.04–1.10; P < .001), longer intensive care unit (ICU) stay (aOR, 1.41; 95% CI, 1.01–1.98; P = .045), and immunodeficiency (aOR, 3.68; 95% CI, 1.16–11.66; P = .027). Risk factors for CPO colonization were prior high-dependency unit (HDU) stay (aOR, 11.46; 95% CI, 1.27–103.09; P = .030) and endocrine, nutritional, and metabolic (ENM) diseases (aOR, 3.41; 95% CI, 1.02–11.33; P = .046). Risk factors for CPO infection versus non-CPO infection were prolonged hospitalization (aOR, 1.02; 95% CI, 1.00–1.03; P = .038) and HDU stay (aOR, 1.13; 95% CI, 1.02–1.26; P = .024). No differences in mortality rates were detected between CPO and non-CPO patients. CPO infection was associated with longer hospital stay than non-CPO infection (P = .041).Conclusions:A history of (prolonged) hospitalization, prolonged ICU or HDU stay; ENM diseases; and being immunocompromised increased risk for CPO. CPO infection was not associated with increased mortality but was associated with prolonged hospital stay.

Helminths are parasitic worms that infect over a billion people worldwide. The pathological consequences from infection are due in part, to parasite-induced changes in host metabolic pathways. Here, we analyse the changes in host metabolic profiles, in response to the first Schistosoma haematobium infection and treatment in Zimbabwean children. A cohort of 83 schistosome-negative children (2–5 years old) as determined by parasitological examination, guardian interviews and examination of medical records, was recruited at baseline. Children were followed up after three months for parasitological diagnosis of their first S. haematobium infection, by detection of parasite eggs excreted in urine. Children positive for infection were treated with the antihelminthic drug praziquantel, and treatment efficacy checked three months after treatment. Blood samples were taken at each time point, and capillary electrophoresis mass spectrometry in conjunction with multivariate analysis were used to compare the change in serum metabolite profiles in schistosome-infected versus uninfected children. Following baseline at the three-month follow up, 11 children had become infected with S. haematobium (incidence = 13.3%). Our results showed that infection with S. haematobium was associated with significant increases (>2-fold) in discriminatory metabolites, linked primarily with energy (G6P, 3-PG, AMP, ADP) and purine (AMP, ADP) metabolism. These observed changes were commensurate with schistosome infection intensity, and levels of the affected metabolites were reduced following treatment, albeit not significantly. This study demonstrates that early infection with S. haematobium is associated with alterations in host energy and purine metabolism. Taken together, these changes are consistent with parasite-related clinical manifestations of malnutrition, poor growth and poor physical and cognitive performance observed in schistosome-infected children., Author summary Schistosomiasis is a parasitic disease caused by helminth worms called schistosomes, and millions of people worldwide are infected. Studies in animal models indicate that the infection can cause alterations in metabolism of the host, leading to the observed clinical manifestations of the disease. However, there are limited human studies on the impact of schistosome infection on host metabolism, and none describing the changes that occur early in infection. There are also no studies relating to the species Schistosoma haematobium, which causes disease manifested in both the urinary and genital organs. To address this, we analysed the metabolic changes in response to the first S. haematobium infection and treatment in Zimbabwean preschool-aged children. We determined for the first time, that within three months of first infection, there are significant changes in metabolite profiles related to host energy (G6P, 3-PG, AMP, ADP) and purine (AMP, ADP) metabolism. These changes were associated with infection burden and were resolved within three months, following curative treatment. Our findings provide further understanding into the early host metabolic responses to the infection, consistent with clinical manifestations such as malnutrition, poor growth and poor physical and cognitive performance observed in schistosome-infected children. This demonstrates the need for early treatment of the infection, and will inform the development of appropriate interventions such as nutraceuticals in child feeding programs, aimed at reducing disease consequences. Further mechanistic studies will contribute to more understanding of the relationship between metabolic alterations and schistosomiasis in young children.

