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.

Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models.

The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fully realised.

Investigating One Health risks for human colonisation with extended spectrum ß-lactamase-producing Escherichia coli and Klebsiella pneumoniae in Malawian households: a longitudinal cohort study.

BACKGROUND: Low-income countries have high morbidity and mortality from drug-resistant infections, especially from enteric bacteria such as Escherichia coli. In these settings, sanitation infrastructure is of variable and often inadequate quality, creating risks of extended-spectrum ß-lactamase (ESBL)-producing Enterobacterales transmission. We aimed to describe the prevalence, distribution, and risks of ESBL-producing Enterobacterales colonisation in sub-Saharan Africa using a One Health approach. METHODS: Between April 29, 2019, and Dec 3, 2020, we recruited 300 households in Malawi for this longitudinal cohort study: 100 each in urban, peri-urban, and rural settings. All households underwent a baseline visit and 195 were selected for longitudinal follow-up, comprising up to three additional visits over a 6 month period. Data on human health, antibiotic usage, health-seeking behaviours, structural and behavioural environmental health practices, and animal husbandry were captured alongside human, animal, and environmental samples. Microbiological processing determined the presence of ESBL-producing E coli and Klebsiella pneumoniae, and hierarchical logistic regression was performed to evaluate the risks of human ESBL-producing Enterobacterales colonisation. FINDINGS: A paucity of environmental health infrastructure and materials for safe sanitation was identified across all sites. A total of 11 975 samples were cultured, and ESBL-producing Enterobacterales were isolated from 1190 (41·8%) of 2845 samples of human stool, 290 (29·8%) of 973 samples of animal stool, 339 (66·2%) of 512 samples of river water, and 138 (46·0%) of 300 samples of drain water. Multivariable models illustrated that human ESBL-producing E coli colonisation was associated with the wet season (adjusted odds ratio 1·66, 95% credible interval 1·38-2·00), living in urban areas (2·01, 1·26-3·24), advanced age (1·14, 1·05-1·25), and living in households where animals were observed interacting with food (1·62, 1·17-2·28) or kept inside (1·58, 1·00-2·43). Human ESBL-producing K pneumoniae colonisation was associated with the wet season (2·12, 1·63-2·76). INTERPRETATION: There are extremely high levels of ESBL-producing Enterobacterales colonisation in humans and animals and extensive contamination of the wider environment in southern Malawi. Urbanisation and seasonality are key risks for ESBL-producing Enterobacterales colonisation, probably reflecting environmental drivers. Without adequate efforts to improve environmental health, ESBL-producing Enterobacterales transmission is likely to persist in this setting. FUNDING: Medical Research Council, National Institute for Health and Care Research, and Wellcome Trust. TRANSLATION: For the Chichewa translation of the abstract see Supplementary Materials section.

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.

A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic.

The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks. We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation. We evaluate the model's predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks). The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance.

Drivers of Resistance in Uganda and Malawi (DRUM): a protocol for the evaluation of One-Health drivers of Extended Spectrum Beta Lactamase (ESBL) resistance in Low-Middle Income Countries (LMICs).

In sub-Saharan Africa (sSA), there is high morbidity and mortality from severe bacterial infection and this is compounded by antimicrobial resistance, in particular, resistance to 3rd-generation cephalosporins. This resistance is typically mediated by extended-spectrum beta lactamases (ESBLs). To interrupt ESBL transmission it will be important to investigate how human behaviour, water, sanitation, and hygiene (WASH) practices, environmental contamination, and antibiotic usage in both urban and rural settings interact to contribute to transmission of ESBL E. coli and ESBL K. pneumoniae between humans, animals, and the environment. Here we present the protocol for the Drivers of Resistance in Uganda and Malawi (DRUM) Consortium, in which we will collect demographic, geospatial, clinical, animal husbandry and WASH data from a total of 400 households in Uganda and Malawi. Longitudinal human, animal and environmental sampling at each household will be used to isolate ESBL E. coli and ESBL K. pneumoniae. This will be complimented by a Risks, Attitudes, Norms, Abilities and Self-Regulation (RANAS) survey and structured observations to understand the contextual and psychosocial drivers of regional WASH practices. Bacterial isolates and plate sweeps will be further characterised using a mixture of short-,long-read and metagenomic whole-genome sequencing. These datasets will be integrated into agent-based models to describe the transmission of EBSL resistance in Uganda and Malawi and allow us to inform the design of interventions for interrupting transmission of ESBL-bacteria.

