Remote sensing and disease control in China: past, present and future

Moving towards improved surveillance and earlier diagnosis of aquatic pathogens: From traditional methods to emerging technologies

Early and accurate diagnosis is key to mitigating the impact of infectious diseases, along with efficient surveillance. This however is particularly challenging in aquatic environments due to hidden biodiversity and physical constraints. Traditional diagnostics, such as visual diagnosis and histopathology, are still widely used, but increasingly technological advances such as portable next generation sequencing (NGS) and artificial intelligence (AI) are being tested for early diagnosis. The most straightforward methodologies, based on visual diagnosis, rely on specialist knowledge and experience but provide a foundation for surveillance. Future computational remote sensing methods, such as AI image diagnosis and drone surveillance, will ultimately reduce labour costs whilst not compromising on sensitivity, but they require capital and infrastructural investment. Molecular techniques have advanced rapidly in the last 30 years, from standard PCR through loop-mediated isothermal amplification (LAMP) to NGS approaches, providing a range of technologies that support the currently popular eDNA diagnosis. There is now vast potential for transformative change driven by developments in human diagnostics. Here we compare current surveillance and diagnostic technologies with those that could be used or developed for use in the aquatic environment, against three gold standard ideals of high sensitivity, specificity, rapid diagnosis, and cost-effectiveness.

Operationalizing the Exposome Using Passive Silicone Samplers

The exposome, which is defined as the cumulative effect of environmental exposures and corresponding biological responses, aims to provide a comprehensive measure for evaluating non-genetic causes of disease. Operationalization of the exposome for environmental health and precision medicine has been limited by the lack of a universal approach for characterizing complex exposures, particularly as they vary temporally and geographically. To overcome these challenges, passive sampling devices (PSDs) provide a key measurement strategy for deep exposome phenotyping, which aims to provide comprehensive chemical assessment using untargeted high-resolution mass spectrometry for exposome-wide association studies. To highlight the advantages of silicone PSDs, we review their use in population studies and evaluate the broad range of applications and chemical classes characterized using these samplers. We assess key aspects of incorporating PSDs within observational studies, including the need to preclean samplers prior to use to remove impurities that interfere with compound detection, analytical considerations, and cost. We close with strategies on how to incorporate measures of the external exposome using PSDs, and their advantages for reducing variability in exposure measures and providing a more thorough accounting of the exposome. Continued development and application of silicone PSDs will facilitate greater understanding of how environmental exposures drive disease risk, while providing a feasible strategy for incorporating untargeted, high-resolution characterization of the external exposome in human studies.

Land use and land cover change and its impacts on dengue dynamics in China: A systematic review

Background

Dengue is a prioritized public health concern in China. Because of the larger scale, more frequent and wider spatial distribution, the challenge for dengue prevention and control has increased in recent years. While land use and land cover (LULC) change was suggested to be associated with dengue, relevant research has been quite limited. The “Open Door” policy introduced in 1978 led to significant LULC change in China. This systematic review is the first to review the studies on the impacts of LULC change on dengue dynamics in China. This review aims at identifying the research evidence, research gaps and provide insights for future research.

Methods

A systematic literature review was conducted following the PRISMA protocol. The combinations of search terms on LULC, dengue and its vectors were searched in the databases PubMed, Web of Science, and Baidu Scholar. Research conducted on China published from 1978 to December 2019 and written in English or Chinese was selected for further screening. References listed in articles meeting the inclusion criteria were also reviewed and included if again inclusion criteria were met to minimize the probability of missing relevant research.

Results

28 studies published between 1978 and 2017 were included for the full review. Guangdong Province and southern Taiwan were the major regional foci in the literature. The majority of the reviewed studies observed associations between LULC change factors and dengue incidence and distribution. Conflictive evidence was shown in the studies about the impacts of green space and blue space on dengue in China. Transportation infrastructure and urbanization were repeatedly suggested to be positively associated with dengue incidence and spread. The majority of the studies reviewed considered meteorological and sociodemographic factors when they analyzed the effects of LULC change on dengue. Primary and secondary remote sensing (RS) data were the primary source for LULC variables. In 21 of 28 studies, a geographic information system (GIS) was used to process data of environmental variables and dengue cases and to perform spatial analysis of dengue.

