Satellite Observations and Malaria: New Opportunities for Research and Applications

Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing

Dengue fever has been spreading rapidly worldwide, with a notably high prevalence in South American countries such as Brazil. Its transmission dynamics are governed by the vector population dynamics and the interactions among humans, vectors, and pathogens, which are further shaped by environmental factors. Calculating these environmental indicators is challenging due to the limited spatial coverage of weather station observations and the time-consuming processes involved in downloading and processing local data, such as satellite imagery. This issue is exacerbated in large-scale studies, making it difficult to develop comprehensive and publicly accessible datasets of disease-influencing factors. Addressing this challenge necessitates the efficient data integration methods and the assembly of multi-factorial datasets to aid public health authorities in understanding dengue transmission mechanisms and improving risk prediction models. In response, we developed a population-weighted dataset of 12 dengue risk factors, covering 558 microregions in Brazil over 1252 epidemiological weeks from 2001 to 2024. This dataset and the associated methodology streamline data processing for researchers and can be adapted for other vector-borne disease studies.

Increasing the resolution of malaria early warning systems for use by local health actors

BACKGROUND

The increasing availability of electronic health system data and remotely-sensed environmental variables has led to the emergence of statistical models capable of producing malaria forecasts. Many of these models have been operationalized into malaria early warning systems (MEWSs), which provide predictions of malaria dynamics several months in advance at national and regional levels. However, MEWSs rarely produce predictions at the village-level, the operational scale of community health systems and the first point of contact for the majority of rural populations in malaria-endemic countries.

METHODS

This study developed a hyper-local MEWS for use within a health-system strengthening intervention in rural Madagascar. It combined bias-corrected, village-level case notification data with remotely sensed environmental variables at spatial scales as fine as a 10 m resolution. A spatio-temporal hierarchical generalized linear regression model was trained on monthly malaria case data from 195 communities from 2017 to 2020 and evaluated via cross-validation. The model was then integrated into an automated workflow with environmental data updated monthly to create a continuously updating MEWS capable of predicting malaria cases up to three months in advance at the village-level. Predictions were transformed into indicators relevant to health system actors by estimating the quantities of medical supplies required at each health clinic and the number of cases remaining untreated at the community level.

RESULTS

The statistical model was able to accurately reproduce village-level case data, performing nearly five times as well as a null model during cross-validation. The dynamic environmental variables, particularly those associated with standing water and rice field dynamics, were strongly associated with malaria incidence, allowing the model to accurately predict future incidence rates. The MEWS represented an improvement of over 50% compared to existing stock order quantification methods when applied retrospectively.

CONCLUSION

This study demonstrates the feasibility of developing an automatic, hyper-local MEWS leveraging remotely-sensed environmental data at fine spatial scales. As health system data become increasingly digitized, this method can be easily applied to other regions and be updated with near real-time health data to further increase performance.

Larviciding for malaria control and elimination in Africa

BACKGROUND

Global progress toward malaria elimination and eradication goals has stagnated in recent years, with many African countries reporting increases in malaria morbidity and mortality. Insecticide-treated nets and indoor residual spraying are effective, but the emergence and increased intensity of insecticide resistance and the challenge of outdoor transmission are undermining their impact. New tools are needed to get back on track towards global targets. This Perspective explores the major challenges hindering wider-scale implementation of larviciding in Africa and identifies potential solutions and opportunities to overcome these barriers.

LARVICIDING IN AFRICA

OVERVIEW, CHALLENGES, AND SOLUTIONS: Larviciding is a valuable vector control tool with strong potential for regional scale-up. There is considerable evidence of its effectiveness, and the World Health Organization (WHO) recommends it as a supplemental intervention. However, malaria programmes hoping to implement larviciding face significant barriers, including (1) poor global technical, policy, and funding support; (2) fragmented implementation and experience; (3) high complexity of delivery and impact evaluation; and (4) limited access to the full range of WHO prequalified larvicide products. Strategic barriers related to global policy and donor hesitancy can be overcome through a coordinated demonstration of cost-effectiveness. Technological advancements and strengthened operational capacity have already overcome technical barriers related to larvicide delivery, targeting, coverage, and evaluation. Developing a Community of Practice platform for larviciding has strong potential to consolidate efforts, addressing the challenge of fragmented implementation and experience. Such a platform can serve as a resource center for African malaria programmes, collating and disseminating technical guidance, facilitating the exchange of best practices, and aiding malaria programmes and partners in designing and evaluating larviciding projects.

