Emerging Variants of Canine Enteric Coronavirus Associated with Outbreaks of Gastroenteric Disease.

A 2022 canine gastroenteritis outbreak in the United Kingdom was associated with circulation of a new canine enteric coronavirus closely related to a 2020 variant with an additional spike gene recombination. The variants are unrelated to canine enteric coronavirus-like viruses associated with human disease but represent a model for coronavirus population adaptation.

Risk Factors, Temporal Dependence, and Seasonality of Human Extended-Spectrum ß-Lactamases-Producing Escherichia coli and Klebsiella pneumoniae Colonization in Malawi: A Longitudinal Model-Based Approach.

BACKGROUND: Sub-Saharan Africa has the highest estimated death rate attributable to antimicrobial resistance, especially from extended-spectrum ß-lactamase-producing Enterobacterales (ESBL-E). However, the dynamics of human colonization in the community with ESBL-E are not well described. Inadequate water, sanitation, and hygiene infrastructure and associated behaviors are believed to play an important role in transmission of ESBL-E, and an improved understanding of the temporal dynamics of within-household transmission could help inform the design of future policies. METHODS: In this 18-month study, using microbiological data and household surveys, we built a multivariable hierarchical harmonic logistic regression model to identify risk factors for colonization with ESBL-producing Escherichia coli and Klebsiella pneumoniae, reflecting household structure and temporal correlation of colonization status. RESULTS: Being male was associated with a lower risk of colonization with ESBL-producing E. coli (odds ratio [OR], 0.786; credible interval [CrI], .678-.910), whereas the use of a tube well or a borehole was associated with an increased risk (OR, 1.550; CrI, 1.003-2.394). For ESBL-producing K. pneumoniae, recent antibiotic exposure increased risk of colonization (OR, 1.281; CrI, 1.049-1.565), whereas sharing plates decreased that risk (OR, 0.672; CrI, .460-.980). Finally, the temporal correlation range of 8 to 11 weeks provided evidence that within-household transmission occurs within this time frame. CONCLUSIONS: We describe different risks for colonization with different enteric bacterial species. Our findings suggest interventions to reduce transmission targeted at the household level need to focus on improving water, sanitation, and hygiene infrastructure and associated behaviors, whereas at the community level, they should focus on both environmental hygiene and antibiotic stewardship.

EpiBeds: Data informed modelling of the COVID-19 hospital burden in England.

The first year of the COVID-19 pandemic put considerable strain on healthcare systems worldwide. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, EpiBeds, which was coupled to a model of the generalised epidemic. In this model, individuals progress through different pathways (e.g. may recover, die, or progress to intensive care and recover or die) and data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow the different clinical pathways, the reproduction number of the generalised epidemic, and to make short-term predictions of hospital bed demand. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland at national and regional scales.

Contact patterns of UK home delivery drivers and their use of protective measures during the COVID-19 pandemic: a cross-sectional study.

OBJECTIVES: To quantify contact patterns of UK home delivery drivers and identify protective measures adopted during the pandemic. METHODS: We conducted a cross-sectional online survey to measure the interactions of 170 UK delivery drivers during a working shift between 7 December 2020 and 31 March 2021. RESULTS: Delivery drivers had a mean number of 71.6 (95% CI 61.0 to 84.1) customer contacts per shift and 15.0 (95% CI 11.2 to 19.2) depot contacts per shift. Maintaining physical distancing with customers was more common than at delivery depots. Prolonged contact (more than 5 min) with customers was reported by 5.4% of drivers on their last shift. We found 3.0% of drivers had tested positive for SARS-CoV-2 since the start of the pandemic and 16.8% of drivers had self-isolated due to a suspected or confirmed case of COVID-19. In addition, 5.3% (95% CI 2.3% to 10.2%) of participants reported having worked while ill with COVID-19 symptoms, or with a member of their household having a suspected or confirmed case of COVID-19. CONCLUSION: Delivery drivers had a large number of face-to-face customer and depot contacts per shift compared with other working adults during this time. However, transmission risk may be curtailed as contact with customers was of short duration. Most drivers were unable to maintain physical distance with customers and at depots at all times. Usage of protective items such as face masks and hand sanitiser was widespread.

