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BackgroundWater resources development (WRD), specifically infrastructural man-made water bodies such as dams and irrigation schemes, are built to improve water supply, provide energy, and enhance food security. However, dams and irrigation schemes may lead to a dramatic increase in the prevalence of schistosomiasis. Methodology/Principal findingsWe conducted a scoping review of WRD impacts on schistosomiasis transmission risk in Africa using electronic databases including Scopus, Web of Science, and grey literature. From 1483 retrieved records, we assessed 186 full-text papers and identified 122 articles covering 54 dams and irrigation schemes in 32 African countries. We found that, although the relationship between WRD and schistosomiasis transmission risk is well-documented in the scientific literature, the vast majority of the approximately 1,600 medium- to large-sized dams currently operating in endemic regions of Africa lack before-and-after prevalence data necessary to evaluate their actual impact on schistosomiasis transmission. Our analysis revealed that rigorous epidemiological data to assess WRD’s effects exist for only 11 dams across 9 countries. Additionally, only a limited number of studies provided information on schistosomiasis control methods, surveillance, or monitoring for WRD. Few countries have implemented engineering and biological snail control measures, some of which have proven effective, enabling us to identify successful interventions employed at various stages of the WRD lifecycle. Lastly, to assess these measures in detail, we selected case studies from Africa that illustrate the success and challenges of schistosomiasis control with regard to WRD, thus gaining insights of the global relevance of lessons learnt for the future development of water resources. Conclusions/SignificanceOur analysis highlighted that an integrated and coordinated approach is vital for the successful control of schistosomiasis transmission risk associated with Water Resources Development. We provide key recommendations which could be adopted by the Continental Africa Water Investment Programme (AIP) with the ultimate goal of decreasing prevalence and moving towards elimination. 

BackgroundSchistosomiasis, a chronic parasitic disease, remains a public health issue in tropical and subtropical regions, especially in low and moderate-income countries lacking assured access to safe water and proper sanitation. A national prevalence survey carried out by the Brazilian Ministry of Health from 2011 to 2015 found a decrease in human infection rates to 1%, with 19 out of 26 states still classified as endemic areas. There is a risk of schistosomiasis reemerging as a public health concern in low-endemic regions. This study proposes an integrated landscape-based approach to aid surveillance and control strategies for schistosomiasis in low-endemic areas. Methodology/Principal findingsIn the Middle Paranapanema river basin, specific landscapes linked to schistosomiasis were identified using a comprehensive methodology. This approach merged remote sensing, environmental, socioeconomic, epidemiological, and malacological data. A team of experts identified ten distinct landscape categories associated with varying levels of schistosomiasis transmission potential. These categories were used to train a supervised classification machine learning algorithm, resulting in a 92.5% overall accuracy and a 6.5% classification error. Evaluation revealed that 74.6% of collected snails from water collections in five key municipalities within the basin belonged to landscape types with higher potential forS. mansoniinfection. Landscape connectivity metrics were also analysed. Conclusions/SignificanceThis study highlights the role of integrated landscape-based analyses in informing strategies for eliminating schistosomiasis. The methodology has produced new schistosomiasis risk maps covering the entire basin. The region’s low endemicity can be partly explained by the limited connectivity among grouped landscape-units more prone to triggering schistosomiasis transmission. Nevertheless, changes in social, economic, and environmental landscapes, especially those linked to the rising pace of incomplete urbanization processes in the region, have the potential to increase risk of schistosomiasis transmission. This study will help target interventions to bring the region closer to schistosomiasis elimination. 

