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Aedes aegypti and A. albopictus mosquitoes are the main vectors for dengue virus (DENV) and other arboviruses, including Zika virus (ZIKV). Understanding the factors that affect transmission of arboviruses from mosquitoes to humans is a priority because it could inform public health and targeted interventions. Reasoning that interactions among viruses in the vector insect might affect transmission, we analysed the viromes of 815 urban Aedes mosquitoes collected from 12 countries worldwide. Two mosquito-specific viruses, Phasi Charoen-like virus (PCLV) and Humaita Tubiacanga virus (HTV), were the most abundant in A. aegypti worldwide. Spatiotemporal analyses of virus circulation in an endemic urban area revealed a 200% increase in chances of having DENV in wild A. aegypti mosquitoes when both HTV and PCLV were present. Using a mouse model in the laboratory, we showed that the presence of HTV and PCLV increased the ability of mosquitoes to transmit DENV and ZIKV to a vertebrate host. By transcriptomic analysis, we found that in DENV-infected mosquitoes, HTV and PCLV block the downregulation of histone H4, which we identify as an important proviral host factor in vivo.

Anopheles stephensi, an Asian urban malaria vector, continues to expand across Africa. We investigated the role of An. stephensi in malaria transmission following a dry season outbreak in Dire Dawa, Ethiopia, from April to July 2022, using a prospective case control design. Plasmodium falciparum microscopy-positive febrile patients (n = 101) and microscopy-negative controls (n = 189) were identified and their contacts (n = 662) screened. Spatial clustering of P. falciparum infections detected in the case contacts but not the controls which was strongly associated with detection of An. stephensi and the detection of sporozoites provides the strongest evidence to date for a role of An. stephensi in driving an urban malaria outbreak in Africa. Importantly, this outbreak involved clonal propagation of drug and diagnostic resistant parasites. This study provides the first epidemiological direct evidence linking An. stephensi with increase in malaria transmission in Africa, highlighting the major public health threat of this fast-spreading invasive mosquito.

IntroductionIn addition to the direct impact of COVID-19 infections on health and mortality, a growing body of literature indicates there are wide-ranging indirect impacts of the COVID-19 pandemic and associated public health measures on population health and wellbeing. Exploring these indirect impacts in the context of a socially deprived UK coastal town will help identify priority areas to focus COVID-19 recovery efforts on.MethodsData on primary care diagnosis, hospital admissions, and several socioeconomic outcomes between 2016 and Spring 2021 in the UK town of Fleetwood were collected and analysed in an exploratory analysis looking at pre- and post-COVID-19 patterns in health and social outcomes. Weekly and monthly trends were plotted by time and differences between periods examined using Chi-squared and t-tests.ResultsInitial falls in hospital admissions and diagnoses of conditions in primary care in March 2020 were followed by sustained changes to health service activity for specific diagnostic and demographic groups, including for chronic kidney disease and young people. Increases in the number of people receiving Universal Credit and children eligible for free school meals appear to be greater for those in the least deprived areas of the town.DiscussionThese exploratory findings provide initial evidence of the sustained impact of the pandemic across several health and social outcomes. Examining these trends in multivariate analyses will further test these associations and establish the strength of the medium term impact of the pandemic on the population of this coastal town. Advanced modelling of this data is ongoing and will be published shortly.

SummaryEmbryonic mesenchymal cells are dispersed within an extracellular matrix but can coalesce to form condensates with key developmental roles. Cells within condensates undergo fate and morphological changes, and induce cell fate changes in nearby epithelia to produce structures including hair follicles, feathers or intestinal villi. Here, by imaging of mouse and chicken embryonic skin, we find that mesenchymal cells undergo much of their dispersal in early interphase, in a stereotyped process of displacement driven by three hours of rapid and persistent migration, followed by a long period of low motility. The cell division plane and the elevated migration speed and persistence of newly born mesenchymal cells are mechanosensitive, aligning with tension in the tissue. This early G1 migratory behaviour disperses mesenchymal cells and allows the daughters of recent divisions to travel long distances to enter dermal condensates, demonstrating an unanticipated effect of a cell cycle sub-phase on core mesenchymal behaviour.HighlightsAfter mesenchymal cell division the speed and persistence of daughter cell migration is elevated for 180 minutesMesenchymal cell division and migration are directed by tissue tensionNewly born mesenchymal cells are uniquely responsive to tissue strainNewly born mesenchymal cells are preferentially recruited to dermal condensatesIncreased dispersal of newly born cells enables long distance travel to dermal condensatesGraphical abstract

