Agricultural expansion raises groundwater and increases flooding in the South American plains

Regional effects of farming on hydrology are associated mostly with irrigation. In this work, we show how rainfed agriculture can also leave large-scale imprints. The extent and speed of farming expansion across the South American plains over the past four decades provide an unprecedented case of the effects of rainfed farming on hydrology. Remote sensing analysis shows that as annual crops replaced native vegetation and pastures, floods gradually doubled their coverage, increasing their sensitivity to precipitation. Groundwater shifted from deep (12 to 6 meters) to shallow (4 to 0 meters) states, reducing drawdown levels. Field studies and simulations suggest that declining rooting depths and evapotranspiration in croplands are the causes of this hydrological transformation. These findings show the escalating flooding risks associated with rainfed agriculture expansion at subcontinental and decadal scales.

Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.

Unravelling the impact of insecticide-treated bed nets on childhood malaria in Malawi

BACKGROUND

To achieve malaria elimination it is essential to understand the impact of insecticide-treated net (ITNs) programmes. Here, the impact of ITN access and use on malaria prevalence in children in Malawi was investigated using Malaria Indicator Survey (MIS) data.

METHODS

MIS data from 2012, 2014 and 2017 were used to investigate the relationship between malaria prevalence in children (6-59 months) and ITN use. Generalized linear modelling (GLM), geostatistical mixed regression modelling and non-stationary GLM were undertaken to evaluate trends, spatial patterns and local dynamics, respectively.

RESULTS

Malaria prevalence in Malawi was 27.1% (95% CI 23.1-31.2%) in 2012 and similar in both 2014 (32.1%, 95% CI 25.5-38.7) and 2017 (23.9%, 95% CI 20.3-27.4%). ITN coverage and use increased during the same time period, with household ITN access growing from 19.0% (95% CI 15.6-22.3%) of households with at least 1 ITN for every 2 people sleeping in the house the night before to 41.7% (95% CI 39.1-44.4%) and ITN use from 41.1% (95% CI 37.3-44.9%) of the population sleeping under an ITN the previous night to 57.4% (95% CI 55.0-59.9%). Both the geostatistical and non-stationary GLM regression models showed child malaria prevalence had a negative association with ITN population access and a positive association with ITN use although affected by large uncertainties. The non-stationary GLM highlighted the spatital heterogeneity in the relationship between childhood malaria and ITN dynamics across the country.

CONCLUSION

Malaria prevalence in children under five had a negative association with ITN population access and a positive association with ITN use, with spatial heterogeneity in these relationships across Malawi. This study presents an important modelling approach that allows malaria control programmes to spatially disentangle the impact of interventions on malaria cases.

How the Air Clean Plan and carbon mitigation measures co-benefited China in PM2.5 reduction and health from 2014 to 2020

China implemented a stringent Air Clean Plan (ACP) since 2013 to address environmental and health risks caused by ambient fine particulate matter (PM_2.5). However, the policy effectiveness of ACP and co-benefits of carbon mitigation measures to environment and health are still largely unknown. Using satellite-based PM_2.5 products produced in our previous study, concentration-response functions, and the logarithmic mean Divisia index (LMDI) method, we analyzed the spatiotemporal dynamics of premature deaths attributable to PM_2.5 exposure, and quantitatively estimated the policy benefits of ACP and carbon mitigation measures. We found the annual PM_2.5 concentrations in China decreased by 33.65 % (13.41 μg m^-3) from 2014 to 2020, accompanied by a decrease in PM_2.5-attributable premature deaths of 0.23 million (95 % confidence interval (CI): 0.22-0.27), indicating the huge benefits of China ACP for human health and environment. However, there were still 1.12 million (95 % CI: 0.79-1.56) premature deaths caused by the exposure of PM_2.5 in mainland China in 2020. Among all ACP measures, clean production (contributed 55.98 % and 51.14 % to decrease in PM_2.5 and premature deaths attributable to PM_2.5) and energy consumption control (contributed 32.58 % and 29.54 % to decrease in PM_2.5 and premature deaths attributable to PM_2.5) made the largest contribution during the past seven years. Nevertheless, the environmental and health benefits of ACP are not fully synergistic in different regions, and the effectiveness of ACP measures reduced from 2018 to 2020. The co-effects of CO₂ and PM_2.5 has become one of the major drivers for PM_2.5 and premature deaths reduction since 2018, confirming the clear environment and health co-benefits of carbon mitigation measures. Our study suggests, with the saturation of clean production and source control, more targeted region-specific strategies and synergistic air pollution-carbon mitigation measures are critical to achieving the WHO's Air Quality Guideline target and the UN's Sustainable Development Goal Target in China.

A Semi-Supervised Deep Rule-Based Approach for Complex Satellite Sensor Image Analysis

Large-scale (large-area), fine spatial resolution satellite sensor images are valuable data sources for Earth observation while not yet fully exploited by research communities for practical applications. Often, such images exhibit highly complex geometrical structures and spatial patterns, and distinctive characteristics of multiple land-use categories may appear at the same region. Autonomous information extraction from these images is essential in the field of pattern recognition within remote sensing, but this task is extremely challenging due to the spectral and spatial complexity captured in satellite sensor imagery. In this research, a semi-supervised deep rule-based approach for satellite sensor image analysis (SeRBIA) is proposed, where large-scale satellite sensor images are analysed autonomously and classified into detailed land-use categories. Using an ensemble feature descriptor derived from pre-trained AlexNet and VGG-VD-16 models, SeRBIA is capable of learning continuously from both labelled and unlabelled images through self-adaptation without human involvement or intervention. Extensive numerical experiments were conducted on both benchmark datasets and real-world satellite sensor images to comprehensively test the validity and effectiveness of the proposed method. The novel information mining technique developed here can be applied to analyse large-scale satellite sensor images with high accuracy and interpretability, across a wide range of real-world applications.

Unmet need for COVID-19 vaccination coverage in Kenya

COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 - 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 - 16.74) - 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 - 36.96) - 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.

A taxonomic-based joint species distribution model for presence-only data

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.

Spatial analysis of G.f.fuscipes abundance in Uganda using Poisson and Zero-Inflated Poisson regression models

BACKGROUND

Tsetse flies are the major vectors of human trypanosomiasis of the form Trypanosoma brucei rhodesiense and T.b.gambiense. They are widely spread across the sub-Saharan Africa and rendering a lot of challenges to both human and animal health. This stresses effective agricultural production and productivity in Africa. Delimiting the extent and magnitude of tsetse coverage has been a challenge over decades due to limited resources and unsatisfactory technology. In a bid to overcome these limitations, this study attempted to explore modelling skills that can be applied to spatially estimate tsetse abundance in the country using limited tsetse data and a set of remote-sensed environmental variables.

METHODOLOGY

Entomological data for the period 2008-2018 as used in the model were obtained from various sources and systematically assembled using a structured protocol. Data harmonisation for the purposes of responsiveness and matching was carried out. The key tool for tsetse trapping was itemized as pyramidal trap in many instances and biconical trap in others. Based on the spatially explicit assembled data, we ran two regression models; standard Poisson and Zero-Inflated Poisson (ZIP), to explore the associations between tsetse abundance in Uganda and several environmental and climatic covariates. The covariate data were constituted largely by satellite sensor data in form of meteorological and vegetation surrogates in association with elevation and land cover data. We finally used the Zero-Inflated Poisson (ZIP) regression model to predict tsetse abundance due to its superiority over the standard Poisson after model fitting and testing using the Vuong Non-Nested statistic.

