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BackgroundLeptospirosis is a zoonosis caused by pathogenic species of bacteria belonging to the genusLeptospira. Most studies infer the epidemiological patterns of a single serogroup or aggregate all serogroups to estimate overall seropositivity, thus not exploring the risks of exposure to distinct serogroups. The present study aims to delineate the demographic, socioeconomic and environmental factors associated with seropositivity ofLeptospiraserogroup Icterohaemorraghiae and serogroup Cynopteri in an urban high transmission setting for leptospirosis in Brazil.Methods/Principal FindingsWe performed a cross-sectional serological study in five urban informal communities in the city of Salvador, Brazil. During the years 2018, 2020 2021, we recruited 2.808 residents and collected blood samples for serological analysis using microagglutination assays. We used a mixed-effect multinomial logistic regression model to identify risk factors associated with seropositivity for each serogroup. Seropositivity to Cynopteri increased with age in years (OR 1.03; 95% CI 1.01-1.06) and was higher in those living in houses with unplaster walls (exposed brick) (OR 1.68; 95% CI 1.09-2.59) and where cats were present near the household (OR 2.00; 95% CI 1.03-3.88). Seropositivity to Icterohaemorrhagiae also increased with age in years (OR 1.02; 95% CI 1.01-1.03) but was higher in males (OR 1.51; 95% CI 1.09-2.10), in those with work-related exposures (OR 1.71; 95% CI 1.10-2.66) or who had contact with sewage (OR 1.42; 95% CI 1.00-2.03). Spatial analysis showed differences in distribution of seropositivity to serogroups Icterohaemorrhagiae and Cynopteri within the five districts where study communities were situated.Conclusions/SignificanceOur data suggests distinct epidemiological patterns associated with serogroups Icterohaemorrhagiae and Cynopteri within the high-risk urban environment for leptospirosis and with differences of spatial niches. Future studies must identify the different pathogenic serogroups circulating in low-income areas, and further evaluate the potential role of cats in the transmission of the serogroup Cynopteri in urban settings.

Soil-transmitted helminth (STH) infections are among the most common neglected tropical diseases worldwide, causing high morbidity and mortality rates in endemic areas. STH impact assessment surveys provide information on the changing epidemiological situation so that control programme managers can adapt the frequency of any continuing preventive chemotherapy (PC). The cost associated with conducting these surveys is an important factor, especially in developing countries with severely limited financial resources. Using three case studies based on historical data on baseline prevalence, environmental risk-factors and treatment history from Kenya, Sierra Leone and Zimbabwe, we show that a model-based geostatistics approach drastically reduces the cost of STH impact surveys by comparison with a survey conducted according to the current WHO guidelines, whilst maintaining the same overall accuracy. The framework that we develop is disease-agnostic and could easily be repurposed for use with other NTDs.

Despite important implications for human health, distribution, abundance and behaviour of most medically-relevant snakes remain poorly understood. Such data deficiencies hamper efforts to characterise the causal pathways of snakebite envenoming and to prioritise management options in the areas at greatest risk. We estimated the spatial patterns of abundance of seven medically-relevant snake species from Sri Lanka, a snakebite hotspot, and combined them with indices of species’ relative abundance, aggressiveness and envenoming severity obtained from an expert opinion survey to test whether these fundamental ecological traits could explain spatial patterns of snakebite and envenoming incidence. The spatial intensity of snake occurrence records in relation to independent environmental factors (fundamental niches and land cover) was analysed with point process models. Then, with the estimated patterns of abundance, we tested which species’ abundances added together, with and without weightings for aggressiveness, envenoming severity and relative abundance, best correlate with per-capita geographic incidence patterns of snakebite and envenoming. We found that weighting abundance patterns by species’ traits increased correlation with incidence. The best performing combination had three species weighted by aggressiveness and abundance, with a correlation of r = 0.47 (P < 0.01) with snakebite incidence. An envenoming severity and relative abundance-weighted combination of two species was the most strongly associated with envenoming incidence (r = 0.46, P = 0). Synthesis and applications. We show that snakebite risk is explained by abundance, aggressiveness and envenoming severity of the snake species most frequently involved in envenoming cases. Incorporating causality via ecological information of key snake species is critical for snakebite risk mapping, help to tailor preventive measures for dominant snake species and deploy the necessary antivenom therapies.

