More Like this

1. A need for null models in understanding disease transmission: the example of Mycobacterium ulcerans (Buruli ulcer disease)

   https://doi.org/10.1093/femsre/fuab045  

   Receveur, Joseph P ; Bauer, Alexandra ; Pechal, Jennifer L ; Picq, Sophie ; Dogbe, Magdalene ; Jordan, Heather R ; Rakestraw, Alex W ; Fast, Kayla ; Sandel, Michael ; Chevillon, Christine ; et al ( January 2022 , FEMS Microbiology Reviews)

   ABSTRACT Understanding the interactions of ecosystems, humans and pathogens is important for disease risk estimation. This is particularly true for neglected and newly emerging diseases where modes and efficiencies of transmission leading to epidemics are not well understood. Using a model for other emerging diseases, the neglected tropical skin disease Buruli ulcer (BU), we systematically review the literature on transmission of the etiologic agent, Mycobacterium ulcerans (MU), within a One Health/EcoHealth framework and against Hill's nine criteria and Koch's postulates for making strong inference in disease systems. Using this strong inference approach, we advocate a null hypothesis for MU transmission and other understudied disease systems. The null should be tested against alternative vector or host roles in pathogen transmission to better inform disease management. We propose a re-evaluation of what is necessary to identify and confirm hosts, reservoirs and vectors associated with environmental pathogen replication, dispersal and transmission; critically review alternative environmental sources of MU that may be important for transmission, including invertebrate and vertebrate species, plants and biofilms on aquatic substrates; and conclude with placing BU within the context of other neglected and emerging infectious diseases with intricate ecological relationships that lead to disease in humans, wildlife and domesticmore »animals.« less
2. Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa

   https://doi.org/10.3390/rs14061345  

   Liu, Zac Yung-Chun ; Chamberlin, Andrew J. ; Tallam, Krti ; Jones, Isabel J. ; Lamore, Lance L. ; Bauer, John ; Bresciani, Mariano ; Wolfe, Caitlin M. ; Casagrandi, Renato ; Mari, Lorenzo ; et al ( March 2022 , Remote Sensing)

   Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 200 million people worldwide, mostly in sub-Saharan Africa, and is clearly associated with the construction of dams and water resource management infrastructure in tropical and subtropical areas. Changes to hydrology and salinity linked to water infrastructure development may create conditions favorable to the aquatic vegetation that is suitable habitat for the intermediate snail hosts of schistosome parasites. With thousands of small and large water reservoirs, irrigation canals, and dams developed or under construction in Africa, it is crucial to accurately assess the spatial distribution of high-risk environments that are habitat for freshwater snail intermediate hosts of schistosomiasis in rapidly changing ecosystems. Yet, standard techniques for monitoring snails are labor-intensive, time-consuming, and provide information limited to the small areas that can be manually sampled. Consequently, in low-income countries where schistosomiasis control is most needed, there are formidable challenges to identifying potential transmission hotspots for targeted medical and environmental interventions. In this study, we developed a new framework to map the spatial distribution of suitable snail habitat across large spatial scales in the Senegal River Basin by integrating satellite data, high-definition, low-cost drone imagery, and an artificial intelligence (AI)-powered computermore »vision technique called semantic segmentation. A deep learning model (U-Net) was built to automatically analyze high-resolution satellite imagery to produce segmentation maps of aquatic vegetation, with a fast and robust generalized prediction that proved more accurate than a more commonly used random forest approach. Accurate and up-to-date knowledge of areas at highest risk for disease transmission can increase the effectiveness of control interventions by targeting habitat of disease-carrying snails. With the deployment of this new framework, local governments or health actors might better target environmental interventions to where and when they are most needed in an integrated effort to reach the goal of schistosomiasis elimination.« less
3. Identification of Snails and Schistosoma of Medical Importance via Convolutional Neural Networks: A Proof-of-Concept Application for Human Schistosomiasis

   https://doi.org/10.3389/fpubh.2021.642895  

   Tallam, Krti ; Liu, Zac Yung-Chun ; Chamberlin, Andrew J. ; Jones, Isabel J. ; Shome, Pretom ; Riveau, Gilles ; Ndione, Raphael A. ; Bandagny, Lydie ; Jouanard, Nicolas ; Eck, Paul Van ; et al ( July 2021 , Frontiers in Public Health)

