scholarly journals Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa

2021 ◽  
Vol 17 (3) ◽  
pp. e1008811
Author(s):  
Andrew J. Basinski ◽  
Elisabeth Fichet-Calvet ◽  
Anna R. Sjodin ◽  
Tanner J. Varrelman ◽  
Christopher H. Remien ◽  
...  
Keyword(s):  
Machine Learning ◽  
West Africa ◽  
Human Population ◽  
Risk Model ◽  
Lassa Virus ◽  
Training Models ◽  
Infection Dynamics ◽  
Wildlife Vaccination ◽  
Risk Forecast ◽  
Human Infections

Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections.

scholarly journals Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus incidence in West Africa

2020 ◽  
Author(s):  
Andrew J. Basinski ◽  
Elisabeth Fichet-Calvet ◽  
Anna R. Sjodin ◽  
Tanner J. Varrelman ◽  
Christopher H. Remien ◽  
...  
Keyword(s):  
Machine Learning ◽  
West Africa ◽  
Human Population ◽  
Annual Number ◽  
Focal Region ◽  
Lassa Virus ◽  
Human Virus ◽  
Persistent Problem ◽  
Animal Host ◽  
Geographical Regions

AbstractForecasting how the risk of pathogen spillover changes over space is essential for the effective deployment of interventions such as human or wildlife vaccination. However, due to the sporadic nature of spillover events and limited availability of data, developing and validating robust predictions is challenging. Recent efforts to overcome this obstacle have capitalized on machine learning to predict spillover risk. Past approaches combine infection data from both humans and reservoir to train models that assess risk across broad geographical regions. In doing so, these models blend data sources that separately describe pathogen risk posed by the reservoir and the realized rate of spillover into the human population. We develop a novel approach that models as separate stages: 1) the contributions of spillover risk from the reservoir and pathogen distribution, and 2) the resulting incidence of pathogen in the human population. Our methodology allows for a rigorous assessment of whether forecasts of spillover risk can reliably predict the realized spillover rate into humans, as measured by seroprevalence. In addition to providing a rigorous cross-validation of risk predictions, this methodology could shed light on human habits that modulate or amplify the resultant spillover. We apply our method to Lassa virus, a zoonotic pathogen that poses a high threat of emergence in West Africa. The resulting framework is the first forecast to quantify the extent to which predictions of spillover risk from the reservoir explain regional variation in human seroprevalence. We use predictions generated by the model to revise existing estimates for the annual number of new human Lassa infections. Our model predicts that between 935,200 – 3,928,000 humans are infected by Lassa virus each year, an estimate that exceeds current conventional wisdom.Author SummaryThe 2019 emergence of SARS-2 coronavirus is a grim reminder of the threat animal-borne pathogens pose to human health. Even prior to SARS-2, the spillover of so-called zoonotic pathogens was a persistent problem, with pathogens such as Ebola and Lassa regularly but unpredictably causing outbreaks. Machine-learning models that can anticipate when and where animal-to-human virus transmission is most likely to occur could help guide surveillance effort, as well as preemptive countermeasures to pandemics, like information campaigns or vaccination programs. We develop a novel machine learning framework that uses data-sets describing the distribution of a virus within its host and the range of its animal host, along with human immunity data, to infer rates of animal-to-human transmission across a focal region. By training the model on data from the animal host, our framework allows rigorous validation of spillover predictions on human data. We apply our framework to Lassa fever, a viral disease of West Africa that is spread to humans by rodents, and update estimates of symptomatic and asymptomatic Lassa virus infections in humans. Our results suggest that Nigeria is most at risk for the emergence of new strains of Lassa virus, and therefore should be prioritized for outbreak-surveillance.

scholarly journals Bayesian estimation of Lassa virus epidemiological parameters: implications for spillover prevention using wildlife vaccination

2019 ◽  
Author(s):  
Scott L. Nuismer ◽  
Christopher H. Remien ◽  
Andrew Basinski ◽  
Tanner Varrelman ◽  
Nathan Layman ◽  
...  
Keyword(s):  
West Africa ◽  
Surveillance Data ◽  
Model Parameters ◽  
Lassa Virus ◽  
Vaccination Programs ◽  
Public Health Burden ◽  
Health Burden ◽  
Human Infections ◽  
Rodent Populations ◽  
Epidemiological Parameters