CONCLUSION

The global shift toward targeted and adaptive interventions enables the incorporation of larviciding into an expanded vector control toolbox. As more African countries implement larvicide programmes, establishing a regional Community of Practice platform for exchanging experiences and best practices is necessary to strengthen the evidence base for cost-effective implementation, advocate for support, and inform policy recommendations, thus supporting Africa's progress toward malaria elimination.

Harmonizing Multisource Data to Inform Vector-Borne Disease Risk Management Strategies

In the last few decades, we have witnessed the emergence of new vector-borne diseases (VBDs), the globalization of endemic VBDs, and the urbanization of previously rural VBDs. Data harmonization forms the basis of robust decision-support systems designed to protect at-risk communities from VBD threats. Strong interdisciplinary partnerships, protocols, digital infrastructure, and capacity-building initiatives are essential for facilitating the coproduction of robust multisource data sets. This review provides a foundation for researchers and practitioners embarking on data harmonization efforts to (a) better understand the links among environmental degradation, climate change, socioeconomic inequalities, and VBD risk; (b) conduct risk assessments, health impact attribution, and projection studies; and (c) develop robust early warning and response systems. We draw upon best practices in harmonizing data for two well-studied VBDs, dengue and malaria, and provide recommendations for the evolution of research and digital technology to improve data harmonization for VBD risk management.

Managing vector-borne diseases in a geoAI-enabled society. Malaria as an example

More than 17 % of all infectious diseases are caused by vector-borne diseases resulting in more than 1 billion cases and over 1 million deaths each year. Of these malaria continues to be a global burden in over eighty countries. As societies become more digitalised, the availability of geospatially enabled health and disease information will become more abundant. With this, the ability to assess health and disease risks in real-time will become a reality. The purpose of this study was to examine how geographic information, geospatial technologies and spatial data science are being used to reduce the burden of vector-borne diseases such as malaria and explore the opportunities that lie ahead with GeoAI and other geospatial technology advancements. Malaria is a dynamic and complex system and as such a range of data and approaches are needed to tackle different parts of the malaria cycle at different local and global scales. Geospatial technologies provide an integrated framework vital for monitoring, analysing and managing vector-borne diseases. GeoAI and technological advancements are useful for enhancing real-time assessments, accelerating the decision making process and spatial targeting of interventions. Training is needed to enhance the use of geospatial information for the management of vector-borne diseases.

Spatial Machine Learning for Exploring the Variability in Low Height-For-Age From Socioeconomic, Agroecological, and Climate Features in the Northern Province of Rwanda

Childhood stunting is a serious public health concern in Rwanda. Although stunting causes have been documented, we still lack a more in-depth understanding of their local factors at a more detailed geographic level. We cross-sectionally examined 615 height-for-age prevalence observations in the Northern Province of Rwanda, linked with their related covariates, to explore the spatial heterogeneity in the low height-for-age prevalence by fitting linear and non-linear spatial regression models and explainable machine learning. Specifically, complemented with generalized additive models, we fitted the ordinary least squares (OLS), a standard geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) models to characterize the imbalanced distribution of stunting risk factors and uncover the nonlinear effect of significant predictors, explaining the height-for-age variations. The results reveal that 27% of the children measured were stunted, and that likelihood was found to be higher in the districts of Musanze, Gakenke, and Gicumbi. The local MGWR model outperformed the ordinary GWR and OLS, with coefficients of determination of 0.89, 0.84, and 0.25, respectively. At specific ranges, the study shows that height-for-age decreases with an increase in the number of days a child was left alone, elevation, and rainfall. In contrast, land surface temperature is positively associated with height-for-age. However, variables like the normalized difference vegetation index, slope, soil fertility, and urbanicity exhibited bell-shaped and U-shaped non-linear associations with the height-for-age prevalence. Identifying areas with the highest rates of stunting will help determine the most effective measures for reducing the burden of undernutrition.