Abstract Aedes aegyptimosquitos are the primary vector for dengue, chikungunya, and Zika viruses and tend to breed in small containers of water, with a propensity to breed in small piles of trash and abandoned tires. This study piloted the use of aerial imaging to map and classify potentialAe. aegyptibreeding sites with a specific focus on trash, including discarded tires. Aerial images of coastal and inland sites in Kenya were obtained using an unmanned aerial vehicle. Aerial images were reviewed for identification of trash and suspected trash mimics, followed by extensive community walk-throughs to identify trash types and mimics by description and ground photography. An expert panel reviewed aerial images and ground photos to develop a classification scheme and evaluate the advantages and disadvantages of aerial imaging versus walk-through trash mapping. A trash classification scheme was created based on trash density, surface area, potential for frequent disturbance, and overall likelihood of being a productiveAe. aegyptibreeding site. Aerial imaging offers a novel strategy to characterize, map, and quantify trash at risk of promotingAe. aegyptiproliferation, generating opportunities for further research on trash associations with disease and trash interventions. 

Abstract Many infectious pathogens spend a significant portion of their life cycles in the environment or in animal hosts, where ecological interactions with natural enemies may influence pathogen transmission to people. Yet, our understanding of natural enemy opportunities for human disease control is lacking, despite widespread uptake and success of natural enemy solutions for pest and parasite management in agriculture.Here we explore three reasons why conserving, restoring or augmenting specific natural enemies in the environment could offer a promising complement to conventional clinical strategies to fight environmentally mediated pathogens and parasites. (a) Natural enemies of human infections abound in nature, largely understudied and undiscovered; (b) natural enemy solutions could provide ecological options for infectious disease control where conventional interventions are lacking; and, (c) many natural enemy solutions could provide important co‐benefits for conservation and human well‐being.We illustrate these three arguments with a broad set of examples whereby natural enemies of human infections have been used or proposed to curb human disease burden, with some clear successes. However, the evidence base for most proposed solutions is sparse, and many opportunities likely remain undiscovered, highlighting opportunities for future research. A freePlain Language Summarycan be found within the Supporting Information of this article. 

Background: Estimating the infection fatality rate (IFR) for emerging diseases is elusive due to the presence of asymptomatic or mildly symptomatic infections and variable testing capacity. IFR estimates are also affected by region-specific differences in sampling regimes, demographics, and healthcare resources. Methods: Here we present a novel regression approach using population testing and readily available case fatality rates (CFR) to estimate the IFR during an outbreak. The approach is based on few assumptions and can be used for a wide range of emerging diseases. We validate the use of the method using commonly reported COVID-19 testing data. Results: Our new statistical approach reveals a conservative global IFR of 0.90 % (CI: 0.70 %, 1.16 %) for COVID-19 across the 139 countries affected before May 2020. Deviation of countries’ reported CFR from the estimator did not correlate with demography, per capita GDP, or healthcare access and quality, suggesting variation is due to differing testing regimes or reporting guidelines by country. Conclusions: This method can be used retrospectively or for future disease outbreaks when other data are limited. 

Abstract Vector‐borne diseases (VBDs) are embedded within complex socio‐ecological systems. While research has traditionally focused on the direct effects of VBDs on human morbidity and mortality, it is increasingly clear that their impacts are much more pervasive. VBDs are dynamically linked to feedbacks between environmental conditions, vector ecology, disease burden, and societal responses that drive transmission. As a result, VBDs have had profound influence on human history. Mechanisms include: (1) killing or debilitating large numbers of people, with demographic and population‐level impacts; (2) differentially affecting populations based on prior history of disease exposure, immunity, and resistance; (3) being weaponised to promote or justify hierarchies of power, colonialism, racism, classism and sexism; (4) catalysing changes in ideas, institutions, infrastructure, technologies and social practices in efforts to control disease outbreaks; and (5) changing human relationships with the land and environment. We use historical and archaeological evidence interpreted through an ecological lens to illustrate how VBDs have shaped society and culture, focusing on case studies from four pertinent VBDs: plague, malaria, yellow fever and trypanosomiasis. By comparing across diseases, time periods and geographies, we highlight the enormous scope and variety of mechanisms by which VBDs have influenced human history. 