Inferring transmission trees to guide targeting of interventions against visceral leishmaniasis and post-kala-azar dermal leishmaniasis.

Understanding of spatiotemporal transmission of infectious diseases has improved significantly in recent years. Advances in Bayesian inference methods for individual-level geo-located epidemiological data have enabled reconstruction of transmission trees and quantification of disease spread in space and time, while accounting for uncertainty in missing data. However, these methods have rarely been applied to endemic diseases or ones in which asymptomatic infection plays a role, for which additional estimation methods are required. Here, we develop such methods to analyze longitudinal incidence data on visceral leishmaniasis (VL) and its sequela, post-kala-azar dermal leishmaniasis (PKDL), in a highly endemic community in Bangladesh. Incorporating recent data on VL and PKDL infectiousness, we show that while VL cases drive transmission when incidence is high, the contribution of PKDL increases significantly as VL incidence declines (reaching 55% in this setting). Transmission is highly focal: 85% of mean distances from inferred infectors to their secondary VL cases were <300 m, and estimated average times from infector onset to secondary case infection were <4 mo for 88% of VL infectors, but up to 2.9 y for PKDL infectors. Estimated numbers of secondary cases per VL and PKDL case varied from 0 to 6 and were strongly correlated with the infector's duration of symptoms. Counterfactual simulations suggest that prevention of PKDL could have reduced overall VL incidence by up to 25%. These results highlight the need for prompt detection and treatment of PKDL to achieve VL elimination in the Indian subcontinent and provide quantitative estimates to guide spatiotemporally targeted interventions against VL.

Synergistic interventions to control COVID-19: Mass testing and isolation mitigates reliance on distancing.

Stay-at-home orders and shutdowns of non-essential businesses are powerful, but socially costly, tools to control the pandemic spread of SARS-CoV-2. Mass testing strategies, which rely on widely administered frequent and rapid diagnostics to identify and isolate infected individuals, could be a potentially less disruptive management strategy, particularly where vaccine access is limited. In this paper, we assess the extent to which mass testing and isolation strategies can reduce reliance on socially costly non-pharmaceutical interventions, such as distancing and shutdowns. We develop a multi-compartmental model of SARS-CoV-2 transmission incorporating both preventative non-pharmaceutical interventions (NPIs) and testing and isolation to evaluate their combined effect on public health outcomes. Our model is designed to be a policy-guiding tool that captures important realities of the testing system, including constraints on test administration and non-random testing allocation. We show how strategic changes in the characteristics of the testing system, including test administration, test delays, and test sensitivity, can reduce reliance on preventative NPIs without compromising public health outcomes in the future. The lowest NPI levels are possible only when many tests are administered and test delays are short, given limited immunity in the population. Reducing reliance on NPIs is highly dependent on the ability of a testing program to identify and isolate unreported, asymptomatic infections. Changes in NPIs, including the intensity of lockdowns and stay at home orders, should be coordinated with increases in testing to ensure epidemic control; otherwise small additional lifting of these NPIs can lead to dramatic increases in infections, hospitalizations and deaths. Importantly, our results can be used to guide ramp-up of testing capacity in outbreak settings, allow for the flexible design of combined interventions based on social context, and inform future cost-benefit analyses to identify efficient pandemic management strategies.

Outbreak of Severe Vomiting in Dogs Associated with a Canine Enteric Coronavirus, United Kingdom.

The lack of population health surveillance for companion animal populations leaves them vulnerable to the effects of novel diseases without means of early detection. We present evidence on the effectiveness of a system that enabled early detection and rapid response a canine gastroenteritis outbreak in the United Kingdom. In January 2020, prolific vomiting among dogs was sporadically reported in the United Kingdom. Electronic health records from a nationwide sentinel network of veterinary practices confirmed a significant increase in dogs with signs of gastroenteric disease. Male dogs and dogs living with other vomiting dogs were more likely to be affected. Diet and vaccination status were not associated with the disease; however, a canine enteric coronavirus was significantly associated with illness. The system we describe potentially fills a gap in surveillance in neglected populations and could provide a blueprint for other countries.