New directions for malaria vector control using geography and geospatial analysis

As we strive towards the ambitious goal of malaria elimination, we must embrace integrated strategies and interventions. Like many diseases, malaria is heterogeneously distributed. This inherent spatial component means that geography and geospatial data is likely to have an important role in malaria control strategies. For instance, focussing interventions in areas where malaria risk is highest is likely to provide more cost-effective malaria control programmes. Equally, many malaria vector control strategies, particularly interventions like larval source management, would benefit from accurate maps of malaria vector habitats - sources of water that are used for malarial mosquito oviposition and larval development. In many landscapes, particularly in rural areas, the formation and persistence of these habitats is controlled by geographical factors, notably those related to hydrology. This is especially true for malaria vector species like Anopheles funestsus that show a preference for more permanent, often naturally occurring water sources like small rivers and spring-fed ponds. Previous work has embraced geographical concepts, techniques, and geospatial data for studying malaria risk and vector habitats. But there is much to be learnt if we are to fully exploit what the broader geographical discipline can offer in terms of operational malaria control, particularly in the face of a changing climate. This chapter outlines potential new directions related to several geographical concepts, data sources and analytical approaches, including terrain analysis, satellite imagery, drone technology and field-based observations. These directions are discussed within the context of designing new protocols and procedures that could be readily deployed within malaria control programmes, particularly those within sub-Saharan Africa, with a particular focus on experiences in the Kilombero Valley and the Zanzibar Archipelago, United Republic of Tanzania.

Assessment of environmental and spatial factors influencing the establishment of Anopheles gambiae larval habitats in the malaria endemic province of Woleu-Ntem, northern Gabon

BACKGROUND

This study aimed to assess the spatial distribution of Anopheles mosquito larval habitats and the environmental factors associated with them, as a prerequisite for the implementation of larviciding.

METHODS

The study was conducted in December 2021, during the transition period between the end of the short rainy season (September-November) and the short dry season (December-February). Physical, biological, and land cover data were integrated with entomological observations to collect Anopheles larvae in three major towns: Mitzic, Oyem, and Bitam, using the "dipping" method during the transition from rainy to dry season. The collected larvae were then reared in a field laboratory established for the study period. After the Anopheles mosquitoes had emerged, their species were identified using appropriate morphological taxonomic keys. To determine the influence of environmental factors on the breeding of Anopheles mosquitoes, multiple-factor analysis (MFA) and a binomial generalized linear model were used.

RESULTS

According to the study, only 33.1% out of the 284 larval habitats examined were found to be positive for Anopheles larvae, which were primarily identified as belonging to the Anopheles gambiae complex. The findings of the research suggested that the presence of An. gambiae complex larvae in larval habitats was associated with various significant factors such as higher urbanization, the size and type of the larval habitats (pools and puddles), co-occurrence with Culex and Aedes larvae, hot spots in ambient temperature, moderate rainfall, and land use patterns.

CONCLUSIONS

The results of this research mark the initiation of a focused vector control plan that aims to eradicate or lessen the larval habitats of An. gambiae mosquitoes in Gabon's Woleu Ntem province. This approach deals with the root causes of malaria transmission through larvae and is consistent with the World Health Organization's (WHO) worldwide objective to decrease malaria prevalence in regions where it is endemic.

Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments

BACKGROUND

In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies.

RESULTS

We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance.

