Guest editors' preface. Global mapping of infectious diseases: methods, examples and emerging applications

Rickettsial Infections among the Undifferentiated Febrile Patients Attending a Tertiary Care Teaching Hospital of Northern India: A Longitudinal Study

Background

Acute undifferentiated febrile illness (AUFI) is one of the most daunting challenges a physician faces in such settings. Among AUFI, rickettsial infections are most common and related infections (such as anaplasmosis, ehrlichiosis, and Q fever) which are caused by an unusual type of bacteria that can live only inside the cells of another organism. The present study was therefore planned with an objective to estimate the prevalence of rickettsial infection among patients of undifferentiated fever and to determine any association of socio-demographic characteristics with rickettsial disease.

Materials and Methods

Patients presenting with febrile illness and admitted or attending out-patient department of Sher-i-Kashmir Institute of Medical Sciences, Srinagar was approached and recruited in the study. Weil Felix Assay, enzyme-linked immunosorbent assay and indirect immunofluorescence assay were done to detect the anti-rickettsial antibodies. Serological evidence of a fourfold increase in IgG-specific antibody titer reactive with spotted fever group rickettsial antigen by indirect immunofluorescence antibody assays between paired serum specimens was considered a confirmatory diagnosis for the rickettsial disease.

Results

Most of the patients were males 61.6%, and most 46.2% were in the age group of 20 -39 years. Most of the patients, 80.8% belonged to rural areas, and 48% belonged to the upper middle (II) class of the socio-economic class according to modified Kuppuswamy scale. Of the studied participants, a majority, 47.0%, were determined undiagnosed, while 15.4% studied participants were diagnosed to have a rickettsial disease. In patients positive for typhus group, 67.8% were IgM positive, 28.5% were IgG positive, and only 3% were positive for IgM and IgG. In patients positive for Scrub Typhus Group, 32.7% were positive for IgM, and 62.0% were positive for IgG, and only 5.0% were positive for both IgM and IgG. In patients positive for spotted fever group, 36.1% were positive for IgM, and 58.5% were positive for IgG, and only 5.5% were positive for both IgM and IgG. The prevalence of rickettsial disease was found to be 11.3%.

Conclusion

Rickettsial diseases, typhoid and brucellosis, were the most prevalent diseased diagnosed among patients reporting to hospitals with undifferentiated febrile illness. Clinicians must consider rickettsial diseases as one of the differential diagnosis while treating patients with fever.

Morphological re-description and molecular identification of Tabanidae (Diptera) in East Africa

Biting flies of the family Tabanidae are important vectors of human and animal diseases across continents. However, records of Africa tabanids are fragmentary and mostly cursory. To improve identification, documentation and description of Tabanidae in East Africa, a baseline survey for the identification and description of Tabanidae in three eastern African countries was conducted. Tabanids from various locations in Uganda (Wakiso District), Tanzania (Tarangire National Park) and Kenya (Shimba Hills National Reserve, Muhaka, Nguruman) were collected. In Uganda, octenol baited F-traps were used to target tabanids, while NG2G traps baited with cow urine and acetone were employed in Kenya and Tanzania. The tabanids were identified using morphological and molecular methods. Morphologically, five genera (Ancala, Tabanus, Atylotus, Chrysops and Haematopota) and fourteen species of the Tabanidae were identified. Among the 14 species identified, six belonged to the genus Tabanus of which two (T. donaldsoni and T. guineensis) had not been described before in East Africa. The greatest diversity of tabanid species were collected from the Shimba Hills National Reserve, while collections from Uganda (around the shores of Lake Victoria) had the fewest number of species. However, the Ancala genus was found in Uganda, but not in Kenya or Tanzania. Maximum likelihood phylogenies of mitochondrial cytochrome c oxidase 1 (COI) genes sequenced in this study show definite concordance with morphological species identifications, except for Atylotus. This survey will be critical to building a complete checklist of Tabanidae prevalent in the region, expanding knowledge of these important vectors of human and animal diseases.