ObjectiveTo estimate continuous positive airway pressure (CPAP) length of treatment effect on survival of hospitalised COVID-19 patients in a medium-sized UK Hospital, and how this effect changes according to the patient’s comorbidity and COVID-19 route of acquisition (community or nosocomial) during the two waves in 2020.SettingThe acute inpatient unit in Wrightington, Wigan and Leigh Teaching Hospitals National Health Service (NHS) Foundation Trust (WWL), a medium-sized NHS Trust in north-west of England.DesignRetrospective cohort of all confirmed COVID-19 patients admitted in WWL during 2020.Participants1830 patients (568 first wave, 1262 s wave) with antigen confirmed COVID-19 disease and severe acute respiratory syndrome admitted between 17 March 2020 (first confirmed COVID-19 case) and 31 December 2020.Outcome measureCOVID-19 survival rate in all patients and survival rate in potentially hospital-acquired COVID-19 (PHA) patients were modelled using a predictor set which include comorbidities (eg, obesity, diabetes, chronic ischaemic heart disease (IHD), chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD)), wave, age, sex and care home residency, and interventions (remdesivir, dexamethasone, CPAP, intensive care unit (ICU), intubation). Secondary outcome measure was CPAP length, which was modelled using the same predictors of the survival rate.ResultsMortality rate in the second wave was significantly lower than in the first wave (43.4% vs 28.1%, pConclusionsThe experience of an Acute Trust during the COVID-19 outbreak of 2020 is documented and indicates the importance of care home and hospitals in disease acquisition. Death rates fell between the first and second wave only for community-acquired COVID-19 patients. The fall was associated to CPAP length, especially for some comorbidities. While uncovering some risk and protective factors of mortality in COVID-19 studies, the study also unravels how little is known about PHA COVID-19 and the interaction between CPAP and some comorbidities.

BackgroundPublic health interventions are essential to prevent a long tail of costly, avoidable and worsening ill health in coastal communities following the COVID-19 pandemic, yet no research exists to guide policy and practice as to which groups within coastal communities are vulnerable and most in need of such interventions. Within this aim, we explore engrained and emerging vulnerabilities of food insecurity, health and well-being for different demographic groups within the deprived coastal community of Fleetwood, Lancashire, UK, before and after the pandemic.MethodsRoutinely collected data of free school meal eligibility, community mental health referrals and hospital admissions between 28 March 2016 and 31 December 2021 were aggregated by locality and deprivation within Fleetwood. Temporal autoregressive models, generalised linear mixed models and survival analyses were employed to compare trends and associations in food insecurity, health and well-being indicators against deprivation indices, demographics, comorbidities (including COVID-19), the COVID-19 pandemic period and locality.ResultsAreas with better housing and income, but higher health and disability deprivation, showed increased levels of free school meal eligibility following the pandemic. Mental health was insensitive to the first 14 months of pandemic yet is worsened by unemployment deprivation and cardiovascular and respiratory comorbidities, with a greater predisposition to poor mental health in adolescents and young adults. After accounting for the effect of COVID-19, hospital mortality risk increased with demographic influences in fitting with the typology of coastal communities having an older population, struggling healthcare and a greater prevalence of comorbidities.ConclusionsPublic health managers and policy makers seeking to prevent worsening health and well-being within coastal communities following the pandemic should focus on broader-scale patterns reflecting entrenched poor health typical of coastal communities, and emerging food insecurity within specific demographic and deprivation groups at finer scales.

Mosquito surveillance data can be used for predicting mosquito distribution and dynamics as they relate to human disease. Often these data are collected by independent agencies and aggregated to state and national level portals to characterize broad spatial and temporal dynamics. These larger repositories may also share the data for use in mosquito and/or disease prediction and forecasting models. Assumed, but not always confirmed, is consistency of data across agencies. Subtle differences in reporting may be important for development and the eventual interpretation of predictive models. Using mosquito vector surveillance data from Arizona as a case study, we found differences among agencies in how trapping practices were reported. Inconsistencies in reporting may interfere with quantitative comparisons if the user has only cursory familiarity with mosquito surveillance data. Some inconsistencies can be overcome if they are explicit in the metadata while others may yield biased estimates if they are not changed in how data are recorded. Sharing of metadata and collaboration between modelers and vector control agencies is necessary for improving the quality of the estimations. Efforts to improve sharing, displaying, and comparing vector data from multiple agencies are underway, but existing data must be used with caution.

