Network inference from multimodal data: A review of approaches from infectious disease transmission

Nextcast: a software suite to analyse and model toxicogenomics data

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Toxicogenomics is emerging as a valid approach to characterise the mechanism of action of chemicals.

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Structured pipelines for toxicogenomics increase standardisation and regulatory acceptance.

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We developed the Nextcast software suite for robust analysis and modelling of toxicogenomic data.

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Nextcast offers customisable modular pipelines to tackle multiple biological questions.

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).

Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review

In order to better understand transmission dynamics and appropriately target control and preventive measures, studies have aimed to identify who-infected-whom in actual outbreaks. Numerous reconstruction methods exist, each with their own assumptions, types of data, and inference strategy. Thus, selecting a method can be difficult. Following PRISMA guidelines, we systematically reviewed the literature for methods combing epidemiological and genomic data in transmission tree reconstruction. We identified 22 methods from the 41 selected articles. We defined three families according to how genomic data was handled: a non-phylogenetic family, a sequential phylogenetic family, and a simultaneous phylogenetic family. We discussed methods according to the data needed as well as the underlying sequence mutation, within-host evolution, transmission, and case observation. In the non-phylogenetic family consisting of eight methods, pairwise genetic distances were estimated. In the phylogenetic families, transmission trees were inferred from phylogenetic trees either simultaneously (nine methods) or sequentially (five methods). While a majority of methods (17/22) modeled the transmission process, few (8/22) took into account imperfect case detection. Within-host evolution was generally (7/8) modeled as a coalescent process. These practical and theoretical considerations were highlighted in order to help select the appropriate method for an outbreak.

Current Trends of Nanobiosensors for Point-of-Care Diagnostics

In order to provide better-quality health care, it is very important that high standards of health care management are achieved by making timely decisions based on rapid diagnostics, smart data analysis, and informatics analysis. Point-of-care testing ensures fast detection of analytes near to the patients facilitating a better disease diagnosis, monitoring, and management. It also enables quick medical decisions since the diseases can be diagnosed at an early stage which leads to improved health outcomes for the patients enabling them to start early treatment. In the recent past, various potential point-of-care devices have been developed and they are paving the way to next-generation point-of-care testing. Biosensors are very critical components of point-of-care devices since they are directly responsible for the bioanalytical performance of an essay. As such, they have been explored for their prospective point-of-care applications necessary for personalized health care management since they usually estimate the levels of biological markers or any chemical reaction by producing signals mainly associated with the concentration of an analyte and hence can detect disease causing markers such as body fluids. Their high selectivity and sensitivity have allowed for early diagnosis and management of targeted diseases; hence, facilitating timely therapy decisions and combination with nanotechnology can improve assessment of the disease onset and its progression and help to plan for treatment of many diseases. In this review, we explore how nanotechnology has been utilized in the development of nanosensors and the current trends of these nanosensors for point-of-care diagnosis of various diseases.

Incorporating genomic methods into contact networks to reveal new insights into animal behavior and infectious disease dynamics

Utilization of contact networks has provided opportunities for assessing the dynamic interplay between pathogen transmission and host behavior. Genomic techniques have, in their own right, provided new insight into complex questions in disease ecology, and the increasing accessibility of genomic approaches means more researchers may seek out these tools. The integration of network and genomic approaches provides opportunities to examine the interaction between behavior and pathogen transmission in new ways and with greater resolution. While a number of studies have begun to incorporate both contact network and genomic approaches, a great deal of work has yet to be done to better integrate these techniques. In this review, we give a broad overview of how network and genomic approaches have each been used to address questions regarding the interaction of social behavior and infectious disease, and then discuss current work and future horizons for the merging of these techniques.

Statistical outbreak detection by joining medical records and pathogen similarity

We present a statistical inference model for the detection and characterization of outbreaks of hospital associated infection. The approach combines patient exposures, determined from electronic medical records, and pathogen similarity, determined by whole-genome sequencing, to simultaneously identify probable outbreaks and their root-causes. We show how our model can be used to target isolates for whole-genome sequencing, improving outbreak detection and characterization even without comprehensive sequencing. Additionally, we demonstrate how to learn model parameters from reference data of known outbreaks. We demonstrate model performance using semi-synthetic experiments.

Quantifying the spatial spread of dengue in a non-endemic Brazilian metropolis via transmission chain reconstruction

The ongoing geographical expansion of dengue is inducing an epidemiological transition in many previously transmission-free urban areas, which are now prone to annual epidemics. To analyze the spatiotemporal dynamics of dengue in these settings, we reconstruct transmission chains in Porto Alegre, Brazil, by applying a Bayesian inference model to geo-located dengue cases from 2013 to 2016. We found that transmission clusters expand by linearly increasing their diameter with time, at an average rate of about 600 m month⁻¹. The majority (70.4%, 95% CI: 58.2–79.8%) of individual transmission events occur within a distance of 500 m. Cluster diameter, duration, and epidemic size are proportionally smaller when control interventions were more timely and intense. The results suggest that a large proportion of cases are transmitted via short-distance human movement (<1 km) and a limited contribution of long distance commuting within the city. These results can assist the design of control policies, including insecticide spraying and strategies for active case finding.

There is increasing urgency to understand the spatiotemporal dynamics of dengue in non-endemic regions. Here, the authors reconstruct likely dengue transmission chains in the city of Porto Alegre based on geo-located cases only, and find that most transmission events occur over short-distances.

Quantifying the spatial spread of dengue in a non-endemic Brazilian metropolis via transmission chain reconstruction

The ongoing geographical expansion of dengue is inducing an epidemiological transition in many previously transmission-free urban areas, which are now prone to annual epidemics. To analyze the spatiotemporal dynamics of dengue in these settings, we reconstruct transmission chains in Porto Alegre, Brazil, by applying a Bayesian inference model to geo-located dengue cases from 2013 to 2016. We found that transmission clusters expand by linearly increasing their diameter with time, at an average rate of about 600 m month^-1. The majority (70.4%, 95% CI: 58.2-79.8%) of individual transmission events occur within a distance of 500 m. Cluster diameter, duration, and epidemic size are proportionally smaller when control interventions were more timely and intense. The results suggest that a large proportion of cases are transmitted via short-distance human movement (<1 km) and a limited contribution of long distance commuting within the city. These results can assist the design of control policies, including insecticide spraying and strategies for active case finding.

Application of nanodiagnostics in point-of-care tests for infectious diseases

Although tremendous efforts have been put into the treatment of infectious diseases to prevent epidemics and mortality, it is still one of the major health care issues that have a profound impact on humankind. Therefore, the development of specific, sensitive, accurate, rapid, low-cost, and easy-to-use diagnostic tools is still in urgent demand. Nanodiagnostics, defined as the application of nanotechnology to medical diagnostics, can offer many unique opportunities for more successful and efficient diagnosis and treatment for infectious diseases. In this review, we provide an overview of the nanodiagnostics for infectious diseases from nanoparticle-based, nanodevice-based, and point-of-care test (POCT) platforms. Most importantly, emphasis focused on the recent trends in the nanotechnology-based POCT system. The current state-of-the-art and most promising point-of-care nanodiagnostic technologies, including miniaturized diagnostic magnetic resonance platform, magnetic barcode assay system, cell phone-based polarized light microscopy platform, cell phone-based dongle platform, and paper-based POCT platform, for infectious diseases were fully examined. The limitations, challenges, and future trends of the nanodiagnostics in POCTs for infectious diseases are also discussed.