Conclusions

The effects of LULC change on the dynamics of dengue in China varied in different periods and regions. The application of RS and GIS enriches the means and dimensions to explore the relations between LULC change and dengue. Further comprehensive regional research is necessary to assess the influence of LULC change on local dengue transmission to provide practical advice for dengue prevention and control.

Dengue is a major public health concern in China. The rapid development of urbanization along with climate change increases the challenge for dengue prevention and control. Previous research has mainly focused on the meteorological variables whereas land use and land cover (LULC) change received comparatively less attention. Our review identified that the regional research hotspots of dengue epidemics in China were Guangdong Province and southern Taiwan. Though inconsistent, most included studies somehow observed associations between at least one of the LULC change factors and dengue. A geographical information system (GIS) was widely used to perform spatial analysis in the selected literature. Its application provided a novel view to describe the relationships between environmental factors and the situation of dengue, which enabled scholars to explore more characteristics of dengue transmission. Meanwhile, the use of remote sensing (RS) enriched the means of environmental monitoring. However, there are research gaps in the area of dengue and LULC change, such as the less consideration of dengue vector study, the lack of interplays between factors, and the lack of considering interventions and policies. Furthermore, because of different research settings, results from these studies were difficult to compare. Thus, further comprehensive and comparable investigations are necessary to better understand the effects of LULC change on dengue in China. This review is the first to expound the studies on the associations between LULC change and dengue dynamics in China. It demonstrates the findings and methodologies and provided insights for future research.

Comprehensive Risk Assessment of Schistosomiasis Epidemic Based on Precise Identification of Oncomelania hupensis Breeding Grounds-A Case Study of Dongting Lake Area

Spatio-temporal epidemic simulation, assessment, and risk monitoring serve as the core to establishing and improving the national public health emergency management system. In this study, we investigated Oncomelania hupensis breeding grounds and analyzed the locational and environmental preferences of snail breeding in Dongting Lake (DTL), Hunan, China. Using geographic information systems and remote sensing technology, we identified schistosomiasis risk areas and explored the factors affecting the occurrence and transmission of the disease. Several key conclusions were drawn. (1) From 2006 to 2016, the spatial change of potential O. hupensis breeding risk showed a diminishing trend from the eastern and northern regions to southwest DTL. Environmental changes in the eastern DTL region resulted in the lakeside and hydrophilic agglomerations of the O. hupensis populations. The shift in snail breeding grounds from a fragmented to centralized distribution indicates the weakening mobility of the O. hupensis population, the increasing independence of solitary groups, and the growing dependence of the snail population to the local environment. (2) The spatial risk distribution showed a descending gradient from west Dongting area to the east and an overall pattern of high in the periphery of large lakes and low in other areas. The cold-spot areas had their cores in Huarong County and Anxiang County and were scattered throughout the peripheral areas. The hot-spot areas had their center at Jinshi City, Nanxian County, and the southern part of Huarong County. The areas with increased comprehensive risks changed from centralized and large-scale development to fragmented shrinkage with increased partialization in the core area. The risk distribution's center shifted to the northwest. The spatial risk distribution exhibited enhanced concentricity along the major axis and increased dispersion along the minor axis.