Species distribution models (SDMs) are an important class of model for mapping taxa spatially and are a key tool for tackling biodiversity loss. However, most common SDMs depend on presence–absence data and, despite the accumulation and exponential growth of biological occurrence data across the globe, the available data are predominantly presence-only (i.e. they lack real absences). Although presence-only SDMs do exist, they inevitably require assumptions about absences of the considered taxa and they are specified mostly for single species and, thus, do not exploit fully the information in related taxa. This greatly limits the utility of global biodiversity databases such as GBIF. Here, we present a Bayesian-based SDM for multiple species that operates directly on presence-only data by exploiting the joint distribution between the multiple ecological processes and, crucially, identifies the sampling effort per taxa which allows inference on absences. The model was applied to two case studies. One, focusing on taxonomically diverse taxa over central Mexico and another focusing on the monophyletic family Cactacea over continental Mexico. In both cases, the model was able to identify the ecological and sampling effort processes for each taxon using only the presence observations, environmental and anthropological data.

Eastern equine encephalitis (EEE) is a rare but lethal mosquito-borne zoonotic disease. Recent years have seen incursion into new areas of the USA, and in 2019 the highest number of human cases in decades. Due to the low detection rate of EEE, previous studies were unable to quantify large-scale and recent EEE ecological dynamics. We used Bayesian spatial generalized-linear mixed model to quantify the spatiotemporal dynamics of human EEE incidence in the northeastern USA. In addition, we assessed whether equine EEE incidence has predictive power for human cases, independently from other environmental variables. The predictors of the model were selected based on variable importance. Human incidence increased with temperature seasonality, but decreased with summer temperature, summer, fall, and winter precipitation. We also found EEE transmission in equines strongly associated with human infection (OR: 1.57; 95% CI: 1.52–1.60) and latitudes above 41.9°N after 2018. The study designed for sparse dataset described new and known relationships between human and animal EEE and environmental factors, including geographical directionality. Future models must include equine cases as a risk factor when predicting human EEE risks. Future work is still necessary to ascertain the establishment of EEE in northern latitudes and the robustness of the available data.

◦AimWe propose a Bayesian framework for modelling species distributions using presence-only biodiversity occurrences obtained from historical opportunistic surveys.◦LocationGlobal applicability with two case studies in south-east Mexico.◦MethodsThe framework defines a bivariate spatial process separable into ecological and sampling effort processes that jointly generate occurrence observations of biodiversity records. Presence-only data are conceived as incomplete observations where some presences have been filtered out. A choosing principle is used to separate out presences, missing data and absences relative to the species of interest and the sampling observations. The framework provides three modelling alternatives for accounting the spatial autocorrelation structure: independent latent variables (model I); common latent spatial random effect (model II); and correlated latent spatial random effects (model III).The framework was compared against the Maximum Entropy (MaxEnt) algorithm in two case studies: one for the prediction of pines (Class: Pinopsida), using botanical records as sampling observations and another for the prediction of Flycatchers (Family: Tyranidae), using bird sightings as sampling records.◦ăResultsIn both case studies, at least one of the proposed models achieved higher predictive accuracy than MaxEnt. The model with correlated spatial effects fit best when the sampling effort was informative, while the one with a shared spatial effect was more suitable in cases with high proportion of non sampled sites.◦Main ConclusionsOur approach provides a flexible framework for presence-only SDMs aided by a sampling effort process informed by the accumulated observations of independent and heterogeneous surveys. For the two case studies, the framework provided a model with a higher predictive accuracy than an optimised version MaxEnt.

Aedes aegypti and Aedes albopictus are major mosquito vectors for arthropod-borne viruses (arboviruses) such as dengue (DENV) and Zika (ZIKV) viruses. Mosquitoes also carry insect-specific viruses (ISVs) that may affect the transmission of arboviruses. Here, we analyzed the global virome in urban Aedes mosquitoes and observed that two insect-specific viruses, Phasi Charoen-like virus (PCLV) and Humaita Tubiacanga virus (HTV), were the most prevalent in A. aegypti worldwide except for African cities, where transmission of arboviruses is low. Spatiotemporal analysis revealed that presence of HTV and PCLV led to a 200% increase in the chances of having DENV in wild mosquitoes. In the laboratory, we showed that HTV and PCLV prevented downregulation of histone H4, a previously unrecognized proviral host factor, and rendered mosquitoes more susceptible to DENV and ZIKV. Altogether, our data reveals a molecular basis for the regulation of A. aegypti vector competence by highly prevalent ISVs that may impact how we analyze the risk of arbovirus outbreaks.