CONCLUSIONS

Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

Early warning systems, mobile technology, and cholera aversion: Evidence from rural Bangladesh

In Bangladesh, cholera poses a significant environmental health risk. Yet, information about the severity of cholera risk is limited as risk varies over time and changing weather patterns make historical cholera risk predictions less reliable. In this paper, we examine how households use geographically and temporally personalized cholera risk predictions to inform their beliefs and behaviors related to cholera and its aversion. We estimate how access to a smartphone application containing monthly cholera risk predictions unique to a user's home location affects households' beliefs about their cholera risk and their water use and hygiene behaviors. We find that households with access to this application feel more equipped to respond to environmental and health risks and reduce their reliance on surface water for bathing and washing-a common cholera transmission pathway. We do not find that households invest additional resources into drinking water treatment, nor do we find reductions in self-reported cholera incidence. Further, households with a static, non-personalized app containing public health information about cholera exhibit similar patterns of beliefs updating. Taken together, our results suggest that access to dynamic risk information can help households make safer water choices, yet improving design and credibility remain important dimensions for increasing application usability.

Impact of climate and land use/land cover changes on malaria incidence in the Ecuadorian Amazon

Malaria transmission is influenced by climate and land use/land cover change (LULC). This study examines the impact of climate and LULC on malaria risk in the Ecuadorian Amazon. Weekly malaria surveillance data between 2008 and 2019 from Ecuador's Ministry of Public Health were combined with hydrometeorological and LULC data. Cross-correlation analyses identified time lags. Bayesian spatiotemporal models estimated annual LULC rates of change (ARC) by census area and assessed the effects on Plasmodium vivax and Plasmodium falciparum incidence. ARC for the five land cover classes (forest, agriculture, urban, shrub vegetation, water) ranged from -1 to 4% with agriculture increasing across areas. Forest and shrub vegetation ARC were significantly associated with both Plasmodium vivax and Plasmodium falciparum. Temperature and terrestrial water content showed consistent negative relationships with both species. Precipitation had varying effects on Plasmodium vivax (null) and Plasmodium falciparum (increase) incidence. Shrubs and forest expansion, increased temperature, and terrestrial water content reduced malaria incidence, while increased precipitation had varying effects. Relationships between malaria, LULC, and climate are complex, influencing risk profiles. These findings aid decision-making and guide further research in the region.

Assessment of malaria risk in Southeast Asia: a systematic review

BACKGROUND

Several countries in Southeast Asia are nearing malaria elimination, yet eradication remains elusive. This is largely due to the challenge of focusing elimination efforts, an area where risk prediction can play an essential supporting role. Despite its importance, there is no standard numerical method to quantify the risk of malaria infection. Thus, there is a need for a consolidated view of existing definitions of risk and factors considered in assessing risk to analyse the merits of risk prediction models. This systematic review examines studies of the risk of malaria in Southeast Asia with regard to their suitability in addressing the challenges of malaria elimination in low transmission areas.

METHODS

A search of four electronic databases over 2010-2020 retrieved 1297 articles, of which 25 met the inclusion and exclusion criteria. In each study, examined factors included the definition of the risk and indicators of malaria transmission used, the environmental and climatic factors associated with the risk, the statistical models used, the spatial and temporal granularity, and how the relationship between environment, climate, and risk is quantified.

RESULTS

This review found variation in the definition of risk used, as well as the environmental and climatic factors in the reviewed articles. GLM was widely adopted as the analysis technique relating environmental and climatic factors to malaria risk. Most of the studies were carried out in either a cross-sectional design or case-control studies, and most utilized the odds ratio to report the relationship between exposure to risk and malaria prevalence.

CONCLUSIONS

Adopting a standardized definition of malaria risk would help in comparing and sharing results, as would a clear description of the definition and method of collection of the environmental and climatic variables used. Further issues that need to be more fully addressed include detection of asymptomatic cases and considerations of human mobility. Many of the findings of this study are applicable to other low-transmission settings and could serve as a guideline for further studies of malaria in other regions.

Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance

Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d'Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.