Data mining and machine learning approaches for prediction modelling of schistosomiasis disease vectors: Epidemic disease prediction modelling

This research presents viable solutions for prediction modelling of schistosomiasis disease based on vector density. Novel training models proposed in this work aim to address various aspects of interest in the artificial intelligence applications domain. Topics discussed include data imputation, semi-supervised labelling and synthetic instance simulation when using sparse training data. Innovative semi-supervised ensemble learning paradigms are proposed focusing on labelling threshold selection and stringency of classification confidence levels. A regression-correlation combination (RCC) data imputation method is also introduced for handling of partially complete training data. Results presented in this work show data imputation precision improvement over benchmark value replacement using proposed RCC on 70% of test cases. Proposed novel incremental transductive models such as ITSVM have provided interesting findings based on threshold constraints outperforming standard SVM application on 21% of test cases and can be applied with alternative environment-based epidemic disease domains. The proposed incremental transductive ensemble approach model enables the combination of complimentary algorithms to provide labelling for unlabelled vector density instances. Liberal (LTA) and strict training approaches provided varied results with LTA outperforming Stacking ensemble on 29.1% of test cases. Proposed novel synthetic minority over-sampling technique (SMOTE) equilibrium approach has yielded subtle classification performance increases which can be further interrogated to assess classification performance and efficiency relationships with synthetic instance generation.

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.

Spatiotemporal incidence of Zika and associated environmental drivers for the 2015-2016 epidemic in Colombia

Despite a long history of mosquito-borne virus epidemics in the Americas, the impact of the Zika virus (ZIKV) epidemic of 2015–2016 was unexpected. The need for scientifically informed decision-making is driving research to understand the emergence and spread of ZIKV. To support that research, we assembled a data set of key covariates for modeling ZIKV transmission dynamics in Colombia, where ZIKV transmission was widespread and the government made incidence data publically available. On a weekly basis between January 1, 2014 and October 1, 2016 at three administrative levels, we collated spatiotemporal Zika incidence data, nine environmental variables, and demographic data into a single downloadable database. These new datasets and those we identified, processed, and assembled at comparable spatial and temporal resolutions will save future researchers considerable time and effort in performing these data processing steps, enabling them to focus instead on extracting epidemiological insights from this important data set. Similar approaches could prove useful for filling data gaps to enable epidemiological analyses of future disease emergence events.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.

Natural and human induced factors influencing the abundance of Schistosoma host snails in Zambia

Schistosomiasis remains a global public health problem affecting about 240 million people. In Zambia, 2 million are infected while 3 million live with the risk of getting infected. Research and interventions relating to schistosomiasis are mainly linked to disease epidemiology. Malacological and ecological aspects of the disease are superficially understood. Developing effective control measures requires an understanding of interacting environmental and socioeconomic factors of host snails vis-a-vis schistosomiasis. Therefore, the present work involved collecting social and environmental data in a large field study in two zones in Zambia that are different in terms of temperature and rainfall amounts. Social data collected through questionnaires included demographic, educational and knowledge of schistosomiasis disease dynamics. Environmental data included physicochemical factors, aquatic plants and snails. Gender (P < 0.001) significantly influences livelihood strategies, while age (P = 0.069) and level of education (P = 0.086) have a moderate influence in zone I. In zone III, none of these factors (age, P = 0.378; gender, P = 0.311; education, P = 0.553) play a significant role. Environmental parameters explained 43 and 41 % variation in species composition for zones I and III, respectively. Most respondents' (52 %, 87 %) perception is that there are more cases of bilharzia in hot season than in other seasons (rainy season 23 %, 7 %; cold season 8 %, 0 % and year round 17 %, 6 %) for zone I and zone III, respectively.

Spatial and Temporal Variation in Aedes aegypti and Aedes albopictus (Diptera: Culicidae) Numbers in the Yogyakarta Area of Java, Indonesia, With Implications for Wolbachia Releases