COVID-19 infection typically causes pneumonia with bilateral changes on Chest radiograph. There is significant hypoxia and use of oxygen for patients admitted to hospital is standard. The use of Continuous Positive Airway Pressure (CPAP) in patients with COVID-19 has now become established as a common clinical practice based on recent experience. It is given as part of “best endeavours” treatment in the absence of sufficient evidence to guide best practice. The use of CPAP as a step up in clinical care is now common but has a poor evidence base.Using routinely collected data, the use of CPAP as a supportive non-invasive ventilatory treatment is described in 35 patients with COVID infection. Patients given early CPAP and in particular within 48 hours of admission, are shown to have a better outcome (a significant probability of lower mortality) than patients who received late CPAP (more than 48 hours after admission).Although the analysis is affected by a small sample size, the results have shown good evidence that supports the early use of CPAP in patients with COVID-19 infection.

Biospytial is a modular open source knowledge engine designed to import, organise, analyse and visualise big spatial ecological datasets using the power of graph theory. Specifically, it handles species occurrences and their taxonomic classification for performing ecological analysis on biodiversity and species distributions. The engine uses a hybrid graph-relational approach to store and access information. The data are linked with relationships that are stored in a graph database, while tabular and geospatial (vector and raster) data are stored in a relational database management system (RDBMS). The graph data structure provides a scalable design that eases the problem of merging datasets from different sources. The linkage relationships use semantic structures (objects and predicates) to answer scientific questions represented as complex data structures stored in the graph database. In this sense, we used species occurrences, taxonomic classification, and climatic datasets to build a knowledge graph of the Tree of Life embedded in an environmental and geographical grid. Biospytial comprises three interconnected components: i) a Geospatial Processing unit (GPU) supported by a RDBMS with geoprocessing capabilities, ii) a Graph Storage and Querying Unit, and iii) a graph-relational package, called: The Biospytial Computing Engine (BCE) that integrates all the system’s components. It also includes tools like: interactive notebooks (Jupyter), graph analytic libraries (NetworkX) and statistical frameworks (PyMC3). The Biospytial approach reduces the complexity of joining datasets using multiple primary-foreign key relations, a drawback in RDBMS. Applied to ecological data, it allows the discovery and inference of relationships using the interconnected network of taxonomic and spatial relationships. Its modular and scalable design makes it possible to run and distribute several instances simultaneously, allowing fast and efficient handling of big and complex ecological datasets. An example applied to the conservation of threatened species from the IUCN Red List using the co-occurrence of jaguars (Panthera onca) is included. This example demonstrates the engine’s capabilities in performing basic taxonomic trees manipulation, analysis and visualization of taxonomic groups co-occurring in space.

ObjectiveTo evaluate the role of continuous positive air pressure (CPAP) in the management of respiratory failure associated with COVID-19 infection. Early clinical management with limited use of CPAP (3% of patients) was compared with a later clinical management strategy which had a higher proportion of CPAP use (15%).DesignRetrospective case-controlled service evaluation for a single UK National Health Service (NHS) Trust during March–June 2020 designed and conducted solely to estimate the effects of current care.SettingThe acute inpatient unit in Wrightington, Wigan and Leigh Teaching Hospitals NHS Foundation Trust, a medium-sized English NHS Trust.Participants206 patients with antigen confirmed COVID-19 disease and severe acute respiratory syndrome admitted between 17 March 2020 and 3 April 2020 for the early group (controls), and between 10 April 2020 and 11 May 2020 for the late group (cases). Follow-up for all cases was until 11 June by which time all patients had a final outcome of death or discharge. Both groups were composed of 103 patients. Cases and controls were matched by age and sex.Outcome measureThe outcome measure was the proportion of patients surviving at time t (time from the positive result of COVID-19 test to discharge/death date). The predictors were CPAP intervention, intubation, residence in care homes and comorbidities (renal, pulmonary, cardiac, hypertension and diabetes). A stratified Cox proportional hazard for clustered data (via generalised estimating equations) and model selection algorithms were employed to identify the effect of CPAP on patients’ survival and the effect on gas exchange as measured by alveolar arterial (A-a) gradient and timing of CPAP treatment on CPAP patients’ survival.ResultsCPAP was found to be significantly (HR 0.38, 95% CI 0.36 to 0.40) associated with lower risk of death in patients with hospital stay equal to, or below 7 days. However, for longer hospitalisation CPAP was found to be associated with increased risk of death (HR 1.72, 95% CI 1.40 to 2.12). When CPAP was initiated within 4 days of hospital admission, the survival probability was above 73% (95% CI 53% to 99%). In addition, lower A-a gradient was associated with lower risk of death in CPAP patients (HR 1.011, 95% CI 1.010 to 1.013). The selected model (best fit) was stratified by sex and clustered by case/control groups. The predictors were age, intubation, hypertension and the residency from care homes, which were found to be statistically significantly associated with patient’s death/discharge.ConclusionsCPAP is a simple and cost-effective intervention. It has been established for care of other respiratory disorders but not for COVID-19 respiratory failure. This evaluation establishes that CPAP as a potentially viable treatment option for this group of patients during the first days of hospital admission. As yet there is limited availability of quantitative research on CPAP use for COVID-19. Whist this work is hampered by both the relatively small sample size and retrospective design (which reduced the ability to control potential confounders), it represents evidence of the significant benefit of early CPAP intervention. This evaluation should stimulate further research questions and larger study designs on the potential benefit of CPAP for COVID-19 infections. Globally, this potentially beneficial low cost and low intensity therapy could have added significance economically for healthcare provision in less developed countries.