Deforestation impacts on dengue incidence in the Brazilian Amazon

The objective of the study is to perform the spatial analysis of the conditioning factors for the increase in the incidence rate of dengue cases in municipalities located in the Amazon biome, in the period from 2016 to 2021. Three statistical approaches were applied: Moran's index, ordinary least squares regression, and geographically weighted regression. The results revealed that the incidence rates of dengue cases cluster in two areas, both located in the south of the Amazon biome, which is associated with the Arc of Deforestation. The variable deforestation influences the increase in dengue incidence rates revealed by the OLS and GWR model. The adjusted R² of the GWR model was 0.70, that is, the model explains about 70% of the total case variation of dengue incidence rates in the Amazon biome. The results of the study evidence the need for public policies aimed at the prevention and combat of deforestation in the Amazon region.

Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology

BACKGROUND

Although malaria transmission has experienced an overall decline in sub-Saharan Africa, urban malaria is now considered an emerging health issue due to rapid and uncontrolled urbanization and the adaptation of vectors to urban environments. Fine-scale hazard and exposure maps are required to support evidence-based policies and targeted interventions, but data-driven predictive spatial modelling is hindered by gaps in epidemiological and entomological data. A knowledge-based geospatial framework is proposed for mapping the heterogeneity of urban malaria hazard and exposure under data scarcity. It builds on proven geospatial methods, implements open-source algorithms, and relies heavily on vector ecology knowledge and the involvement of local experts.

METHODS

A workflow for producing fine-scale maps was systematized, and most processing steps were automated. The method was evaluated through its application to the metropolitan area of Dakar, Senegal, where urban transmission has long been confirmed. Urban malaria exposure was defined as the contact risk between adult Anopheles vectors (the hazard) and urban population and accounted for socioeconomic vulnerability by including the dimension of urban deprivation that is reflected in the morphology of the built-up fabric. Larval habitat suitability was mapped through a deductive geospatial approach involving the participation of experts with a strong background in vector ecology and validated with existing geolocated entomological data. Adult vector habitat suitability was derived through a similar process, based on dispersal from suitable breeding site locations. The resulting hazard map was combined with a population density map to generate a gridded urban malaria exposure map at a spatial resolution of 100 m.

RESULTS

The identification of key criteria influencing vector habitat suitability, their translation into geospatial layers, and the assessment of their relative importance are major outcomes of the study that can serve as a basis for replication in other sub-Saharan African cities. Quantitative validation of the larval habitat suitability map demonstrates the reliable performance of the deductive approach, and the added value of including local vector ecology experts in the process. The patterns displayed in the hazard and exposure maps reflect the high degree of heterogeneity that exists throughout the city of Dakar and its suburbs, due not only to the influence of environmental factors, but also to urban deprivation.

CONCLUSIONS

This study is an effort to bring geospatial research output closer to effective support tools for local stakeholders and decision makers. Its major contributions are the identification of a broad set of criteria related to vector ecology and the systematization of the workflow for producing fine-scale maps. In a context of epidemiological and entomological data scarcity, vector ecology knowledge is key for mapping urban malaria exposure. An application of the framework to Dakar showed its potential in this regard. Fine-grained heterogeneity was revealed by the output maps, and besides the influence of environmental factors, the strong links between urban malaria and deprivation were also highlighted.

Comparing Satellite and Ground-Based Measurements of Environmental Suitability for Vector Mosquitoes in an Urban Landscape

Exposure to mosquito-borne diseases is influenced by landscape patterns and microclimates associated with land cover. These influences can be particularly strong in heterogeneous urban landscapes where human populations are concentrated. We investigated how land cover and climate influenced abundances of Ae. albopictus (Skuse) (Diptera: Culicidae) and Cx. quinquefasciatus (Say) (Diptera: Culicidae) in Norman, Oklahoma (United States). From June-October 2019 and May-October 2020 we sampled mosquitoes along an urban-rural gradient using CO2 baited BG Sentinel traps. Microclimate sensors at these sites measured temperature and humidity. We mapped environmental variables using satellite images from Landsat, Sentinel-2, and VIIRS, and the CHIRPS rainfall dataset. We also obtained meteorological data from the closest weather station. We compared statistical models of mosquito abundance based on microclimate, satellite, weather station, and land cover data. Mosquitoes were more abundant on trap days with higher temperature and relative humidity. Rainfall 2 wk prior to the trap day negatively affected mosquito abundances. Impervious surface cover was positively associated with Cx. quinquefasciatus and tree cover was negatively associated with Ae. albopictus. Among the data sources, models based on satellite variables and land cover data had the best fits for Ae. albopictus (R2 = 0.7) and Cx. quinquefasciatus (R2 = 0.51). Models based on weather station or microclimate data had weaker fits (R2 between 0.09 and 0.17) but were improved by adding land cover variables (R2 between 0.44 and 0.61). These results demonstrate the potential for using satellite remote sensing for mosquito habitat analyses in urban areas.