In 2012, the World Health Organisation (WHO) set out a roadmap for eliminating schistosomiasis as a public health problem by 2025. To achieve this target, preschool-aged children (PSAC; aged 6 years and below) will need to be included in schistosomiasis treatment programmes. As the global community discusses the tools and approaches for treating this group, one of the main questions that remains unanswered is how to quantify infection in this age group to inform treatment strategies. The aim of this study was thus to determine whether a relationship exists between levels of schistosome infection in PSAC and school-aged children (SAC), that can be used to determine unknown schistosome infection prevalence levels in PSAC. A systematic search of publications reporting schistosomiasis prevalence in African PSAC and SAC was conducted. The search strategy was formulated using the PRISMA guidelines and SPIDER search strategy tool. The published data was subjected to regression analysis to determine if a relationship exists between infection levels in PSAC and SAC. The interaction between SAC and community treatment history was also entered in the regression model to determine if treatment history significantly affected the relationship between PSAC and SAC prevalence. The results showed that a significant positive relationship exists between infection prevalence levels in PSAC and SAC for Schistosoma mansoni (r = 0.812, df (88, 1), p =

This study was designed to determine whether vaccination of pigs with conventional WIV or virus-vectored vaccines reduces pH1N1 swine influenza virus shedding following challenge and can prevent transmission to naive in-contact animals. Even when viral shedding was significantly reduced following challenge, infection was transmissible to susceptible cohoused recipients., Swine influenza A virus (swIAV) infection causes substantial economic loss and disease burden in humans and animals. The 2009 pandemic H1N1 (pH1N1) influenza A virus is now endemic in both populations. In this study, we evaluated the efficacy of different vaccines in reducing nasal shedding in pigs following pH1N1 virus challenge. We also assessed transmission from immunized and challenged pigs to naive, directly in-contact pigs. Pigs were immunized with either adjuvanted, whole inactivated virus (WIV) vaccines or virus-vectored (ChAdOx1 and MVA) vaccines expressing either the homologous or heterologous influenza A virus hemagglutinin (HA) glycoprotein, as well as an influenza virus pseudotype (S-FLU) vaccine expressing heterologous HA. Only two vaccines containing homologous HA, which also induced high hemagglutination inhibitory antibody titers, significantly reduced virus shedding in challenged animals. Nevertheless, virus transmission from challenged to naive, in-contact animals occurred in all groups, although it was delayed in groups of vaccinated animals with reduced virus shedding. IMPORTANCE This study was designed to determine whether vaccination of pigs with conventional WIV or virus-vectored vaccines reduces pH1N1 swine influenza A virus shedding following challenge and can prevent transmission to naive in-contact animals. Even when viral shedding was significantly reduced following challenge, infection was transmissible to susceptible cohoused recipients. This knowledge is important to inform disease surveillance and control strategies and to determine the vaccine coverage required in a population, thereby defining disease moderation or herd protection. WIV or virus-vectored vaccines homologous to the challenge strain significantly reduced virus shedding from directly infected pigs, but vaccination did not completely prevent transmission to cohoused naive pigs.

The Infectious Diseases of East African Livestock (IDEAL) project was a longitudinal cohort study of calf health which was conducted in Western Kenya between 2007–2010. A total of 548 East African shorthorn zebu calves were recruited at birth and followed at least every 5 weeks during the first year of life. Comprehensive clinical and epidemiological data, blood and tissue samples were collected at every visit. These samples were screened for over 100 different pathogens or infectious exposures, using a range of diagnostic methods. This manuscript describes this comprehensive dataset and bio-repository, and how to access it through a single online site (http://data.ctlgh.org/ideal/). This provides extensive filtering and searching capabilities. These data are useful to illustrate outcomes of multiple infections on health, investigate patterns of morbidity and mortality due to parasite infections, and to study genotypic determinants of immunity and disease., Measurement(s) Health • disease • genotype • Phenotypic variability Technology Type(s) Examination • single-nucleotide polymorphism analysis Sample Characteristic - Organism Bos indicus Sample Characteristic - Environment farm Sample Characteristic - Location Kenya Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12459737