RESULTS

A total of 1,187 tsetse sampling points were identified and considered as representative for the country. The model results indicated the significance and level of responsiveness of each covariate in influencing tsetse abundance across the study area. Woodland vegetation, elevation, temperature, rainfall, and dry season normalised difference vegetation index (NDVI) were important in determining tsetse abundance and spatial distribution at varied scales. The resultant prediction map shows scaled tsetse abundance with estimated fitted numbers ranging from 0 to 59 flies per trap per day (FTD). Tsetse abundance was found to be largest at low elevations, in areas of high vegetative activity, in game parks, forests and shrubs during the dry season. There was very limited responsiveness of selected predictors to tsetse abundance during the wet season, matching the known fact that tsetse disperse most significantly during wet season.

CONCLUSIONS

A methodology was advanced to enable compilation of entomological data for 10 years, which supported the generation of tsetse abundance maps for Uganda through modelling. Our findings indicate the spatial distribution of the G. f. fuscipes as; low 0-5 FTD (48%), medium 5.1-35 FTD (18%) and high 35.1-60 FTD (34%) grounded on seasonality. This approach, amidst entomological data shortages due to limited resources and absence of expertise, can be adopted to enable mapping of the vector to provide better decision support towards designing and implementing targeted tsetse and tsetse-transmitted African trypanosomiasis control strategies.

Chronic kidney disease of unknown origin is associated with environmental urbanisation in Belfast, UK

Chronic kidney disease (CKD), a collective term for many causes of progressive renal failure, is increasing worldwide due to ageing, obesity and diabetes. However, these factors cannot explain the many environmental clusters of renal disease that are known to occur globally. This study uses data from the UK Renal Registry (UKRR) including CKD of uncertain aetiology (CKDu) to investigate environmental factors in Belfast, UK. Urbanisation has been reported to have an increasing impact on soils. Using an urban soil geochemistry database of elemental concentrations of potentially toxic elements (PTEs), we investigated the association of the standardised incidence rates (SIRs) of both CKD and CKD of uncertain aetiology (CKDu) with environmental factors (PTEs), controlling for social deprivation. A compositional data analysis approach was used through balances (a special class of log contrasts) to identify elemental balances associated with CKDu. A statistically significant relationship was observed between CKD with the social deprivation measures of employment, income and education (significance levels of 0.001, 0.01 and 0.001, respectively), which have been used as a proxy for socio-economic factors such as smoking. Using three alternative regression methods (linear, generalised linear and Tweedie models), the elemental balances of Cr/Ni and As/Mo were found to produce the largest correlation with CKDu. Geogenic and atmospheric pollution deposition, traffic and brake wear emissions have been cited as sources for these PTEs which have been linked to kidney damage. This research, thus, sheds light on the increasing global burden of CKD and, in particular, the environmental and anthropogenic factors that may be linked to CKDu, particularly environmental PTEs linked to urbanisation.

Association between community-based self-reported COVID-19 symptoms and social deprivation explored using symptom tracker apps: a repeated cross-sectional study in Northern Ireland

OBJECTIVES

The aim of the study was to investigate the spatial and temporal relationships between the prevalence of COVID-19 symptoms in the community-level and area-level social deprivation.

DESIGN

Spatial mapping, generalised linear models, using time as a factor and spatial-lag models were used to explore the relationship between self-reported COVID-19 symptom prevalence as recorded through two smartphone symptom tracker apps and a range of socioeconomic factors using a repeated cross-sectional study design.

SETTING

In the community in Northern Ireland, UK. The analysis period included the earliest stages of non-pharmaceutical interventions and societal restrictions or 'lockdown' in 2020.

PARTICIPANTS

Users of two smartphone symptom tracker apps recording self-reported health information who recorded their location as Northern Ireland, UK.

PRIMARY OUTCOME MEASURES

Population standardised self-reported COVID-19 symptoms and correlation between population standardised self-reported COVID-19 symptoms and area-level characteristics from measures of multiple deprivation including employment levels and population housing density, derived as the mean number of residents per household for each census super output area.

RESULTS

Higher self-reported prevalence of COVID-19 symptoms was associated with the most deprived areas (p<0.001) and with those areas with the lowest employment levels (p<0.001). Higher rates of self-reported COVID-19 symptoms within the age groups, 18-24 and 25-34 years were found within the most deprived areas during the earliest stages of non-pharmaceutical interventions and societal restrictions ('lockdown').

CONCLUSIONS

Through spatial regression of self-reporting COVID-19 smartphone data in the community, this research shows how a lens of social deprivation can deepen our understanding of COVID-19 transmission and prevention. Our findings indicate that social inequality, as measured by area-level deprivation, is associated with disparities in potential COVID-19 infection, with higher prevalence of self-reported COVID-19 symptoms in urban areas associated with area-level social deprivation, housing density and age.

Adjusting for Conditional Bias in Process Model Simulations of Hydrological Extremes: An Experiment Using the North Wyke Farm Platform

Peak flow events can lead to flooding which can have negative impacts on human life and ecosystem services. Therefore, accurate forecasting of such peak flows is important. Physically-based process models are commonly used to simulate water flow, but they often under-predict peak events (i.e., are conditionally biased), undermining their suitability for use in flood forecasting. In this research, we explored methods to increase the accuracy of peak flow simulations from a process-based model by combining the model’s output with: a) a semi-parametric conditional extreme model and b) an extreme learning machine model. The proposed 3-model hybrid approach was evaluated using fine temporal resolution water flow data from a sub-catchment of the North Wyke Farm Platform, a grassland research station in south-west England, United Kingdom. The hybrid model was assessed objectively against its simpler constituent models using a jackknife evaluation procedure with several error and agreement indices. The proposed hybrid approach was better able to capture the dynamics of the flow process and, thereby, increase prediction accuracy of the peak flow events.

Risk assessment of the step-by-step return-to-work policy in Beijing following the COVID-19 epidemic peak

Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. As the virus spread worldwide causing a global pandemic, China reduced transmission at considerable social and economic cost. Post-lockdown, resuming work safely, that is, while avoiding a second epidemic outbreak, is a major challenge. Exacerbating this challenge, Beijing hosts many residents and workers with origins elsewhere, making it a relatively high-risk region in which to resume work. Nevertheless, the step-by-step approach taken by Beijing appears to have been effective so far. To learn from the epidemic progression and return-to-work measures undertaken in Beijing, and to inform efforts to avoid a second outbreak of COVID-19, we simulated the epidemiological progression of COVID-19 in Beijing under the real scenario of multiple stages of resuming work. A new epidemic transmission model was developed from a modified SEIR model for SARS, tailored to the situation of Beijing and fitted using multi-source data. Because of strong spatial heterogeneity amongst the population, socio-economic factors and medical capacity of Beijing, the risk assessment was undertaken spatiotemporally with respect to each district of Beijing. The epidemic simulation confirmed that the policy of resuming work step-by step, as implemented in Beijing, was sufficient to avoid a recurrence of the epidemic. Moreover, because of the structure of the model, the simulation provided insights into the specific factors at play at different stages of resuming work, allowing district-specific recommendations to be made with respect to monitoring at different stages of resuming work . As such, this research provides important lessons for other cities and regions dealing with outbreaks of COVID-19 and implementing return-to-work policies.

