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      Neglected Tropical Diseases in the Anthropocene: The Cases of Zika, Ebola, and Other Infections

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      PLoS Neglected Tropical Diseases

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          Abstract

          While we advance through a geological epoch that increasingly reflects human intervention on a massive scale, we might expect to see the continued expansion of epidemic neglected tropical diseases, as we have recently seen for Zika and Ebola virus infections. Emerging evidence indicates that the Holocene, our most recent geological epoch that began at the end of the last ice age almost 12,000 years ago, has undergone some fundamental changes because of human activity. Since the origins of agriculture and deforestation and later accelerating with the industrial revolution, followed by rapid 20th century population growth extending into the nuclear age, our planet has undergone a fundamental and seemingly irreversible geological shift [1]. According to many (but not all) prominent Earth scientists, humans have profoundly altered the planet, thereby ushering in a new and so-called Anthropocene epoch (Fig 1). 10.1371/journal.pntd.0004648.g001 Fig 1 Geological epochs over the last 5 million years. In a January 2016 article in Science, Colin Waters from the British Geological Survey and his colleagues provide important geochemical evidence to support designating the end of the Holocene as the Anthropocene [1]. It includes data showing increasing lead levels after World War II, altered soil nitrogen and phosphorous levels because of increased fertilizer use, and the appearance of newly created radionuclides, beginning with the atomic bomb tests in the New Mexico desert at Los Alamos [1]. Alongside these human-induced geochemical signatures are elevated carbon dioxide and methane levels and sharp increases in average global temperatures [1]. Levels of concrete and plastic have also dramatically increased in recent years, while in parallel, there has been massive loss of animal and plant species [2]. Species extinctions have reached unprecedented levels [1,3]. In this late Anthropocene epoch, we have seen significant increases in the incidence or prevalence rates of several neglected tropical diseases (NTDs), due partly or mostly to human-induced changes to our planet. This is especially true for NTDs transmitted by invertebrate vectors, including mosquitoes, kissing bugs, and snails, as well as highly lethal zoonotic virus infections from bats and other mammals. For example, in the Americas, dengue fever reemerged in the 1980s, while chikungunya and Zika virus infections have aggressively spread across the Latin American and Caribbean region. Venezuela in particular has seen dramatic increases in malaria and most of its neglected tropical diseases (NTDs), including Chagas disease, schistosomiasis, and Zika virus infection, for which unprecedented urban foci are also occurring [4]. Across the Atlantic Ocean, Southern Europe has of late seen the emergence or reemergence of malaria in Greece, West Nile virus infection and chikungunya in Italy and Spain, dengue in Portugal, and schistosomiasis on the French island of Corsica [5]. The Middle East and North Africa (MENA) region is now considered one of the worst-affected global hotspots for NTDs and other emerging infections such as leishmaniasis, schistosomiasis, and MERS coronavirus infection; measles and polio have also returned [6]. Ebola caused thousands of deaths and overwhelmed the health systems of Guinea, Liberia, and Sierra Leone in West Africa in 2014–2015 [7], while East Africa and the Sahel are considered among the most important regions for kala-azar and multiple other NTDs [8]. Schistosomiasis continues to increase throughout Africa, where it is now a major cofactor in its AIDS epidemic [9]. Southeast Asia has seen the rise of Nipah and Hendra virus from bats, in addition to drug resistant malaria, enterovirus 71, melioidosis, and foodborne trematodiases transmitted by snails [10]. Several human activities that characterize the Anthropocene account for the increases in NTDs. It is instructive to see how some of these factors illustrated in Fig 2 helped to facilitate the emergence of two of the most devastating NTDs in 2014 and 2015—Ebola and Zika virus infections, respectively, as well as other high-disease-burden NTDs such as the cutaneous and visceral forms of leishmaniasis and schistosomiasis. 10.1371/journal.pntd.0004648.g002 Fig 2 The major forces arising out of the Anthropocene now promoting the emergence of catastrophic neglected tropical diseases (NTDs). Poverty and Blue Marble Health As has been said very frequently in our editorials, poverty is front and center. NTDs are most common in the setting of poverty [11,12], while simultaneously helping to perpetuate poverty through their long-standing negative effects on maternal and child health and human productivity and labor [13,14]. Ebola has so far emerged almost exclusively in impoverished nations such as the Democratic Republic of Congo or Guinea, Liberia, and Sierra Leone, while Zika is disproportionately affecting impoverished areas such as Brazil’s poorest northeastern provinces. In the case of Zika or other vector-borne NTDs such as leishmaniasis and Chagas disease, poverty equates to poor quality housing, in addition to uncollected garbage and standing water in poor neighborhoods that allow certain insects to breed nearby. For these reasons, we might expect poor countries such as Haiti or Jamaica to suffer greatly from the advance of Zika in the Caribbean region. Yet another feature of Zika, leishmaniasis, Chagas disease, and other NTDs are their propensity to strike the poorest people who live in the wealthier group of 20 countries, such as Brazil or Mexico. The concept of “blue marble health” has been invoked to describe the surprising disease burden of NTDs among the poor living in these countries [15]. Today, most of the world’s NTDs paradoxically occur in the world’s largest economies, but mostly among the disenfranchised poor in those nations [15]. Political Destabilization, Conflict, and Post-conflict Next to poverty, these forces may account for the largest risk factor for NTDs [6,16,17]. The long-standing atrocities and civil and international conflicts decimated the health systems of Guinea, Liberia, and Sierra Leone, thereby allowing Ebola and Lassa fever to flourish [7,17], while these same forces facilitated the rapid spread and lethality of human African trypanosomiasis and kala-azar in Africa [8,18]. Conflict and post-conflict settings are central to the massive epidemic of cutaneous leishmaniasis in the Middle East [6]. While Zika has so far not been linked to these factors, the political destabilization in Venezuela could become a contributory factor. Deforestation In Asia, deforestation may have increased human and bat contact to promote Nipah and Hendra viruses and SARS [10]. Deforestation is also an important factor promoting the expansion of vector borne NTDs, including leishmaniasis [19]. Deforestation has been noted to have some possible links with the emergence of Ebola [20]. For instance, the Guinea forest region where Ebola emerged in 2014 has been severely and adversely affected by clear-cut logging [7]. Dams Bodies of fresh water arising from large-scale hydroelectric projects can help aquatic snails to proliferate, including those that transmit schistosomiasis in Africa and foodborne trematodiases in Asia. Dams and newly formed reservoirs of fresh water also create mosquito breeding sites for arbovirus infections and malaria and also facilitate waterborne intestinal infections [21]. In China, on the other hand, the Three Gorges Dam on the Yangtze River has assisted flood control and so far has not been shown to promote the emergence of Schistosoma japonicum infection [22]. Urbanization and Human Migrations The large-scale movement of human populations into cities can create crowded conditions, which together with destruction of the environment favor arthropod vectors [23], including the Aedes aegypti mosquito that transmits Zika, dengue, chikungunya, and yellow fever. Accelerated urbanization that outpaces sanitation and sewage control infrastructures is also a key factor in the endemicity of leptospirosis and enteric NTDs [24,25]. Overall, human migrations of immunologically naïve populations to endemic regions have accounted for rapid spread of infections, as well as the converse—infected populations introducing new diseases. The 2014 Brazilian FIFA World Cup soccer games may have been a factor in bringing Asian populations with Zika into areas where Aedes aegypti is found, although this hypothesis has since been dismissed [26]. However, the annual Hajj, the Muslim pilgrimage to Mecca that brings millions of people to Saudi Arabia, has also been postulated as having helped introduce dengue [27,28], as it could Zika virus infection in 2016 or 2017. The Hajj has also had a role in the emergence of meningococcal disease and other acute respiratory infections [29,30]. Climate Change and El Niño Events Major climate change events in the Anthropocene include increased temperatures and altered rainfall patterns that expand arthropod vector habitats and ranges. Such factors could be responsible for accelerating the geographic expansions of arbovirus infections [31,32], leishmaniasis [33], and Chagas disease [34], but this concept requires further exploration. In addition, we are just beginning to understand the role of El Niño events in promoting these and other NTDs [35,36]. The fact that Zika virus expanded dramatically in an El Niño year is also of interest, but as yet, there are no proven links. Many of the factors highlighted above were either manufactured or shaped by human activity. The dramatic expansions in the number of cases of arbovirus infections caused by dengue, chikungunya, and now Zika in recent years, together with a recent explosive Ebola outbreak in 2014–2015, give us pause to evaluate human influence on the biosphere and to recognize that beyond globalization, the Anthropocene could become a dominant theme for spreading NTDs or creating catastrophic human epidemics in the years to come.