Malaria Transmission Intensity Likely Modifies RTS, S/AS01 Efficacy Due to a Rebound Effect in Ghana, Malawi, and Gabon

BACKGROUND

RTS,S/AS01 is the first malaria vaccine to be approved and recommended for widespread implementation by the World Health Organization (WHO). Trials reported lower vaccine efficacies in higher-incidence sites, potentially due to a "rebound" in malaria cases in vaccinated children. When naturally acquired protection in the control group rises and vaccine protection in the vaccinated wanes concurrently, malaria incidence can become greater in the vaccinated than in the control group, resulting in negative vaccine efficacies.

METHODS

Using data from the 2009-2014 phase III trial (NCT00866619) in Lilongwe, Malawi; Kintampo, Ghana; and Lambaréné, Gabon, we evaluate this hypothesis by estimating malaria incidence in each vaccine group over time and in varying transmission settings. After estimating transmission intensities using ecological variables, we fit models with 3-way interactions between vaccination, time, and transmission intensity.

RESULTS

Over time, incidence decreased in the control group and increased in the vaccine group. Three-dose efficacy in the lowest-transmission-intensity group (0.25 cases per person-year [CPPY]) decreased from 88.2% to 15.0% over 4.5 years, compared with 81.6% to -27.7% in the highest-transmission-intensity group (3 CPPY).

CONCLUSIONS

These findings suggest that interventions, including the fourth RTS,S dose, that protect vaccinated individuals during the potential rebound period should be implemented for high-transmission settings.

Enhancing the Uptake of Earth Observation Products and Services in Africa Through a Multi-level Transdisciplinary Approach

Africa stands to gain from Earth Observation (EO) science, products and applications. However, its use and application remain below potential on the continent. This article examines how EO can better serve the needs of African users. First, we argue that a successful uptake of EO services is conditional on understanding the African context and matching EO development and deployment to it. Using reference cases, we find that actors outside Africa drive most EO initiatives, whereas country-level expenditures on EO remain low. Recent developments, such as the African space policy and strategy, and initiatives in partnerships with Africa-based organisations to develop a community of practice on EO hold the potential to fill the identified gaps. The analysis indicates that most EO users are either government organisations or researchers, with very few cases involving other types of users. It is generally assumed that users at the local levels are educated and digitally literate, or that the transmission of EO-based knowledge is achieved by government officers and researchers. Although still very few, potentials are emerging for the private sector to deploy EO products and services such as crop or index-based insurance directly to farmers. These private initiatives have prospects for further developing indigenous EO capacity as envisioned in the African space policy and strategy. We then formulate recommendations for a transdisciplinary approach that integrates user contexts, attributes and needs to enhance the uptake of EO products and services in Africa. We conclude by proposing actions to close some of the identified gaps and seize emerging opportunities.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10712-022-09724-1.

Cloud-based applications for accessing satellite Earth observations to support malaria early warning

Malaria epidemics can be triggered by fluctuations in temperature and precipitation that influence vector mosquitoes and the malaria parasite. Identifying and monitoring environmental risk factors can thus provide early warning of future outbreaks. Satellite Earth observations provide relevant measurements, but obtaining these data requires substantial expertise, computational resources, and internet bandwidth. To support malaria forecasting in Ethiopia, we developed software for Retrieving Environmental Analytics for Climate and Health (REACH). REACH is a cloud-based application for accessing data on land surface temperature, spectral indices, and precipitation using the Google Earth Engine (GEE) platform. REACH can be implemented using the GEE code editor and JavaScript API, as a standalone web app, or as package with the Python API. Users provide a date range and data for 852 districts in Ethiopia are automatically summarized and downloaded as tables. REACH was successfully used in Ethiopia to support a pilot malaria early warning project in the Amhara region. The software can be extended to new locations and modified to access other environmental datasets through GEE.

