Health and economic impacts of Lassa vaccination campaigns in West Africa.

Lassa fever is a zoonotic disease identified by the World Health Organization (WHO) as having pandemic potential. This study estimates the health-economic burden of Lassa fever throughout West Africa and projects impacts of a series of vaccination campaigns. We also model the emergence of "Lassa-X" - a hypothetical pandemic Lassa virus variant - and project impacts of achieving 100 Days Mission vaccination targets. Our model predicted 2.7M (95% uncertainty interval: 2.1M-3.4M) Lassa virus infections annually, resulting over ten years in 2.0M (793.8K-3.9M) disability-adjusted life years (DALYs). The most effective vaccination strategy was a population-wide preventive campaign primarily targeting WHO-classified "endemic" districts. Under conservative vaccine efficacy assumptions, this campaign averted $20.1M ($8.2M-$39.0M) in lost DALY value and $128.2M ($67.2M-$231.9M) in societal costs (International dollars 2021). Reactive vaccination in response to local outbreaks averted just one-tenth the health-economic burden of preventive campaigns. In the event of Lassa-X emerging, spreading throughout West Africa and causing approximately 1.2M DALYs within two years, 100 Days Mission vaccination averted 22% of DALYs given a vaccine 70% effective against disease, and 74% of DALYs given a vaccine 70% effective against both infection and disease. These findings suggest how vaccination could alleviate Lassa fever's burden and assist in pandemic preparedness.

An osteopathic orientation to interprofessional education.

CONTEXT: The osteopathic tenets may serve as a useful guideline for an interprofessional program. There is an alignment between the osteopathic tenets and the concept of interprofessional education (IPE). IPE occurs when students from two or more professions work with each other to collaborate or improve healthcare outcomes. Holistic treatment is fundamental in both instances, and the interrelatedness of structure and function requires acknowledgment of all healthcare professionals' roles in treating a patient. IPE allows students to gain a better understanding of their own professional roles and the roles of their fellow healthcare providers in treating patients more effectively. OBJECTIVES: The objectives of this analysis are to evaluate the ability of an interprofessional summer workshop/lecture utilizing an osteopathic focus to educate students from different healthcare colleges about the interconnectedness of the systems of the human body and how working with a team-based approach will ultimately benefit their collective patients. A secondary objective was to determine the students' perceptions before and after the lecture/workshop to see if there were any perceived differences among students in different healthcare professions at either time. METHODS: This was a retrospective data analysis conducted on pretest/posttest surveys completed by 73 incoming students from six different healthcare colleges participating in the Summer Preparedness and Readiness Course (SPaRC), held annually at Western University of Health Sciences (WUHS) in Pomona, California. Analysis was conducted on responses collected during the SPaRC programs of 2013, 2016, and 2019. Participants were given surveys containing five questions scored on a five-point Likert scale. The surveys were given before and after an integrated lecture/hands-on workshop presented at SPaRC that reviewed multiple studies showing the utility of connecting the healthcare professions to best treat a patient. RESULTS: A total of 73 students responded to both the prelecture and postlecture surveys. When the number of positive scores were totaled from students from all colleges, there was an increase in positive responses from 190 (52.2 %) in prelecture surveys when compared to 336 (92.3 %) in postlecture surveys. A Wilcoxon signed-rank test suggested that the lecture workshop elicited a significant improvement in scores from prelecture to postlecture for all students (Z=-6.976, p=0.000). Median scores improved from 3.60 at baseline to 4.40 after the lecture/workshop. Secondary analysis conducted utilizing Kruskal-Wallis H to examine the differences between the responses of the different colleges prelecture and postlecture showed no significant differences prelecture (H [6]=7.58, p=0.271) and a significant difference between postlecture answers (H [6]=14.04, p=0.029). A series of post hoc independent Kruskal-Wallis H analyses was conducted to identify where differences were, and the only identifiable difference after Bonferroni corrections was between students from the Doctor of Osteopathic Medicine college and the Physician Assistant's college after the lecture/survey (p=0.041). CONCLUSIONS: An interprofessional program with the osteopathic principles of focusing on body unity and relatedness of structure and function may serve as a helpful tool for uniting healthcare professionals in their ultimate goal of better serving their patients.