An enhanced care package to improve asthma management in Malawian children: a randomised controlled trial.

BACKGROUND: Shortages of clinical staff make chronic asthma care challenging in low-income countries. We evaluated an outpatient asthma care package for children, including task-shifting of asthma management roles. METHODS: We conducted a non-blinded individually randomised controlled trial at a tertiary-level government hospital in Blantyre, Malawi. Children aged 6-15 years diagnosed with asthma were recruited from outpatient clinic, stratified by Childhood Asthma Control Test (cACT) score and allocated 1:1 from a concealed file, accessed during electronic questionnaire completion. The intervention, delivered by non-physicians, comprised clinical assessment, optimisation of inhaled treatment, individualised asthma education. The control group received standard care from outpatient physicians. Primary outcome for intention-to-treat analysis was change in cACT score at 3 months. Secondary outcomes included asthma exacerbations requiring emergency healthcare and school absence. FINDINGS: Between September 2018 and December 2019, 120 children (59 intervention; 61 control) were recruited; 65.8% males, with mean (SD) age 9.8 (2.8) years, mean (SD) baseline cACT 20.3 (2.6). At 3 months, intervention children (n=56) had a greater mean (SD) change in cACT score from baseline (2.7 (2.8) vs 0.6 (2.8)) compared with standard care participants (n=59); a difference of 2.1 points (95% CI: 1.1 to 3.1, p<0.001). Fewer intervention children attended emergency healthcare (7.3% vs 25.4%, p=0.02) and missed school (20.0% vs 62.7%, p<0.001) compared with standard care children. INTERPRETATION: The intervention resulted in decreased asthma symptoms and exacerbations. Wider scale-up could present substantial benefits for asthmatic patients in resource-limited settings. TRIAL REGISTRATION NUMBER: PACTR201807211617031.

Non-communicable respiratory disease and air pollution exposure in Malawi: a prospective cohort study.

RATIONALE: There are no population-based studies from sub-Saharan Africa describing longitudinal lung function in adults. OBJECTIVES: To explore the lung function trajectories and their determinants, including the effects of air pollution exposures and the cleaner-burning biomass-fuelled cookstove intervention of the Cooking and Pneumonia Study (CAPS), in adults living in rural Malawi. METHODS: We assessed respiratory symptoms and exposures, spirometry and measured 48-hour personal exposure to fine particulate matter (PM2.5) and carbon monoxide (CO), on three occasions over 3 years. Longitudinal data were analysed using mixed-effects modelling by maximum likelihood estimation. MEASUREMENTS AND MAIN RESULTS: We recruited 1481 adults, mean (SD) age 43.8 (17.8) years, including 523 participants from CAPS households (271 intervention; 252 controls), and collected multiple spirometry and air pollution measurements for 654 (44%) and 929 (63%), respectively. Compared with Global Lung Function Initiative African-American reference ranges, mean (SD) FEV1 (forced expiratory volume in 1 s) and FVC (forced vital capacity) z-scores were -0.38 (1.14) and -0.19 (1.09). FEV1 and FVC were determined by age, sex, height, previous TB and body mass index, with FEV1 declining by 30.9 mL/year (95% CI: 21.6 to 40.1) and FVC by 38.3 mL/year (95% CI: 28.5 to 48.1). There was decreased exposure to PM2.5 in those with access to a cookstove but no effect on lung function. CONCLUSIONS: We did not observe accelerated lung function decline in this cohort of Malawian adults, compared with that reported in healthy, non-smoking populations from high-income countries; this suggests that the lung function deficits we measured in adulthood may have origins in early life.

Anticipating future learning affects current control decisions: A comparison between passive and active adaptive management in an epidemiological setting.