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          The Role of Human Movement in the Transmission of Vector-Borne Pathogens

          Introduction For vector-borne pathogens heterogeneity in patterns of contact between susceptible hosts and infectious agents is common [1],[2],[3]. Some hosts will be exposed to, harbor, and pass on more parasites than others. Variation in contact patterns can amplify [4],[5] or dampen [6] the rate of transmission, even as it also potentially reduces disease prevalence and epidemic stability (i.e., likelihood of an outbreak; [7]). Understanding and describing what drives heterogeneous contact patterns is thus important for designing improved disease surveillance and prevention programs [3]. If the characteristics of hosts most often infectious or important for transmission are known they could be targeted to more efficiently prevent disease [8]. To be useful for targeted control across different contexts the mechanisms underlying heterogeneous contact patterns must be elucidated. Here we examine the role of individual human movement as a critical behavioral factor underlying observed patterns of vector-borne pathogen transmission, because movement determines exposure to infectious agents; i.e., bites from infected mosquito vectors. Little is known about individual human movement patterns and even less about their epidemiological consequences, even though such knowledge would be a valuable contribution to the understanding and control of many vector-borne diseases. We begin our investigation of this topic by reviewing studies of human movement. Next, based on an existing typology, we examine the relevance of movement patterns to the dynamics of different diseases. Using the mosquito-borne virus dengue as an example, we develop a conceptual model that illustrates how human and vector behavior can influence pathogen transmission dynamics. We end by outlining key issues important to the design of future research and explaining potential benefits to disease prevention of an improved understanding of host movement. A Framework: Movement and Scale Historically epidemiologists have viewed human movement from the perspective of populations of susceptible hosts moving into high risk areas or infected hosts moving into susceptible populations as explanation for disease occurrence and spread. Indeed, across different scales and diseases, movements of hosts affect pathogen transmission in a variety of ways. Thirty years ago Prothero [9] provided a typology to facilitate study of the role of human movements in epidemiology based on his experience in Africa. Drawing on geography literature concerned with understanding human movement [10],[11],[12], Prothero highlighted the difference between circulatory movements, where individuals return home after some period, and migratory movements, which tend to be permanent changes of residence (see Figure 1 in [11]). He further characterized movements by their ‘spatial scale’, which he categorized in terms of a rural-urban gradient, and temporal scale based on the time and timing of displacements. He qualified these categories in terms of their relevance to public health. For instance, seasonal movements from one rural area to another for agriculture could potentially expose individuals to different ‘ecological zones’ where the risk of malaria or African trypanosomiasis is high [13]. His argument was that knowing something about the nature of such movements would help explain the incidence and prevalence of disease in a population and provide informed options for control [9]. In Figure 1 we generalize Prothero's typology in terms of the spatial and temporal scale (sensu [14]) of human movement and extend it to include most vector-borne disease contexts. 10.1371/journal.pntd.0000481.g001 Figure 1 A framework for human movements and their relevance to vector-borne pathogen transmission. Movements are characterized in terms of their spatial and temporal scale, which are defined in terms of physical displacement (Δxy) and time spent (Δt, frequency and duration). Generally, movements of greater spatial displacement involve more time, but this is not necessarily always the case. At broad spatial scales (e.g., national, international) individual movements drive pathogen introduction and reintroduction (far right, Figure 1). Global spread of dengue virus via shipping routes was characterized by periodic, large, spatial displacements [15]. Globalization and air transportation have changed the dynamic of pathogen spread by dramatically shortening the time required to travel around the globe [16],[17],[18]. The recent chikungunya epidemic in the Indian Ocean that subsequently spread to Italy is an example [19]. At finer scales (e.g., regional, urban-rural, intra-urban; far left of Figure 1), movement associated with work, recreation, transient migration, and other phenomena is important to patterns of pathogen transmission and spread [9],[20]. Movements into high-risk areas not only lead to individual infection, but can also contribute to local transmission when infected hosts return home and infect competent vectors. For example, in the Chocó region of Colombia most malaria transmission occurs in rural areas and many cases diagnosed in the city of Quibdó are due to travel to these areas [21]. Transmission also occurs locally within Quibdó [22], however, most likely because of infected travelers returning and infecting competent vectors. Understanding the origin of infections and the relative importance of human movement at different scales to both local and regional transmission dynamics would increase effectiveness of disease prevention programs by, for example, identifying individuals at greatest risk of contracting and transmitting pathogen. Generally, a key significance of human movement for vector-borne disease at any scale lies with exposure to vectors. Exposure is local in space and time and variation in exposure due to individual host movement could strongly influence the transmission dynamics of pathogens. For instance, circulatory movements associated with working in rural areas and variation in movement patterns among cultures may explain heterogeneous patterns of onchocerciasis incidence. While men in Cameroon and Guatemala both experience similar parasite loads reflecting exposure to vectors when working in fields, women in the 2 countries show different patterns of infection partly due to differences in exposure [23]. The type of movement most relevant for exposure will depend on site specific differences, the ecology of the arthropod vector, human behavior, and the relative scale of host and vector movement. For pathogens transmitted by vectors able to move long distances in search of a host, fine scale host movements may not be important, while they are for pathogens transmitted by sessile vectors. Aedes aegypti—the principal vector of dengue virus—bites during the day [24], disperses only short distances [25] and is heterogeneously distributed within urban areas [26],[27]. Conversely, humans move frequently at local scales (bottom-left of Figure 1), allocating different amounts of time to multiple locations on a regular basis. This will influence individual risk of infection with dengue virus [28] and thus overall patterns of transmission [29],[30],[31]. Methods The dynamics of human movement, the locations used and the paths between them, is conceptualized by the ‘activity space’ model developed in the 60's by human geographers [12],[32],[33]. This model, much like the ‘home-range’ concept in ecology, is effective because organisms exhibit habitual behavior in their use of space [34]. For our purposes of studying dengue, the ‘activity space’ refers to those few locations where humans commonly spend most of their time [32],[35] and ‘movement’ refers to the use of these locations. Thus, exposure to host-seeking female Ae. aegypti is the sum of exposure across an individual's activity space. For other vectors and pathogens, human movements per se (e.g., walking between the house and a water source) and/or visits to less common destinations could be relevant for the transmission of other pathogens (e.g., African trypanosomiasis) depending on the behavior of the vector and the relative scales of vector and host movement. The activity space model represents movement associated with the regular activity of individuals [36]. We present a version of this model in Figure 2 for understanding how movements within an urban area might contribute to risk of exposure. Risk at locations within an individual person's activity space will vary depending on the number of infected, host seeking vectors present and their biting behavior. For instance, visits to locations during the day are of minimal risk for bites from nocturnal An. gambiae, but are relatively high for day active Ae. aegypti (Figure 2). Exposure to vector bites may also depend on how long a person stays at a given location. If vectors are stimulated by the arrival of an individual to a location (as may be the case for Ae. aegypti and Aedes albopictus [37],[38]), then a bite is most likely to occur early after arrival (i.e. the cumulative probability of a bite during a visit, e(t), accumulates rapidly). Alternatively, for vectors like triatomine bugs, which are less opportunistic than mosquitoes, long visits will be expected to pose a higher risk of host-vector contact (e(t) slowly accumulates over time). How vectors respond to hosts arriving at a site is important because it weights the risk of visits differently depending on their frequency and duration. If a vector is stimulated to host seek by the arrival of a host, then multiple short visits to that site will carry greater risk than a single long visit of equivalent total duration. 