   In recent decades, computer vision has proven remarkably effective in addressing diverse issues in public health, from determining the diagnosis, prognosis, and treatment of diseases in humans to predicting infectious disease outbreaks. Here, we investigate whether convolutional neural networks (CNNs) can also demonstrate effectiveness in classifying the environmental stages of parasites of public health importance and their invertebrate hosts. We used schistosomiasis as a reference model. Schistosomiasis is a debilitating parasitic disease transmitted to humans via snail intermediate hosts. The parasite affects more than 200 million people in tropical and subtropical regions. We trained our CNN, a feed-forward neural network, on a limited dataset of 5,500 images of snails and 5,100 images of cercariae obtained from schistosomiasis transmission sites in the Senegal River Basin, a region in western Africa that is hyper-endemic for the disease. The image set included both images of two snail genera that are relevant to schistosomiasis transmission – that is, Bulinus spp. and Biomphalaria pfeifferi – as well as snail images that are non-component hosts for human schistosomiasis. Cercariae shed from Bi. pfeifferi and Bulinus spp. snails were classified into 11 categories, of which only two, S. haematobium and S. mansoni , are major etiological agentsmore »of human schistosomiasis. The algorithms, trained on 80% of the snail and parasite dataset, achieved 99% and 91% accuracy for snail and parasite classification, respectively, when used on the hold-out validation dataset – a performance comparable to that of experienced parasitologists. The promising results of this proof-of-concept study suggests that this CNN model, and potentially similar replicable models, have the potential to support the classification of snails and parasite of medical importance. In remote field settings where machine learning algorithms can be deployed on cost-effective and widely used mobile devices, such as smartphones, these models can be a valuable complement to laboratory identification by trained technicians. Future efforts must be dedicated to increasing dataset sizes for model training and validation, as well as testing these algorithms in diverse transmission settings and geographies.« less
4. Reactivation of latent infections with migration shapes population-level disease dynamics

   https://doi.org/10.1098/rspb.2020.1829  

   Becker, Daniel J. ; Ketterson, Ellen D. ; Hall, Richard J. ( September 2020 , Proceedings of the Royal Society B: Biological Sciences)

   Annual migration is common across animal taxa and can dramatically shape the spatial and temporal patterns of infectious disease. Although migration can decrease infection prevalence in some contexts, these energetically costly long-distance movements can also have immunosuppressive effects that may interact with transmission processes in complex ways. Here, we develop a mechanistic model for the reactivation of latent infections driven by physiological changes or energetic costs associated with migration (i.e. ‘migratory relapse’) and its effects on disease dynamics. We determine conditions under which migratory relapse can amplify or reduce infection prevalence across pathogen and host traits (e.g. infectious periods, virulence, overwinter survival, timing of relapse) and transmission phenologies. We show that relapse at either the start or end of migration can dramatically increase prevalence across the annual cycle and may be crucial for maintaining pathogens with low transmissibility and short infectious periods in migratory populations. Conversely, relapse at the start of migration can reduce the prevalence of highly virulent pathogens by amplifying culling of infected hosts during costly migration, especially for highly transmissible pathogens and those transmitted during migration or the breeding season. Our study provides a mechanistic foundation for understanding the spatio-temporal patterns of relapsing infections in migratory hosts,more »with implications for zoonotic surveillance and understanding how infection patterns will respond to shifts in migratory propensity associated with environmental change. Further, our work suggests incorporating within-host processes into population-level models of pathogen transmission may be crucial for reconciling the range of migration–infection relationships observed across migratory species.« less
5. Tracking the assembly of nested parasite communities: using β-diversity to understand variation in parasite richness and composition over time and scale

   Moss*, W. E. ( January 2020 , Journal of animal ecology)

   Community composition is driven by a few key assembly processes: ecological selection, drift and dispersal. Nested parasite communities represent a powerful study system for understanding the relative importance of these processes and their relationship with biological scale. Quantifying β‐diversity across scales and over time additionally offers mechanistic insights into the ecological processes shaping the distributions of parasites and therefore infectious disease. To examine factors driving parasite community composition, we quantified the parasite communities of 959 amphibian hosts representing two species (the Pacific chorus frog, Pseudacris regilla and the California newt, Taricha torosa) sampled over 3 months from 10 ponds in California. Using additive partitioning, we estimated how much of regional parasite richness (γ‐diversity) was composed of within‐host parasite richness (α‐diversity) and turnover (β‐diversity) at three biological scales: across host individuals, across species and across habitat patches (ponds). We also examined how β‐diversity varied across time at each biological scale. Differences among ponds comprised the majority (40%) of regional parasite diversity, followed by differences among host species (23%) and among host individuals (12%). Host species supported parasite communities that were less similar than expected by null models, consistent with ecological selection, although these differences lessened through time, likely due to highmore »dispersal rates of infectious stages. Host individuals within the same population supported more similar parasite communities than expected, suggesting that host heterogeneity did not strongly impact parasite community composition and that dispersal was high at the individual host-level. Despite the small population sizes of within‐host parasite communities, drift appeared to play a minimal role in structuring community composition. Dispersal and ecological selection appear to jointly drive parasite community assembly, particularly at larger biological scales. The dispersal ability of aquatic parasites with complex life cycles differs strongly across scales, meaning that parasite communities may predictably converge at small scales where dispersal is high, but may be more stochastic and unpredictable at larger scales. Insights into assembly mechanisms within multi‐host, multi‐parasite systems provide opportunities for understanding how to mitigate the spread of infectious diseases within human and wildlife hosts.« less