Highlights • Vector indices are often poor predictors of geographic disease risk. • When longitudinal vector surveillance data is available, vector population dynamics parameters can be estimated. • Vector population dynamics are investigated as a measure of disease risk. • In this work we have shown that mosquito intrinsic growth rate has a larger predictive accuracy for dengue incidence than mosquito abundance. • In the light of this result we encourage vector surveillance and control agencies/scientist to explore the use of intrinsic growth rate as index for disease risk., Understanding geographic population dynamics of mosquitoes is an essential requirement for estimating the risk of mosquito-borne disease transmission and geographically targeted interventions. However, the use of population dynamics measures, such as the intrinsic growth rate, as predictors in spatio-temporal point processes has not been investigated before. In this work we compared the predictive accuracy of four spatio-temporal log-Gaussian Cox models: (i) With no predictors; (ii) mosquito abundance as predictor; (iii) intrinsic growth rate as predictor; (iv) intrinsic growth rate and mosquito abundance as predictors. This analysis is based on Aedes aegypti mosquito surveillance and human dengue data obtained from the urban area of Caratinga, Brazil. We used a statistical Moran Curve approach to estimate the intrinsic growth rate and a zero inflated Poisson kriging model for estimating mosquito abundance at locations of dengue cases. The incidence of dengue cases was positively associated with mosquito intrinsic growth rate and this model outperformed, in terms of predictive accuracy, the abundance and the null models. The latter includes only the spatio-temporal random effect but no predictors. In the light of these results we suggest that the intrinsic growth rate should be investigated further as a potential tool for predicting the risk of dengue transmission and targeting health interventions for vector-borne diseases.

Culicoides-borne arboviruses of livestock impair animal health, livestock production and livelihoods worldwide. As these arboviruses are multi-host, multi-vector systems, predictions to improve targeting of disease control measures require frameworks that quantify the relative impacts of multiple abiotic and biotic factors on disease patterns. We develop such a framework to predict long term (1992–2009) average patterns in bluetongue (BT), caused by bluetongue virus (BTV), in sheep in southern India, where annual BT outbreaks constrain the livelihoods and production of small-holder farmers. In Bayesian spatial general linear mixed models, host factors outperformed landscape and climate factors as predictors of disease patterns, with more BT outbreaks occurring on average in districts with higher densities of susceptible sheep breeds and buffalo. Since buffalo are resistant to clinical signs of BT, this finding suggests they are a source of infection for sympatric susceptible sheep populations. Sero-monitoring is required to understand the role of buffalo in maintaining BTV transmission and whether they must be included in vaccination programs to protect sheep adequately. Landscape factors, namely the coverage of post-flooding, irrigated and rain-fed croplands, had weak positive effects on outbreaks. The intimate links between livestock host, vector composition and agricultural practices in India require further investigation at the landscape scale.

