Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia

Identifying and assessing pond best management practice under future climate scenarios

Climate change can play important roles in the hydrological processes within watershed with ponds as the Best Management Practice (BMP). Unlike several other studies, this study integrated remote sensing technique with hydrological model to identify and simulate pond BMP. Limited studies have been carried out to evaluate pond BMP in relation to the climate change impacts on hydrology and water quality particularly in Mississippi watersheds. The objective of this study was to classify ponds on satellite imagery within the Big Sunflower River Watershed (BSRW) using Google Earth Engine (GEE) and incorporate this data with Soil and Water Assessment Tool (SWAT) model to evaluate future hydrological and water quality outputs. The SWAT model was calibrated and validated against streamflow (R2 and NSE values from 0.81 to 0.56) and sediment (R2 and NSE values from 0.91 to 0.40). Future climate data for the mid (2040-2060) and late (2079-2099) centuries were utilized to create climate change scenarios (e.g., RCP 4.5 and 8.5). Results of this study projected that the average annual flow and sediment load will increase by 26-46%, and 107-150% respectively by the late century compared to the baseline period (2002-2021). However, the projected sediment load with modified pond BMP data used in the SWAT model could decrease average annual sediment load by 44-46% under both RCP scenarios. Seasonal data analysis determined that spring, summer, and fall sediment loads were projected to decrease up to 42%, 52%, and 46% respectively under both RCP scenarios due to pond BMP. This study can be useful for the development of climate-smart management strategies in agricultural watersheds.

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.

Satellite Observations and Malaria: New Opportunities for Research and Applications

Satellite remote sensing provides a wealth of information about environmental factors that influence malaria transmission cycles and human populations at risk. Long-term observations facilitate analysis of climate–malaria relationships, and high-resolution data can be used to assess the effects of agriculture, urbanization, deforestation, and water management on malaria. New sources of very-high-resolution satellite imagery and synthetic aperture radar data will increase the precision and frequency of observations. Cloud computing platforms for remote sensing data combined with analysis-ready datasets and high-level data products have made satellite remote sensing more accessible to nonspecialists. Further collaboration between the malaria and remote sensing communities is needed to develop and implement useful geospatial data products that will support global efforts toward malaria control, elimination, and eradication.

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources

Random forests (RF) is a supervised machine learning algorithm, which has recently started to gain prominence in water resources applications. However, existing applications are generally restricted to the implementation of Breiman’s original algorithm for regression and classification problems, while numerous developments could be also useful in solving diverse practical problems in the water sector. Here we popularize RF and their variants for the practicing water scientist, and discuss related concepts and techniques, which have received less attention from the water science and hydrologic communities. In doing so, we review RF applications in water resources, highlight the potential of the original algorithm and its variants, and assess the degree of RF exploitation in a diverse range of applications. Relevant implementations of random forests, as well as related concepts and techniques in the R programming language, are also covered.

Identifying Environmental Risk Factors and Mapping the Distribution of West Nile Virus in an Endemic Region of North America

Understanding the geographic distribution of mosquito-borne disease and mapping disease risk are important for prevention and control efforts. Mosquito-borne viruses (arboviruses), such as West Nile virus (WNV), are highly dependent on environmental conditions. Therefore, the use of environmental data can help in making spatial predictions of disease distribution. We used geocoded human case data for 2004-2017 and population-weighted control points in combination with multiple geospatial environmental data sets to assess the environmental drivers of WNV cases and to map relative infection risk in South Dakota, USA. We compared the effectiveness of (1) land cover and physiography data, (2) climate data, and (3) spectral data for mapping the risk of WNV in South Dakota. A final model combining all data sets was used to predict spatial patterns of disease transmission and characterize the associations between environmental factors and WNV risk. We used a boosted regression tree model to identify the most important variables driving WNV risk and generated risk maps by applying this model across the entire state. We found that combining multiple sources of environmental data resulted in the most accurate predictions. Elevation, late-season humidity, and early-season surface moisture were the most important predictors of disease distribution. Indices that quantified interannual variability of climatic conditions and land surface moisture were better predictors than interannual means. We suggest that combining measures of interannual environmental variability with static land cover and physiography variables can help to improve spatial predictions of arbovirus transmission risk.

Applications of Space Technologies to Global Health: Scoping Review

Background

Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations’ Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health.

Objective

The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges.

Methods

We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs’ Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified.

Results

A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use.

Conclusions

Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.

Paleopathological Considerations on Malaria Infection in Korea before the 20th Century

Malaria, one of the deadliest diseases in human history, still infects many people worldwide. Among the species of the genus Plasmodium, P. vivax is commonly found in temperate-zone countries including South Korea. In this article, we first review the history of malarial infection in Korea by means of studies on Joseon documents and the related scientific data on the evolutionary history of P. vivax in Asia. According to the historical records, malarial infection was not unusual in pre-20th-century Korean society. We also found that certain behaviors of the Joseon people might have affected the host-vector-pathogen relationship, which could explain why malarial infection prevalence was so high in Korea at that time. In our review of genetic studies on P. vivax, we identified substantial geographic differentiation among continents and even between neighboring countries. Based on these, we were able to formulate a strategy for future analysis of ancient Plasmodium strains in Korea.