Background and Aims In hierarchically reticulate venation patterns, smaller orders of veins form areoles in which stomata are located. This study aimed to quantify the spatial relationship among stomata at the areole level. Methods For each of 12 leaves of M. cavaleriei var. platypetala, we assumed that stomatal characteristics were symmetrical on either side of the midrib, and divided the leaf surface on one side of the midrib into six layers equidistantly spaced along the apical–basal axis. We then further divided each layer into three positions equidistantly spaced from midrib to leaf margin, resulting in a total of 18 sampling locations. In addition, for 60 leaves, we sampled three positions from midrib to margin within only the widest layer of the leaf. Stomatal density and mean nearest neighbour distance (MNND) were calculated for each section. A replicated spatial point pattern approach quantified stomatal spatial relationships at different distances (0–300 μm). Key Results A tendency towards regular arrangement (inhibition as opposed to attraction or clustering) was observed between stomatal centres at distances Conclusions Spatial inhibition might be caused by the one-cell-spacing rule, resulting in more regular arrangement of stomata, and it was found to exist at distances up to ~100 μm. This work implies that leaf hydraulic architecture, consisting of both vascular and mesophyll properties, is sufficient to prevent important spatial variability in water supply at the areole level.

Background Diverse environmental exposures and risk factors have been implicated in the transmission of Salmonella Typhi, but the dominant transmission pathways through the environment to susceptible humans remain unknown. Here, we use spatial, bacterial genomic, and hydrological data to refine our view of typhoid transmission in an endemic setting. Methods A total of 546 patients presenting to Queen Elizabeth Central Hospital in Blantyre, Malawi, with blood culture–confirmed typhoid fever between April 2015 and January 2017 were recruited to a cohort study. The households of a subset of these patients were geolocated, and 256 S. Typhi isolates were whole-genome sequenced. Pairwise single-nucleotide variant distances were incorporated into a geostatistical modeling framework using multidimensional scaling. Results Typhoid fever was not evenly distributed across Blantyre, with estimated minimum incidence ranging across the city from 100 cases per 100 000 population per year. Pairwise single-nucleotide variant distance and physical household distances were significantly correlated (P = .001). We evaluated the ability of river catchment to explain the spatial patterns of genomics observed, finding that it significantly improved the fit of the model (P = .003). We also found spatial correlation at a smaller spatial scale, of households living Conclusions These findings reinforce the emerging view that hydrological systems play a key role in the transmission of typhoid fever. By combining genomic and spatial data, we show how multifaceted data can be used to identify high incidence areas, explain the connections between them, and inform targeted environmental surveillance, all of which will be critical to shape local and regional typhoid control strategies.

We present "interoperability" as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring spatial-temporal coronavirus disease 2019 (COVID-19) prevalence and reproduction numbers in England., Comment: 26 pages, 10 figures, for associated mpeg file Movie 1 please see https://www.dropbox.com/s/kn9y1v6zvivfla1/Interoperability_of_models_Movie_1.mp4?dl=0

We investigated a Gaussian conditional geostatistical spatio-temporal model (CGSTM) aiming to fit data observed at non-fixed locations over discrete times, based only on the observed locations. The model specifies the process state at the current time conditioning on the process state in the recent past. Particularly, the process mean uses a weighting function governing the spatio-temporal model evolution and handling the interaction between space and time. The CGSTM provides attractive features, such as it belongs to the dynamic linear model framework, models non-fixed locations over time and easily provides forecasting maps k-steps ahead. Likelihood estimation and inference are based on a Kalman filter-based algorithm. Equivalent closed form of a covariance and precision matrices of the spatio-temporal joint-distribution was obtained. We performed a simulation study considering locations of a real data example, which presents data locations varying over time. A second simulation study was ran using various scenarios for parameter values and number of observations in time and space, observing consistency and unbiasedness of model estimators. Thirdly, The model was fitted to the average monthly rainfall dataset, with 678 temporal registers at 32 stations located in western Parana, Brazil. The rainfall station locations suffered geographical changes from 1961 to 2017. In this modelling, we used explanatory variables and provided forecasting maps.