10.1371/journal.pntd.0000481.g002 Figure 2 The activity space model. Space is plotted in the xy plane and time on the z axis. In this example daily movements for a week are represented. Points in the xy plane are sites visited and the polygon depicts the activity area. Vertical arrows indicate time spent at a site. Thickness of arrows indicates frequency of visitation and length shows duration. Red arrows are for the home and here we assume a person is in the home every night of the week. Dashed lines represent movement between sites with velocity indicated by the angle of the line. Grayed-out regions of the cube represent night-time. Not shown is variation in vector abundance among sites. Plotted along the back axis for time are representative curves of biting rates, a(t), for Ae. aegypti (green), a day biting mosquito, and Anopheles gambiae (black), a night biting mosquito. Plotted to the right of the large black arrow is a cumulative biting probability, e(t), as a function of time spent in the location. See text for more detail. In summary, a person's risk of exposure to an infective vector can be represented with a simple exposure model for indirectly transmitted disease: (1) Here, the risk of exposure (i.e., being bitten by a vector) for individual i, ri , over some observation period is simply the sum across sites visited, j, of vector abundance, Vj , conditioned on the time and duration of all visits to that site, k, as determined by vector behavior (where K is the total number of visits during the observation period). The biting rate, ak , is the number of bites expected per visit and is drawn from the day biting rate distribution for the times of the visit. (2) How vectors respond to the appearance of a host at a site is captured by ek , the cumulative probability of a bite given the time spent in the site, and is bounded by the unit interval. (3) Visits, k, are defined by an arrival time, t0 , and a departure time, t1 , in hours and are in reference to a single day. At the limit (where t1 −t0  = 24 hours), ak becomes the day biting rate, a, and ek goes to 1 and we recover the model often assumed for vector-borne diseases where exposure occurs in the household. Note that although we imply here that a site comprises a household or other edifice because of our focus on dengue, in truth it simply demarcates a location where the abundance and activity of vectors is independent of other locations and is defined by the scale of vector movement. Site-specific exposure risk is calculated as: (4) and has units of bites*humans for the observation period. Note that in this formulation, risk among individuals using the same site is assumed to be independent (i.e., the expected number of bites at a site is the product of humans present and vector activity). This may not be realistic if hosts occupy a site at the same time, which would be expected to dilute the number of bites individual hosts receive, and can be corrected (see below) by incorporating the actual amount of time individual humans spend in a location. The estimate of risk, rj , can be used to estimate the transmission rate, R0 , which is the number of secondary infections expected from the introduction of a single infective individual into a wholly susceptible population. Woolhouse et al. (1997) use the following approximation for R0 : (5) where vj is the proportion of vectors at site j, hj is the proportion of hosts living in site j, and J is the total number of sites. Risk as estimated above is incorporated by replacing vj with site associated risk, rj , discounted by the proportional use of that site within some interval by people, hj : (6) For example, if a site is used by 2 individuals for 6 hours each over a week, hj  = (2 humans * 6 hours)/(24 hours/day * 7 days) = 0.07 humans. The activity space model elaborated here illustrates that host and vector behavior are very important for determining who gets bitten and has the greatest risk of contracting or transmitting a pathogen. Results The activity space model when coupled with our knowledge of vector behavior provides a tool for determining what human movements are important for transmission (e.g., Figure 1). Specifically, it allows us to identify places and individuals that contribute disproportionately to pathogen transmission dynamics. For example, consider the following scenario depicted in Figure 3 for a human population at risk for dengue virus infection like the one we are studying in Iquitos, Peru (Figure 3, Text S1 and Table S1). Briefly, individuals spend their time at a number of different sites, both commercial and residential, during their regular weekly activities (Sites, first column in Figure 3). Sites have different numbers of female mosquitoes and are visited at different rates and for different durations. We can estimate the risk of exposure to host-seeking female mosquitoes (ri ) for each person (columns 1–13 in Figure 3) at each site (rows in Figure 3) and then estimate R0 . In this particular example, R0 as approximated when accounting only for the home (eq. 5) is 1.3 and the site with the highest estimated risk is house 5 (in bold in column under R0 ). If we account for exposure at all locations in addition to the home and assume the biting rate at night is 10% of the rate during the day [39], our estimate of R0 (eq. 6) jumps nearly 3-fold and the most important site is 13, a clinic (in bold under R0e). This latter result arises because of the relatively large number of bites per person expected at that site, determined largely by the significant amount of time a single person spends there (e.g., their workplace). In this example, all individuals except individual 10 experience the greatest exposure to bites in their homes because that is where they spend the most time. Individual 10, however, experiences the highest risk at site 4, which represents their workplace. This individual is also at the greatest risk in the host population. 10.1371/journal.pntd.0000481.g003 Figure 3 Example scenario of risk of exposure due to individual movements. Individuals (i, represented by columns) live in and visit a number of sites (j, rows) for different durations and frequencies during a regular week. Each site is infested with a number of female mosquitoes, V. Grey shading indicates the home of each individual. Risk of a mosquito bite, ri , is calculated as described in the text and is presented here for each individual given the number of visits and time spent at different locations during a typical week. Numbers in bold are maxima for each column. Here the probability of a mosquito bite at night (in the home) is assumed to be 10% of all other times. The sum of individual risk is shown along the bottom of the figure. Overall transmission rate estimated without, R0 , and with exposure, R0 e, considered are shown in the bottom-right and underlined. See Text S1 and Table S1 for further details. This example illustrates that the key sites are not necessarily those of greatest vector abundance, as is commonly assumed. For this example scenario, R0j increases monotonically with vector abundance when transmission is assumed to occur only in the home (Figure 4). When exposure rates are accounted for, however, there is no relationship between R0j and vector abundance (Figure 4). Similarly, people living where vector abundance is greatest are not necessarily at greater risk. Human movement and subsequent variation in exposure thus becomes more important than vector density per se. Because heterogeneity in contact patterns has a large influence of the rate of pathogen transmission, variation in exposure rates due to individual movement patterns could have considerable impact on disease dynamics [40],[41]. 10.1371/journal.pntd.0000481.g004 Figure 4 Estimates of R0 plotted against vector density at sites. R0 is calculated assuming exposure occurs only within homes, R0 e is calculated taking exposure rates into account based on representative activity patterns of several hypothetical individuals living in a community like Iquitos, Peru, where we are studying dengue transmission (Figure 3). Discussion To fully understand the implications of movements, however, data should be incorporated into network, individual-based or metapopulation models [5],[42],[43]. Network models, in particular, capture heterogeneity explicitly and intuitively, allowing precise prediction of trends and patterns in human infection and disease [44]. For dengue, one imagines a dynamic network of individuals most likely to become infected or infect mosquitoes and of locations where transmission is most likely to occur [29]. These are the key nodes of pathogen transmission that, if identified and understood, would be excellent targets for intervention (e.g., [8]). The value of estimating actual exposure rates and incorporating these into models to better understand pathogen dynamics is clear for dengue, which is mostly transmitted when people are engaged in daily activities [29]. For this reason we are currently monitoring human movements in Iquitos, Peru. The activity space model as we describe it, however, highlights that movements may be important for the transmission of many pathogens typically thought to be transmitted at night when hosts are inactive. Sand fly vectors of American visceral leishmaniasis are active at dusk [45], move short distances [46], and are heterogeneously distributed among homes [47], which, in combination with human behavior, may be key to understanding leishmaniasis incidence patterns [48],[49]. Similarly, Michael et al. [50] found that 27% of Culex quinquefasciatus resting within households had fed on hosts from outside that home despite its nocturnal habit, with implications for transmission of lymphatic filariasis. There are thus many reasons for increased examination of individual human movement patterns. Measuring Movements As an aid to future research, in the remainder of this article we discuss key issues and considerations for designing studies of human movement based on our experiences with dengue. Spatial scale The first question to ask when one seeks to measure human movements and evaluate their role in pathogen transmission concerns spatial scale. This can be determined by the disease dynamic of interest; e.g., spread of a pathogen to new geographic areas vs. sustained transmission at a given locale. If the question concerns local transmission, then relevant movements will be those placing susceptible hosts in high risk locations at times when infection risk is high. General information about a particular system may guide this process. Assumptions regarding the importance of movements should be made cautiously because heterogeneity in exposure can have a dramatic effect on infection risk. Type of movement Next, one should ask what to measure. The term ‘movement’ is used somewhat ambiguously. Are we interested in just the sites where individuals spend their time on a regular basis (high spatial and temporal resolution) or whether they are in the home/city or elsewhere? Do we want travel information (outside of an urban area) that specifies exactly where people go or just a general notion? Are specific routes important, or should only destinations be considered? These details will, again, depend on the question, system, and resources and methods for measuring movements. Where we work in Peru, dengue transmission is primarily focused in urban areas of Iquitos and the mosquito vector, Ae. aegypti, is not found in the majority of rural areas outside of the city. As such, we are comfortable excluding movements to rural areas because people are unlikely to be infected there. We only need know that they were not in Iquitos, and where they were is only important if that location has dengue as well. If we were studying malaria, we might do the opposite and ignore movements within urban Iquitos where malaria is not transmitted. In our study of local dengue transmission we want high spatial and temporal resolution because Ae. aegypti cluster at the scale of individual households and bite during the day [26]. For malaria, regional movements to and from fishing or logging camps are a likely dynamic driving transmission patterns and simply knowing to which camps individuals move to on a periodic or seasonal basis and the routes taken should be sufficient to understand the spatial dynamics of that disease (G. Devine, personal communication). Measurement method A third question concerns how to measure movements. A number of methods and technologies are currently available that allow tracking of individual movements (Table 1). The choice of the appropriate method is dependent on the scale of the study and the disease in question. If the scale of interest is broad, then data from transit networks may be suitable, as has been done in studies of the global spread of SARS and influenza [17],[51],[52]. For finer scales, lack of appropriate means for measuring movements is one reason so little has yet been done in a rigorous, quantitative fashion (Vasquez-Prokopec et al. unpublished). The technology has long been available in some form, but has proved too cumbersome and expensive for large scale use with humans. Indirect devices commonly used in the social sciences, such as travel diaries, are a good source of information when used rigorously, but have seen limited use in the study of indirectly transmitted pathogens, perhaps because of inherent bias and imperfect recall. 