Infectious disease epidemics present a difficult task for policymakers, requiring the implementation of control strategies under significant time constraints and uncertainty. Mathematical models can be used to predict the outcome of control interventions, providing useful information to policymakers in the event of such an epidemic. However, these models suffer in the early stages of an outbreak from a lack of accurate, relevant information regarding the dynamics and spread of the disease and the efficacy of control. As such, recommendations provided by these models are often incorporated in an ad hoc fashion, as and when more reliable information becomes available. In this work, we show that such trial-and-error-type approaches to management, which do not formally take into account the resolution of uncertainty and how control actions affect this, can lead to sub-optimal management outcomes. We compare three approaches to managing a theoretical epidemic: a non-adaptive management (AM) approach that does not use real-time outbreak information to adapt control, a passive AM approach that incorporates real-time information if and when it becomes available, and an active AM approach that explicitly incorporates the future resolution of uncertainty through gathering real-time information into its initial recommendations. The structured framework of active AM encourages the specification of quantifiable objectives, models of system behaviour and possible control and monitoring actions, followed by an iterative learning and control phase that is able to employ complex control optimisations and resolve system uncertainty. The result is a management framework that is able to provide dynamic, long-term projections to help policymakers meet the objectives of management. We investigate in detail the effect of different methods of incorporating up-to-date outbreak information. We find that, even in a highly simplified system, the method of incorporating new data can lead to different results that may influence initial policy decisions, with an active AM approach to management providing better information that can lead to more desirable outcomes from an epidemic.

Lung health and exposure to air pollution in Malawian children (CAPS): a cross-sectional study.

BACKGROUND: Non-communicable lung disease and exposure to air pollution are major problems in sub-Saharan Africa. A high burden of chronic respiratory symptoms, spirometric abnormalities and air pollution exposures has been found in Malawian adults; whether the same would be true in children is unknown. METHODS: This cross-sectional study of children aged 6-8 years, in rural Malawi, included households from communities participating in the Cooking and Pneumonia Study (CAPS), a trial of cleaner-burning biomass-fuelled cookstoves. We assessed; chronic respiratory symptoms, anthropometry, spirometric abnormalities (using Global Lung Initiative equations) and personal carbon monoxide (CO) exposure. Prevalence estimates were calculated, and multivariable analyses were done. RESULTS: We recruited 804 children (mean age 7.1 years, 51.9% female), including 476 (260 intervention; 216 control) from CAPS households. Chronic respiratory symptoms (mainly cough (8.0%) and wheeze (7.1%)) were reported by 16.6% of children. Average height-for-age and weight-for-age z-scores were -1.04 and -1.10, respectively. Spirometric abnormalities (7.1% low forced vital capacity (FVC); 6.3% obstruction) were seen in 13.0% of children. Maximum CO exposure and carboxyhaemoglobin levels (COHb) exceeded WHO guidelines in 50.1% and 68.5% of children, respectively. Children from CAPS intervention households had lower COHb (median 3.50% vs 4.85%, p=0.006) and higher FVC z-scores (-0.22 vs -0.44, p=0.05) than controls. CONCLUSION: The substantial burden of chronic respiratory symptoms, abnormal spirometry and air pollution exposures in children in rural Malawi is concerning; effective prevention and control strategies are needed. Our finding of potential benefit in CAPS intervention households calls for further research into clean-air interventions to maximise healthy lung development in children.

Real-time decision-making during emergency disease outbreaks.

In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak.

sourceR: Classification and source attribution of infectious agents among heterogeneous populations.

Zoonotic diseases are a major cause of morbidity, and productivity losses in both human and animal populations. Identifying the source of food-borne zoonoses (e.g. an animal reservoir or food product) is crucial for the identification and prioritisation of food safety interventions. For many zoonotic diseases it is difficult to attribute human cases to sources of infection because there is little epidemiological information on the cases. However, microbial strain typing allows zoonotic pathogens to be categorised, and the relative frequencies of the strain types among the sources and in human cases allows inference on the likely source of each infection. We introduce sourceR, an R package for quantitative source attribution, aimed at food-borne diseases. It implements a Bayesian model using strain-typed surveillance data from both human cases and source samples, capable of identifying important sources of infection. The model measures the force of infection from each source, allowing for varying survivability, pathogenicity and virulence of pathogen strains, and varying abilities of the sources to act as vehicles of infection. A Bayesian non-parametric (Dirichlet process) approach is used to cluster pathogen strain types by epidemiological behaviour, avoiding model overfitting and allowing detection of strain types associated with potentially high "virulence". sourceR is demonstrated using Campylobacter jejuni isolate data collected in New Zealand between 2005 and 2008. Chicken from a particular poultry supplier was identified as the major source of campylobacteriosis, which is qualitatively similar to results of previous studies using the same dataset. Additionally, the software identifies a cluster of 9 multilocus sequence types with abnormally high 'virulence' in humans. sourceR enables straightforward attribution of cases of zoonotic infection to putative sources of infection. As sourceR develops, we intend it to become an important and flexible resource for food-borne disease attribution studies.