Background: The third wave of COVID-19 in England peaked in January 2022 resulting from the rapid transmission of the Omicron variant. However, rates of hospitalisations and deaths were substantially lower than in the first and second waves Methods: In the REal-time Assessment of Community Transmission-1 (REACT-1) study we obtained data from a random sample of 94,950 participants with valid throat and nose swab results by RT-PCR during round 18 (8 February to 1 March 2022). Findings: We estimated a weighted mean SARS-CoV-2 prevalence of 2.88% (95% credible interval [CrI] 2.76-3.00), with a within-round reproduction number (R) overall of 0.94 (0.91-0.96). While within-round weighted prevalence fell among children (aged 5 to 17 years) and adults aged 18 to 54 years, we observed a level or increasing weighted prevalence among those aged 55 years and older with an R of 1.04 (1.00-1.09). Among 1,195 positive samples with sublineages determined, only one (0.1% [0.0-0.5]) corresponded to AY.39 Delta sublineage and the remainder were Omicron: N=390, 32.7% (30.0-35.4) were BA.1; N=473, 39.6% (36.8-42.5) were BA.1.1; and N=331, 27.7% (25.2-30.4) were BA.2. We estimated an R additive advantage for BA.2 (vs BA.1 or BA.1.1) of 0.40 (0.36-0.43). The highest proportion of BA.2 among positives was found in London. Interpretation: In February 2022, infection prevalence in England remained high with level or increasing rates of infection in older people and an uptick in hospitalisations. Ongoing surveillance of both survey and hospitalisations data is required. Funding: Department of Health and Social Care, England.

The elimination of onchocerciasis through community-based Mass Drug Administration (MDA) of ivermectin (Mectizan) is hampered by co-endemicity of Loa loa, as individuals who are highly co-infected with Loa loa parasites can suffer serious and occasionally fatal neurological reactions from the drug. The test-and-not-treat strategy of testing all individuals participating in MDA has some operational constraints including the cost and limited availability of LoaScope diagnostic tools. As a result, a Loa loa Antibody (Ab) Rapid Test was developed to offer a complementary way of determining the prevalence of loiasis. We develop a joint geostatistical modelling framework for the analysis of Ab and Loascope data to delineate whether an area is safe for MDA. Our results support the use of a two-stage strategy, in which Ab testing is used to identify areas that, with acceptably high probability, are safe or unsafe for MDA, followed by Loascope testing in areas whose safety status is uncertain. This work therefore contributes to the global effort towards the elimination of onchocerciasis as a public health problem by potentially reducing the time and cost required to establish whether an area is safe for MDA.

ObjectivesTo survey on the availability and use of primary care services in slum populations.DesignRetrospective, cross-sectional, household, individual and healthcare provider surveys.SettingSeven slum sites in four countries (Nigeria, Kenya, Pakistan and Bangladesh).ParticipantsResidents of slums and informal settlements.Primary and secondary outcome measuresPrimary care consultation rates by type of provider and facility.ResultsWe completed 7692 household, 7451 individual adult and 2633 individual child surveys across seven sites. The majority of consultations were to doctors/nurses (in clinics or hospitals) and pharmacies rather than single-handed providers or traditional healers. Consultation rates with a doctor or nurse varied from 0.2 to 1.5 visits per person-year, which was higher than visit rates to any other type of provider in all sites except Bangladesh, where pharmacies predominated. Approximately half the doctor/nurse visits were in hospital outpatient departments and most of the remainder were to clinics. Over 90% of visits across all sites were for acute symptoms rather than chronic disease. Median travel times were between 15 and 45 min and the median cost per visit was between 2% and 10% of a household’s monthly total expenditure. Medicines comprised most of the cost. More respondents reported proximity (54%–78%) and service quality (31%–95%) being a reason for choosing a provider than fees (23%–43%). Demand was relatively inelastic with respect to both price of consultation and travel time.ConclusionsPeople in slums tend to live sufficiently close to formal doctor/nurse facilities for their health-seeking behaviour to be influenced by preference for provider type over distance and cost. However, costs, especially for medicines are high in relation to income and use rates remain significantly below those of high-income countries.

Background In line with movement restrictions and physical distancing essential for the control of the COVID-19 pandemic, WHO recommended postponement of all neglected tropical disease (NTD) control activities that involve community-based surveys, active case finding, and mass drug administration in April, 2020. Following revised guidance later in 2020, and after interruptions to NTD programmes of varying lengths, NTD programmes gradually restarted in the context of an ongoing pandemic. However, ongoing challenges and service gaps have been reported. This study aimed to evaluate the potential effect of the programmatic interruptions and strategies to mitigate this effect. Methods For seven NTDs, namely soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis, trachoma, visceral leishmaniasis, and human African trypanosomiasis, we used mathematical transmission models to simulate the effect of programme interruptions on the dynamics of each of these diseases in different endemic settings. We also explored the potential benefit of implementing mitigation strategies, primarily in terms of minimising the delays to control targets. Findings We show that the effect of the COVID-19-induced interruption in terms of delay to achieving elimination goals might in some cases be much longer than the duration of the interruption. For schistosomiasis, onchocerciasis, trachoma, and visceral leishmaniasis, a mean delay of 2–3 years for a 1-year interruption is predicted in areas of highest prevalence. We also show that these delays can largely be mitigated by measures such as additional mass drug administration or enhanced case-finding. Interpretation The COVID-19 pandemic has brought infectious disease control to the forefront of global consciousness. It is essential that the NTDs, so long neglected in terms of research and financial support, are not overlooked, and remain a priority in health service planning and funding. Funding Bill & Melinda Gates Foundation, Medical Research Council, and the UK Foreign, Commonwealth & Development Office.