Maplaria: a user friendly web-application for spatio-temporal malaria prevalence mapping.

BACKGROUND: Model-based geostatistical (MBG) methods have been extensively used to map malaria risk using community survey data in low-resource settings where disease registries are incomplete or non-existent. However, the wider adoption of MBG methods by national control programmes to inform health policy decisions is hindered by the lack of advanced statistical expertise and suitable computational equipment. Here, Maplaria, an interactive, user-friendly web-application that allows users to upload their own malaria prevalence data and carry out geostatistical prediction of annual malaria prevalence at any desired spatial scale, is introduced. METHODS: In the design of the Maplaria web application, two main criteria were considered: the application should be able to classify subnational divisions into the most likely endemicity levels; the web application should allow only minimal input from the user in the set-up of the geostatistical inference process. To achieve this, the process of fitting and validating the geostatistical models is carried out by statistical experts using publicly available malaria survey data from the Harvard database. The stage of geostatistical prediction is entirely user-driven and allows the user to upload malaria data, as well as vector data that define the administrative boundaries for the generation of spatially aggregated inferences. RESULTS: The process of data uploading and processing is split into a series of steps spread across screens through the progressive disclosure technique that prevents the user being immediately overwhelmed by the length of the form. Each of these is illustrated using a data set from the Malaria Indicator carried out in Tanzania in 2017 as an example. CONCLUSIONS: Maplaria application provides a user-friendly solution to the problem making geostatistical methods more accessible to users that have not undertaken formal training in statistics. The application is a useful tool that can be used to foster ownership, among policy makers, of disease risk maps and promote better use of data for decision-making in low resource settings.

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.

Spatiotemporal modelling of pregabalin prescribing in England with effect of deprivation.

OBJECTIVE: This paper aims to understand spatial and temporal trends in pregabalin prescribing and the relationship with deprivation across England at both general practice and clinical commissioning group (CCG) levels. DESIGN: A set of 207 independent generalised additive models are employed to model the spatiotemporal trend of pregabalin prescribed and dispensed per 1000 population, adjusting for deprivation. The response variable is pregabalin prescribed in milligrams, with weighted Index of Multiple Deprivation (IMD), geographical location and time as predictors. The set of active prescribing facilities grouped within CCG is the unit of analysis. SETTING: National Health Service open prescribing data; all general practices in England, UK between January 2015 and June 2017. POPULATION: All patients registered to general practices in England, UK. RESULTS: Adjusting for deprivation, a North-South divide is shown in terms of prescribing trends, with the North of England showing increasing prescribing rates during the study period on average, while in the South of England rates are on average decreasing. Approximately 60% of general practices showed increasing prescribing rate, with the highest being 4.03 (1.75 for the most decreasing). There were no apparent spatial patterns in baseline prescription rates at the CCG level. Weighted IMD score proved to be statistically significant in 138 of 207 CCGs. Two-thirds of CCGs showed more pregabalin prescribed in areas of greater deprivation. Whether the prescribing rate is high due to high baseline prescription rate or increasing rates needs to be specifically looked at. CONCLUSIONS: The spatial temporal modelling demonstrated that the North of England has a significantly higher chance to see increase in pregablin prescriptions compared with the South, adjusted for weighted IMD. Weighted IMD has shown positive impact on pregabalin prescriptions for 138 CCGs.

A real-time spatio-temporal syndromic surveillance system with application to small companion animals.

Lack of disease surveillance in small companion animals worldwide has contributed to a deficit in our ability to detect and respond to outbreaks. In this paper we describe the first real-time syndromic surveillance system that conducts integrated spatio-temporal analysis of data from a national network of veterinary premises for the early detection of disease outbreaks in small animals. We illustrate the system's performance using data relating to gastrointestinal disease in dogs and cats. The data consist of approximately one million electronic health records for dogs and cats, collected from 458 UK veterinary premises between March 2014 and 2016. For this illustration, the system predicts the relative reporting rate of gastrointestinal disease amongst all presentations, and updates its predictions as new data accrue. The system was able to detect simulated outbreaks of varying spatial geometry, extent and severity. The system is flexible: it generates outcomes that are easily interpretable; the user can set their own outbreak detection thresholds. The system provides the foundation for prompt detection and control of health threats in companion animals.

Seasonal Influenza Vaccination amongst Medical Students: A Social Network Analysis Based on a Cross-Sectional Study.