BackgroundSevere Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has challenged public health agencies globally. In order to effectively target government responses, it is critical to identify the individuals most at risk of coronavirus disease-19 (COVID-19), developing severe clinical signs, and mortality. We undertook a systematic review of the literature, to present the current status of scientific knowledge in these areas and describe the need for unified global approaches, moving forwards, as well as lessons learnt for future pandemics. MethodsMedline, Embase and Global Health were searched to the end of April 2020, as well as the Web of Science. Search terms were specific to the SARS-CoV-2 virus and COVID-19. Comparative studies of risk factors from any setting, population group and in any language were included. Titles, abstracts and full texts were screened by two reviewers and extracted in duplicate into a standardised form. Data were extracted on risk factors for COVID-19 disease, severe disease, or death and were narratively and descriptively synthesised. Results1,238 papers were identified post-deduplication. 33 met our inclusion criteria, of which 26 were from China. Six assessed the risk of contracting the disease, 20 the risk of having severe disease and ten the risk of dying. Age, gender and co-morbidities were commonly assessed as risk factors. The weight of evidence showed increasing age to be associated with severe disease and mortality, and general comorbidities with mortality. Only seven studies presented multivariable analyses and power was generally limited. A wide range of definitions were used for disease severity. ConclusionsThe volume of literature generated in the short time since the appearance of SARS-CoV-2 has been considerable. Many studies have sought to document the risk factors for COVID-19 disease, disease severity and mortality; age was the only risk factor based on robust studies and with a consistent body of evidence. Mechanistic studies are required to understand why age is such an important risk factor. At the start of pandemics, large, standardised, studies that use multivariable analyses are urgently needed so that the populations most at risk can be rapidly protected. This review was registered on PROSPERO as CRD42020177714.

Acute respiratory infections (ARIs) impose a major public health burden on fragile healthcare systems of developing Southeast Asian countries such as Vietnam. The epidemiology, genetic diversity and transmission patterns of respiratory viral pathogens that circulate in this region are not well characterized. We used RT-PCR to screen for 14 common respiratory viruses in nasal/throat samples from 4326 ARI patients from 5 sites in Vietnam during 2012-2016. 64% of patients tested positive for viruses; 14% tested positive multiple co-infecting viruses. The most frequently detected viruses were Respiratory syncytial virus (RSV, 23%), Human Rhinovirus (HRV, 13%), Influenza A virus (IAV, 11%) and Human Bocavirus (HBoV, 7%). RSV infections peaked in July to October, were relatively more common in children 5 years in the central region. Coinfection with IAV or RSV was associated with increased disease severity compared with patients only infected with HBoV or HRV. Over a hundred genomes belonging to 13 families and 24 genera were obtained via metagenomic sequencing, including novel viruses and viruses less commonly associated with ARIs. Phylogenetic and phylogeographic analyses further indicated that neighboring countries were the most likely source of many virus lineages causing ARIs in Vietnam and estimated the period that specific lineages have been circulating. Our study illustrates the value of applying the state-of-the-art virus diagnostic methods (multiplex RT-PCR and metagenomic sequencing) and phylodynamic analyses at a national level to generate an integrated picture of viral ARI epidemiology.

Antimicrobial resistance (AMR), a cross-cutting and increasing threat to global health (1–3), is a complex problem with multiple and interconnected drivers. Reliable surveillance data that accurately describe and characterize the global occurrence and distribution of AMR are essential for tracking changes in resistance over time, setting national and global priorities, assessing the impacts of interventions, identifying new kinds of resistance, and supporting investigation of (international) outbreaks of resistant pathogens. AMR surveillance data can also inform development of treatment guidelines. Yet it has proven difficult to achieve these objectives on a global scale, and especially in low- and middle-income countries (LMICs), largely because current surveillance systems deliver data that are extremely variable in quality and quantity and highly heterogeneous in terms of which population is sampled (usually a category of hospital patients) and what drug-bug combinations are included (1). Here, we outline a plan for a global AMR surveillance system based on applying next-generation sequencing (NGS) to human sewage that will be especially helpful for community AMR surveillance, which is difficult to achieve in other ways, and will provide an affordable surveillance option in resource-poor settings.NGS is a powerful technology that has transformed the health data landscape. Among many other benefits, it has drastically improved our ability to determine the presence of AMR genes (bacterial genes known to confer resistance to an antimicrobial drug) in single isolates and to quantify them in complex microbiomes (4, 5). Millions of random DNA fragments sequenced by NGS can be mapped to reference sequence databases, and the number of reads coming from any of several thousand known AMR genes can be counted to provide easily shared information on their occurrence and abundance.Increasing numbers of people globally are connected to sewage treatment systems (6) and, as recently highlighted by the World Bank (3), metagenomics-based, near realtime quantification of AMR genes in sewage is a potentially useful surveillance tool even in remote locations without microbiology laboratories (7). Such an approach could quickly plug current gaps in the geographic, population, and agent coverage of AMR surveillance, especially by providing data on AMR outside hospitals (90% of antibiotic usage in humans occurs outside hospitals) (see the figure). It could also provide information on environmental transmission in populations exposed to raw sewage.