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

Integrating malaria surveillance with climate data for outbreak detection and forecasting: the EPIDEMIA system

Background

Early indication of an emerging malaria epidemic can provide an opportunity for proactive interventions. Challenges to the identification of nascent malaria epidemics include obtaining recent epidemiological surveillance data, spatially and temporally harmonizing this information with timely data on environmental precursors, applying models for early detection and early warning, and communicating results to public health officials. Automated web-based informatics systems can provide a solution to these problems, but their implementation in real-world settings has been limited.

Methods

The Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system was designed and implemented to integrate disease surveillance with environmental monitoring in support of operational malaria forecasting in the Amhara region of Ethiopia. A co-design workshop was held with computer scientists, epidemiological modelers, and public health partners to develop an initial list of system requirements. Subsequent updates to the system were based on feedback obtained from system evaluation workshops and assessments conducted by a steering committee of users in the public health sector.

Results

The system integrated epidemiological data uploaded weekly by the Amhara Regional Health Bureau with remotely-sensed environmental data freely available from online archives. Environmental data were acquired and processed automatically by the EASTWeb software program. Additional software was developed to implement a public health interface for data upload and download, harmonize the epidemiological and environmental data into a unified database, automatically update time series forecasting models, and generate formatted reports. Reporting features included district-level control charts and maps summarizing epidemiological indicators of emerging malaria outbreaks, environmental risk factors, and forecasts of future malaria risk.

Conclusions

Successful implementation and use of EPIDEMIA is an important step forward in the use of epidemiological and environmental informatics systems for malaria surveillance. Developing software to automate the workflow steps while remaining robust to continual changes in the input data streams was a key technical challenge. Continual stakeholder involvement throughout design, implementation, and operation has created a strong enabling environment that will facilitate the ongoing development, application, and testing of the system.

Mapping intra-urban malaria risk using high resolution satellite imagery: a case study of Dar es Salaam

Background

With more than half of Africa’s population expected to live in urban settlements by 2030, the burden of malaria among urban populations in Africa continues to rise with an increasing number of people at risk of infection. However, malaria intervention across Africa remains focused on rural, highly endemic communities with far fewer strategic policy directions for the control of malaria in rapidly growing African urban settlements. The complex and heterogeneous nature of urban malaria requires a better understanding of the spatial and temporal patterns of urban malaria risk in order to design effective urban malaria control programs. In this study, we use remotely sensed variables and other environmental covariates to examine the predictability of intra-urban variations of malaria infection risk across the rapidly growing city of Dar es Salaam, Tanzania between 2006 and 2014.

Methods

High resolution SPOT satellite imagery was used to identify urban environmental factors associated malaria prevalence in Dar es Salaam. Supervised classification with a random forest classifier was used to develop high resolution land cover classes that were combined with malaria parasite prevalence data to identify environmental factors that influence localized heterogeneity of malaria transmission and develop a high resolution predictive malaria risk map of Dar es Salaam.

Results

Results indicate that the risk of malaria infection varied across the city. The risk of infection increased away from the city centre with lower parasite prevalence predicted in administrative units in the city centre compared to administrative units in the peri-urban suburbs. The variation in malaria risk within Dar es Salaam was shown to be influenced by varying environmental factors. Higher malaria risks were associated with proximity to dense vegetation, inland water and wet/swampy areas while lower risk of infection was predicted in densely built-up areas.

Conclusions

The predictive maps produced can serve as valuable resources for municipal councils aiming to shrink the extents of malaria across cities, target resources for vector control or intensify mosquito and disease surveillance. The semi-automated modelling process developed can be replicated in other urban areas to identify factors that influence heterogeneity in malaria risk patterns and detect vulnerable zones. There is a definite need to expand research into the unique epidemiology of malaria transmission in urban areas for focal elimination and sustained control agendas.

Electronic supplementary material

The online version of this article (doi:10.1186/s12942-016-0051-y) contains supplementary material, which is available to authorized users.

Seasonal associations of climatic drivers and malaria in the highlands of Ethiopia

BACKGROUND

The impacts of interannual climate fluctuations on vector-borne diseases, especially malaria, have received considerable attention in the scientific literature. These effects can be significant in semi-arid and high-elevation areas such as the highlands of East Africa because cooler temperature and seasonally dry conditions limit malaria transmission. Many previous studies have examined short-term lagged effects of climate on malaria (weeks to months), but fewer have explored the possibility of longer-term seasonal effects.

METHODS

This study assessed the interannual variability of malaria occurrence from 2001 to 2009 in the Amhara region of Ethiopia. We tested for associations of climate variables summarized during the dry (January-April), early transition (May-June), and wet (July-September) seasons with malaria incidence in the early peak (May-July) and late peak (September-December) epidemic seasons using generalized linear models. Climate variables included land surface temperature (LST), rainfall, actual evapotranspiration (ET), and the enhanced vegetation index (EVI).

RESULTS

We found that both early and late peak malaria incidence had the strongest associations with meteorological conditions in the preceding dry and early transition seasons. Temperature had the strongest influence in the wetter western districts, whereas moisture variables had the strongest influence in the drier eastern districts. We also found a significant correlation between malaria incidence in the early and the subsquent late peak malaria seasons, and the addition of early peak malaria incidence as a predictor substantially improved models of late peak season malaria in both of the study sub-regions.

CONCLUSIONS

These findings suggest that climatic effects on malaria prior to the main rainy season can carry over through the rainy season and affect the probability of malaria epidemics during the late malaria peak. The results also emphasize the value of combining environmental monitoring with epidemiological surveillance to develop forecasts of malaria outbreaks, as well as the need for spatially stratified approaches that reflect the differential effects of climatic variations in the different sub-regions.