INTRODUCTION: The Chief Medical Officer for England recommends that healthcare workers have a seasonal influenza vaccination in an attempt to protect both patients and NHS staff. Despite this, many healthcare workers do not have a seasonal influenza vaccination. Social network analysis is a well-established research approach that looks at individuals in the context of their social connections. We examine the effects of social networks on influenza vaccination decision and disease dynamics. METHODS: We used a social network analysis approach to look at vaccination distribution within the network of the Lancaster Medical School students and combined these data with the students' beliefs about vaccination behaviours. We then developed a model which simulated influenza outbreaks to study the effects of preferentially vaccinating individuals within this network. RESULTS: Of the 253 eligible students, 217 (86%) provided relational data, and 65% of responders had received a seasonal influenza vaccination. Students who were vaccinated were more likely to think other medical students were vaccinated. However, there was no clustering of vaccinated individuals within the medical student social network. The influenza simulation model demonstrated that vaccination of well-connected individuals may have a disproportional effect on disease dynamics. CONCLUSIONS: This medical student population exhibited vaccination coverage levels similar to those seen in other healthcare groups but below recommendations. However, in this population, a lack of vaccination clustering might provide natural protection from influenza outbreaks. An individual student's perception of the vaccination coverage amongst their peers appears to correlate with their own decision to vaccinate, but the directionality of this relationship is not clear. When looking at the spread of disease within a population it is important to include social structures alongside vaccination data. Social networks influence disease epidemiology and vaccination campaigns designed with information from social networks could be a future target for policy makers.

Mapping English GP prescribing data: a tool for monitoring health-service inequalities.

OBJECTIVE: The aim of this paper was to show that easily interpretable maps of local and national prescribing data, available from open sources, can be used to demonstrate meaningful variations in prescribing performance. DESIGN: The prescription dispensing data from the National Health Service (NHS) Information Centre for the medications metformin hydrochloride and methylphenidate were compared with reported incidence data for the conditions, diabetes and attention deficit hyperactivity disorder, respectively. The incidence data were obtained from the open source general practitioner (GP) Quality and Outcomes Framework. These data were mapped using the Ordnance Survey CodePoint Open data and the data tables stored in a PostGIS spatial database. Continuous maps of spending per person in England were then computed by using a smoothing algorithm and areas whose local spending is substantially (at least fourfold) and significantly (p<0.05) higher than the national average are then highlighted on the maps. SETTING: NHS data with analysis of primary care prescribing. POPULATION: England, UK. RESULTS: The spatial mapping demonstrates that several areas in England have substantially and significantly higher spending per person on metformin and methyphenidate. North Kent and the Wirral have substantially and significantly higher spending per child on methyphenidate. CONCLUSIONS: It is possible, using open source data, to use statistical methods to distinguish chance fluctuations in prescribing from genuine differences in prescribing rates. The results can be interactively mapped at a fine spatial resolution down to individual GP practices in England. This process could be automated and reported in real time. This can inform decision-making and could enable earlier detection of emergent phenomena.

Advancing insights into methods for studying environment-health relationships: a multidisciplinary approach to understanding Legionnaires' disease.

Recent years have witnessed important advances in the analysis of spatially referenced health data. This paper uses GIS and point pattern modelling to address previously unanswered questions regarding the spatial epidemiology of Legionnaires' disease. We demonstrate a distance effect for proximity of residence to cooling towers; mixed support for a directional effect; and some evidence relating to multiple sources. In uncovering complex conceptual and technical problems in the spatial modelling of infection risk we also extended the limits of existing point pattern techniques. We advocate further multidisciplinary research to advance methodological developments for understanding spatial environment-health relationships.

Short communication: Negative spatial association between lymphatic filariasis and malaria in West Africa.

OBJECTIVE: To determine the relationship between human lymphatic filariasis, caused by Wuchereria bancrofti, and falciparum malaria, which are co-endemic throughout West Africa. METHODS: We used geographical information systems and spatial statistics to examine the prevalence of lymphatic filariasis in relation to malaria prevalence, mosquito species distributions, vegetation and climate. RESULTS: A negative spatial association between W. bancrofti and falciparum malaria prevalence exists. Interspecies competition between parasites, seasonality, differences in the distribution and vector competence of Anopheles vectors, agricultural practices and insecticide resistance may be factors driving current (and potentially future) spatial distributions. CONCLUSION: Further investigating these factors will become crucial as large-scale lymphatic filariasis and malaria control programmes are implemented in West Africa that may influence the epidemiology of both diseases.