Visceral leishmaniasis (VL) in Brazil is a neglected vector-borne tropical parasitic disease that is responsible for several thousand human deaths every year. The numbers of cases more than doubled between 1990 and 2016. Transmission occurs when sand flies become infected after feeding on infected dogs (the reservoir host) and then subsequently on humans. A major component of the VL control effort is the identification and euthanasia of infected dogs to remove them as a source of infection. Rapid, accurate identification of infected dogs would be key to this strategy.Here we demonstrate the potential of a volatile organic chemical analyser (eNose) to rapidly and accurately identify dogs infected with Leishmania infantum parasites. The eNose could discriminate between the odours present in the hair of infected and uninfected dogs with greater than 95% sensitivity and 95% specificity. The device was sufficiently sensitive to be able to identify infected dogs even when parasite loads in the circulating blood were very low.Future improvements to VOC analyser technology, portability and ease of use suggest that this methodology could significantly improve the diagnosis of VL infected dogs in Brazil and elsewhere and with other parasitic diseases such as Malaria, Chaga’s Disease and Leishmania in humans.

Vector-borne disease control relies on efficient vector surveillance, mostly carried out using traps whose number and locations are often determined by expert opinion rather than a rigorous quantitative sampling design. In this work we propose a framework for ecological sampling design which in its preliminary stages can take into account environmental conditions obtained from open data (i.e. remote sensing and meteorological stations) not necessarily designed for ecological analysis. These environmental data are used to delimit the area into ecologically homogeneous strata. By employing Bayesian statistics within a model-based sampling design, the traps are deployed among the strata using a mixture of random and grid locations which allows balancing predictions and model-fitting accuracies. Sample sizes and the effect of ecological strata on sample sizes are estimated from previous mosquito sampling campaigns open data. Notably, we found that a configuration of 30 locations with four households each (120 samples) will have a similar accuracy in the predictions of mosquito abundance as 200 random samples. In addition, we show that random sampling independently from ecological strata, produces biased estimates of the mosquito abundance. Finally, we propose standardizing reporting of sampling designs to allow transparency and repetition/re-use in subsequent sampling campaigns.

Land cover change analyses are common and, especially in the absence of explanatory variables, they are mainly carried out by employing qualitative methods such as transition matrices or raster operations. These methods do not provide any estimation of the statistical significance of the changes, or the uncertainty of the model and data, and are usually limited in supporting explicit biological/ecological interpretation of the processes determining the changes. Here we show how the original nearest-neighbour contingency table, proposed by Dixon to evaluate spatial segregation, has been extended to the temporal domain to map the intensity, statistical significance and uncertainty of land cover changes. This index was then employed to quantify the changes in cork oak forest cover between 1998 and 2016 in the Sa Serra region of Sardinia (Italy). The method showed that most statistically significant cork oak losses were concentrated in the centre of Sa Serra and characterised by high intensity. A spatial binomial-logit generalised linear model estimated the probability of changes occurring in the area but not the type of change. We show how the spatio-temporal Dixon’s index can be an attractive alternative to other land cover change analysis methods, since it provides a robust statistical framework and facilitates direct biological/ecological interpretation.

Background Dengue is a vector-borne disease caused by the dengue virus (DENV). Despite the crucial role of Aedes mosquitoes in DENV transmission, pure vector indices poorly correlate with human infections. Therefore there is great need for a better understanding of the spatial and temporal scales of DENV transmission between mosquitoes and humans. Here, we have systematically monitored the circulation of DENV in individual Aedes spp. mosquitoes and human patients from Caratinga, a dengue endemic city in the state of Minas Gerais, in Southeast Brazil. From these data, we have developed a novel stochastic point process pattern algorithm to identify the spatial and temporal association between DENV infected mosquitoes and human patients. Methods The algorithm comprises of: (i) parameterization of the variogram for the incidence of each DENV serotype in mosquitoes; (ii) identification of the spatial and temporal ranges and variances of DENV incidence in mosquitoes in the proximity of humans infected with dengue; and (iii) analysis of the association between a set of environmental variables and DENV incidence in mosquitoes in the proximity of humans infected with dengue using a spatio-temporal additive, geostatistical linear model. Results DENV serotypes 1 and 3 were the most common virus serotypes detected in both mosquitoes and humans. Using the data on each virus serotype separately, our spatio-temporal analyses indicated that infected humans were located in areas with the highest DENV incidence in mosquitoes, when incidence is calculated within 2.5–3 km and 50 days (credible interval 30–70 days) before onset of symptoms in humans. These measurements are in agreement with expected distances covered by mosquitoes and humans and the time for virus incubation. Finally, DENV incidence in mosquitoes found in the vicinity of infected humans correlated well with the low wind speed, higher air temperature and northerly winds that were more likely to favor vector survival and dispersal in Caratinga. Conclusions We have proposed a new way of modeling bivariate point pattern on the transmission of arthropod-borne pathogens between vector and host when the location of infection in the latter is known. This strategy avoids some of the strong and unrealistic assumptions made by other point-process models. Regarding virus transmission in Caratinga, our model showed a strong and significant association between high DENV incidence in mosquitoes and the onset of symptoms in humans at specific spatial and temporal windows. Together, our results indicate that vector surveillance must be a priority for dengue control. Nevertheless, localized vector control at distances lower than 2.5 km around premises with infected vectors in densely populated areas are not likely to be effective. Electronic supplementary material The online version of this article (10.1186/s13071-018-2662-6) contains supplementary material, which is available to authorized users.