Drivers and potential distribution of anthrax occurrence and incidence at national and sub-county levels across Kenya from 2006 to 2020 using INLA.

Anthrax is caused by, Bacillus anthracis, a soil-borne bacterium that infects grazing animals. Kenya reported a sharp increase in livestock anthrax cases from 2005, with only 12% of the sub-counties (decentralised administrative units used by Kenyan county governments to facilitate service provision) accounting for almost a third of the livestock cases. Recent studies of the spatial extent of B. anthracis suitability across Kenya have used approaches that cannot capture the underlying spatial and temporal dependencies in the surveillance data. To address these limitations, we apply the first Bayesian approach using R-INLA to analyse a long-term dataset of livestock anthrax case data, collected from 2006 to 2020 in Kenya. We develop a spatial and a spatiotemporal model to investigate the distribution and socio-economic drivers of anthrax occurrence and incidence at the national and sub-county level. The spatial model was robust to geographically based cross validation and had a sensitivity of 75% (95% CI 65-75) against withheld data. Alarmingly, the spatial model predicted high intensity of anthrax across the Northern counties (Turkana, Samburu, and Marsabit) comprising pastoralists who are often economically and politically marginalized, and highly predisposed to a greater risk of anthrax. The spatiotemporal model showed a positive link between livestock anthrax risk and the total human population and the number of exotic dairy cattle, and a negative association with the human population density, livestock producing households, and agricultural land area. Public health programs aimed at reducing human-animal contact, improving access to healthcare, and increasing anthrax awareness, should prioritize these endemic regions.

Joint spatiotemporal modelling reveals seasonally dynamic patterns of Japanese encephalitis vector abundance across India.

Predicting vector abundance and seasonality, key components of mosquito-borne disease (MBD) hazard, is essential to determine hotspots of MBD risk and target interventions effectively. Japanese encephalitis (JE), an important MBD, is a leading cause of viral encephalopathy in Asia with 100,000 cases estimated annually, but data on the principal vector Culex tritaeniorhynchus is lacking. We developed a Bayesian joint-likelihood model that combined information from available vector occurrence and abundance data to predict seasonal vector abundance for C. tritaeniorhynchus (a constituent of JE hazard) across India, as well as examining the environmental drivers of these patterns. Using data collated from 57 locations from 24 studies, we find distinct seasonal and spatial patterns of JE vector abundance influenced by climatic and land use factors. Lagged precipitation, temperature and land use intensity metrics for rice crop cultivation were the main drivers of vector abundance, independent of seasonal, or spatial variation. The inclusion of environmental factors and a seasonal term improved model prediction accuracy (mean absolute error [MAE] for random cross validation = 0.48) compared to a baseline model representative of static hazard predictions (MAE = 0.95), signalling the importance of seasonal environmental conditions in predicting JE vector abundance. Vector abundance varied widely across India with high abundance predicted in northern, north-eastern, eastern, and southern regions, although this ranged from seasonal (e.g., Uttar Pradesh, West Bengal) to perennial (e.g., Assam, Tamil Nadu). One-month lagged predicted vector abundance was a significant predictor of JE outbreaks (odds ratio 2.45, 95% confidence interval: 1.52-4.08), highlighting the possible development of vector abundance as a proxy for JE hazard. We demonstrate a novel approach that leverages information from sparse vector surveillance data to predict seasonal vector abundance-a key component of JE hazard-over large spatial scales, providing decision-makers with better guidance for targeting vector surveillance and control efforts.

Geographical drivers and climate-linked dynamics of Lassa fever in Nigeria.