There are believed to be thousands of as yet unknown viruses ‘out there’, circulating in wildlife and possibly our domestic animals too. Which of these viruses might have, or might acquire, the potential to cause the next global pandemic in humans?

Summary Profound changes are occurring in the epidemiology of schistosomiasis, a neglected tropical disease caused by a chronic infection with parasitic helminths of the genus Schistosoma . Schistosomiasis currently affects 240 million people worldwide, mostly in sub-Saharan Africa. The advent and proliferation of mass drug administration (MDA) programmes using the drug praziquantel is resulting in substantial increases in the number of people, mainly children aged 6–14 years, being effectively treated, approaching the point where most people in endemic areas will receive one or more treatments during their lifetimes. Praziquantel treatment not only cures infection but also frees the host from the powerful immunomodulatory action of the parasites. The treatment simultaneously enhances exposure to key parasite antigens, accelerating the development of protective acquired immunity, which would take many years to develop naturally. At a population level, these changes constitute a substantial alteration to schistosome ecology in that the parasites are more likely to be exposed not only to praziquantel directly but also to hosts with altered immune phenotypes. Here, we consider the consequences of this for schistosome biology, immunoepidemiology, and public health. We anticipate that there could be substantial effects on chronic pathology, natural immunity, vaccine development strategies, immune disorders, and drug efficacy. This makes for a complex picture that will only become apparent over decades. We recommend careful monitoring and assessment to accompany the roll-out of MDA programmes to ensure that the considerable health benefits to populations are achieved and sustained.

Helminth parasites have been shown to have systemic effects in the host. Using shotgun metagenomic sequencing, we characterise the gut microbiome and resistome of 113 Zimbabwean preschool-aged children (1–5 years). We test the hypothesis that infection with the human helminth parasite, Schistosoma haematobium, is associated with changes in gut microbial and antimicrobial resistance gene abundance/diversity. Here, we show that bacteria phyla Bacteroidetes, Firmicutes, Proteobacteria, and fungi phyla Ascomycota, Microsporidia, Zoopagomycota dominate the microbiome. The abundance of Proteobacteria, Ascomycota, and Basidiomycota differ between schistosome-infected versus uninfected children. Specifically, infection is associated with increases in Pseudomonas, Stenotrophomonas, Derxia, Thalassospira, Aspergillus, Tricholoma, and Periglandula, with a decrease in Azospirillum. We find 262 AMR genes, from 12 functional drug classes, but no association with individual-specific data. To our knowledge, we describe a novel metagenomic dataset of Zimbabwean preschool-aged children, indicating an association between urogenital schistosome infection and changes in the gut microbiome., Osakunor et al. show that infection of Zimbabwean preschool-aged children with Schistosoma haematobium correlates with abundance changes in Pseudomonas, Stenotrophomonas, Derxia, Thalassospira, Aspergillus, Tricholoma, Periglandula, and Azospirillum. This study provides a microbiome and resistome dataset of African preschool-aged children.