In Brazil, human and canine visceral leishmaniasis is caused by infection with Leishmania infantum, a Protist parasite transmitted by blood-feeding female Lutzomyia longipalpis sand flies. The objective of this study was to determine if the odour of hamsters, infected with Le. infantum, was more attractive than the odour of the same hamsters, before they were infected. The attractiveness of odour collected from individual hamsters (n = 13), before they were infected, was compared in a longitudinal study, with the attractiveness of the odour of the same hamster in a Y-tube olfactometer bioassay, at a late stage of infection. The odour of six of the golden hamsters was significantly more attractive to 50% of the female sand flies at the end of infection compared to before infection and the odour of four of the golden hamsters was significantly more attractive to 75% of the female sand flies at the end of infection. These results strongly indicate that hamsters infected with Le. infantum become significantly more attractive to a greater proportion of female sand flies as the infection progresses.

Public health authorities are required to prepare for future threats and need predictions of the likely impact of climate change on public health risks. They may get overwhelmed by the volume of heterogeneous information in scientific articles and risk relying purely on the public opinion articles which focus mainly on global warming trends, and leave out many other relevant factors. In the current paper, we discuss various scientific approaches investigating climate change and its possible impact on public health and discuss their different roles and functions in unraveling the complexity of the subject. It is not our objective to review the available literature or to make predictions for certain diseases or countries, but rather to evaluate the applicability of scientific research articles on climate change to evidence-based public health decisions. In the context of mosquito borne diseases, we identify common pitfalls to watch out for when assessing scientific research on the impact of climate change on human health. We aim to provide guidance through the plethora of scientific papers and views on the impact of climate change on human health to those new to the subject, as well as to remind public health experts of its multifactorial and multidisciplinary character.

Human mobility continues to increase in terms of volumes and reach, producing growing global connectivity. This connectivity hampers efforts to eliminate infectious diseases such as malaria through reintroductions of pathogens, and thus accounting for it becomes important in designing global, continental, regional, and national strategies. Recent works have shown that census-derived migration data provides a good proxy for internal connectivity, in terms of relative strengths of movement between administrative units, across temporal scales. To support global malaria eradication strategy efforts, here we describe the construction of an open access archive of estimated internal migration flows in endemic countries built through pooling of census microdata. These connectivity datasets, described here along with the approaches and methods used to create and validate them, are available both through the WorldPop website and the WorldPop Dataverse Repository.

Background: There are limited data on the epidemiology, diagnosis and optimal management of nontuberculous mycobacterial (NTM) disease in children.Methods: Retrospective cohort study of NTM cases over a 10-year-period at a tertiary referral hospital in Australia.Results: A total of 140 children with NTM disease, including 107 with lymphadenitis and 25 with skin and soft tissue infections (SSTIs), were identified. The estimated incidence of NTM disease was 0.6–1.6 cases / 100,000 children / year; no increasing trend was observed over the study period. Temporal analyses revealed a seasonal incidence cycle around 12 months, with peaks in late winter/spring and troughs in autumn. Mycobacterium-avium-complex accounted for most cases (77.8%), followed by Mycobacterium ulcerans (14.4%) and Mycobacterium marinum (3.3%). Polymerase chain reaction testing had higher sensitivity than culture and microscopy for acid-fast bacilli (92.0%, 67.2% and 35.7%, respectively). The majority of lymphadenitis cases underwent surgical excision (97.2%); multiple recurrences in this group were less common in cases treated with clarithromycin and rifampicin compared with clarithromycin alone or no anti-mycobacterial drugs (0% versus 7.1%; OR:0.73). SSTI recurrences were also less common in cases treated with two anti-mycobacterial drugs compared with one or none (10.5% versus 33.3%; OR:0.23).Conclusions: There was seasonal variation in the incidence of NTM disease, analogous to recently published observations in tuberculosis, which have been linked to seasonal variation in vitamin D. Our finding that anti-mycobacterial combination therapy was associated with a reduced risk of recurrences in patients with NTM lymphadenitis or SSTI requires further confirmation in prospective trials.