10.1371/journal.pntd.0000481.t001 Table 1 Methods for measuring human movement. Method Description Pros Cons Ideal use Recall Commonly used in studies of exercise and physical activity, in diary or close-ended formats Captures both quantitative and qualitative information; used internationally in chronic disease research. Subject to memory decay, social desirability, and other response biases. Have been used primarily in developed countries. Not as primary outcome but to validate and inform electronic instrumentation and other more objective measures Telemetry Commonly used in wildlife studies, involves a transmitter placed on an individual and antennas (fixed or mobile) for locating the transmitter. Can be inexpensive, long battery life of transmitters, well established method, range dependent. Short range, Difficult to get precise location information, expensive for large scale use (i.e. establishing an array of antennas), interference in urban areas. Wildlife diseases, not practical for use with humans. RFID Radio Frequency Identification Device, used to track inventories, individuals in hospitals. Involve a small ‘tag’ and an antenna to detect tag. Tag is very small, easy to wear, and battery lasts a very long time. Short range, requires network of antennas to track movements in an area, which can become expensive. Very good option for tracking movements to and from predefined locations, e.g., for movements to commonly used water sources. GPS Global Positioning System. Global, satellite-based, location aware system. Only requires a receiver, works everywhere, provides exact positional information, devices are becoming very small and inexpensive. Large data post-processing requirement, short battery life, custom devices are expensive while commercial options not tailored to research use. Reductions in cost and device size make GPS the best option for tracking movements where cellular phone use is not universal. GSM-GPS GSM assisted GPS. Devices use the GSM cellular network to improve the satellite signal and provide positional information when satellites are out of reach due to interference. Same as GPS with the additional benefit of location information inside buildings and other places the satellite signal cannot reach. Additional positional information depends on cellular network, feature requires data transmission, network fees and arrangements necessary, very short battery life. Because the additional advantage of these devices relies on a cellular network, either GPS or cellular phones will often be better options. Cellular phone The position of cellular phones can be approximated through triangulation using the cellular network. Where cellular phone use is universal, movement data can acquired from network providers without any inconvenience to study participants. Potential for bias (positions are recorded when phones are used), low spatial precision, requires network agreement, privacy issues, most individuals need personal phones. For large scale studies of the collective dynamics of populations, regional movements and movements within large metropolitan areas Cellular phone, AGPS Assisted-GPS on cellular phones works by the same mechanism as GSM-GPS, utilizing the cellular network to assist in acquiring positional information. High spatial precision, potential for high coverage where cellular phone use is common, no need to purchase devices. Dependent on cellular network, requires data transmission, may require custom software or other means to acquire data while avoiding privacy issues. Can be very expensive without a special arrangement with a network provider. Most useful for studying movements in developed countries were cellular network coverage is high and most people have personal phones. Also good for urban areas where GPS signal is imperfect. All available technologies have pros and cons (Table 1). GPS has often been considered to measure exposure, but because of cost, size, battery life, and other technical limitations has yet to be used incisively to study human movements (Vasquez-Prokopec et al. unpublished). Cellular phones hold promise where the technology is available and use is universal (e.g., [35]), but are awkward to use for prospective studies and in low-resource settings and come with privacy issues. GPS seems to hold the greatest potential for the combination of low cost, ease of use, spatial accuracy, and fewer privacy issues than cellular phones because only location information is recorded. We are currently using a GPS device in Iquitos, Peru that weighs less than 25 g, records for >3 days continuously, and is under $50 (Vasquez-Prokopec et al. unpublished). Size and battery life of tracking devices are critical in human studies because they are key to acceptance by participants for long term use (minimizing coverage bias, Paz Soldan et al., unpublished, [53]). Except for cellular phones owned by participants, any currently available device is only useful for prospective investigation. To evaluate the role of movements on disease dynamics retrospectively–that is, after identifying an infected individual–the options are limited. Cellular phones may be useful in certain contexts: e.g., where the technology is accepted and widely used (Table 1). Otherwise, instruments reliant on recall such as diaries, questionnaires, or interviews are required. These methods are imperfect, yet can provide valuable information when coupled with other tools. For instance, Geographic Information Systems permit production of detailed maps for a region that can be used to elicit recall of visits to certain sites (Paz Soldan et al. unpublished). Recall instruments should be sensitive to the local social and cultural contexts. As such, active collaboration with social scientists versed in the local culture is critical for the development of an interview device with sufficient sensitivity. Technologies such as GPS can be used to facilitate development of a recall instrument and to validate it. Location aware technologies, however, are not a gold standard for measuring movement because of precision and accuracy limitations, problems of compliance and use (Paz Soldan et al. unpublished), and other factors that can disrupt tracking (e.g., interference from buildings). Moreover, GPS does not indicate an individual's activity, which could be critical for determining risk [e.g., did they enter the house or stay on the sidewalk? 53]. Combining objective (e.g., GPS) and recall methods may be the best way to efficiently follow individual movements on a large scale and to qualify those movements with regard to disease risk. Observation interval A fourth question concerns how long to observe individual movements. The answer will depend on the question being asked and available resources. In the case of dengue, infection can occur up to 2 weeks prior to the manifestation of symptoms. For a retrospective study, 14–15 days would be the right observation period. Conversely, in a prospective study the length of the observation period will depend on the relative importance of rare movements. Studies of human movements in developed societies reveal markedly regular patterns, especially during the work-week [32],[35],[54],[55],[56]. Conversely, there may be significant instability in movements on weekends or at other times (e.g., vacations). For regular movements during the work week, at least 2 weeks of observation are needed. For more variable movements/times, substantially longer observation periods will be necessary [54]. The need for long-term observation reinforces the need to ensure acceptability of tracking devices by the study population and emphasizes the importance of device wearability (Paz Soldan et al. unpublished, Vasquez-Prokopec et al. unpublished). Data management Although gathering movement information is becoming more feasible, handling movement data remains a challenge [53]. GPS and other devices provide tracks of movements that must be processed into data useable in analyses: for example, the locations visited, frequency of visitation, and time spent during visits. Tools are becoming available to facilitate data processing (e.g., [57],[58],[59]) that integrate with existing GIS and statistical software packages (e.g., Arc-GIS, R). Such tools will facilitate data analysis. Conclusions Because patterns of contact between pathogens and susceptible hosts are heterogeneous, disease interventions can be made more effective and efficient by targeting the key points or ‘nodes’ of transmission [3]. Even where heterogeneous patterns are clearly documented, not knowing the factors driving such patterns impedes one's ability to effectively target control. Is a biting preference toward young adults [60] because they are intrinsically more attractive to a host-seeking mosquito or, because of their behavior, they are more likely to be exposed to mosquitoes? Although many different causes of host-vector contact heterogeneity have been proposed [summarized by 6], variation in exposure due to human behavior is likely to be key across disease systems. The role of other risk factors (e.g., host-preference) will always be conditioned by exposure rates. The study of human movement is thus critical to the identification of key individuals and key locations. Nevertheless, movements have largely been neglected in studies of indirectly transmitted disease even though it is becoming increasingly easy to measure. Quantifying and describing human movements promises more than just characterization of key heterogeneities. Quantification of the collective dynamics of human populations provides information necessary for models intended to predict disease outbreak and spread and to evaluate control alternatives to halt epidemics [8],[35],[51]. Buscarino et al. [61], for instance, predict that movements within a population have an important effect on the epidemic threshold, lowering this as individuals move over larger distances more frequently. Additionally, quantifying movements and applying that information to a variety of diseases creates the opportunity to identify common places where infection occurs across diseases and, thus, the potential to leverage public health programs by allowing limited resources to be targeted to the most important locations for more than one disease. Rigorous examination of the role of human movement across different scales will significantly improve understanding of pathogen transmission, which will be critical to increasing the effectiveness of disease prevention programs. As transmission rates are reduced through intervention efforts, we expect the importance of heterogeneity in exposure to increase and to play an even more important role in pathogen persistence. Characterization of movements will thus not only facilitate the elimination of disease, it will help to prevent its return. Supporting Information Text S1 Calculating individual risk. (0.06 MB DOC) Click here for additional data file. Table S1 Example space time budget from human movements. (0.05 MB XLS) Click here for additional data file.