Spatial and temporal epidemiological analysis in the Big Data era

Concurrent with global economic development in the last 50 years, the opportunities for the spread of existing diseases and emergence of new infectious pathogens, have increased substantially. The activities associated with the enormously intensified global connectivity have resulted in large amounts of data being generated, which in turn provides opportunities for generating knowledge that will allow more effective management of animal and human health risks. This so-called Big Data has, more recently, been accompanied by the Internet of Things which highlights the increasing presence of a wide range of sensors, interconnected via the Internet. Analysis of this data needs to exploit its complexity, accommodate variation in data quality and should take advantage of its spatial and temporal dimensions, where available. Apart from the development of hardware technologies and networking/communication infrastructure, it is necessary to develop appropriate data management tools that make this data accessible for analysis. This includes relational databases, geographical information systems and most recently, cloud-based data storage such as Hadoop distributed file systems. While the development in analytical methodologies has not quite caught up with the data deluge, important advances have been made in a number of areas, including spatial and temporal data analysis where the spectrum of analytical methods ranges from visualisation and exploratory analysis, to modelling. While there used to be a primary focus on statistical science in terms of methodological development for data analysis, the newly emerged discipline of data science is a reflection of the challenges presented by the need to integrate diverse data sources and exploit them using novel data- and knowledge-driven modelling methods while simultaneously recognising the value of quantitative as well as qualitative analytical approaches. Machine learning regression methods, which are more robust and can handle large datasets faster than classical regression approaches, are now also used to analyse spatial and spatio-temporal data. Multi-criteria decision analysis methods have gained greater acceptance, due in part, to the need to increasingly combine data from diverse sources including published scientific information and expert opinion in an attempt to fill important knowledge gaps. The opportunities for more effective prevention, detection and control of animal health threats arising from these developments are immense, but not without risks given the different types, and much higher frequency, of biases associated with these data.

Implications of temperature variation for malaria parasite development across Africa

Temperature is an important determinant of malaria transmission. Recent work has shown that mosquito and parasite biology are influenced not only by average temperature, but also by the extent of the daily temperature variation. Here we examine how parasite development within the mosquito (Extrinsic Incubation Period) is expected to vary over time and space depending on the diurnal temperature range and baseline mean temperature in Kenya and across Africa. Our results show that under cool conditions, the typical approach of using mean monthly temperatures alone to characterize the transmission environment will underestimate parasite development. In contrast, under warmer conditions, the use of mean temperatures will overestimate development. Qualitatively similar patterns hold using both outdoor and indoor temperatures. These findings have important implications for defining malaria risk. Furthermore, understanding the influence of daily temperature dynamics could provide new insights into ectotherm ecology both now and in response to future climate change.

Global mapping of infectious disease

The primary aim of this review was to evaluate the state of knowledge of the geographical distribution of all infectious diseases of clinical significance to humans. A systematic review was conducted to enumerate cartographic progress, with respect to the data available for mapping and the methods currently applied. The results helped define the minimum information requirements for mapping infectious disease occurrence, and a quantitative framework for assessing the mapping opportunities for all infectious diseases. This revealed that of 355 infectious diseases identified, 174 (49%) have a strong rationale for mapping and of these only 7 (4%) had been comprehensively mapped. A variety of ambitions, such as the quantification of the global burden of infectious disease, international biosurveillance, assessing the likelihood of infectious disease outbreaks and exploring the propensity for infectious disease evolution and emergence, are limited by these omissions. An overview of the factors hindering progress in disease cartography is provided. It is argued that rapid improvement in the landscape of infectious diseases mapping can be made by embracing non-conventional data sources, automation of geo-positioning and mapping procedures enabled by machine learning and information technology, respectively, in addition to harnessing labour of the volunteer ‘cognitive surplus’ through crowdsourcing.