The Moderate Resolution Imaging Spectroradiometer MODIS on NASA's Terra and Aqua satellites conducts continuous monitoring of the Earth's land surface and oceans. Recently, a sharp discontinuity averaging 1.9°C has been noticed at 60° N in both MODIS daytime and night-time land surface temperature LST products. This linear artefact arises because the CO2 high cloud test in the operational code for the generation of the MODIS cloud mask product is used only in the non-polar region between 60° N and 60° S. The resulting discontinuity clearly has negative implications for any statistical applications of these temperature data. In this technical note we present a new algorithm, which minimizes this discontinuity. The method uses edge detection and elimination based on a mixture of Sobel and non-linear Laplacian filters edge detection and quantification, cubic splines edge modelling, and a controllable power function for image restoration. The implementation of this algorithm is demonstrated on an image of average minimum night-time LST between 2001 and 2008.

The 2006 bluetongue (BT) outbreak in northwestern Europe had devastating effects on cattle and sheep in that intensively farmed area. The role of wind in disease spread, through its effect on Culicoides dispersal, is still uncertain, and remains unquantified. We examine here the relationship between farm-level infection dates and wind speed and direction within the framework of a novel model involving both mechanistic and stochastic steps. We consider wind as both a carrier of host semio-chemicals, to which midges might respond by upwind flight, and as a transporter of the midges themselves, in a more or less downwind direction. For completeness, we also consider midge movement independent of wind and various combinations of upwind, downwind and random movements. Using stochastic simulation, we are able to explain infection onset at 94 per cent of the 2025 affected farms. We conclude that 54 per cent of outbreaks occurred through (presumably midge) movement of infections over distances of no more than 5 km, 92 per cent over distances of no more than 31 km and only 2 per cent over any greater distances. The modal value for all infections combined is less than 1 km. Our analysis suggests that previous claims for a higher frequency of long-distance infections are unfounded. We suggest that many apparent long-distance infections resulted from sequences of shorter-range infections; a ‘stepping stone’ effect. Our analysis also found that downwind movement (the only sort so far considered in explanations of BT epidemics) is responsible for only 39 per cent of all infections, and highlights the effective contribution to disease spread of upwind midge movement, which accounted for 38 per cent of all infections. The importance of midge flight speed is also investigated. Within the same model framework, lower midge active flight speed (of 0.13 rather than 0.5 m s −1 ) reduced virtually to zero the role of upwind movement, mainly because modelled wind speeds in the area concerned were usually greater than such flight speed. Our analysis, therefore, highlights the need to improve our knowledge of midge flight speed in field situations, which is still very poorly understood. Finally, the model returned an intrinsic incubation period of 8 days, in accordance with the values reported in the literature. We argue that better understanding of the movement of infected insect vectors is an important ingredient in the management of future outbreaks of BT in Europe, and other devastating vector-borne diseases elsewhere.

Cork oak is one of the most valuable natural forest genera in the Mediterranean basin. Modelling cork oak growth has been a challenge for foresters in recent years because of strong site and genetic influences, below-ground competition, management regimes and age effects. Because cork productivity is related to forest height, which is, in turn, related directly with site characteristics, an increase in the accuracy of height prediction implies improved productivity estimation. A Bayesian maximum entropy (BME) geostatistical model was applied to characterize the space-time pattern of height of young cork oak in a forest stand from central Sardinia in the years 2000, 2002, 2003, 2006 and 2008. Cork oak height maps were produced for each of the 5 years. The main goals were to analyse and interpret through time (i) the changes in spatial correlation and (ii) the changes in spatial distribution of cork oak height. The plantation was characterized by an increasing spatial dependence through time, whereas the temporal range was 2 years. Cork oak height was significantly correlated with wind speed (reduced by a neighbouring forest) in all the years implying a single trend. The correlations were larger for 2006 and 2008 than for previous years. Three other environmental variables (shade, elevation and slope) were less significant and their influence restricted to 2 years only. This research has several implications for the management of cork oak in the young phase.