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            Outbreak of Ebola Virus Disease in Guinea: Where Ecology Meets Economy

            Ebola virus is back, this time in West Africa, with over 350 cases and a 69% case fatality ratio at the time of this writing [1]. The culprit is the Zaire ebolavirus species, the most lethal Ebola virus known, with case fatality ratios up to 90%. The epicenter and site of first introduction is the region of Guéckédou in Guinea's remote southeastern forest region, spilling over into various other regions of Guinea as well as to neighboring Liberia and Sierra Leone (Figure 1). News of this outbreak engenders three basic questions: (1) What in the world is Zaire ebolavirus doing in West Africa, far from its usual haunts in Central Africa? (2) Why Guinea, where no Ebola virus has ever been seen before? (3) Why now? We'll have to wait for the outbreak to conclude and more data analysis to occur to answer these questions in detail, and even then we may never know, but some educated speculation may be illustrative. 10.1371/journal.pntd.0003056.g001 Figure 1 Map of the three countries (Guinea, Liberia, and Sierra Leone) involved in the 2013–2014 outbreak of Ebola virus disease as of June 20, 2014. The putative first virus introduction and epicenter are in the vicinity of the town of Guéckédou in the Guinea Forest Region. CDC: http://www.cdc.gov/vhf/ebola/resources/distribution-map-guinea-outbreak.html. The Ebolavirus genus is comprised of five species, Zaire, Sudan, Taï Forest, Bundibugyo, and Reston, each associated with a consistent case fatality and more or less well-identified endemic area (Figure 2). Zaire ebolavirus had been previously found only in three Central African countries—the Democratic Republic of the Congo, Republic of the Congo, and Gabon. Thus, the logical assumption when Ebola virus turned up in Guinea was that this would be the Taï Forest species previously noted in Guinea's neighbor, Côte d'Ivoire. 10.1371/journal.pntd.0003056.g002 Figure 2 African countries where endemic transmission of Ebola virus has been noted. How did Zaire ebolavirus get all the way over to West Africa? The two possibilities appear to be that the virus has always been present the region, but we just never noticed, or that it was recently introduced. The initial report and phylogenetic analyses on the Guinea outbreak suggested that the Zaire ebolavirus found in Guinea is a distinct strain from that noted in Central Africa [1], thus suggesting that the virus may not be a newcomer to the region. However, subsequent reworking and interpretations of the limited genetic data have cast some doubt on this conclusion [2]. If Zaire ebolavirus had been circulating for some time in Guinea, one might expect greater sequence variation than the 97% homogeneity noted relative to that isolated from Central Africa [1]. Phylogenetic arguments aside, if Ebola virus was present in Guinea, wouldn't we have seen cases before? Not necessarily. Many pathogens may be maintained in animals with which humans normally have little contact, thus providing limited opportunity for infection. Furthermore, the proportion of infected animals may often be very low, so even frequent contact may not result in pathogen transmission. Even if human Ebola virus infection has occurred, it may not be recognized; contrary to popular concept, the clinical presentation of viral hemorrhagic fever is often very nonspecific, with frank bleeding seen in a minority of cases, so cases may be mistaken for other, more common diseases or, in the case of Guinea, Lassa fever, which is endemic in the area of the outbreak [3]. Nor are laboratory diagnostics routinely available in West Africa for most viral hemorrhagic fevers [4]. Ebola virus testing of human serum samples collected as far back as 1996 as part of surveillance for Lassa fever in the same region as the current outbreak could help reveal whether humans had exposure to Ebola virus prior to this outbreak [3]. We are presently organizing with collaborators to conduct ELISA antigen testing, PCR, and cell culture for Ebola virus on samples from persons who met the case definition for viral hemorrhagic fever but tested negative for Lassa fever. We will also test all samples for IgG antibody to Ebola virus to explore the prevalence of past exposure. Could Zaire ebolavirus have been recently introduced into Guinea from Central Africa? Introduction from a human traveler seems unlikely; there is little regular travel or trade between Central Africa and Guinea, and Guéckédou, the remote epicenter and presumed area of first introduction, is far off the beaten path, a minimum 12 hour drive over rough roads from the capitals of Guinea, Liberia, or Sierra Leone (Figure 1). Furthermore, with the average incubation period as well as time from disease onset until death in fatal cases both a little over a week, a human traveler would have to make the trip from Central Africa to Guéckédou rather rapidly. If Ebola virus was introduced into Guinea from afar, the more likely traveler was a bat. Although a virus has not yet been isolated, PCR and serologic evidence accumulated over the past decade suggests that fruit bats are the likely reservoir for Ebola virus. The hammer-headed fruit bat (Hypsignathus monstrosus), Franquet's epauletted fruit bat (Epomops franqueti), and the little collared fruit bat (Myonycteris torquata) are among the leading candidates [5]–[9]. Many of these species are common across sub-Saharan Africa, including in Guinea, and/or may migrate long distances, raising the possibility that one of these wayward flyers may have carried Ebola virus to Guinea [8]. Introduction into humans may have then occurred through exposures related to hunting and consumption of fruit bats, as has been suspected in Ebola virus outbreaks in Gabon [8]. Similar customs have been reported in Guinea, prompting the Guinean government to impose a ban on bat sale and consumption early on in the outbreak. Field collections and laboratory testing for Ebola viruses of bats collected from the Guinea forest region should shed light on the presence or absence of these various species in the area and possible Ebola virus infection. Indeed, a team of ecologists is already on the ground beginning this work. But why Guinea and why Guéckédou? Certainly this is not the only place bats migrate. Unfortunately, Ebola virus outbreaks typically constitute yet another health and economic burden to Africa's most disadvantaged populations. Despite the frequently promulgated image of Ebola virus mysteriously and randomly emerging from the forest, the sites of attack are far from random; large hemorrhagic fever virus outbreaks almost invariable occur in areas in which the economy and public health system have been decimated from years of civil conflict or failed development [10]–[13]. Biological and ecological factors may drive emergence of the virus from the forest, but clearly the sociopolitical landscape dictates where it goes from there—an isolated case or two or a large and sustained outbreak. The effect of a stalled economy and government is 3-fold. First, poverty drives people to expand their range of activities to stay alive, plunging deeper into the forest to expand the geographic as well as species range of hunted game and to find wood to make charcoal and deeper into mines to extract minerals, enhancing their risk of exposure to Ebola virus and other zoonotic pathogens in these remote corners. Then, the situation is compounded when the unlucky infected person presents to an impoverished and neglected healthcare facility where a supply of gloves, clean needles, and disinfectants is not a given, leaving patients and healthcare workers alike vulnerable to nosocomial transmission. The cycle is further amplified as persons infected in the hospital return to their homes incubating Ebola virus. This classic pattern was noted in Guinea, where early infection of a healthcare worker in Guéckédou triggered spread to surrounding prefectures and eventually to the capital, Conakry [1]. Lastly, with an outbreak now coming into full force, inefficient and poorly resourced governments struggle to respond, as we are seeing all too clearly with this outbreak of Ebola virus disease in West Africa, which is now by far the largest on record. The response challenge is compounded in this case by infected persons crossing the highly porous borders of the three implicated countries, requiring intergovernmental coordination, with all the inherent logistical challenges in remote areas with poor infrastructure and communication networks and, in this case, significant language barriers. Guinea, Liberia, and Sierra Leone, sadly, fit the bill for susceptibility to more severe outbreaks. While the devastating effects of the civil wars in Liberia and Sierra Leone are evident and well documented, readers may be less familiar with the history of Guinea, where decades of inefficient and corrupt government have left the country in a state of stalled or even retrograde development. Guinea is one of the poorest countries in the world, ranking 178 out of 187 countries on the United Nations Development Programme Human Development Index (just behind Liberia [174] and Sierra Leone [177]). More than half of Guineans live below the national poverty line and about 20% live in extreme poverty. The Guinea forest region, traditionally comprised of small and isolated populations of diverse ethnic groups who hold little power and pose little threat to the larger groups closer to the capital, has been habitually neglected, receiving little attention or capital investment. Rather, the region was systematically plundered and the forest decimated by clear-cut logging, leaving the “Guinea Forest Region” largely deforested (Figure 3). 10.1371/journal.pntd.0003056.g003 Figure 3 The area known as the Guinea Forest Region, now largely deforested because of logging and clearing and burning of the land for agriculture. Photo credit: Daniel Bausch. The forest region also shares borders with Sierra Leone, Liberia, and Cote d'Ivoire, three countries suffering civil war in recent decades. Consequently, the region has found itself home to tens of thousands of refugees fleeing these conflicts, adding to both the ecologic and economic burden. A United Nations High Commission for Refugees census of camps in the forest region in 2004 registered 59,000 refugees. Although the formal refugee camps have now been dismantled with improved political stability in the surrounding countries, the impact on the region is long lasting. Having worked in Guinea for a decade (1998–2008) on research projects based very close to the epicenter of the current Ebola virus outbreak, one of the authors (DGB) witnessed this “de-development” first-hand; on every trip back to Guinea, on every long drive from Conakry to the forest region, the infrastructure seemed to be further deteriorated—the once-paved road was worse, the public services less, the prices higher, the forest thinner (Figures 3 and 4). 10.1371/journal.pntd.0003056.g004 Figure 4 Scenes of the degraded infrastructure of the Guinea forest region. A. Once-paved, but now deteriorated road; B, C, and D. Street views of the dilapidated town of Guéckédou, the epicenter of the Ebola virus disease outbreak. Photos credit: Frederique Jacquerioz. Guinea fell further into governmental and civil disarray after former president Lansana Conté's death in 2008 left a power vacuum, with a series of coup d'états and periods of violence. Although the political situation has now somewhat stabilized, the country struggles to progress; socioeconomic indicators such as life expectancy (56 years) and growth national income (GNI) per capita ($440) have crept up in the past few years, but still remain disparagingly low. Despite a wealth of mineral and other natural resources, Guinea still possesses the eighth lowest GNI per capita in the world, and the incidence of poverty has been steadily increasing since 2003. Lastly, why is this outbreak of Ebola virus happening now? As best as can be determined, the first case of Ebola virus disease in Guinea occurred in December 2013, at the beginning of the dry season, a finding consistent with observations from other countries that outbreaks often begin during the transition from the rainy to dry seasons [14]–[18]. Sharply drier conditions at the end of the rainy seasons have been cited as one triggering event [17]. Although more in-depth analysis of the environmental conditions in Guinea over the period in question remain to be conducted, inhabitants in the region do indeed anecdotally report an exceptionally arid and prolonged dry season, perhaps linked to the extreme deforestation of the area over recent decades. At present, we can only speculate that these drier ecologic conditions somehow influence the number or proportion of Ebola virus–infected bats and/or the frequency of human contact with them. The precise factors that result in an Ebola virus outbreak remain unknown, but a broad examination of the complex and interwoven ecology and socioeconomics may help us better understand what has already happened and be on the lookout for what might happen next, including determining regions and populations at risk. Although the focus is often on the rapidity and efficacy of the short-term international response, attention to these admittedly challenging underlying factors will be required for long-term prevention and control.