Biospytial: spatial graph-based computing for ecological Big Data

BACKGROUND

The exponential accumulation of environmental and ecological data together with the adoption of open data initiatives bring opportunities and challenges for integrating and synthesising relevant knowledge that need to be addressed, given the ongoing environmental crises.

FINDINGS

Here we present Biospytial, a modular open source knowledge engine designed to import, organise, analyse and visualise big spatial ecological datasets using the power of graph theory. The engine uses a hybrid graph-relational approach to store and access information. A graph data structure uses linkage relationships to build semantic structures represented as complex data structures stored in a graph database, while tabular and geospatial data are stored in an efficient spatial relational database system. We provide an application using information on species occurrences, their taxonomic classification and climatic datasets. We built a knowledge graph of the Tree of Life embedded in an environmental and geographical grid to perform an analysis on threatened species co-occurring with jaguars (Panthera onca).

CONCLUSIONS

The Biospytial approach reduces the complexity of joining datasets using multiple tabular relations, while its scalable design eases the problem of merging datasets from different sources. Its modular design makes it possible to distribute several instances simultaneously, allowing fast and efficient handling of big ecological datasets. The provided example demonstrates the engine's capabilities in performing basic graph manipulation, analysis and visualizations of taxonomic groups co-occurring in space. The example shows potential avenues for performing novel ecological analyses, biodiversity syntheses and species distribution models aided by a network of taxonomic and spatial relationships.

Collective influence of household and community capitals on agricultural employment as a measure of rural poverty in the Mahanadi Delta, India

The main determinants of agricultural employment are related to households' access to private assets and the influence of inherited social-economic stratification and power relationships. However, despite the recommendations of rural studies which have shown the importance of multilevel approaches to rural poverty, very few studies have explored quantitatively the effects of common-pool resources and household livelihood capitals on agricultural employment. Understanding the influence of access to both common-pool resources and private assets on rural livelihoods can enrich our understanding of the drivers of rural poverty in agrarian societies, which is central to achieving sustainable development pathways. Based on a participatory assessment conducted in rural communities in India, this paper differentiates two levels of livelihood capitals (household capitals and community capitals) and quantifies them using national census data and remotely sensed satellite sensor data. We characterise the effects of these two levels of livelihood capitals on precarious agricultural employment by using multilevel logistic regression. Our study brings a new perspective on livelihood studies and rural economics by demonstrating that common-pool resources and private assets do not have the same effect on agricultural livelihoods. It identifies that a lack of access to human, financial and social capitals at the household level increases the levels of precarious agricultural employment, such as daily-wage agricultural labour. Households located in communities with greater access to collective natural capital are less likely to be agricultural labourers. The statistical models also show that proximity to rural centres and access to financial infrastructures increase the likelihood of being a landless agricultural labourer. These findings suggest that investment in rural infrastructure might increase livelihood vulnerability, if not accompanied by an improvement in the provisioning of complementary rural services, such as access to rural finance, and by the implementation of agricultural tenancy laws to protect smallholders' productive assets.

Photoperiod controls vegetation phenology across Africa

Vegetation phenology is driven by environmental factors such as photoperiod, precipitation, temperature, insolation, and nutrient availability. However, across Africa, there's ambiguity about these drivers, which can lead to uncertainty in the predictions of global warming impacts on terrestrial ecosystems and their representation in dynamic vegetation models. Using satellite data, we undertook a systematic analysis of the relationship between phenological parameters and these drivers. The analysis across different regions consistently revealed photoperiod as the dominant factor controlling the onset and end of vegetation growing season. Moreover, the results suggest that not one, but a combination of drivers control phenological events. Consequently, to enhance our predictions of climate change impacts, the role of photoperiod should be incorporated into vegetation-climate and ecosystem modelling. Furthermore, it is necessary to define clearly the responses of vegetation to interactions between a consistent photoperiod cue and inter-annual variation in other drivers, especially under a changing climate.

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.

Strengthening surveillance systems for malaria elimination: a global landscaping of system performance, 2015-2017

BACKGROUND

Surveillance is a core component of an effective system to support malaria elimination. Poor surveillance data will prevent countries from monitoring progress towards elimination and targeting interventions to the last remaining at-risk places. An evaluation of the performance of surveillance systems in 16 countries was conducted to identify key gaps which could be addressed to build effective systems for malaria elimination.

METHODS

A standardized surveillance system landscaping was conducted between 2015 and 2017 in collaboration with governmental malaria programmes. Malaria surveillance guidelines from the World Health Organization and other technical bodies were used to identify the characteristics of an optimal surveillance system, against which systems of study countries were compared. Data collection was conducted through review of existing material and datasets, and interviews with key stakeholders, and the outcomes were summarized descriptively. Additionally, the cumulative fraction of incident infections reported through surveillance systems was estimated using surveillance data, government records, survey data, and other scientific sources.

RESULTS

The landscaping identified common gaps across countries related to the lack of surveillance coverage in remote communities or in the private sector, the lack of adequate health information architecture to capture high quality case-based data, poor integration of data from other sources such as intervention information, poor visualization of generated information, and its lack of availability for making programmatic decisions. The median percentage of symptomatic cases captured by the surveillance systems in the 16 countries was estimated to be 37%, mostly driven by the lack of treatment-seeking in the public health sector (64%) or, in countries with large private sectors, the lack of integration of this sector within the surveillance system.

CONCLUSIONS

The landscaping analysis undertaken provides a clear framework through which to identify multiple gaps in current malaria surveillance systems. While perfect systems are not required to eliminate malaria, closing the gaps identified will allow countries to deploy resources more efficiently, track progress, and accelerate towards malaria elimination. Since the landscaping undertaken here, several countries have addressed some of the identified gaps by improving coverage of surveillance, integrating case data with other information, and strengthening visualization and use of data.

Exploring the effect of human and animal population growth on vector-borne disease transmission with an agent-based model of Rhodesian human African trypanosomiasis in eastern province, Zambia

This paper presents the development of an agent-based model (ABM) to investigate Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) disease transmission. The ABM model, fitted at a fine spatial scale, was used to explore the impact of a growing host population on the spread of disease along a 75 km transect in the Luangwa Valley, Zambia. The model was used to gain a greater understanding of how increases in human and domestic animal population could impact the contact network between vector and host, the subsequent transmission patterns, and disease incidence outcomes in the region. Modelled incidence rates showed increases in rHAT transmission in both humans and cattle. The primary demographic attribution of infection switched dramatically from young children of both sexes attending school, to adult women performing activities with shorter but more frequent trips, such as water and firewood collection, with men more protected due to the presence of cattle in their routines. The interpretation of model output provides a plausible insight into both population development and disease transmission in the near future in the region and such techniques could aid well-targeted mitigation strategies in the future.