AbstractLassa virus is a significant burden on human health throughout its endemic region in West Africa, with most human infections the result of spillover from the primary rodent reservoir of the virus, the natal multimammate mouse,M. natalensis. Here we develop a Bayesian methodology for estimating epidemiological parameters of Lassa virus within its rodent reservoir and for generating probabilistic predictions for the efficacy of rodent vaccination programs. Our approach uses Approximate Bayesian Computation (ABC) to integrate mechanistic mathematical models, remotely-sensed precipitation data, and Lassa virus surveillance data from rodent populations. Using simulated data, we show that our method accurately estimates key model parameters, even when surveillance data are available from only a relatively small number of points in space and time. Applying our method to previously published data from two villages in Guinea estimates the time-averagedR0of Lassa virus to be 1.658 and 1.453 for rodent populations in the villages of Bantou and Tanganya, respectively. Using the posterior distribution for model parameters derived from these Guinean populations, we evaluate the likely efficacy of vaccination programs relying on distribution of vaccine-laced baits. Our results demonstrate that effective and durable reductions in the risk of Lassa virus spillover into the human population will require repeated distribution of large quantities of vaccine.Author SummaryLassa virus is a chronic source of illness throughout West Africa, and is considered to be a threat for widespread emergence. Because most human infections result from contact with infected rodents, interventions that reduce the number of rodents infected with Lassa virus represent promising opportunities for reducing the public health burden of this disease. Evaluating how well alternative interventions are likely to perform is complicated by our relatively poor understanding of viral epidemiology within the reservoir population. Here we develop a novel statistical approach that couples mathematical models and viral surveillance data from rodent populations to robustly estimate key epidemiological parameters. Applying our method to existing data from Guinea yields well-resolved parameter estimates and allows us to simulate a variety of rodent vaccination programs. Together, our results demonstrate that rodent vaccination alone is unlikely to be an effective tool for reducing that public health burden of Lassa fever within West Africa.

Determining Ancestry between Rodent- and Human-Derived Lassa Virus Sequences in Endemic Foci within West Africa: Indications of Reverse Zoonosis

2019 ◽  
Author(s):  
Ayodeji Olayemi ◽  
Adetunji Samuel Adesina ◽  
Thomas Strecker ◽  
N’Faly Magassouba ◽  
Elisabeth Fichet-Calvet
Keyword(s):  
West Africa ◽  
Lassa Virus

scholarly journals New risk model is able to identify patients with a low risk of progression in systemic sclerosis

RMD Open ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e001524
Author(s):  
Nina Marijn van Leeuwen ◽  
Marc Maurits ◽  
Sophie Liem ◽  
Jacopo Ciaffi ◽  
Nina Ajmone Marsan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Sclerosis ◽  
High Risk ◽  
Disease Progression ◽  
Risk Model ◽  
Immunosuppressive Drugs ◽  
Low Risk ◽  
Expert Assessment ◽  
Risk Of Progression

ObjectivesTo develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.MethodsA machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prospective Leiden SSc cohort and fulfilling the ACR/EULAR 2013 criteria were included. Disease progression was defined as progression in ≥1 organ system, and/or start of immunosuppression or death. Using elastic-net-regularisation, and including 90 independent clinical variables (100% complete), we trained the model on 75% and validated it on 25% of the patients, optimising on negative predictive value (NPV) to minimise the likelihood of missing progression. Probability cutoffs were identified for low and high risk for disease progression by expert assessment.ResultsOf the 492 SSc patients (follow-up range: 2–10 years), disease progression during follow-up was observed in 52% (median time 4.9 years). Performance of the model in the test set showed an AUC-ROC of 0.66. Probability score cutoffs were defined: low risk for disease progression (<0.197, NPV:1.0; 29% of patients), intermediate risk (0.197–0.223, NPV:0.82; 27%) and high risk (>0.223, NPV:0.78; 44%). The relevant variables for the model were: previous use of cyclophosphamide or corticosteroids, start with immunosuppressive drugs, previous gastrointestinal progression, previous cardiovascular event, pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.ConclusionOur machine-learning-assisted model for progression enabled us to classify 29% of SSc patients as ‘low risk’. In this group, annual assessment programmes could be less extensive than indicated by international guidelines.

scholarly journals An Application of Imipenem Discs or P. aeruginosa ATCC 27853 Reference Strain Increases Sensitivity of Carbapenem Inactivation Method for Non-Fermenting Gram-Negative Bacteria