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              Examining the Relationship between Urogenital Schistosomiasis and HIV Infection

              Introduction An estimated 207 million people worldwide are infected with schistosomes [1], and 85% of these cases occur in Africa [1]–[3]. Schistosomiasis is a disease of poverty that arises in areas with poor sanitation where people come into contact with urine- or feces-contaminated water as part of their daily lives [4]. Individuals living in endemic countries are most commonly infected during childhood, and the prevalence peaks between the ages of 10 and 20 years [5], [6]. For those who are continually reinfected by contaminated water, schistosomiasis causes a chronic disease over decades. While the mortality caused by schistosomiasis is low, the morbidity is high, and includes anemia, stunted growth, and decreased ability to learn in children [1]. For these reasons, the World Health Organization (WHO) recommends annual treatment for school-aged children in areas of high endemicity [7]. Schistosoma haematobium causes more than half (at least 112 million) of worldwide schistosome infections [8]. Formerly known as urinary schistosomiasis, S. haematobium infection was recently renamed “urogenital schistosomiasis” in recognition that the disease affects both the urinary and genital tracts in up to 75% of infected individuals [9]. Adult S. haematobium worms inhabit the venules surrounding organs of the pelvis where they lay between 20 and 200 eggs daily [4]. These eggs subsequently penetrate the vessel wall and move towards the lumen of the bladder. An important proportion of the eggs become sequestered in the tissue of pelvic organs such as the urinary bladder, lower ureters, cervix, vagina, prostate gland, and seminal vesicles, where they cause chronic inflammation in the affected organs. This results in a number of symptoms and signs including pelvic pain, postcoital bleeding, and an altered cervical epithelium in women [10]–[11], and ejaculatory pain, hematospermia and leukocytospermia in men [12]–[13]. Epidemiologic mapping studies of HIV and S. haematobium in Africa depict a substantial overlap, in many regions, between areas in which S. haematobium is endemic and areas in which women have a high prevalence of HIV infection ( Figure 1 ) [14]. In particular, HIV studies report an unexplained gender quotient disfavoring women over men [15]–[16]. In rural women whose limited access to clean water more often puts them at risk for schistosomiasis, HIV prevalence also peaks at younger ages than in urban women [17]. While some of this skewing has been attributed to social, behavioral, and cultural norms [17]–[20], this unexplained gender quotient also suggests that risk factors for HIV acquisition may be different between rural and urban populations [17]. 10.1371/journal.pntd.0001396.g001 Figure 1 Geographical overlap of S. haematobium and HIV infections [14] -[15]. Several cross-sectional studies have reported associations between urogenital schistosomiasis and HIV, but the infection still receives relatively little attention. Global Burden of Disease (e.g. disability-adjusted life years [DALY]) calculations treat schistosomiasis cases as a single sequel, leading others to argue that the estimates should be much higher [21]–[23]. Furthermore, DALY calculations have neither examined urogenital schistosomiasis as an entity separate from intestinal schistosomiasis nor considered it as a population-attributable risk factor for HIV transmission. Thus urogenital schistosomiasis remains a neglected disease, particularly in women and men of reproductive age. This comprehensive literature review was undertaken to examine the evidence for a cause-effect relationship between urogenital schistosomiasis and HIV/AIDS. Our aim is to support discussions of urogenital schistosomiasis as an urgent public health challenge [24]. Methods We conducted a broad review of the literature by performing a systematic search of online databases including PUBMED/MEDINE, EMBASE, POPLINE, GLOBAL HEALTH, and WEB OF KNOWLEDGE. We used search terms beginning with the text string ‘schistosom’ in all possible combinations with ‘HIV’, ‘HIV/AIDS’, and other related keywords including ‘urinary,’ ‘genital’, ‘gynecology’, and ‘adolescent.’ Case reports were excluded. We subsequently limited our search to articles published in the past 30 years which overlap with the HIV pandemic. We also reviewed the most current editions of widely-used infectious diseases textbooks [6], [25]–[28] and WHO websites for relevant publications. We screened titles and abstracts for relevance, and subsequently reviewed the full texts of manuscripts that were potentially pertinent. Notable manuscripts and key learning points that emerged during our review are summarized in Tables 1 and 2. 10.1371/journal.pntd.0001396.t001 Table 1 Five key papers in the field. 1 Kjetland EF, Ndhlovu PD, Gomo E, Mduluza T, Midzi N, et al (2006) Association between genital schistosomiasis and HIV in rural Zimbabwean women. AIDS 20(4): 593–600. 2 Leutscher PDC, Pedersen M, Raharisolo C, Jensen JS, Hoffmann S, et al (2005) Increased prevalence of leukocytes and elevated cytokine levels in semen from Schistosoma haematobium-infected individuals. J Infect Dis 191: 1639–47. 3 Secor WE (2006) Interactions between schistosomiasis and HIV-1. Parasit Immunol 28: 597–603. 4 Kallestrup P, Zinyama R, Gomo E, Butterworth AE, Mudenge B, et al (2005) Schistosomiasis and HIV-1 infection in rural Zimbabwe: effect of treatment of schistosomiasis on CD4 count and plasma HIV-1 RNA load. J Infect Dis 192(11): 1956–61. 5 Poggensee G and Feldmeier H (2001) Female genital schistosomiasis: facts and hypotheses. Acta Tropica 79: 193–210. 10.1371/journal.pntd.0001396.t002 Table 2 Key learning points. 1 Schistosoma haematobium infection has been recently renamed “urogenital schistosomiasis” by the World Health Organization because of its tissue-damaging effects of both the urinary and genital tracts. 2 S. haematobium ova in the female genital tract (typically the cervix and vagina) cause epithelial inflammation leading to erosions and ulcerations. These epithelial breaches may facilitate HIV viral entry and binding to immune cells present in the altered epithelium. 3 In men with S. haematobium infection, genital organs and semen show inflammatory alterations that may imply an increased risk of HIV transmission from men to women. 4 Chronic schistosomiasis promotes a Th2-type immune environment in the host that may increase susceptibility to HIV infection and viral propagation. 5 In HIV-positive individuals, co-infection with schistosomes appears to raise HIV RNA levels and decrease CD4+ T-cell counts more rapidly than in those without co-infection. This could lead to a more rapid disease progression and a higher viral load which, in turn, enhances virus shedding through genital secretions. Results and Discussion Local Effects of Urogenital Schistosomiasis and Susceptibility to HIV Infection in Women Macroscopic Tissue Damage Reported symptoms of female genital schistosomiasis include infertility, pelvic discomfort, dyspareunia, contact and spontaneous bleeding, itching, and giant granulomata that appear as tumors [10]–[11], [29]–[30]. The condition has been associated with mucosal edema, erosion and ulcerations leading to a friable epithelium [10], most commonly on the cervix [29]. Similar breaches in the integrity of the genital epithelium, whether due to trauma or to sexually-transmitted ulcerative diseases, have been associated with an increased risk of HIV infection. The epithelial damage caused by S. haematobium infection, which has been observed to persist even after treatment, has been suggested to ease viral entry and therefore could serve as a facilitating factor for acquisition of HIV [11], [31]–[32]. Schistosomiasis in the female genital tract has been postulated to pose a greater risk than bacterial genital ulcer disease because, unlike many common sexually-transmitted infections (STIs), it often is not restricted to a single localized sore that allows the rest of the vulval, vaginal or cervical epithelium to remain intact [33]. As they can become sequestered in any genital organ, schistosomal ova cause a spectrum of clinical pathology. “Grainy sandy patches,” which are tiny cervical abnormalities similar to sandy patches in the bladder, have been recognized as pathognomonic for female genital schistosomiasis [10]. Grainy sandy patches have been described as rough small grains located superficially within the mucosa [10]. A second type of sandy patch, the “homogeneous yellow sandy patch,” has also been associated with S. haematobium infection but is less specific since it is also associated with STIs [10]. Grainy sandy patches are often encircled by irregularly-formed blood vessels believed to represent egg-induced neovascularization [10], [34]. This may explain the contact bleeding that is associated with genital tract S. haematobium infection and could be one mechanism by which HIV risk is enhanced. Microscopic Tissue Damage Sequestered schistosomal ova have been shown to evoke a complex cellular and humoral immune response in tissue. The ova of S. haematobium can induce areas of inflammation or of large granulomata in the female genital tract, with recruitment of plasma cells, lymphocytes, granulocytes, macrophages, and eosinophils to the site [35]–[36]. These inflammatory cells express CD4+ T-cell receptors, which are the primary targets for HIV. This activity at cellular level in genital lesions caused by schistosomiasis is potentially comparable to lesions caused by primary and secondary syphilis and herpes simplex virus infections (HSV-1 and HSV-2), in which affected tissue was shown to have more HIV receptors than healthy tissue nearby [37]. Monocytes and CD4+ T-cells in individuals infected with S. mansoni have been reported to display higher surface densities of Schistosoma-induced chemokine receptors CCR5 and CXCR4 [38]. Inflammation provoked by ova in genital tissue has thus been postulated to recruit these activated immune cells expressing CD4, CXCR4, and CCR5 receptors into the epithelium, facilitating rapid binding of the virus after penetration through an ulcerated, friable epithelium [39]. In support of this hypothesis, rhesus macaques with acute S. mansoni infection were found to be more susceptible to development of systemic HIV infection after rectal HIV exposure than were macaques without S. mansoni [40]. Because macaques did not have statistically-significant differences in the rates of HIV infection after intravenous HIV exposure, the authors concluded that the increased host susceptibility to HIV in the setting of schistosomal infection that they observed was predominantly due to mucosal inflammation [40]. Findings from Clinico-Epidemiological Studies A few cross-sectional studies in women support an association between urogenital schistosomiasis and HIV infection (Table 3). In Zimbabwe, women with urinary schistosomiasis had a higher HIV prevalence (33.3%) than women without urinary schistosomiasis (HIV prevalence of 25.6%, p = 0.053) [41]. Women with S. haematobium ova in the genital tract had nearly a three-fold increased risk of having HIV [34]. In Tanzania, women with S. haematobium infection had a four-fold increased risk of HIV [42]. 