Schistosomiasis, a debilitating parasitic disease of poverty affecting more than 250 million people worldwide, is contracted upon contact with the larval form of the parasite, known as cercaria, emerging from infected freshwater snails, the obligate intermediate host of the parasite. Understanding how infectious larvae can be transported in rivers and irrigation canals is crucial to fine-tune environmental interventions targeting the parasite and its intermediate host. Specifically, lateral cavities along many tropical rivers act as water access points but can also entrap parasitic larvae and provide low-velocity environments for snail-supporting vegetation to flourish, creating potential areas of high schistosomiasis infection. In this paper, the circulation of larvae in a typical transmission site along the Lampsar River in Senegal is modeled under a range of wind and vegetation conditions to better understand how such environmental factors affect their transport. We found that wind direction has a large influence on the distribution and abundance of parasitic larvae at the water access point, whereas increasing wind speed scales velocities but does not affect flow patterns. The area of coverage of vegetation can significantly alter flow magnitudes and circulation patterns for the same wind speed and direction. Increasing vegetation coverage generally leads to an increase in larvae residence time in the side pond, but the relationship is non-monotonic with five regimes of residence time behavior based on vegetation patch radius. The results suggest that there is an optimal patch radius at which larvae residence time and velocity deviations within the side pond are maximized. 

Species distribution models (SDMs) are increasingly popular tools for profiling disease risk in ecology, particularly for infectious diseases of public health importance that include an obligate non-human host in their transmission cycle. SDMs can create high-resolution maps of host distribution across geographical scales, reflecting baseline risk of disease. However, as SDM computational methods have rapidly expanded, there are many outstanding methodological questions. Here we address key questions about SDM application, using schistosomiasis risk in Brazil as a case study. Schistosomiasis is transmitted to humans through contact with the free-living infectious stage ofSchistosomaspp. parasites released from freshwater snails, the parasite’s obligate intermediate hosts. In this study, we compared snail SDM performance across machine learning (ML) approaches (MaxEnt, Random Forest, and Boosted Regression Trees), geographic extents (national, regional, and state), types of presence data (expert-collected and publicly-available), and snail species (Biomphalaria glabrata,B.straminea, andB.tenagophila). We used high-resolution (1km) climate, hydrology, land-use/land-cover (LULC), and soil property data to describe the snails’ ecological niche and evaluated models on multiple criteria. Although all ML approaches produced comparable spatially cross-validated performance metrics, their suitability maps showed major qualitative differences that required validation based on local expert knowledge. Additionally, our findings revealed varying importance of LULC and bioclimatic variables for different snail species at different spatial scales. Finally, we found that models using publicly-available data predicted snail distribution with comparable AUC values to models using expert-collected data. This work serves as an instructional guide to SDM methods that can be applied to a range of vector-borne and zoonotic diseases. In addition, it advances our understanding of the relevant environment and bioclimatic determinants of schistosomiasis risk in Brazil. 

The geographical range of schistosomiasis is affected by the ecology of schistosome parasites and their obligate host snails, including their response to temperature. Previous models predicted schistosomiasis’ thermal optimum at 21.7°C, which is not compatible with the temperature in sub-Saharan Africa (SSA) regions where schistosomiasis is hyperendemic. We performed an extensive literature search for empirical data on the effect of temperature on physiological and epidemiological parameters regulating the free-living stages ofS.mansoniandS.haematobiumand their obligate host snails, i.e.,Biomphalariaspp. andBulinusspp., respectively. We derived nonlinear thermal responses fitted on these data to parameterize a mechanistic, process-based model of schistosomiasis. We then re-cast the basic reproduction number and the prevalence of schistosome infection as functions of temperature. We found that the thermal optima for transmission ofS.mansoniandS.haematobiumrange between 23.1–27.3°C and 23.6–27.9°C (95% CI) respectively. We also found that the thermal optimum shifts toward higher temperatures as the human water contact rate increases with temperature. Our findings align with an extensive dataset of schistosomiasis prevalence in SSA. The refined nonlinear thermal-response model developed here suggests a more suitable current climate and a greater risk of increased transmission with future warming for more than half of the schistosomiasis suitable regions with mean annual temperature below the thermal optimum.