Needs Assessment of Southeastern United States Vector Control Agencies: Capacity Improvement Is Greatly Needed to Prevent the Next Vector-Borne Disease Outbreak

A national 2017 vector control capacity survey was conducted to assess the United States’ (U.S.’s) ability to prevent emerging vector-borne disease. Since that survey, the southeastern U.S. has experienced continued autochthonous exotic vector-borne disease transmission and establishment of invasive vector species. To understand the current gaps in control programs and establish a baseline to evaluate future vector control efforts for this vulnerable region, a focused needs assessment survey was conducted in early 2020. The southeastern U.S. region was targeted, as this region has a high probability of novel vector-borne disease introduction. Paper copies delivered in handwritten envelopes and electronic copies of the survey were delivered to 386 unique contacts, and 150 returned surveys were received, corresponding to a 39% response rate. Overall, the survey found vector control programs serving areas with over 100,000 residents and those affiliated with public health departments had more core capabilities compared to smaller programs and those not affiliated with public health departments. Furthermore, the majority of vector control programs in this region do not routinely monitor for pesticide resistance. Taken as a whole, these results suggest that the majority of the southeastern U.S. is vulnerable to vector-borne disease outbreaks. Results from this survey raise attention to the critical need of providing increased resources to bring all vector control programs to a competent level, ensuring that public health is protected from the threat of vector-borne disease.

A framework to integrate innovations in invasion science for proactive management

Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions of U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge are essential to keeping pace with growing impacts of invasions under climate change. Although the rapid development of diverse technologies and approaches has produced tools with the potential to greatly accelerate invasion research and management, innovation has far outpaced implementation and coordination. Technological and methodological syntheses are urgently needed to close the growing implementation gap and facilitate interdisciplinary collaboration and synergy among evolving disciplines. A broad review is necessary to demonstrate the utility and relevance of work in diverse fields to generate actionable science for the ongoing invasion crisis. Here, we review such advances in relevant fields including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, and others, and present a generalized framework for distilling existing and emerging data into products for proactive IAS research and management. This integrated workflow provides a pathway for scientists and practitioners in diverse disciplines to contribute to applied invasion biology in a coordinated, synergistic, and scalable manner.

Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data through Google Earth Engine

In many studies regarding the field of malaria, environmental factors have been acquired in single-time, multi-time or a short-time series using remote sensing and meteorological data. Selecting the best periods of the year to monitor the habitats of Anopheles larvae can be effective in better and faster control of malaria outbreaks. In this article, high-risk times for three regions in Iran, including Qaleh-Ganj, Sarbaz and Bashagard counties with a history of malaria prevalence were estimated. For this purpose, a series of environmental factors affecting the growth and survival of Anopheles were used over a seven-year period through the Google Earth Engine. The results of this study indicated two high-risk times for Qaleh-Ganj and Bashagard counties and three high-risk times for Sarbaz county over the course of a year observing an increase in the abundance of Anopheles mosquitoes. Further evaluation of the results against the entomological data available in previous studies showed that the high-risk times predicted in this study were consistent with an increase in the abundance of Anopheles mosquitoes in the study areas. The proposed method is extremely useful for temporal prediction of the increase in abundance of Anopheles mosquitoes in addition to the use of optimal data aimed at monitoring the exact location of Anopheles habitats.