10.1371/journal.pntd.0001396.t003 Table 3 Primary studies in individuals with schistosomiasis and HIV infection. ARTICLE MAJOR FINDING LIMITATIONS Epidemiological Studies Ndhlovu P et al, Trans RoySoc Trop Med Hyg 2007 [41] Women in Zimbabwe with urinary schistosomiasis had a higher prevalence of HIV than those without urinary schistosomiasis(33% vs. 26%, p = 0.053). Cross-sectional studies and thereforeunable to demonstrate causality. Kjetland EF et al, AIDS2006 [32] HIV was associated with female genital schistosomiasis inZimbabwean women (OR = 2.9, 95% CI [1.1–7.5]). Downs JA et al, Am J TropMed Hyg 2011 [42] HIV was associated with S. haematobium infection in Tanzanianwomen (OR = 4.0 [1.2–13.5]). Immunological Studies in Individuals with HIV/ S. mansoni Co-Infection Mwinzi PN et al, J Infect Dis2001 [56] S. mansoni-infected individuals who were also HIV-positive hadlower levels of Th2-type cytokines than those without HIV,implying HIV-mediated Th2-type CD4+ T-cell descruction. Unable to demonstrate definitively that lowercytokine levels reflect Th2-type cell destructionby HIV rather than being caused by another mechanism. Secor WE et al, InfectImmunol 2003 [38] Densities of the chemokine receptors CCR5 and CXCR4 on CD4+T-cells in HIV-positive (and also HIV-negative) individuals with S.mansoni co-infection were reduced following praziquantel treatment. Very small observational study; possibilityof selection bias not addressed. Observational Studies Assessing Effect of Praziquantel Treatment for S. mansoni on HIV-RNA levels in Individuals with HIV Infection Lawn SD et al, AIDS2000 [62] HIV RNA levels increased significantly over a mean of 5.6 monthsof follow-up. Observational studies: -Effects may be moreattributable to length of follow-up time and toeffects of HIV infection than to praziquantel treatment.-No control groups or randomization. Elliott AM et al, Trans RoySoc Trop Med Hyg 2003 [63] HIV RNA levels transiently increased (at 5 weeks after treatment)and then returned to pre-treatment baseline by 4 months. Brown M et al, J Infect Dis2004 [64] HIV RNA levels were not significantly different pre- andpost- treatment in HIV/S. mansoni co-infected individuals. Modjarrad K et al, J InfectDis 2005 [65] HIV RNA levels increased (nonsignificantly) over the 16-weekpost-treatment follow-up. Brown M et al, J InfectDis 2005 [66] HIV RNA levels transiently increased 1 month after treatmentand then returned to pre-treatment levels by month 5. Randomized Trial Assessing Effect of Praziquantel Treatment for S. mansoni in Individuals with HIV Infection Kallestrup P et al, J InfectDis 2005 [60] HIV-positive patients who were randomized to receivepraziquantel immediately had smaller HIV RNA level increasesand increased CD4+ T-cell count compared with thoserandomized to treatment after 3 months. Randomized but not blinded so potentialfor bias in follow-up. Taken together, these studies included more than 1000 African women from rural communities with the consistent finding that S. haematobium infection was associated with HIV, with odds ratios between 2.9 and 4.0. Each of these studies is limited by its cross-sectional nature, which allows determination only of an association, rather than of a cause-effect relationship, between S. haematobium infection and HIV. Because S. haematobium infection, typically acquired in childhood, normally precedes HIV acquisition, it seems likely that S. haematobium infection may increase the susceptibility to HIV infection when girls reach sexual maturity. Definitive proof of this hypothesis can only be ascertained through longitudinal studies that are carefully planned to demonstrate a cause-effect relationship. The design of prospective studies to establish a causal relationship would be complex and needs to address difficult ethical issues concerning gynecological examination of pre-teenage and teenage girls. Moreover, withholding praziquantel from young women with documented S. haematobium infection and following them prospectively as a control group in a study for incident HIV infections is not ethical. For these reasons, recent study designs have focused on comparing different mass treatment strategies among cohorts of girls at different ages in S. haematobium-endemic communities. For example, young adolescent schoolgirls would receive early, regular praziquantel prophylaxis and be compared, at the time they commence sexual activity, to older adolescent girls who received fewer treatments before becoming sexually-active and to older adolescent “control” girls from the same villages who are sexually active and did not receive prophylaxis. The hypothesis is that early, frequent praziquantel treatment will prevent development of genital lesions in adolescence and will consequently reduce HIV infections in girls and women after sexual debut [14], [43]. Another possibility would be to use recently-developed serum schistosomal antigen tests, such as the Circulating Anodic Antigen (CAA) [44], [45], to analyze banked serum from prior HIV seroincidence studies. These study designs also highlight additional research needs in the field, such as optimization of the praziquantel regimen for treatment of urogenital schistosomiasis and the need for a standardized, agreed-upon case definition for clinical diagnosis and measurement of morbidity caused by urogenital schistosomiasis. Female Genital Schistosomiasis and Shedding of HIV Concurrent S. haematobium infection may also increase the ease with which HIV-positive women transmit HIV infection to their sexual partners. A recent meta-analysis found that a variety of genital tract infections were associated with HIV-1 viral shedding in the female genital tract [46]. This effect was most pronounced in conditions that resulted in the recruitment of high concentrations of leukocytes to the genital epithelium, including nonspecific cervicitis, genital ulcer diseases, Chlamydia trachomatis and Neisseria gonorrhea infections, and vulvovaginal candidiasis. The authors hypothesized that, because leukocytes typically harbor HIV, conditions that lead to higher genital tract leukocyte concentrations are those that most heighten the risk of sexual HIV transmission from women to men during sexual intercourse. Given the recruitment of leukocytes to the genital tract by S. haematobium infection [35], [36], it seems possible that, through this mechanism, women who are co-infected with HIV and S. haematobium may more easily transmit HIV to their sexual partners. Male Urogenital Schistosomiasis and HIV Infection Genital schistosomiasis in men can involve several male reproductive organs. Since the penis is not affected by ova-induced lesions, male genital schistosomiasis is not believed to increase the risk of HIV acquisition through local effects [43] but rather through schistosomiasis-related immunomodulatory effects. Moreover, the infection could increase risk for HIV transmission by inciting inflammation in the male genital tract. Men with severe urogenital schistosomiasis have been found to have a higher prevalence of lymphocytes and eosinophils in seminal fluid than those without infection [47]. Infected men are also reported to have significantly higher levels of interleukin (IL)-4, IL-6, IL-10, and tumor necrosis factor-alpha in their semen. These cytokines may recruit more HIV-infected cells to the semen, upregulate viral replication, and increase the concentration of HIV virus in semen [47]. Six months after anti-schistosomal treatment, the concentrations of seminal lymphocytes and eosinophils were lower and the levels of cytokines were reduced. Another analysis of seminal fluid in S. haematobium-infected men demonstrated lower volumes of semen, higher levels of eosinophilic cationic protein (an established marker of inflammation and morbidity in urogenital schistosomiasis), and higher rates of sperm apoptosis, which lessened after praziquantel treatment [48]. With a mechanism similar to that discussed for women in the preceding section, it has been demonstrated that HIV-positive men with concomitant genital tract infections, such as urethritis, have higher concentrations of seminal HIV-1 RNA than those without dual infections [49]. The chronic inflammation and recruitment of lymphocytes and eosinophils to the male genital tract may increase the HIV-1 viral load in semen. In this manner, a female sexual partner of an HIV-positive male living in an S. haematobium-endemic area may have a doubly-amplified risk of HIV acquisition: her S. haematobium-infected partner's semen may contain disproportionately high concentrations of HIV RNA, and her own S. haematobium infection may increase the ease with which HIV can establish infection following exposure. Taken together, these data suggest that egg-induced inflammation in the male genital tract could be a risk factor for HIV transmission from men to women. Schistosomiasis in Children and Adolescents and HIV Transmission Schistosomal lesions are commoner in the vulva and the lower vagina before puberty, while in adult women they are more frequent in the cervix, uterus, ovaries and fallopian tubes [50]. These clinically-apparent lesions and the resulting compromise of the vaginal epithelium, therefore, are already present before a girl's first sexual intercourse. This is in contrast to lesions caused by STIs, which can develop only after sexual intercourse [32]. The presence of schistosomal lesions already in childhood makes it likely that schistosomal infection typically precedes HIV infection and that the temporal association reflects the fact that urogenital schistosomiasis is a risk factor for HIV acquisition rather than vice-versa [10]. Important risk factors for urinary tract morbidity in adulthood are cumulative intensity and