Lassa fever is a longstanding public health concern in West Africa. Recent molecular studies have confirmed the fundamental role of the rodent host (Mastomys natalensis) in driving human infections, but control and prevention efforts remain hampered by a limited baseline understanding of the disease's true incidence, geographical distribution and underlying drivers. Here, we show that Lassa fever occurrence and incidence is influenced by climate, poverty, agriculture and urbanisation factors. However, heterogeneous reporting processes and diagnostic laboratory access also appear to be important drivers of the patchy distribution of observed disease incidence. Using spatiotemporal predictive models we show that including climatic variability added retrospective predictive value over a baseline model (11% decrease in out-of-sample predictive error). However, predictions for 2020 show that a climate-driven model performs similarly overall to the baseline model. Overall, with ongoing improvements in surveillance there may be potential for forecasting Lassa fever incidence to inform health planning.

A Pilot Study on the Effects of Nut Consumption on Cardiovascular Biomarkers.

BACKGROUND: Cardiovascular disease (CVD) is a leading cause of morbidity and mortality in the United States and changes in lifestyle can minimize the likelihood of succumbing to heart disease. Anti-inflammatory agents are commonly used to reduce the chronic inflammatory state behind the pathogenesis of CVD. Multiple studies have been published correlating nut consumption with a reduction in both heart attacks and strokes. The goal of this study is to determine to what extent the consumption of almonds, hazelnuts, and walnuts have on the blood markers associated with cardiac disease and inflammation. METHODS: This was a six-week study in which subject's baseline values act as controls. Fasting blood draws occurred at week 0, week 2, and after four weeks of intervention (week 6). All participants had undesirable lipid profiles and no medications related to heart disease. RESULTS: Total cholesterol (TC): high-density lipoprotein (HDL-C) ratio was lowered a statistically significant amount at the six-week time point (3.89 ± 0.74) compared to both the zero-week (4.93 ± 1.16, p < 0.01) and two-week (4.63 ± 1.20, p < 0.5) timepoints. Low-density lipoprotein (LDL) measurements were lowered a statistically significant amount at the six-week time point (135.6 ± 15.0 mg/dL) compared to the zero-week (159.7 ± 12.3 mg/dL, p < 0.01). Erythrocyte sedimentation rate (ESR) was lowered a statistically significant amount at six-week time point (10.44 ± 5.05 mm/h) compared to the zero-week (14.44 ± 5.12 mm/h, p < 0.01). CONCLUSIONS: Blood markers associated with CVD specifically and the general marker for inflammation associated with many chronic diseases can be favorably modified with the consumption of specific nuts as demonstrated by this study.

Zoonotic host diversity increases in human-dominated ecosystems.

Land use change-for example, the conversion of natural habitats to agricultural or urban ecosystems-is widely recognized to influence the risk and emergence of zoonotic disease in humans1,2. However, whether such changes in risk are underpinned by predictable ecological changes remains unclear. It has been suggested that habitat disturbance might cause predictable changes in the local diversity and taxonomic composition of potential reservoir hosts, owing to systematic, trait-mediated differences in species resilience to human pressures3,4. Here we analyse 6,801 ecological assemblages and 376 host species worldwide, controlling for research effort, and show that land use has global and systematic effects on local zoonotic host communities. Known wildlife hosts of human-shared pathogens and parasites overall comprise a greater proportion of local species richness (18-72% higher) and total abundance (21-144% higher) in sites under substantial human use (secondary, agricultural and urban ecosystems) compared with nearby undisturbed habitats. The magnitude of this effect varies taxonomically and is strongest for rodent, bat and passerine bird zoonotic host species, which may be one factor that underpins the global importance of these taxa as zoonotic reservoirs. We further show that mammal species that harbour more pathogens overall (either human-shared or non-human-shared) are more likely to occur in human-managed ecosystems, suggesting that these trends may be mediated by ecological or life-history traits that influence both host status and tolerance to human disturbance5,6. Our results suggest that global changes in the mode and the intensity of land use are creating expanding hazardous interfaces between people, livestock and wildlife reservoirs of zoonotic disease.