of mosquito vector populations, particularly through Wolbachia endosymbionts. The success of these strategies depends on understanding the dynamics of vector populations. In preparation for Wolbachia releases around Yogyakarta, we have studied Aedes populations in five hamlets. Adult monitoring with BioGent- Sentinel (BG-S) traps indicated that hamlet populations had different dynamics across the year; while there was an increase in Aedes aegypti (L.) and Aedes albopictus (Skuse) numbers in the wet season, species abundance remained relatively stable in some hamlets but changed markedly (>2 fold) in others. Local rainfall a month prior to monitoring partly predicted numbers of Ae. aegypti but not Ae. albopictus. Site differences in population size indicated by BG-S traps were also evident in ovitrap data. Egg or larval collections with ovitraps repeated at the same location suggested spatial autocorrelation (<250 m) in the areas of the hamlets where Ae. aegypti numbers were high. Overall, there was a weak negative association (r<0.43) between Ae. aegypti and Ae. albopictus numbers in ovitraps when averaged across collections. Ae. albopictus numbers in ovitraps and BG-S traps were positively correlated with vegetation around areas where traps were placed, while Ae. aegypti were negatively correlated with this feature. These data inform intervention strategies by defining periods when mosquito densities are high, highlighting the importance of local site characteristics on populations, and suggesting relatively weak interactions between Ae. aegypti and Ae. albopictus. They also indicate local areas within hamlets where consistently high mosquito densities may influence Wolbachia invasions and other interventions.

Integrated mapping of lymphatic filariasis and podoconiosis: lessons learnt from Ethiopia

BACKGROUND

The World Health Organization (WHO), international donors and partners have emphasized the importance of integrated control of neglected tropical diseases (NTDs). Integrated mapping of NTDs is a first step for integrated planning of programmes, proper resource allocation and monitoring progress of control. Integrated mapping has several advantages over disease specific mapping by reducing costs and enabling co-endemic areas to be more precisely identified. We designed and conducted integrated mapping of lymphatic filariasis (LF) and podoconiosis in Ethiopia; here we present the methods, challenges and lessons learnt.

METHODS

Integrated mapping of 1315 communities across Ethiopia was accomplished within three months. Within these communities, 129,959 individuals provided blood samples that were tested for circulating Wuchereria bancrofti antigen using immunochromatographic card tests (ICT). Wb123 antibody tests were used to further establish exposure to LF in areas where at least one ICT positive individual was detected. A clinical algorithm was used to reliably diagnose podoconiosis by excluding other potential causes of lymphoedema of the lower limb.

RESULTS

A total of 8110 individuals with leg swelling were interviewed and underwent physical examination. Smartphones linked to a central database were used to collect data, which facilitated real-time data entry and reduced costs compared to traditional paper-based data collection approach; their inbuilt Geographic Positioning System (GPS) function enabled simultaneous capture of geographical coordinates. The integrated approach led to efficient use of resources and rapid mapping of an enormous geographical area and was well received by survey staff and collaborators. Mobile based technology can be used for such large scale studies in resource constrained settings such as Ethiopia, with minimal challenges.

CONCLUSIONS

This was the first integrated mapping of podoconiosis and LF globally. Integrated mapping of podoconiosis and LF is feasible and, if properly planned, can be quickly achieved at nationwide scale.

Anopheles species associations in Southeast Asia: indicator species and environmental influences

Background

Southeast Asia presents a high diversity of Anopheles. Environmental requirements differ for each species and should be clarified because of their influence on malaria transmission potential. Monitoring projects collect vast quantities of entomological data over the whole region and could bring valuable information to malaria control staff but collections are not always standardized and are thus difficult to analyze. In this context studying species associations and their relation to the environment offer some opportunities as they are less subject to sampling error than individual species.

Methods

Using asymmetrical similarity coefficients, indirect clustering and the search of indicator species, this paper identified species associations. Environmental influences were then analysed through canonical and discriminant analysis using climatic and topographic data, land cover in a 3 km buffer around villages and vegetation indices.

Results

Six groups of sites characterized the structure of the species assemblage. Temperature, rainfall and vegetation factors all play a role. Four out of the six groups of sites based on species similarities could be discriminated using environmental information only.

Conclusions

Vegetation indices derived from satellite imagery proved very valuable with one variable explaining more variance of the species dataset than any other variable. The analysis could be improved by integrating seasonality in the sampling and collecting at least 4 consecutive days.