Influence of climate on malaria transmission depends on daily temperature variation

Malaria transmission is strongly influenced by environmental temperature, but the biological drivers remain poorly quantified. Most studies analyzing malaria-temperature relations, including those investigating malaria risk and the possible impacts of climate change, are based solely on mean temperatures and extrapolate from functions determined under unrealistic laboratory conditions. Here, we present empirical evidence to show that, in addition to mean temperatures, daily fluctuations in temperature affect parasite infection, the rate of parasite development, and the essential elements of mosquito biology that combine to determine malaria transmission intensity. In general, we find that, compared with rates at equivalent constant mean temperatures, temperature fluctuation around low mean temperatures acts to speed up rate processes, whereas fluctuation around high mean temperatures acts to slow processes down. At the extremes (conditions representative of the fringes of malaria transmission, where range expansions or contractions will occur), fluctuation makes transmission possible at lower mean temperatures than currently predicted and can potentially block transmission at higher mean temperatures. If we are to optimize control efforts and develop appropriate adaptation or mitigation strategies for future climates, we need to incorporate into predictive models the effects of daily temperature variation and how that variation is altered by climate change.

Quantifying aggregated uncertainty in Plasmodium falciparum malaria prevalence and populations at risk via efficient space-time geostatistical joint simulation

Risk maps estimating the spatial distribution of infectious diseases are required to guide public health policy from local to global scales. The advent of model-based geostatistics (MBG) has allowed these maps to be generated in a formal statistical framework, providing robust metrics of map uncertainty that enhances their utility for decision-makers. In many settings, decision-makers require spatially aggregated measures over large regions such as the mean prevalence within a country or administrative region, or national populations living under different levels of risk. Existing MBG mapping approaches provide suitable metrics of local uncertainty—the fidelity of predictions at each mapped pixel—but have not been adapted for measuring uncertainty over large areas, due largely to a series of fundamental computational constraints. Here the authors present a new efficient approximating algorithm that can generate for the first time the necessary joint simulation of prevalence values across the very large prediction spaces needed for global scale mapping. This new approach is implemented in conjunction with an established model for P. falciparum allowing robust estimates of mean prevalence at any specified level of spatial aggregation. The model is used to provide estimates of national populations at risk under three policy-relevant prevalence thresholds, along with accompanying model-based measures of uncertainty. By overcoming previously unchallenged computational barriers, this study illustrates how MBG approaches, already at the forefront of infectious disease mapping, can be extended to provide large-scale aggregate measures appropriate for decision-makers.

Reliable disease maps can support rational decision making. These maps are often made by interpolation: taking disease data from field studies and predicting values for the gaps between the data to make a complete map. Such maps always contain uncertainty, however, and measuring this uncertainty is vital so that the reliability of risk maps can be determined. A modern approach called model-based geostatistics (MBG) has led to increasingly sophisticated ways of mapping disease and measuring spatial uncertainty. Many health management decisions are made for administrative areas (e.g., districts, provinces, countries) and disease maps can support these decisions by averaging their values over the regions of interest. Carrying out this aggregation in conjunction with MBG techniques has not previously been possible for very large maps, however, due largely to the computational constraints involved. This study has addressed this problem by developing a new algorithm and allows aggregation of a global MBG disease map over very large areas. It is used to estimate Plasmodium falciparum malaria prevalence and corresponding populations at risk worldwide, aggregated across regions of different sizes. These estimates are a cornerstone for disease burden estimation and are provided in full to facilitate that process.

Integrated mapping of neglected tropical diseases: epidemiological findings and control implications for northern Bahr-el-Ghazal State, Southern Sudan

Background

There are few detailed data on the geographic distribution of most neglected tropical diseases (NTDs) in post-conflict Southern Sudan. To guide intervention by the recently established national programme for integrated NTD control, we conducted an integrated prevalence survey for schistosomiasis, soil-transmitted helminth (STH) infection, lymphatic filariasis (LF), and loiasis in Northern Bahr-el-Ghazal State. Our aim was to establish which communities require mass drug administration (MDA) with preventive chemotherapy (PCT), rather than to provide precise estimates of infection prevalence.