There is still limited information on the diversity of viruses co-circulating in humans and animals. Here, we report data obtained from a large field collection of enteric samples taken from humans, pigs, rodents and other mammal hosts in Vietnam between 2012 and 2016. Each of 2100 stool or rectal swab samples was subjected to virally-enriched agnostic metagenomic sequencing; the short read sequence data are accessible from the European Nucleotide Archive (ENA). We link the sequence data to metadata on host type and demography and geographic location, distinguishing hospital patients, members of a cohort identified as a high risk of zoonotic infections (e.g. abattoir workers, rat traders) and animals. These data are suitable for further studies of virus diversity and virus discovery in humans and animals from Vietnam and to identify viruses found in multiple hosts that are potentially zoonotic., Measurement(s)viral metagenomeTechnology Type(s)DNA SequencingFactor Type(s)related animalSample Characteristic - OrganismHomo sapiens • Rodentia • Chiroptera • Simiiformes • Sus • Carnivora • Capra hircusSample Characteristic - Environmentfecal environmentSample Characteristic - LocationViet Nam Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9889709

To have epidemic potential, a pathogen must be able to spread in human populations, but of human-infective RNA viruses only a minority can do so. We investigated the evolution of human transmissibility through parallel analyses of 1755 virus genome sequences from 39 RNA virus genera. We identified 57 lineages containing human-transmissible species and estimated that at least 74% of these lineages have evolved directly from non-human viruses in other mammals or birds, a public health threat recently designated “Disease X”. Human-transmissible viruses rarely evolve from virus lineages that can infect but not transmit between humans. This result cautions against focussing surveillance and mitigation efforts narrowly on currently known human-infective virus lineages and supports calls for a better understanding of RNA virus diversity in non-human hosts.

A swine influenza A pandemic 2009 H1N1 (pH1N1) virus was used in a pig challenge model to investigate the efficacy of whole inactivated vaccines homologous or heterologous to the challenge virus as well as a commercial vaccine. Nasal shedding of viral RNA was monitored daily by real-time, quantitative RT-PCR (RRT-qPCR) as detailed (1). Here we report the statistical modelling of the viral RNA shedding kinetics. Keywords: Pandemic 2009 swine influenza a virus, Whole inactivated vaccine, Viral kinetics, Statistical modelling RRT-qPCR

Novel infectious diseases continue to emerge within human populations. Predictive studies have begun to identify pathogen traits associated with emergence. However, emerging pathogens vary widely in virulence, a key determinant of their ultimate risk to public health. Here, we use structured literature searches to review the virulence of each of the 214 known human-infective RNA virus species. We then use a machine learning framework to determine whether viral virulence can be predicted by ecological traits, including human-to-human transmissibility, transmission routes, tissue tropisms, and host range. Using severity of clinical disease as a measurement of virulence, we identified potential risk factors using predictive classification tree and random forest ensemble models. The random forest approach predicted literature-assigned disease severity of test data with mean accuracy of 89.4% compared to a null accuracy of 74.2%. In addition to viral taxonomy, the ability to cause systemic infection was the strongest predictor of severe disease. Further notable predictors of severe disease included having neural and/or renal tropism, direct contact or respiratory transmission, and limited (0 < R0 ≤ 1) human-to-human transmissibility. We present a novel, to our knowledge, comparative perspective on the virulence of all currently known human RNA virus species. The risk factors identified may provide novel perspectives in understanding the evolution of virulence and elucidating molecular virulence mechanisms. These risk factors could also improve planning and preparedness in public health strategies as part of a predictive framework for novel human infections., Comparative analysis using machine learning shows that specificity of tissue tropism and transmission biology can act as predictive risk factors for the virulence of human RNA viruses.

Highlights • First study of antimicrobial resistance (AMR) phenotypes in humans and urban livestock. • Highest AMR carriage in humans, pigs and poultry. • AMR more common in larger households. • Urban livestock keeping is not a risk factor for AMR in humans. • Use of animal manure affects the risk factor of AMR in humans., There are substantial limitations in understanding of the distribution of antimicrobial resistance (AMR) in humans and livestock in developing countries. This papers present the results of an epidemiological study examining patterns of AMR in Escherichia coli isolates circulating in sympatric human (n = 321) and livestock (n = 633) samples from 99 households across Nairobi, Kenya. E. coli isolates were tested for susceptibility to 13 antimicrobial drugs representing nine antibiotic classes. High rates of AMR were detected, with 47.6% and 21.1% of isolates displaying resistance to three or more and five or more antibiotic classes, respectively. Human isolates showed higher levels of resistance to sulfonamides, trimethoprim, aminoglycosides and penicillins compared with livestock (P0.05). These findings revealed a high prevalence of AMR E. coli circulating in healthy humans and livestock in Nairobi, with no evidence to suggest that keeping livestock, when treated as a single risk factor, contributed significantly to the burden of AMR in humans, although the presence of livestock waste was significant. These results provide an understanding of the broader epidemiology of AMR in complex and interconnected urban environments.