A Bayesian approach to identifying the role of hospital structure and staff interactions in nosocomial transmission of SARS-CoV-2.

Nosocomial infections threaten patient safety, and were widely reported during the COVID-19 pandemic. Effective hospital infection control requires a detailed understanding of the role of different transmission pathways, yet these are poorly quantified. Using patient and staff data from a large UK hospital, we demonstrate a method to infer unobserved epidemiological event times efficiently and disentangle the infectious pressure dynamics by ward. A stochastic individual-level, continuous-time state-transition model was constructed to model transmission of SARS-CoV-2, incorporating a dynamic staff-patient contact network as time-varying parameters. A Metropolis-Hastings Markov chain Monte Carlo (MCMC) algorithm was used to estimate transmission rate parameters associated with each possible source of infection, and the unobserved infection and recovery times. We found that the total infectious pressure exerted on an individual in a ward varied over time, as did the primary source of transmission. There was marked heterogeneity between wards; each ward experienced unique infectious pressure over time. Hospital infection control should consider the role of between-ward movement of staff as a key infectious source of nosocomial infection for SARS-CoV-2. With further development, this method could be implemented routinely for real-time monitoring of nosocomial transmission and to evaluate interventions.

Prevalence of Endemic Respiratory Viruses During the COVID-19 Pandemic in Urban and Rural Malawi.

Background: We investigated endemic respiratory virus circulation patterns in Malawi, where no lockdown was imposed, during the COVID-19 pandemic. Methods: Within a prospective household cohort in urban and rural Malawi, adult participants provided upper respiratory tract (URT) samples at 4 time points between February 2021 and April 2022. Polymerase chain reaction (PCR) was performed for SARS-CoV-2, influenza, and other endemic respiratory viruses. Results: 1626 URT samples from 945 participants in 542 households were included. Overall, 7.6% (n = 123) samples were PCR- positive for >1 respiratory virus; SARS-CoV-2 (4.4%) and rhinovirus (2.0%) were most common. No influenza A virus was detected. Influenza B and respiratory syncytial virus (RSV) were rare. Higher virus positivity were detected in the rural setting and at earlier time points. Coinfections were infrequent. Conclusions: Endemic respiratory viruses circulated in the community in Malawi during the pandemic, though influenza and RSV were rarely detected. Distinct differences in virus positivity and demographics were observed between urban and rural cohorts.

Enhancing Bayesian risk prediction for epidemics using contact tracing.

Contact-tracing data (CTD) collected from disease outbreaks has received relatively little attention in the epidemic modeling literature because it is thought to be unreliable: infection sources might be wrongly attributed, or data might be missing due to resource constraints in the questionnaire exercise. Nevertheless, these data might provide a rich source of information on the disease transmission rate. This paper presents a novel methodology for combining CTD with rate-based contact network data to improve posterior precision, and therefore predictive accuracy. We present an advancement in Bayesian inference for epidemics that assimilates these data and is robust to partial contact tracing. Using a simulation study based on the British poultry industry, we show how the presence of CTD improves posterior predictive accuracy and can directly inform a more effective control strategy.

Characterizing the evolving SARS-CoV-2 seroprevalence in urban and rural Malawi between February 2021 and April 2022: A population-based cohort study.