Author Correction: Impacts of environmental and socio-economic factors on emergence and epidemic potential of Ebola in Africa.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Impacts of environmental and socio-economic factors on emergence and epidemic potential of Ebola in Africa.

Recent outbreaks of animal-borne emerging infectious diseases have likely been precipitated by a complex interplay of changing ecological, epidemiological and socio-economic factors. Here, we develop modelling methods that capture elements of each of these factors, to predict the risk of Ebola virus disease (EVD) across time and space. Our modelling results match previously-observed outbreak patterns with high accuracy, and suggest further outbreaks could occur across most of West and Central Africa. Trends in the underlying drivers of EVD risk suggest a 1.75 to 3.2-fold increase in the endemic rate of animal-human viral spill-overs in Africa by 2070, given current modes of healthcare intervention. Future global change scenarios with higher human population growth and lower rates of socio-economic development yield a fourfold higher likelihood of epidemics occurring as a result of spill-over events. Our modelling framework can be used to target interventions designed to reduce epidemic risk for many zoonotic diseases.

The effect of global change on mosquito-borne disease.

More than 80% of the global population is at risk of a vector-borne disease, with mosquito-borne diseases being the largest contributor to human vector-borne disease burden. Although many global processes, such as land-use and socioeconomic change, are thought to affect mosquito-borne disease dynamics, research to date has strongly focused on the role of climate change. Here, we show, through a review of contemporary modelling studies, that no consensus on how future changes in climatic conditions will impact mosquito-borne diseases exists, possibly due to interacting effects of other global change processes, which are often excluded from analyses. We conclude that research should not focus solely on the role of climate change but instead consider growing evidence for additional factors that modulate disease risk. Furthermore, future research should adopt new technologies, including developments in remote sensing and system dynamics modelling techniques, to enable a better understanding and mitigation of mosquito-borne diseases in a changing world.

Location-level processes drive the establishment of alien bird populations worldwide.

Human-mediated translocation of species to areas beyond their natural distribution (which results in 'alien' populations1) is a key signature of the Anthropocene2, and is a primary global driver of biodiversity loss and environmental change3. Stemming the tide of invasions requires understanding why some species fail to establish alien populations, and others succeed. To achieve this, we need to integrate the effects of features of the introduction site, the species introduced and the specific introduction event. Determining which, if any, location-level factors affect the success of establishment has proven difficult, owing to the multiple spatial, temporal and phylogenetic axes along which environmental variation may influence population survival. Here we apply Bayesian hierarchical regression analysis to a global spatially and temporally explicit database of introduction events of alien birds4 to show that environmental conditions at the introduction location, notably climatic suitability and the presence of other groups of alien species, are the primary determinants of successful establishment. Species-level traits and the size of the founding population (propagule pressure) exert secondary, but important, effects on success. Thus, current trajectories of anthropogenic environmental change will most probably facilitate future incursions by alien species, but predicting future invasions will require the integration of multiple location-, species- and event-level characteristics.

Environmental limits of Rift Valley fever revealed using ecoepidemiological mechanistic models.

Vector-borne diseases (VBDs) of humans and domestic animals are a significant component of the global burden of disease and a key driver of poverty. The transmission cycles of VBDs are often strongly mediated by the ecological requirements of the vectors, resulting in complex transmission dynamics, including intermittent epidemics and an unclear link between environmental conditions and disease persistence. An important broader concern is the extent to which theoretical models are reliable at forecasting VBDs; infection dynamics can be complex, and the resulting systems are highly unstable. Here, we examine these problems in detail using a case study of Rift Valley fever (RVF), a high-burden disease endemic to Africa. We develop an ecoepidemiological, compartmental, mathematical model coupled to the dynamics of ambient temperature and water availability and apply it to a realistic setting using empirical environmental data from Kenya. Importantly, we identify the range of seasonally varying ambient temperatures and water-body availability that leads to either the extinction of mosquito populations and/or RVF (nonpersistent regimens) or the establishment of long-term mosquito populations and consequently, the endemicity of the RVF infection (persistent regimens). Instabilities arise when the range of the environmental variables overlaps with the threshold of persistence. The model captures the intermittent nature of RVF occurrence, which is explained as low-level circulation under the threshold of detection, with intermittent emergence sometimes after long periods. Using the approach developed here opens up the ability to improve predictions of the emergence and behaviors of epidemics of many other important VBDs.