Despite ∼60 % of human pathogens being of zoonotic origin, the diversity of viruses in non-human hosts remains understudied. To investigate the viral diversity present in Vietnam, the Vietnam Initiative on Zoonotic Infections (VIZIONS) collected 2100 rectal swabs and faecal samples from several non-human hosts (including pigs, rats and bats) as well enteric hospital patients and individuals with high animal contact. Samples underwent metagenomic sequencing (Illumina HiSeq), assembly (MetaSPAdes) and screening for viral sequences (Blastn and DIAMOND Blastp). The majority (87.5%) of viral sequences identified in humans shared high nucleotide identity (>95 %) to previously described viral species, with the exception of sequences assigned to Anelloviridae and Picobirnaviridae. Approximately half (46.5%) of viral sequences identified in non-human hosts shared between 60 to 80% nucleotide identity to its closest match in GenBank. For all hosts other from rats the family with the highest frequency of low identity hits was Picobirnaviridae. Sources of diversity at a viral family level were mostly host specific, with pigs having diverse astrovirus and smacovirus sequences, rats for rotavirus and adenovirus and bats for coronavirus and parvovirus. These results highlight the historic bias of sequencing viruses from human hosts and the need to shift focus to non-human hosts. Pig, bats and rats in Vietnam harbor a large diversity of potentially novel viruses that may be threats to animal and human health.

In medical, scientific and political arenas relating to antimicrobial resistance (AMR) there is currently an intense focus on multi-drug resistant pathogens that render last-line antimicrobial treatments ineffective. We question the current emphasis of attention on resistance to last-line antimicrobials, arguing that tackling resistance to front-line antimicrobials has a greater public health benefit. Using AMR monitoring data on 25 drug-pathogen combinations from across Europe, here we show that the presence of front-line pathogen resistance initiates a cascade of resistance selection that ultimately leads to pathogen resistance to last-line antimicrobials. We then interrogate, by modelling the dynamics of resistance evolution, whether 3 key interventions in the strategic response to AMR are more effectively targeted at front-line or last-line treatment. We show that interventions that make front-line therapy more effective by use of antimicrobial adjuvants or front-line resistance diagnostics or by introduction of a novel, front-line antimicrobial all lead to a larger reduction in mortality and morbidity than the same interventions implemented in last-line therapy. Mass use of a newly discovered antimicrobial in front-line infection management to maximise its public health benefit is contrary to current policy but may provide valuable incentives for drug developers. We demonstrate that funding, publications, and attention to those publications do not reflect the importance of front-line antimicrobials and are disproportionately devoted to last-line antimicrobials that account for less than 10% of antimicrobial prescriptions. While studying resistance to last-line drugs is undoubtedly important, our work relays a strong message to public health agencies, funding bodies, and researchers that allocating resources to front-line infections can be a more effective way to combat the antimicrobial resistance crisis.

Genetic sequence data from pathogens present a novel means to investigate the spread of infectious disease between infected hosts or infected premises, complementing traditional contact-tracing approaches, and much recent work has gone into developing methods for this purpose. The objective is to recover the epidemic transmission tree, which identifies who infected whom. This paper reviews the various approaches that have been taken. The first step is to define a measure of difference between sequences, which must be done while taking into account such factors as recombination and convergent evolution. Three broad categories of method exist, of increasing complexity: those that assume no withinhost genetic diversity or mutation, those that assume no within-host diversity but allow mutation, and those that allow both. Until recently, the assumption was usually made that every host in the epidemic could be identified, but this is now being relaxed, and some methods are intended for sparsely sampled data, concentrating on the identification of pairs of sequences that are likely to be the result of direct transmission rather than inferring the complete transmission tree. Many of the procedures described here are available to researchers as free software.

SCOPUS: no.j, info:eu-repo/semantics/published