OBJECTIVES: This study aimed to investigate the changing SARS-CoV-2 seroprevalence and associated health and sociodemographic factors in Malawi between February 2021 and April 2022. METHODS: In total, four 3-monthly serosurveys were conducted within a longitudinal population-based cohort in rural Karonga District and urban Lilongwe, testing for SARS-CoV-2 S1 immunoglobulin (Ig)G antibodies using an enzyme-linked immunosorbent assay. Population seroprevalence was estimated in all and unvaccinated participants. Bayesian mixed-effects logistic models estimated the odds of seropositivity in the first survey, and of seroconversion between surveys, adjusting for age, sex, occupation, location, and assay sensitivity/specificity. RESULTS: Of the 2005 participants (Karonga, n = 1005; Lilongwe, n = 1000), 55.8% were female and median age was 22.7 years. Between Surveys (SVY) 1 and 4, population-weighted SARS-CoV-2 seroprevalence increased from 26.3% to 89.2% and 46.4% to 93.9% in Karonga and Lilongwe, respectively. At SVY4, seroprevalence did not differ by COVID-19 vaccination status in adults, except for those aged 30+ years in Karonga (unvaccinated: 87.4%, 95% credible interval 79.3-93.0%; two doses: 98.1%, 94.8-99.5%). Location and age were associated with seroconversion risk. Individuals with hybrid immunity had higher SARS-CoV-2 seropositivity and antibody titers, than those infected. CONCLUSION: High SARS-CoV-2 seroprevalence combined with low morbidity and mortality indicate that universal vaccination is unnecessary at this stage of the pandemic, supporting change in national policy to target at-risk groups.

Social mixing patterns in the UK following the relaxation of COVID-19 pandemic restrictions, July-August 2020: a cross-sectional online survey.

OBJECTIVES: To quantify and characterise non-household contact and to identify the effect of shielding and isolating on contact patterns. DESIGN: Cross-sectional study. SETTING AND PARTICIPANTS: Anyone living in the UK was eligible to take part in the study. We recorded 5143 responses to the online questionnaire between 28 July 2020 and 14 August 2020. OUTCOME MEASURES: Our primary outcome was the daily non-household contact rate of participants. Secondary outcomes were propensity to leave home over a 7 day period, whether contacts had occurred indoors or outdoors locations visited, the furthest distance travelled from home, ability to socially distance and membership of support bubble. RESULTS: The mean rate of non-household contacts per person was 2.9 d-1. Participants attending a workplace (adjusted incidence rate ratio (aIRR) 3.33, 95% CI 3.02 to 3.66), self-employed (aIRR 1.63, 95% CI 1.43 to 1.87) or working in healthcare (aIRR 5.10, 95% CI 4.29 to 6.10) reported significantly higher non-household contact rates than those working from home. Participants self-isolating as a precaution or following Test and Trace instructions had a lower non-household contact rate than those not self-isolating (aIRR 0.58, 95% CI 0.43 to 0.79). We found limited evidence that those shielding had reduced non-household contacts compared with non-shielders. CONCLUSION: The daily rate of non-household interactions remained lower than prepandemic levels measured by other studies, suggesting continued adherence to social distancing guidelines. Individuals attending a workplace in-person or employed as healthcare professionals were less likely to maintain social distance and had a higher non-household contact rate, possibly increasing their infection risk. Shielding and self-isolating individuals required greater support to enable them to follow the government guidelines and reduce non-household contact and therefore their risk of infection.

Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates.

Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39-4.13), indicating that 58-76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6-7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090-33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Trends, relationships and case attribution of antibiotic resistance between children and environmental sources in rural India.

Bacterial antibiotic resistance is an important global health threat and the interfaces of antibiotic resistance between humans, animals and the environment are complex. We aimed to determine the associations and overtime trends of antibiotic resistance between humans, animals and water sources from the same area and time and estimate attribution of the other sources to cases of human antibiotic resistance. A total of 125 children (aged 1-3 years old) had stool samples analysed for antibiotic-resistant bacteria at seven time points over two years, with simultaneous collection of samples of animal stools and water sources in a rural Indian community. Newey-West regression models were used to calculate temporal associations, the source with the most statistically significant relationships was household drinking water. This is supported by use of SourceR attribution modelling, that estimated the mean attribution of cases of antibiotic resistance in the children from animals, household drinking water and wastewater, at each time point and location, to be 12.6% (95% CI 4.4-20.9%), 12.1% (CI 3.4-20.7%) and 10.3% (CI 3.2-17.3%) respectively. This underlines the importance of the 'one health' concept and requires further research. Also, most of the significant trends over time were negative, suggesting a possible generalised improvement locally.