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species' ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10-12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit Bayesian SDMs may be the better choice when modelling the spatial distribution of target species.

Understanding the cryptic nature of Lassa fever in West Africa.

Lassa fever (LF) is increasingly recognized by global health institutions as an important rodent-borne disease with severe impacts on some of West Africa's poorest communities. However, our knowledge of LF ecology, epidemiology and distribution is limited, which presents barriers to both short-term disease forecasting and prediction of long-term impacts of environmental change on Lassa virus (LASV) zoonotic transmission dynamics. Here, we synthesize current knowledge to show that extrapolations from past research have produced an incomplete picture of the incidence and distribution of LF, with negative consequences for policy planning, medical treatment and management interventions. Although the recent increase in LF case reports is likely due to improved surveillance, recent studies suggest that future socio-ecological changes in West Africa may drive increases in LF burden. Future research should focus on the geographical distribution and disease burden of LF, in order to improve its integration into public policy and disease control strategies.

Spatial, seasonal and climatic predictive models of Rift Valley fever disease across Africa.

Understanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Niño year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make space- and time-sensitive predictions to better direct future surveillance resources.This article is part of the themed issue 'One Health for a changing world: zoonoses, ecosystems and human well-being'.

The global avian invasions atlas, a database of alien bird distributions worldwide.

The introduction of species to locations where they do not naturally occur (termed aliens) can have far-reaching and unpredictable environmental and economic consequences. Therefore there is a strong incentive to stem the tide of alien species introduction and spread. In order to identify broad patterns and processes of alien invasions, a spatially referenced, global dataset on the historical introductions and alien distributions of a complete taxonomic group is required. Here we present the Global Avian Invasions Atlas (GAVIA)-a new spatial and temporal dataset comprising 27,723 distribution records for 971 alien bird species introduced to 230 countries and administrative areas spanning the period 6000BCE-AD2014. GAVIA was initiated to provide a unified database of records on alien bird introductions, incorporating records from all stages of invasion, including introductions that have failed as well as those that have succeeded. GAVIA represents the most comprehensive resource on the global distribution of alien species in any major taxon, allowing the spatial and temporal dynamics of alien bird distributions to be examined.

The Global Distribution and Drivers of Alien Bird Species Richness.

Alien species are a major component of human-induced environmental change. Variation in the numbers of alien species found in different areas is likely to depend on a combination of anthropogenic and environmental factors, with anthropogenic factors affecting the number of species introduced to new locations, and when, and environmental factors influencing how many species are able to persist there. However, global spatial and temporal variation in the drivers of alien introduction and species richness remain poorly understood. Here, we analyse an extensive new database of alien birds to explore what determines the global distribution of alien species richness for an entire taxonomic class. We demonstrate that the locations of origin and introduction of alien birds, and their identities, were initially driven largely by European (mainly British) colonialism. However, recent introductions are a wider phenomenon, involving more species and countries, and driven in part by increasing economic activity. We find that, globally, alien bird species richness is currently highest at midlatitudes and is strongly determined by anthropogenic effects, most notably the number of species introduced (i.e., "colonisation pressure"). Nevertheless, environmental drivers are also important, with native and alien species richness being strongly and consistently positively associated. Our results demonstrate that colonisation pressure is key to understanding alien species richness, show that areas of high native species richness are not resistant to colonisation by alien species at the global scale, and emphasise the likely ongoing threats to global environments from introductions of species.