Typhoid fever is a major cause of illness and mortality in low- and middle-income settings. We investigated the association of typhoid fever and rainfall in Blantyre, Malawi, where multi-drug-resistant typhoid has been transmitting since 2011. Peak rainfall preceded the peak in typhoid fever by approximately 15 weeks [95% confidence interval (CI) 13.3, 17.7], indicating no direct biological link. A quasi-Poisson generalised linear modelling framework was used to explore the relationship between rainfall and typhoid incidence at biologically plausible lags of 1–4 weeks. We found a protective effect of rainfall anomalies on typhoid fever, at a two-week lag (P = 0.006), where a 10 mm lower-than-expected rainfall anomaly was associated with up to a 16% reduction in cases (95% CI 7.6, 26.5). Extreme flooding events may cleanse the environment of S. Typhi, while unusually low rainfall may reduce exposure from sewage overflow. These results add to evidence that rainfall anomalies may play a role in the transmission of enteric pathogens, and can help direct future water and sanitation intervention strategies for the control of typhoid fever.

Background:Zoonotic spillover from animal reservoirs is responsible for a significant global public health burden, but the processes that promote spillover events are poorly understood in complex urban settings. Endemic transmission of Leptospira, the agent of leptospirosis, in marginalised urban communities occurs through human exposure to an environment contaminated by bacteria shed in the urine of the rat reservoir. However, it is unclear to what extent transmission is driven by variation in the distribution of rats or by the dispersal of bacteria in rainwater runoff and overflow from open sewer systems.Methods:We conducted an eco-epidemiological study in a high-risk community in Salvador, Brazil, by prospectively following a cohort of 1401 residents to ascertain serological evidence for leptospiral infections. A concurrent rat ecology study was used to collect information on the fine-scale spatial distribution of ‘rattiness’, our proxy for rat abundance and exposure of interest. We developed and applied a novel geostatistical framework for joint spatial modelling of multiple indices of disease reservoir abundance and human infection risk.Results:The estimated infection rate was 51.4 (95%CI 40.4, 64.2) infections per 1000 follow-up events. Infection risk increased with age until 30 years of age and was associated with male gender. Rattiness was positively associated with infection risk for residents across the entire study area, but this effect was stronger in higher elevation areas (OR 3.27 95% CI 1.68, 19.07) than in lower elevation areas (OR 1.14 95% CI 1.05, 1.53).Conclusions:These findings suggest that, while frequent flooding events may disperse bacteria in regions of low elevation, environmental risk in higher elevation areas is more localised and directly driven by the distribution of local rat populations. The modelling framework developed may have broad applications in delineating complex animal-environment-human interactions during zoonotic spillover and identifying opportunities for public health intervention.Funding:This work was supported by the Oswaldo Cruz Foundation and Secretariat of Health Surveillance, Brazilian Ministry of Health, the National Institutes of Health of the United States (grant numbers F31 AI114245, R01 AI052473, U01 AI088752, R01 TW009504 and R25 TW009338); the Wellcome Trust (102330/Z/13/Z), and by the Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB/JCB0020/2016). MTE was supported by a Medical Research UK doctorate studentship. FBS participated in this study under a FAPESB doctorate scholarship.

Summary We consider the analysis of continuous repeated measurement outcomes that are collected longitudinally. A standard framework for analysing data of this kind is a linear Gaussian mixed effects model within which the outcome variable can be decomposed into fixed effects, time invariant and time-varying random effects, and measurement noise. We develop methodology that, for the first time, allows any combination of these stochastic components to be non-Gaussian, using multivariate normal variance–mean mixtures. To meet the computational challenges that are presented by large data sets, i.e. in the current context, data sets with many subjects and/or many repeated measurements per subject, we propose a novel implementation of maximum likelihood estimation using a computationally efficient subsampling-based stochastic gradient algorithm. We obtain standard error estimates by inverting the observed Fisher information matrix and obtain the predictive distributions for the random effects in both filtering (conditioning on past and current data) and smoothing (conditioning on all data) contexts. To implement these procedures, we introduce an R package: ngme. We reanalyse two data sets, from cystic fibrosis and nephrology research, that were previously analysed by using Gaussian linear mixed effects models.