African trypanosomiasis is a parasitic disease which affects humans and other animals in 36 sub-Saharan African countries. The disease is transmitted by the tsetse fly, and the human form of the disease is known as sleeping sickness. With human and animal populations growing across Africa, demand for space to settle is on the rise, and people are being forced to occupy increasingly marginal spaces. This behaviour has the potential to increase exposure to pre-existing biological hazards, including vector-borne diseases. This investigation utilises agent-based modelling techniques to investigate the implications of a growing and spreading human and animal population in a region affected by Rhodesian human African trypanosomiasis. The model incorporates previously developed spatial data for the Luangwa Valley case study in Zambia, along with demographic data for its current inhabitants, and a detailed, seasonally-driven tsetse lifecycle. Tsetse and potential human and animal hosts are modelled at the individual level, allowing each contact and infection to be recorded through time. By modelling at a fine-scale, we can incorporate detailed mechanisms for tsetse birth, feeding, reproduction and death, as well as a realistic theoretical human and domestic animal population increase, before considering the possible spatial and demographic impact.

Large-scale prerain vegetation green-up across Africa

Information on the response of vegetation to different environmental drivers, including rainfall, forms a critical input to ecosystem models. Currently, such models are run based on parameters that, in some cases, are either assumed or lack supporting evidence (e.g., that vegetation growth across Africa is rainfall-driven). A limited number of studies have reported that the onset of rain across Africa does not fully explain the onset of vegetation growth, for example, drawing on the observation of prerain flush effects in some parts of Africa. The spatial extent of this prerain green-up effect, however, remains unknown, leaving a large gap in our understanding that may bias ecosystem modelling. This paper provides the most comprehensive spatial assessment to-date of the magnitude and frequency of the different patterns of phenology response to rainfall across Africa and for different vegetation types. To define the relations between phenology and rainfall, we investigated the spatial variation in the difference, in number of days, between the start of rainy season (SRS) and start of vegetation growing season (SOS); and between the end of rainy season (ERS) and end of vegetation growing season (EOS). We reveal a much more extensive spread of prerain green-up over Africa than previously reported, with prerain green-up being the norm rather than the exception. We also show the relative sparsity of postrain green-up, confined largely to the Sudano-Sahel region. While the prerain green-up phenomenon is well documented, its large spatial extent was not anticipated. Our results, thus, contrast with the widely held view that rainfall drives the onset and end of the vegetation growing season across Africa. Our findings point to a much more nuanced role of rainfall in Africa's vegetation growth cycle than previously thought, specifically as one of a set of several drivers, with important implications for ecosystem modelling.

Geographic coverage of demographic surveillance systems for characterising the drivers of childhood mortality in sub-Saharan Africa

A major focus of international health and development goals is the reduction of mortality rates in children under 5 years of age. Achieving this requires understanding the drivers of mortality and how they vary geographically to facilitate the targeting and prioritisation of appropriate interventions. Much of our knowledge on the causes of, and trends in, childhood mortality come from longitudinal demographic surveillance sites, with a renewed focus recently on the establishment and growth of networks of sites from which standardised outputs can facilitate broader understanding of processes. To ensure that the collective outputs from surveillance sites can be used to derive a comprehensive understanding and monitoring system for driving policy on tackling childhood mortality, confidence is needed that existing and planned networks of sites are providing a reliable and representative picture of the geographical variation in factors associated with mortality. Here, we assembled subnational data on childhood mortality as well as key factors known to be associated with it from household surveys in 27 sub-Saharan African countries. We then mapped the locations of existing longitudinal demographic surveillance sites to assess the extent of current coverage of the range of factors, identifying where gaps exist. The results highlight regions with unique combinations of factors associated with childhood mortality that are poorly represented by the current distribution of sites, such as southern Mali, central Nigeria and southern Zambia. Finally, we determined where the establishment of new surveillance systems could improve coverage.

An agent-based model of tsetse fly response to seasonal climatic drivers: Assessing the impact on sleeping sickness transmission rates

BACKGROUND

This paper presents the development of an agent-based model (ABM) to incorporate climatic drivers which affect tsetse fly (G. m. morsitans) population dynamics, and ultimately disease transmission. The model was used to gain a greater understanding of how tsetse populations fluctuate seasonally, and investigate any response observed in Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) disease transmission, with a view to gaining a greater understanding of disease dynamics. Such an understanding is essential for the development of appropriate, well-targeted mitigation strategies in the future.

METHODS

The ABM was developed to model rHAT incidence at a fine spatial scale along a 75 km transect in the Luangwa Valley, Zambia. The model incorporates climatic factors that affect pupal mortality, pupal development, birth rate, and death rate. In combination with fine scale demographic data such as ethnicity, age and gender for the human population in the region, as well as an animal census and a sample of daily routines, we create a detailed, plausible simulation model to explore tsetse population and disease transmission dynamics.

RESULTS

The seasonally-driven model suggests that the number of infections reported annually in the simulation is likely to be a reasonable representation of reality, taking into account the high levels of under-detection observed. Similar infection rates were observed in human (0.355 per 1000 person-years (SE = 0.013)), and cattle (0.281 per 1000 cattle-years (SE = 0.025)) populations, likely due to the sparsity of cattle close to the tsetse interface. The model suggests that immigrant tribes and school children are at greatest risk of infection, a result that derives from the bottom-up nature of the ABM and conditioning on multiple constraints. This result could not be inferred using alternative population-level modelling approaches.

CONCLUSIONS

In producing a model which models the tsetse population at a very fine resolution, we were able to analyse and evaluate specific elements of the output, such as pupal development and the progression of the teneral population, allowing the development of our understanding of the tsetse population as a whole. This is an important step in the production of a more accurate transmission model for rHAT which can, in turn, help us to gain a greater understanding of the transmission system as a whole.

Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq

Crop production and yield estimation using remotely sensed data have been studied widely, but such information is generally scarce in arid and semi-arid regions. In these regions, inter-annual variation in climatic factors (such as rainfall) combined with anthropogenic factors (such as civil war) pose major risks to food security. Thus, an operational crop production estimation and forecasting system is required to help decision-makers to make early estimates of potential food availability. Data from NASA's MODIS with official crop statistics were combined to develop an empirical regression-based model to forecast winter wheat and barley production in Iraq. The study explores remotely sensed indices representing crop productivity over the crop growing season to find the optimal correlation with crop production. The potential of three different remotely sensed indices, and information related to the phenology of crops, for forecasting crop production at the governorate level was tested and their results were validated using the leave-one-year-out approach. Despite testing several methodological approaches, and extensive spatio-temporal analysis, this paper depicts the difficulty in estimating crop yield on an annual base using current satellite low-resolution data. However, more precise estimates of crop production were possible. The result of the current research implies that the date of the maximum vegetation index (VI) offered the most accurate forecast of crop production with an average R²=0.70 compared to the date of MODIS EVI (Avg R²=0.68) and a NPP (Avg R²=0.66). When winter wheat and barley production were forecasted using NDVI, EVI and NPP and compared to official statistics, the relative error ranged from -20 to 20%, -45 to 28% and -48 to 22%, respectively. The research indicated that remotely sensed indices could characterize and forecast crop production more accurately than simple cropping area, which was treated as a null model against which to evaluate the proposed approach.