Methods and Findings

The integrated survey design used anecdotal reports of LF and proximity to water bodies (for schistosomiasis) to guide selection of survey sites. In total, 86 communities were surveyed for schistosomiasis and STH; 43 of these were also surveyed for LF and loiasis. From these, 4834 urine samples were tested for blood in urine using Hemastix reagent strips, 4438 stool samples were analyzed using the Kato-Katz technique, and 5254 blood samples were tested for circulating Wuchereria bancrofti antigen using immunochromatographic card tests (ICT). 4461 individuals were interviewed regarding a history of ‘eye worm’ (a proxy measure for loiasis) and 31 village chiefs were interviewed regarding the presence of clinical manifestations of LF in their community. At the village level, prevalence of Schistosoma haematobium and S. mansoni ranged from 0 to 65.6% and from 0 to 9.3%, respectively. The main STH species was hookworm, ranging from 0 to 70% by village. Infection with LF and loiasis was extremely rare, with only four individuals testing positive or reporting symptoms, respectively. Questionnaire data on clinical signs of LF did not provide a reliable indication of endemicity. MDA intervention thresholds recommended by the World Health Organization were only exceeded for urinary schistosomiasis and hookworm in a few, yet distinct, communities.

Conclusion

This was the first attempt to use an integrated survey design for this group of infections and to generate detailed results to guide their control over a large area of Southern Sudan. The approach proved practical, but could be further simplified to reduce field work and costs. The results show that only a few areas need to be targeted with MDA of PCT, thus confirming the importance of detailed mapping for cost-effective control.

Integrated control of neglected tropical diseases (NTDs) is being scaled up in a number of developing countries, because it is thought to be more cost-effective than stand-alone control programmes. Under this approach, treatments for onchocerciasis, lymphatic filariasis (LF), schistosomiasis, soil-transmitted helminth (STH) infection, and trachoma are administered through the same delivery structure and at about the same time. A pre-requisite for implementation of integrated NTD control is information on where the targeted diseases are endemic and to what extent they overlap. This information is generated through surveys that can be labour-intensive and expensive. In Southern Sudan, all of the above diseases except onchocerciasis require further mapping before a comprehensive integrated NTD control programme can be implemented. To determine where treatment for which disease is required, integrated surveys were conducted for schistosomiasis, STH infection, LF, and loiasis, throughout one of ten states of the country. Our results show that treatment is only required for urinary schistosomiasis and STH in a few, yet separate, geographical area. This illustrates the importance of investing in disease mapping to minimize overall programme costs by being able to target interventions. Integration of survey methodologies for the above disease was practical and efficient, and minimized the effort required to collect these data.

The co-distribution of Plasmodium falciparum and hookworm among African schoolchildren

Background

Surprisingly little is known about the geographical overlap between malaria and other tropical diseases, including helminth infections. This is despite the potential public health importance of co-infection and synergistic opportunities for control.

Methods

Statistical models are presented that predict the large-scale distribution of hookworm in sub-Saharan Africa (SSA), based on the relationship between prevalence of infection among schoolchildren and remotely sensed environmental variables. Using a climate-based spatial model of the transmission potential for Plasmodium falciparum malaria, adjusted for urbanization, the spatial congruence of populations at coincident risk of infection is determined.

Results

The model of hookworm indicates that the infection is widespread throughout Africa and that, of the 179.3 million school-aged children who live on the continent, 50.0 (95% CI: 48.9–51.1) million (27.9% of total population) are infected with hookworm and 45.1 (95% CI: 43.9–46) million are estimated to be at risk of coincident infection.

Conclusion

Malaria and hookworm infection are widespread throughout SSA and over a quarter of school-aged children in sub-Saharan Africa appear to be at risk of coincident infection and thus at enhanced risk of clinical disease. The results suggest that the control of parasitic helminths and of malaria in school children could be viewed as essential co-contributors to promoting the health of schoolchildren.

Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa

A Bayesian geostatistical model was developed to predict the intensity of infection with Schistosoma mansoni in East Africa. Epidemiological data from purpose-designed and standardized surveys were available for 31,458 schoolchildren (90% aged between 6 and 16 years) from 459 locations across the region and used in combination with remote sensing environmental data to identify factors associated with spatial variation in infection patterns. The geostatistical model explicitly takes into account the highly aggregated distribution of parasite distributions by fitting a negative binomial distribution to the data and accounts for spatial correlation. Results identify the role of environmental risk factors in explaining geographical heterogeneity in infection intensity and show how these factors can be used to develop a predictive map. Such a map has important implications for schisosomiasis control programmes in the region.