Background Typhoid fever remains a major source of morbidity and mortality in low-income settings. Its most feared complication is intestinal perforation. However, due to the paucity of diagnostic facilities in typhoid-endemic settings, including microbiology, histopathology, and radiology, the etiology of intestinal perforation is frequently assumed but rarely confirmed. This poses a challenge for accurately estimating burden of disease. Methods We recruited a prospective cohort of patients with confirmed intestinal perforation in 2016 and performed enhanced microbiological investigations (blood and tissue culture, plus tissue polymerase chain reaction [PCR] for Salmonella Typhi). In addition, we used a Poisson generalized linear model to estimate excess perforations attributed to the typhoid epidemic, using temporal trends in S. Typhi bloodstream infection and perforated abdominal viscus at Queen Elizabeth Central Hospital from 2008–2017. Results We recruited 23 patients with intraoperative findings consistent with intestinal perforation. 50% (11/22) of patients recruited were culture or PCR positive for S. Typhi. Case fatality rate from typhoid-associated intestinal perforation was substantial at 18% (2/11). Our statistical model estimates that culture-confirmed cases of typhoid fever lead to an excess of 0.046 perforations per clinical typhoid fever case (95% CI, .03–.06). We therefore estimate that typhoid fever accounts for 43% of all bowel perforation during the period of enhanced surveillance. Conclusions The morbidity and mortality associated with typhoid abdominal perforations are high. By placing clinical outcome data from a cohort in the context of longitudinal surgical registers and bacteremia data, we describe a valuable approach to adjusting estimates of the burden of typhoid fever.

There are concerns that pneumococcal conjugate vaccines (PCVs) in sub-Saharan Africa sub-optimally interrupt Streptococcus pneumoniae vaccine-serotype (VT) carriage and transmission. Here we assess PCV carriage using rolling, prospective nasopharyngeal carriage surveys between 2015 and 2018, 3.6–7.1 years after Malawi’s 2011 PCV13 introduction. Carriage decay rate is analysed using non-linear regression. Despite evidence of reduction in VT carriage over the study period, there is high persistent residual carriage. This includes among PCV-vaccinated children 3–5-year-old (16.1% relative reduction from 19.9% to 16.7%); PCV-unvaccinated children 6–8-year-old (40.5% reduction from 26.4% to 15.7%); HIV-infected adults 18-40-years-old on antiretroviral therapy (41.4% reduction from 15.2% to 8.9%). VT carriage prevalence half-life is similar among PCV-vaccinated and PCV-unvaccinated children (3.26 and 3.34 years, respectively). Compared with high-income settings, there is high residual VT carriage 3.6–7.1 years after PCV introduction. Rigorous evaluation of strategies to augment vaccine-induced control of carriage, including alternative schedules and catch-up campaigns, is required., Impact of pneumococcal conjugate vaccines (PCV) in controlling carriage needs to be evaluated to inform vaccine policy. Here, Swarthout et al. show in a prospective rolling cross-sectional study in Malawi a high residual prevalence of vaccine-serotype S. pneumoniae 7 years after PCV introduction.

The largest randomised controlled trial evaluating results of lung volume reduction surgery (LVRS) was conducted by the National Emphysema Treatment Trial (NETT) that published a series of reports for outcomes up to 24 months. However, patient outcomes were difficult to interpret due to limitations in and the presentation of conventional statistical analyses applied to longitudinal data. We reevaluated the NETT results using longitudinal data methodology to report longer-term outcomes to facilitate interpretation by clinicians and patients who are considering LVRS for emphysema management.Trial data were released by the United States National Institutes of Health and the National Heart, Lung, and Blood Institute and analyzed using a mixed-effects model. Data on the difference in lung function variables between patients receiving LVRS vs medical care out to 5 years were estimated and are presented.The 5-year differences in patients randomised to LVRS were a small but sustained improvement in lung function indicators of forced expiratory volume in 1 second, forced vital capacity, and residual volume of +1.4% (P.001), +3.44% (P.001) and -19.49% (P.001) of the predicted values, respectively. With regards to physiological function, the 5-year difference in patients randomised to LVRS was an overall 0.89 W improvement in maximum workload (P = .069), -4.12 improvement in shortness of breath score (P.001), and 0.088 improvement in quality of well-being score (P = .102).Our results suggest that LVRS continues to have an important role in the treatment of patients with severe emphysema, with long-term benefits to lung function variables and a sustained improvement to the relief of dyspnea.

Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public.