Malaria prevalence metrics in low- and middle-income countries: an assessment of precision in nationally-representative surveys

Background

One pillar to monitoring progress towards the Sustainable Development Goals is the investment in high quality data to strengthen the scientific basis for decision-making. At present, nationally-representative surveys are the main source of data for establishing a scientific evidence base, monitoring, and evaluation of health metrics. However, little is known about the optimal precisions of various population-level health and development indicators that remains unquantified in nationally-representative household surveys. Here, a retrospective analysis of the precision of prevalence from these surveys was conducted.

Methods

Using malaria indicators, data were assembled in nine sub-Saharan African countries with at least two nationally-representative surveys. A Bayesian statistical model was used to estimate between- and within-cluster variability for fever and malaria prevalence, and insecticide-treated bed nets (ITNs) use in children under the age of 5 years. The intra-class correlation coefficient was estimated along with the optimal sample size for each indicator with associated uncertainty.

Findings

Results suggest that the estimated sample sizes for the current nationally-representative surveys increases with declining malaria prevalence. Comparison between the actual sample size and the modelled estimate showed a requirement to increase the sample size for parasite prevalence by up to 77.7% (95% Bayesian credible intervals 74.7–79.4) for the 2015 Kenya MIS (estimated sample size of children 0–4 years 7218 [7099–7288]), and 54.1% [50.1–56.5] for the 2014–2015 Rwanda DHS (12,220 [11,950–12,410]).

Conclusion

This study highlights the importance of defining indicator-relevant sample sizes to achieve the required precision in the current national surveys. While expanding the current surveys would need additional investment, the study highlights the need for improved approaches to cost effective sampling.

Electronic supplementary material

The online version of this article (10.1186/s12936-017-2127-y) contains supplementary material, which is available to authorized users.

Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model

BACKGROUND

Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA).

METHODS

Using nationally representative household survey data from the 2013 Demographic and Health Survey (DHS) in Namibia, individual-level responses (n = 1138) were linked to theoretical estimates of travel time to the nearest public or private health facility. Bayesian Item Response Theory (IRT) models were fitted via Markov Chain Monte Carlo (MCMC) simulation to estimate parameters related to fever treatment and estimate probability of treatment for children under five years. Different models were implemented to evaluate computational needs and the effect of including predictor variables such as rurality. The mean treatment rates were then estimated at regional level.

RESULTS

Modelling results suggested probability of fever treatment was highest in regions with relatively high incidence of malaria historically. The minimum predicted threshold probability of seeking treatment was 0.3 (model 1: 0.340; 95% CI 0.155-0.597), suggesting that even in populations at large distances from facilities, there was still a 30% chance of an individual seeking treatment for fever. The agreement between correctly predicted probability of treatment at individual level based on a subset of data (n = 247) was high (AUC = 0.978), with a sensitivity of 96.7% and a specificity of 75.3%.

CONCLUSION

We have shown how individual responses in national surveys can be transformed to probabilistic measures comparable at population level. Our analysis of household survey data on fever suggested a 30% baseline threshold for fever treatment in Namibia. However, this threshold level is likely to vary by country or endemicity. Although our focus was on fever treatment, the methodology outlined can be extended to multiple health seeking behaviours captured in routine national survey data and to other infectious diseases.

Significance of major international seaports in the distribution of murine typhus in Taiwan

Background

International seaports are hotspots for disease invasion and pathogens can persist in seaports even after ports are abandoned. Transmitted by fleas infected by Rickettsia typhi, murine typhus, a largely neglected and easily misdiagnosed disease, is known to occur primarily in large seaports. However, the significance of seaports in the occurrence of murine typhus has never been validated quantitatively.

Methodology/Principal findings

We studied the spatial distribution of murine typhus, a notifiable disease, in Taiwan. We investigated whether risk of infection was correlated with distance to international seaports and a collection of environmental and socioeconomic factors, using a Bayesian negative binomial conditionally autoregressive model, followed with geographically weighted regression. Seaports that are currently in use and those that operated in the 19^th century for trade with China, but were later abandoned due to siltation were analyzed. A total of 476 human cases of murine typhus were reported during 2000–2014 in the main island of Taiwan, with spatial clustering in districts in southwest and central-west Taiwan. A higher incidence rate (case/population) was associated with a smaller distance to currently in-use international seaports and lower rainfall and temperature, but was uncorrelated with distance to abandoned ports. Geographically weighted regression revealed a geographic heterogeneity in the importance of distance to in-use seaports near the four international seaports of Taiwan.

Conclusions/Significance

Our study suggests that murine typhus is associated with international seaports, especially for those with large trading volume. Thus, one of the costs of global trade in Taiwan might be elevated risks of murine typhus. Globalization has accelerated the spread of infectious diseases, but the burden of disease varies geographically, with regions surrounding major international seaports warranting particular surveillance.

Globalization has hastened the spread of infectious diseases, with seaports as hotspots for disease invasion. Transmitted by fleas infected with the rickettsia Rickettsia typhi, murine typhus occurs worldwide, but its significance as a common causative agent of illness in tropical regions remains largely neglected. Although it is recognized that murine typhus is prevalent primarily in large seaports, the significance of seaports in the occurrence of murine typhus has never been validated quantitatively. We thus investigated whether distribution of murine typhus in Taiwan was associated with international seaports. Notably, abandoned international seaports (abandoned in the 19^th century due to siltation) were also studied to see whether the causative agent of murine typhus might still circulate around the ports even after being abandoned. We found that infection risk of murine typhus was negatively associated with distance to operating seaports but was uncorrelated with nearness to abandoned seaports. In addition, the importance of distance to operating seaports for risk of murine typhus infection varied spatially. Our study highlights elevated disease risk as a cost of international trade and suggests particular surveillance in regions surrounding major international seaports.

Mapping the birch and grass pollen seasons in the UK using satellite sensor time-series

Grass and birch pollen are two major causes of seasonal allergic rhinitis (hay fever) in the UK and parts of Europe affecting around 15-20% of the population. Current prediction of these allergens in the UK is based on (i) measurements of pollen concentrations at a limited number of monitoring stations across the country and (ii) general information about the phenological status of the vegetation. Thus, the current prediction methodology provides information at a coarse spatial resolution only. Most station-based approaches take into account only local observations of flowering, while only a small number of approaches take into account remote observations of land surface phenology. The systematic gathering of detailed information about vegetation status nationwide would therefore be of great potential utility. In particular, there exists an opportunity to use remote sensing to estimate phenological variables that are related to the flowering phenophase and, thus, pollen release. In turn, these estimates can be used to predict pollen release at a fine spatial resolution. In this study, time-series of MERIS Terrestrial Chlorophyll Index (MTCI) data were used to predict two key phenological variables: the start of season and peak of season. A technique was then developed to estimate the flowering phenophase of birch and grass from the MTCI time-series. For birch, the timing of flowering was defined as the time after the start of the growing season when the MTCI value reached 25% of the maximum. Similarly, for grass this was defined as the time when the MTCI value reached 75% of the maximum. The predicted pollen release dates were validated with data from nine pollen monitoring stations in the UK. For both birch and grass, we obtained large positive correlations between the MTCI-derived start of pollen season and the start of the pollen season defined using station data, with a slightly larger correlation observed for birch than for grass. The technique was applied to produce detailed maps for the flowering of birch and grass across the UK for each of the years from 2003 to 2010. The results demonstrate that the remote sensing-based maps of onset flowering of birch and grass for the UK together with the pollen forecast from the Meteorology Office and National Pollen and Aerobiology Research Unit (NPARU) can potentially provide more accurate information to pollen allergy sufferers in the UK.