Technical Workflow Development for Integrating Drone Surveys and Entomological Sampling to Characterise Aquatic Larval Habitats of Anopheles funestus in Agricultural Landscapes in Côte d'Ivoire

Land-use practices such as agriculture can impact mosquito vector breeding ecology, resulting in changes in disease transmission. The typical breeding habitats of Africa's second most important malaria vector Anopheles funestus are large, semipermanent water bodies, which make them potential candidates for targeted larval source management. This is a technical workflow for the integration of drone surveys and mosquito larval sampling, designed for a case study aiming to characterise An. funestus breeding sites near two villages in an agricultural setting in Côte d'Ivoire. Using satellite remote sensing data, we developed an environmentally and spatially representative sampling frame and conducted paired mosquito larvae and drone mapping surveys from June to August 2021. To categorise the drone imagery, we also developed a land cover classification scheme with classes relative to An. funestus breeding ecology. We sampled 189 potential breeding habitats, of which 119 (63%) were positive for the Anopheles genus and nine (4.8%) were positive for An. funestus. We mapped 30.42 km2 of the region of interest including all water bodies which were sampled for larvae. These data can be used to inform targeted vector control efforts, although its generalisability over a large region is limited by the fine-scale nature of this study area. This paper develops protocols for integrating drone surveys and statistically rigorous entomological sampling, which can be adjusted to collect data on vector breeding habitats in other ecological contexts. Further research using data collected in this study can enable the development of deep-learning algorithms for identifying An. funestus breeding habitats across rural agricultural landscapes in Côte d'Ivoire and the analysis of risk factors for these sites.

Epidemiology of the zoonotic malaria Plasmodium knowlesi in changing landscapes

Within the past two decades, incidence of human cases of the zoonotic malaria Plasmodium knowlesi has increased markedly. P. knowlesi is now the most common cause of human malaria in Malaysia and threatens to undermine malaria control programmes across Southeast Asia. The emergence of zoonotic malaria corresponds to a period of rapid deforestation within this region. These environmental changes impact the distribution and behaviour of the simian hosts, mosquito vector species and human populations, creating new opportunities for P. knowlesi transmission. Here, we review how landscape changes can drive zoonotic disease emergence, examine the extent and causes of these changes across Southeast and identify how these mechanisms may be impacting P. knowlesi dynamics. We review the current spatial epidemiology of reported P. knowlesi infections in people and assess how these demographic and environmental changes may lead to changes in transmission patterns. Finally, we identify opportunities to improve P. knowlesi surveillance and develop targeted ecological interventions within these landscapes.

Remote sensing of environmental risk factors for malaria in different geographic contexts

BACKGROUND

Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies.

METHODS

We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (kebele) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models.

RESULTS

We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria.

CONCLUSION

We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences.

Spatial distribution of Culex mosquito abundance and associated risk factors in Hanoi, Vietnam

Japanese encephalitis (JE) is the major cause of viral encephalitis (VE) in most Asian-Pacific countries. In Vietnam, there is no nationwide surveillance system for JE due to lack of medical facilities and diagnoses. Culex tritaeniorhynchus, Culex vishnui, and Culex quinquefasciatus have been identified as the major JE vectors in Vietnam. The main objective of this study was to forecast a risk map of Culex mosquitoes in Hanoi, which is one of the most densely populated cities in Vietnam. A total of 10,775 female adult Culex mosquitoes were collected from 513 trapping locations. We collected temperature and precipitation information during the study period and its preceding month. In addition, the other predictor variables (e.g., normalized difference vegetation index [NDVI], land use/land cover and human population density), were collected for our analysis. The final model selected for estimating the Culex mosquito abundance included centered rainfall, quadratic term rainfall, rice cover ratio, forest cover ratio, and human population density variables. The estimated spatial distribution of Culex mosquito abundance ranged from 0 to more than 150 mosquitoes per 900m2. Our model estimated that 87% of the Hanoi area had an abundance of mosquitoes from 0 to 50, whereas approximately 1.2% of the area showed more than 100 mosquitoes, which was mostly in the rural/peri-urban districts. Our findings provide better insight into understanding the spatial distribution of Culex mosquitoes and its associated environmental risk factors. Such information can assist local clinicians and public health policymakers to identify potential areas of risk for JE virus. Risk maps can be an efficient way of raising public awareness about the virus and further preventive measures need to be considered in order to prevent outbreaks and onwards transmission of JE virus.