duration of S. haematobium infection during early adolescence. Treatment of school-aged children can significantly reduce the cumulative lifetime egg burden as the intensity of infection is greatest during early teenage years [50]. Furthermore, treatment for schistosomiasis during childhood was significantly associated with the absence of cervical sandy patches and contact bleeding in adult women [51]. Thus treatment of S. haematobium infection before and during the teenage years may not only diminish genital schistosomiasis-associated morbidity in adulthood, but may simultaneously decrease the risk of HIV acquisition. Schistosomiasis-Associated Immunomodulation and the Risk of HIV Infection In addition to local and gender-specific effects of S. haematobium infection, schistosomiasis also appears to increase HIV susceptibility through chronic immune modulation. This topic has been studied far more extensively with regard to Schistosoma mansoni [52]. It has also recently been the subject of a comprehensive review [31] and for this reason will be summarized only briefly here. Chronic schistosomiasis alters global immune function and in this manner may also increase susceptibility to HIV infection [38]. It preferentially stimulates the Th2-type immune response, with reciprocal down-regulation of the Th1-type cytotoxic responses [53] which are important in initial control of HIV infection. This is supported by work from Uganda, which demonstrated that HIV-positive patients with S. mansoni infection had decreased Gag-specific cytolytic CD8+ responses [54]. Moreover, CD4+ T-cells with a Th2 phenotype are more readily infected, and subsequently destroyed, by HIV-1 than are Th1 cells [55]. In individuals in Kenya with HIV and S. mansoni co-infection, Th2-type CD4+ T-cells were destroyed more quickly than in HIV-positive individuals without schistosomiasis [56]. Specifically, differences in cell surface receptors may lead to differences in HIV susceptibility between those with and without schistosomiasis. The chemokine receptors CCR5 and CXCR4 are co-receptors for HIV-1 and were found to be more dense on the CD4+ T-cell surfaces of individuals with active S. mansoni infection than on the CD4+ T-cells of individuals who had received prior anti-schistosomal treatment [38]. The levels of these co-receptors dropped in individuals who were studied pre- and post-praziquantel treatment [38]. This highlights the potential role that widespread anti-schistosomal treatment could play in reducing the progression and spread of HIV. Effects of Schistosomiasis on Progression of HIV Infection and Shedding of HIV In addition to potentially increasing susceptibility to HIV infection, evidence suggests that S. haematobium infection may also speed progression of disease by raising plasma HIV RNA concentration (commonly known as “viral load”) in individuals who are co-infected. At the cellular level, the same CCR5 and CXCR4 chemokine receptors that are upregulated in schistosomiasis and facilitate HIV binding in initial infection may also promote cell-to-cell spread of HIV once infection is established [31], [57]. Multiple studies have shown that the plasma HIV RNA level is predictive of both HIV disease progression and risk of transmission of HIV to sexual partners [58], [59]. If the hypothesis is correct that schistosomiasis increases the HIV RNA levels in co-infected individuals, then treatment for schistosomiasis could delay the development of AIDS and decrease the spread of HIV in sub-Saharan Africa. A recent randomized clinical trial conducted in Zimbabwe supports this hypothesis. Patients who were infected with both HIV and S. mansoni were randomized either to praziquantel treatment at enrollment or to praziquantel after three months [60]. Compared with the group in whom treatment was delayed, the early-treatment group experienced significantly smaller declines in CD4+ T-cell counts after three months (mean decline of 1.7 cells/ µL versus 35.2 cells/ µL in the delayed-treatment group) [61]. Notably, the HIV RNA levels in both groups of patients increased during the three months, but the mean increase in the early-treatment group (0.001log10 copies/mL) was significantly lower than in the delayed-treatment group (0.21log10 copies/mL). Earlier non-randomized studies of HIV-positive patients who were treated for S. mansoni infections had found that HIV RNA levels remained stable or increased in patients regardless of treatment [62]–[65]. One notable study that reported significant HIV RNA level increases one month post-treatment noted corresponding increases in S. mansoni-specific Th2-type cytokine responses as well, though both of these reverted to pre-treatment levels by five months post-treatment [66]. Notably, none of the patients in these studies were receiving ART. In light of the findings of the randomized trial in Zimbabwe that did demonstrate a benefit with praziquantel treatment with regard to the viral load [60], it is plausible that the overall observed increases in HIV RNA levels reflect natural progression of untreated HIV infection. In this sense, while treatment for schistosomiasis is clearly not able to substitute for antiretroviral therapy, it may possibly be able to slow HIV disease progression [31]. In support of this hypothesis, two other randomized studies of HIV-infected patients co-infected with either Wucheria bancrofti [67] or soil-transmitted helminths [68] have explored the effects of treatment on parameters of HIV infection. Patients treated for lymphatic filariasis had significant decreases in their HIV RNA levels and insignificant increases in their CD4+ T-cell counts at 12 weeks as compared to pretreatment levels [67]. Patients with ascariasis who received albendazole experienced significantly higher CD4+ T-cell counts at 12 weeks and a trend towards lower HIV RNA levels [68]. Taken together, these studies of treatment in HIV and helminth co-infections support a positive effect of antiparasitic treatment on certain HIV infection parameters. While treatment for schistosomiasis in HIV-positive patients may not decrease HIV RNA levels, it may slow the increase of viral levels. It is also possible that praziquantel treatment may enhance immunocompetence by promoting an increase in CD4+ T-cell counts and an increased NK cell function [69]–[71]. Conclusions Schistosoma haematobium infection is highly prevalent in sub-Saharan Africa. Increasing evidence supports that it is a plausible risk factor for HIV acquisition due both to its local genital tract effects in women, and to its chronic immunomodulatory effects in both men and women. It also could facilitate HIV transmission to the sexual partners of HIV-positive individuals with schistosomal co-infection, and could enhance HIV disease progression. Circumstantial, biological, immunological, and epidemiological evidence is strongly suggestive of a cause-effect relationship between S. haematobium and HIV infection. Our review highlights the need for further innovative research, particularly appropriately-designed longitudinal studies which ultimately would be able to confirm the suggested causality of schistosomiasis in incident HIV infections. Such studies must carefully balance the ethical obligation to ensure treatment for study subjects while simultaneously managing to explore the cause-effect relationship between the two infections. This is by no means an easy task. Consideration should therefore be given to harnessing latent operational research opportunities that exist within the context of ongoing schistosomiasis control programs. These include studies such as exploring the effect of early, regular anti-schistosomal treatment of girls to prevent development of urogenital lesions in adolescence or testing for markers of active schistosomiasis in blood collected from HIV-positive women before their HIV-seroconversion. Meanwhile, in schistosomiasis-endemic areas where coverage for preventive chemotherapy with praziquantel remains low, millions of individuals may be at higher risk for HIV infection. The presumptive causal association with HIV infection notwithstanding, urogenital schistosomiasis by itself leads to significant morbidity that can be lessened with inexpensive preventive chemotherapy. At an annual cost of about 40 cents per person [72]–[73], praziquantel stands as a powerful and economical public health intervention with the potential to prevent the development of urogenital lesions, prolong survival, and decrease new HIV infections on the African continent. In view of the plausible association between urogenital schistosomiasis and HIV transmission in areas where these infections are co-endemic, a salient effect on the health of millions of individuals could presumably be achieved if antischistosomal treatment and HIV prevention interventions were integrated. The WHO-recommended policy of early regular treatment of school-age children with praziquantel needs to be extended to adults and prioritized in national programs as a possible means of further preventing HIV infections in sub-Saharan Africa.
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                Contributors
                Role: Editor
                Journal
                PLoS Negl Trop Dis
                PLoS Negl Trop Dis
                plos
                plosntds
                PLoS Neglected Tropical Diseases
                Public Library of Science (San Francisco, CA USA )
                1935-2727
                1935-2735
                8 April 2016
                April 2016
                : 10
                : 4
                Affiliations
                [1 ]Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, Texas, United States of America
                [2 ]Sabin Vaccine Institute and Texas Children’s Hospital Center for Vaccine Development, Houston, Texas, United States of America
                [3 ]James A. Baker III Institute for Public Policy, Rice University, Houston, Texas, United States of America
                [4 ]Department of Biology, Baylor University, Waco, Texas, United States of America
                University of Texas Medical Branch, UNITED STATES
                Author notes

                The author has declared that no competing interests exist.

                Article
                PNTD-D-16-00216
                10.1371/journal.pntd.0004648
                4825952
                27058728
                © 2016 Peter J. Hotez

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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