A Multi-Host Agent-Based Model for a Zoonotic, Vector-Borne Disease. A Case Study on Trypanosomiasis in Eastern Province, Zambia

BACKGROUND

This paper presents a new agent-based model (ABM) for investigating T. b. rhodesiense human African trypanosomiasis (rHAT) disease dynamics, produced to aid a greater understanding of disease transmission, and essential for development of appropriate mitigation strategies.

METHODS

The ABM was developed to model rHAT incidence at a fine spatial scale along a 75 km transect in the Luangwa Valley, Zambia. The method offers a complementary approach to traditional compartmentalised modelling techniques, permitting incorporation of fine scale demographic data such as ethnicity, age and gender into the simulation.

RESULTS

Through identification of possible spatial, demographic and behavioural characteristics which may have differing implications for rHAT risk in the region, the ABM produced output that could not be readily generated by other techniques. On average there were 1.99 (S.E. 0.245) human infections and 1.83 (S.E. 0.183) cattle infections per 6 month period. The model output identified that the approximate incidence rate (per 1000 person-years) was lower amongst cattle owning households (0.079, S.E. 0.017), than those without cattle (0.134, S.E. 0.017). Immigrant tribes (e.g. Bemba I.R. = 0.353, S.E.0.155) and school-age children (e.g. 5-10 year old I.R. = 0.239, S.E. 0.041) were the most at-risk for acquiring infection. These findings have the potential to aid the targeting of future mitigation strategies.

CONCLUSION

ABMs provide an alternative way of thinking about HAT and NTDs more generally, offering a solution to the investigation of local-scale questions, and which generate results that can be easily disseminated to those affected. The ABM can be used as a tool for scenario testing at an appropriate spatial scale to allow the design of logistically feasible mitigation strategies suggested by model output. This is of particular importance where resources are limited and management strategies are often pushed to the local scale.

Spatio-temporal analysis of malaria vector density from baseline through intervention in a high transmission setting

Background

An increase in effective malaria control since 2000 has contributed to a decline in global malaria morbidity and mortality. Knowing when and how existing interventions could be combined to maximise their impact on malaria vectors can provide valuable information for national malaria control programs in different malaria endemic settings. Here, we assess the effect of indoor residual spraying on malaria vector densities in a high malaria endemic setting in eastern Uganda as part of a cohort study where the use of long-lasting insecticidal nets (LLINs) was high.

Methods

Anopheles mosquitoes were sampled monthly using CDC light traps in 107 households selected randomly. Information on the use of malaria interventions in households was also gathered and recorded via a questionnaire. A Bayesian spatio-temporal model was then used to estimate mosquito densities adjusting for climatic and ecological variables and interventions.

Results

Anopheles gambiae (sensu lato) were most abundant (89.1%; n = 119,008) compared to An. funestus (sensu lato) (10.1%, n = 13,529). Modelling results suggest that the addition of indoor residual spraying (bendiocarb) in an area with high coverage of permethrin-impregnated LLINs (99%) was associated with a major decrease in mosquito vector densities. The impact on An. funestus (s.l.) (Rate Ratio 0.1508; 97.5% CI: 0.0144–0.8495) was twice as great as for An. gambiae (s.l.) (RR 0.5941; 97.5% CI: 0.1432–0.8577).

Conclusions

High coverage of active ingredients on walls depressed vector populations in intense malaria transmission settings. Sustained use of combined interventions would have a long-term impact on mosquito densities, limiting infectious biting.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-016-1917-3) contains supplementary material, which is available to authorized users.

The Sero-epidemiology of Coxiella burnetii in Humans and Cattle, Western Kenya: Evidence from a Cross-Sectional Study

Evidence suggests that the intracellular bacterial pathogen Coxiella burnetii (which causes Q fever) is widespread, with a near global distribution. While there has been increasing attention to Q fever epidemiology in high-income settings, a recent systematic review highlighted significant gaps in our understanding of the prevalence, spatial distribution and risk factors for Q fever infection across Africa. This research aimed to provide a One Health assessment of Q fever epidemiology in parts of Western and Nyanza Provinces, Western Kenya, in cattle and humans. A cross-sectional survey was conducted: serum samples from 2049 humans and 955 cattle in 416 homesteads were analysed for C. burnetii antibodies. Questionnaires covering demographic, socio-economic and husbandry information were also administered. These data were linked to environmental datasets based on geographical locations (e.g., land cover). Correlation and spatial-cross correlation analyses were applied to assess the potential link between cattle and human seroprevalence. Multilevel regression analysis was used to assess the relationships between a range of socio-economic, demographic and environmental factors and sero-positivity in both humans and animals. The overall sero-prevalence of C. burnetii was 2.5% in humans and 10.5% in cattle, but we found no evidence of correlation between cattle and human seroprevalence either within households, or when incorporating spatial proximity to other households in the survey. Multilevel modelling indicated the importance of several factors for exposure to the organism. Cattle obtained from market (as opposed to those bred in their homestead) and those residing in areas with lower precipitation levels had the highest sero-prevalence. For humans, the youngest age group had the highest odds of seropositivity, variations were observed between ethnic groups, and frequent livestock contact (specifically grazing and dealing with abortion material) was also a risk factor. These results illustrate endemicity of C. burnetii in western Kenya, although prevalence is relatively low. The analysis indicates that while environmental factors may play a role in cattle exposure patterns, human exposure patterns are likely to be driven more strongly by livestock contacts. The implication of livestock markets in cattle exposure risks suggests these may be a suitable target for interventions.

Hyalomma ticks on northward migrating birds in southern Spain: Implications for the risk of entry of Crimean-Congo haemorrhagic fever virus to Great Britain

Crimean-Congo haemorrhagic fever virus (CCHFV) is a zoonotic virus transmitted by Hyalomma ticks, the immature stages of which may be carried by migratory birds. In this study, a total of 12 Hyalomma ticks were recovered from five of 228 migratory birds trapped in Spring, 2012 in southern Spain along the East Atlantic flyway. All collected ticks tested negative for CCHFV. While most birds had zero Hyalomma ticks, two individuals had four and five ticks each and the statistical distribution of Hyalomma tick counts per bird is over-dispersed compared to the Poisson distribution, demonstrating the need for intensive sampling studies to avoid underestimating the total number of ticks. Rates of tick exchange on migratory birds during their northwards migration will affect the probability that a Hyalomma tick entering Great Britain is positive for CCHFV. Drawing on published data, evidence is presented that the latitude of a European country affects the probability of entry of Hyalomma ticks on wild birds. Further data on Hyalomma infestation rates and tick exchange rates are required along the East Atlantic flyway to further our understanding of the origin of Hyalomma ticks (i.e., Africa or southern Europe) and hence the probability of entry of CCHFV into GB.