Antibiotics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 875
Author(s):  
Tomasz Bogiel ◽  
Mateusz Rzepka ◽  
Eugenia Gospodarek-Komkowska
Keyword(s):  
Human Population ◽  
Reference Strain ◽  
Resistance Mechanisms ◽  
Gram Negative Bacteria ◽  
Opportunistic Pathogens ◽  
Beta Lactams ◽  
Gram Negative ◽  
E Coli ◽  
Human Infections ◽  
Reliable Diagnostic Method

Non-fermenting Gram-negative rods are one of the most commonly isolated bacteria from human infections. These microorganisms are typically opportunistic pathogens that pose a serious threat to public health due to possibility of transmission in the human population. Resistance to beta-lactams, due to carbapenemases synthesis, is one of the most important antimicrobial resistance mechanisms amongst them. The aim of this study was to evaluate the usefulness of the Carbapenem Inactivation Method (CIM), and its modifications, for the detection of carbapenemase activity amongst non-fermenting Gram-negative rods. This research involved 81 strains of Gram-negative rods. Of the tested strains, 55 (67.9%) synthesized carbapenemases. For non-fermenting rods, 100% sensitivity and specificity was obtained in the version of the CIM test using imipenem discs and E. coli ATCC 25922 strain. The CIM test allows for differentiation of carbapenems resistance mechanisms resulting from carbapenemase synthesis from other resistance types. It is a reliable diagnostic method for the detection of carbapenemase activity amongst non-fermenting Gram-negative rods. Application of imipenem discs and P. aeruginosa ATCC 27853 reference strain increases CIM results sensitivity, while imipenem discs and E. coli ATCC 25922 strain use maintains full precision of the test for non-fermenting rods.

scholarly journals Using Machine Learning Methods Incorporating Individual Reader Annotations to Classify Paediatric Chest Radiographs in Epidemiological Studies

2021 ◽  
Vol 6 ◽  
pp. 309
Author(s):  
Paul Mwaniki ◽  
Timothy Kamanu ◽  
Samuel Akech ◽  
M. J. C Eijkemans
Keyword(s):  
Machine Learning ◽  
Epidemiological Studies ◽  
Chest Radiographs ◽  
World Health ◽  
Data Sets ◽  
Learning Models ◽  
Middle Income ◽  
Training Models ◽  
Model Training ◽  
Machine Learning Models

Introduction: Epidemiological studies that involve interpretation of chest radiographs (CXRs) suffer from inter-reader and intra-reader variability. Inter-reader and intra-reader variability hinder comparison of results from different studies or centres, which negatively affects efforts to track the burden of chest diseases or evaluate the efficacy of interventions such as vaccines. This study explores machine learning models that could standardize interpretation of CXR across studies and the utility of incorporating individual reader annotations when training models using CXR data sets annotated by multiple readers. Methods: Convolutional neural networks were used to classify CXRs from seven low to middle-income countries into five categories according to the World Health Organization's standardized methodology for interpreting paediatric CXRs. We compared models trained to predict the final/aggregate classification with models trained to predict how each reader would classify an image and then aggregate predictions for all readers using unweighted mean. Results: Incorporating individual reader's annotations during model training improved classification accuracy by 3.4% (multi-class accuracy 61% vs 59%). Model accuracy was higher for children above 12 months of age (68% vs 58%). The accuracy of the models in different countries ranged between 45% and 71%. Conclusions: Machine learning models can annotate CXRs in epidemiological studies reducing inter-reader and intra-reader variability. In addition, incorporating individual reader annotations can improve the performance of machine learning models trained using CXRs annotated by multiple readers.

scholarly journals Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics

2020 ◽  
Author(s):  
Malik Magdon-Ismail
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Early Infection ◽  
Data Driven ◽  
Infection Dynamics ◽  
Serious Infection ◽  
Specific Virus ◽  
Different Populations ◽  
Learning Analysis ◽  
Over Time

AbstractWe present a robust data-driven machine learning analysis of the COVID-19 pandemic from its early infection dynamics, specifically infection counts over time. The goal is to extract actionable public health insights. These insights include the infectious force, the rate of a mild infection becoming serious, estimates for asymtomatic infections and predictions of new infections over time. We focus on USA data starting from the first confirmed infection on January 20 2020. Our methods reveal significant asymptomatic (hidden) infection, a lag of about 10 days, and we quantitatively confirm that the infectious force is strong with about a 0.14% transition from mild to serious infection. Our methods are efficient, robust and general, being agnostic to the specific virus and applicable to different populations or cohorts.

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