Extravagance in the commons: Resource exploitation and the frontiers of ecosystem service depletion in the Amazon estuary

Estuaries hold major economic potential due their strategic location, close to seas and inland waterways, thereby supporting intense economic activity. The increasing pace of human development in coastal deltas over the past five decades has also strained local resources and produced extensive changes across both social and ecological systems. The Amazon estuary is located in the Amazon Basin, North Brazil, the largest river basin on Earth and also one of the least understood. A considerable segment of the population living in the estuary is directly dependent on the local extraction of natural resources for their livelihood. Areas sparsely inhabited may be exploited with few negative consequences for the environment. However, recent and increasing pressure on ecosystem services is maximised by a combination of factors such as governance, currency exchange rates, exports of beef and forest products. Here we present a cross methodological approach in identifying the political frontiers of forest cover change in the estuary with consequences for ecosystem services loss. We used a combination of data from earth observation satellites, ecosystem service literature, and official government statistics to produce spatially-explicit relationships linking the Green Vegetation Cover to the availability of ecosystems provided by forests in the estuary. Our results show that the continuous changes in land use/cover and in the economic state have contributed significantly to changes in key ecosystem services, such as carbon sequestration, climate regulation, and the availability of timber over the last thirty years.

A MS-lesion pattern discrimination plot based on geostatistics

INTRODUCTION

A geostatistical approach to characterize MS-lesion patterns based on their geometrical properties is presented.

METHODS

A dataset of 259 binary MS-lesion masks in MNI space was subjected to directional variography. A model function was fit to express the observed spatial variability in x, y, z directions by the geostatistical parameters Range and Sill.

RESULTS

Parameters Range and Sill correlate with MS-lesion pattern surface complexity and total lesion volume. A scatter plot of ln(Range) versus ln(Sill), classified by pattern anisotropy, enables a consistent and clearly arranged presentation of MS-lesion patterns based on geometry: the so-called MS-Lesion Pattern Discrimination Plot.

CONCLUSIONS

The geostatistical approach and the graphical representation of results are considered efficient exploratory data analysis tools for cross-sectional, follow-up, and medication impact analysis.

Moving interdisciplinary science forward: integrating participatory modelling with mathematical modelling of zoonotic disease in Africa

This review outlines the benefits of using multiple approaches to improve model design and facilitate multidisciplinary research into infectious diseases, as well as showing and proposing practical examples of effective integration. It looks particularly at the benefits of using participatory research in conjunction with traditional modelling methods to potentially improve disease research, control and management. Integrated approaches can lead to more realistic mathematical models which in turn can assist with making policy decisions that reduce disease and benefit local people. The emergence, risk, spread and control of diseases are affected by many complex bio-physical, environmental and socio-economic factors. These include climate and environmental change, land-use variation, changes in population and people’s behaviour. The evidence base for this scoping review comes from the work of a consortium, with the aim of integrating modelling approaches traditionally used in epidemiological, ecological and development research. A total of five examples of the impacts of participatory research on the choice of model structure are presented. Example 1 focused on using participatory research as a tool to structure a model. Example 2 looks at identifying the most relevant parameters of the system. Example 3 concentrates on identifying the most relevant regime of the system (e.g., temporal stability or otherwise), Example 4 examines the feedbacks from mathematical models to guide participatory research and Example 5 goes beyond the so-far described two-way interplay between participatory and mathematical approaches to look at the integration of multiple methods and frameworks. This scoping review describes examples of best practice in the use of participatory methods, illustrating their potential to overcome disciplinary hurdles and promote multidisciplinary collaboration, with the aim of making models and their predictions more useful for decision-making and policy formulation.

Fine resolution mapping of population age-structures for health and development applications

The age-group composition of populations varies considerably across the world, and obtaining accurate, spatially detailed estimates of numbers of children under 5 years is important in designing vaccination strategies, educational planning or maternal healthcare delivery. Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings. Focusing on Nigeria, we use nationally representative household surveys and their cluster locations to predict the proportion of the under-five population in 1 × 1 km using a Bayesian hierarchical spatio-temporal model. Results showed that land cover, travel time to major settlements, night-time lights and vegetation index were good predictors and that accounting for fine-scale variation, rather than assuming a uniform proportion of under 5 year olds can result in significant differences in health metrics. The largest gaps in estimated bednet and vaccination coverage were in Kano, Katsina and Jigawa. Geolocated household surveys are a valuable resource for providing detailed, contemporary and regularly updated population age-structure data in the absence of recent census data. By combining these with covariate layers, age-structure maps of unprecedented detail can be produced to guide the targeting of interventions in resource-poor settings.

Exploiting Human Resource Requirements to Infer Human Movement Patterns for Use in Modelling Disease Transmission Systems: An Example from Eastern Province, Zambia

In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra’s algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread.

Seasonality of Plasmodium falciparum transmission: a systematic review

Background

Although Plasmodium falciparum transmission frequently exhibits seasonal patterns, the drivers of malaria seasonality are often unclear. Given the massive variation in the landscape upon which transmission acts, intra-annual fluctuations are likely influenced by different factors in different settings. Further, the presence of potentially substantial inter-annual variation can mask seasonal patterns; it may be that a location has “strongly seasonal” transmission and yet no single season ever matches the mean, or synoptic, curve. Accurate accounting of seasonality can inform efficient malaria control and treatment strategies. In spite of the demonstrable importance of accurately capturing the seasonality of malaria, data required to describe these patterns is not universally accessible and as such localized and regional efforts at quantifying malaria seasonality are disjointed and not easily generalized.

Methods

The purpose of this review was to audit the literature on seasonality of P. falciparum and quantitatively summarize the collective findings. Six search terms were selected to systematically compile a list of papers relevant to the seasonality of P. falciparum transmission, and a questionnaire was developed to catalogue the manuscripts.

Results and discussion

152 manuscripts were identified as relating to the seasonality of malaria transmission, deaths due to malaria or the population dynamics of mosquito vectors of malaria. Among these, there were 126 statistical analyses and 31 mechanistic analyses (some manuscripts did both).

Discussion

Identified relationships between temporal patterns in malaria and climatological drivers of malaria varied greatly across the globe, with different drivers appearing important in different locations. Although commonly studied drivers of malaria such as temperature and rainfall were often found to significantly influence transmission, the lags between a weather event and a resulting change in malaria transmission also varied greatly by location.

Conclusions

The contradicting results of studies using similar data and modelling approaches from similar locations as well as the confounding nature of climatological covariates underlines the importance of a multi-faceted modelling approach that attempts to capture seasonal patterns at both small and large spatial scales.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-015-0849-2) contains supplementary material, which is available to authorized users.

Sleeping sickness and its relationship with development and biodiversity conservation in the Luangwa Valley, Zambia

The Luangwa Valley has a long historical association with Human African Trypanosomiasis (HAT) and is a recognised geographical focus of this disease. It is also internationally acclaimed for its high biodiversity and contains many valuable habitats. Local inhabitants of the valley have developed sustainable land use systems in co-existence with wildlife over centuries, based on non-livestock keeping practices largely due to the threat from African Animal Trypanosomiasis. Historical epidemics of human sleeping sickness have influenced how and where communities have settled and have had a profound impact on development in the Valley. Historical attempts to control trypanosomiasis have also had a negative impact on conservation of biodiversity.

Centralised control over wildlife utilisation has marginalised local communities from managing the wildlife resource. To some extent this has been reversed by the implementation of community based natural resource management programmes in the latter half of the 20^th century and the Luangwa Valley provides some of the earliest examples of such programmes. More recently, there has been significant uncontrolled migration of people into the mid-Luangwa Valley driven by pressure on resources in the eastern plateau region, encouragement from local chiefs and economic development in the tourist centre of Mfuwe. This has brought changing land-use patterns, most notably agricultural development through livestock keeping and cotton production. These changes threaten to alter the endemically stable patterns of HAT transmission and could have significant impacts on ecosystem health and ecosystem services.

In this paper we review the history of HAT in the context of conservation and development and consider the impacts current changes may have on this complex social-ecological system. We conclude that improved understanding is required to identify specific circumstances where win-win trade-offs can be achieved between the conservation of biodiversity and the reduction of disease in the human population.

Tsetse fly (G. f. fuscipes) distribution in the Lake Victoria basin of Uganda

Tsetse flies transmit trypanosomes, the causative agent of human and animal African trypanosomiasis. The tsetse vector is extensively distributed across sub-Saharan Africa. Trypanosomiasis maintenance is determined by the interrelationship of three elements: vertebrate host, parasite and the vector responsible for transmission. Mapping the distribution and abundance of tsetse flies assists in predicting trypanosomiasis distributions and developing rational strategies for disease and vector control. Given scarce resources to carry out regular full scale field tsetse surveys to up-date existing tsetse maps, there is a need to devise inexpensive means for regularly obtaining dependable area-wide tsetse data to guide control activities. In this study we used spatial epidemiological modelling techniques (logistic regression) involving 5000 field-based tsetse-data (G. f. fuscipes) points over an area of 40,000 km², with satellite-derived environmental surrogates composed of precipitation, temperature, land cover, normalised difference vegetation index (NDVI) and elevation at the sub-national level. We used these extensive tsetse data to analyse the relationships between presence of tsetse (G. f. fuscipes) and environmental variables. The strength of the results was enhanced through the application of a spatial autologistic regression model (SARM). Using the SARM we showed that the probability of tsetse presence increased with proportion of forest cover and riverine vegetation. The key outputs are a predictive tsetse distribution map for the Lake Victoria basin of Uganda and an improved understanding of the association between tsetse presence and environmental variables. The predicted spatial distribution of tsetse in the Lake Victoria basin of Uganda will provide significant new information to assist with the spatial targeting of tsetse and trypanosomiasis control.

Trypanosomiasis is a vector-borne disease transmitted to both humans and animals by the tsetse fly. The tsetse vector is distributed across sub-Saharan Africa. Trypanosomiasis maintenance is determined by the interrelationship of three elements: vertebrate host, parasite and the vector responsible for transmission. Mapping the distribution and abundance of tsetse flies assists in predicting trypanosomiasis distributions and developing rational strategies for disease and vector control. This study makes available dependable tsetse fly distribution data (maps) for use by decision makers. The approach makes use of modelling techniques involving limited field-sampled tsetse data points distributed across an area of approximately 40,000km² within the Lake Victoria basin of Uganda. Precipitation, temperature, landcover, normalised difference vegetation index (NDVI, a measure of the amount of green vegetation) and elevation data were used as environmental covariates. We used logistic regression to analyse the relationships between presence of tsetse and the environmental covariates. The results indicated that tsetse are more likely to be present in areas with a greater proportion of riverine vegetation and forest cover. The key outputs are a predicted tsetse distribution map for the Lake Victoria basin of Uganda and an increased understanding of the association between tsetse presence and environmental variables. This will provide a vital resource for the planning and spatial targeting of future tsetse control activities.

Elucidating the impact of temperature variability and extremes on cereal croplands through remote sensing

Remote sensing-derived wheat crop yield-climate models were developed to highlight the impact of temperature variation during thermo-sensitive periods (anthesis and grain-filling; TSP) of wheat crop development. Specific questions addressed are: can the impact of temperature variation occurring during the TSP on wheat crop yield be detected using remote sensing data and what is the impact? Do crop critical temperature thresholds during TSP exist in real world cropping landscapes? These questions are tested in one of the world's major wheat breadbaskets of Punjab and Haryana, north-west India. Warming average minimum temperatures during the TSP had a greater negative impact on wheat crop yield than warming maximum temperatures. Warming minimum and maximum temperatures during the TSP explain a greater amount of variation in wheat crop yield than average growing season temperature. In complex real world cereal croplands there was a variable yield response to critical temperature threshold exceedance, specifically a more pronounced negative impact on wheat yield with increased warming events above 35 °C. The negative impact of warming increases with a later start-of-season suggesting earlier sowing can reduce wheat crop exposure harmful temperatures. However, even earlier sown wheat experienced temperature-induced yield losses, which, when viewed in the context of projected warming up to 2100 indicates adaptive responses should focus on increasing wheat tolerance to heat. This study shows it is possible to capture the impacts of temperature variation during the TSP on wheat crop yield in real world cropping landscapes using remote sensing data; this has important implications for monitoring the impact of climate change, variation and heat extremes on wheat croplands.

Evaluating the impact of the community-based health planning and services initiative on uptake of skilled birth care in Ghana

Background

The Community-based Health Planning and Services (CHPS) initiative is a major government policy to improve maternal and child health and accelerate progress in the reduction of maternal mortality in Ghana. However, strategic intelligence on the impact of the initiative is lacking, given the persistant problems of patchy geographical access to care for rural women. This study investigates the impact of proximity to CHPS on facilitating uptake of skilled birth care in rural areas.

Methods and Findings

Data from the 2003 and 2008 Demographic and Health Survey, on 4,349 births from 463 rural communities were linked to georeferenced data on health facilities, CHPS and topographic data on national road-networks. Distance to nearest health facility and CHPS was computed using the closest facility functionality in ArcGIS 10.1. Multilevel logistic regression was used to examine the effect of proximity to health facilities and CHPS on use of skilled care at birth, adjusting for relevant predictors and clustering within communities. The results show that a substantial proportion of births continue to occur in communities more than 8 km from both health facilities and CHPS. Increases in uptake of skilled birth care are more pronounced where both health facilities and CHPS compounds are within 8 km, but not in communities within 8 km of CHPS but lack access to health facilities. Where both health facilities and CHPS are within 8 km, the odds of skilled birth care is 16% higher than where there is only a health facility within 8km.

Conclusion

Where CHPS compounds are set up near health facilities, there is improved access to care, demonstrating the facilitatory role of CHPS in stimulating access to better care at birth, in areas where health facilities are accessible.

Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa

BACKGROUND

Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI.

METHODS

In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa.

RESULTS

This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found.

CONCLUSIONS

These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality.