Real Time Viral Sub-Strains Discovery in Emerging Infectious Disease Situation – The African Perspective

Abstract Background: The increased number of accessible genomes has prompted large-scale comparative studies for decerning evolutionary knowledge of infectious diseases, but challenges such as non-availability of close reference sequence(s), incompletely assembled or large number of genomes, preclude real time multiple sequence alignment and sub-strain(s) discovery. This paper introduces a cooperatively inspired open-source framework, for intelligent mining of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genomes. We situate this study within the African context, to drive advancement on state-of-the-art, towards intelligent infectious disease characterization and prediction. The outcome is an enriched Knowledge Base, sufficient to provide deep understanding of the viral sub-strains’ identification problem. We also open investigation by gender, which to the best of our knowledge has been ignored in related research. Data for the study came from the Global Initiative on Sharing All Influenza Data database (https://gisaid.org) and processed for precise discovery of viral sub-strains transmission between and within African countries. To localize the transmission route(s) of each isolate excavated and provide appropriate links to similar isolate strain(s), a cognitive solution was imposed on the genome expression patterns discovered by unsupervised self-organizing map (SOM) component planes visualization. The Freidman-Nemenyi’s test was finally performed to validate our claim. Results: Evidence of inter- and intra-genome diversity was noticed. While some isolates (or genomes) clustered differently, implying different evolutionary source (or high-diversity), others clustered closely together, indicating similar evolutionary source (or less-diversity). SOM component planes analysis revealed multiple sub-strains patterns, strongly suggesting local- or intra-community and country to country transmissions. Cognitive maps of both male and female isolates revealed multiple transmission routes. Freidman’s test results showed highly significant difference (p<0.01) among the various isolate groups. Nemenyi’s test revealed groups that differed in their isolates.Conclusion: The proposed framework offers explanations to SARS-CoV-2 diversity and provides real time identification to disease transmission routes, as well as rapid decision support for facilitating inter- and intra-country contact tracing of infected case(s). Intermediate data produced in this paper are helpful to enrich the genome datasets for intelligent characterization and prediction of COVID-19 and related pandemics, as well as the construction of intelligent device for accurate infectious disease monitoring.

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Real Time Viral Sub-Strains Discovery in Emerging Infectious Disease Situation – The African Perspective

10.21203/rs.3.rs-103476/v2 ◽

2020 ◽

Author(s):

Moses Effiong Ekpenyong ◽

Faith-Michael Uzoka ◽

Mercy Edoho ◽

Udoinyang G. Inyang ◽

Ifiok J Udo ◽

...

Keyword(s):

Infectious Disease ◽

Real Time ◽

Disease Transmission ◽

Large Scale ◽

Expression Patterns ◽

Deep Understanding ◽

Contact Tracing ◽

Reference Sequence ◽

African Countries ◽

Significant Difference

Abstract Background: The increased number of accessible genomes has prompted large-scale comparative studies for discerning evolutionary knowledge of infectious diseases, but challenges such as non-availability of close reference sequence(s), incompletely assembled or large number of genomes, preclude real time multiple sequence alignment and sub-strain(s) discovery. This paper introduces a cooperatively inspired open-source framework, for intelligent mining of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genomes. We situate this study within the African context, to drive advancement on state-of-the-art, towards intelligent infectious disease characterization and prediction. The outcome is an enriched Knowledge Base, sufficient to provide deep understanding of the viral sub-strains’ identification problem. We also open investigation by gender, which to the best of our knowledge has been ignored in related research. Data for the study came from the Global Initiative on Sharing All Influenza Data database (https://gisaid.org) and processed for precise discovery of viral sub-strains transmission between and within African countries. To localize the transmission route(s) of each isolate excavated and provide appropriate links to similar isolate strain(s), a cognitive solution was imposed on the genome expression patterns discovered by unsupervised self-organizing map (SOM) component planes visualization. The Freidman-Nemenyi’s test was finally performed to validate our claim. Results: Evidence of inter- and intra-genome diversity was noticed. While some isolates (or genomes) clustered differently, implying different evolutionary source (or high-diversity), others clustered closely together, indicating similar evolutionary source (or less-diversity). SOM component planes analysis revealed multiple sub-strains patterns, strongly suggesting local- or intra-community and country to country transmissions. Cognitive maps of both male and female isolates revealed multiple transmission routes. Freidman’s test results showed highly significant difference (p<0.01) among the various isolate groups. Nemenyi’s test revealed groups that differed in their isolates.Conclusion: The proposed framework offers explanations to SARS-CoV-2 diversity and provides real time identification to disease transmission routes, as well as rapid decision support for facilitating inter- and intra-country contact tracing of infected case(s). Intermediate data produced in this paper are helpful to enrich the genome datasets for intelligent characterization and prediction of COVID-19 and related pandemics, as well as the construction of intelligent device for accurate infectious disease monitoring.

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Real Time Viral Sub-Strains Discovery in Emerging Infectious Disease Situation – The African Perspective

10.21203/rs.3.rs-103476/v1 ◽

2020 ◽

Author(s):

Moses Effiong Ekpenyong ◽

Mercy Edoho ◽

Udoinyang G. Inyang ◽

Faith-Michael Uzoka ◽

Ifiok J Udo ◽

...

Keyword(s):

Infectious Disease ◽

Real Time ◽

Disease Transmission ◽

Large Scale ◽

Expression Patterns ◽

Deep Understanding ◽

Contact Tracing ◽

Reference Sequence ◽

African Countries ◽

Significant Difference

Abstract BackgroundThe increased number of accessible genomes has prompted large-scale comparative studies for decerning evolutionary knowledge of infectious diseases, but challenges such as non-availability of close reference sequence(s), incompletely assembled or large number of genomes, preclude real time multiple sequence alignment and sub-strain(s) discovery. This paper introduces a cooperatively inspired open-source framework, for intelligent mining of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genomes. We situate this study within the African context, to drive advancement on state-of-the-art, towards intelligent infectious disease characterization and prediction. The outcome is an enriched Knowledge Base, sufficient to provide deep understanding of the viral sub-strains’ identification problem. We also open investigation by gender, which to the best of our knowledge has been ignored in related research. Data for the study came from the Global Initiative on Sharing All Influenza Data database (https://gisaid.org) and processed for precise discovery of viral sub-strains transmission between and within African countries. To localize the transmission route(s) of each isolate excavated and provide appropriate links to similar isolate strain(s), a cognitive solution was imposed on the genome expression patterns discovered by unsupervised self-organizing map (SOM) component planes visualization. The Freidman-Nemenyi’s test was finally performed to validate our claim.ResultsEvidence of inter- and intra-genome diversity was noticed. While some isolates (or genomes) clustered differently, implying different evolutionary source (or high-diversity), others clustered closely together, indicating similar evolutionary source (or less-diversity). SOM component planes analysis revealed multiple sub-strains patterns, strongly suggesting local or intra-community and country to country transmissions. Cognitive maps of both male and female isolates revealed multiple transmission routes. Statistical results indicate significant difference between the various isolate groups at the 0.05 level of significance.ConclusionThe proposed framework offers explanations to SARS-CoV-2 diversity and provides real time identification to disease transmission routes, as well as rapid decision support for facilitating inter- and intra-country contact tracing of infected case(s). Intermediate data produced in this paper are helpful to enrich the genome datasets for intelligent characterization and prediction of COVID-19 and related pandemics, as well as the construction of intelligent device for accurate infectious disease monitoring.

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Genome-wide screening of m6A-modified transcript in the Wilms’ tumor

10.21203/rs.3.rs-197376/v1 ◽

2021 ◽

Author(s):

Zhuo Liu ◽

Feng He ◽

Jing Liu ◽

Shengrong OuYang ◽

Zexi Li ◽

...

Keyword(s):

Gene Ontology ◽

Real Time ◽

Real Time Pcr ◽

Wilms Tumor ◽

Expression Patterns ◽

Mrna Levels ◽

Gene Ontology Analysis ◽

Quantitative Real Time Pcr ◽

Related Factors ◽

Significant Difference

Abstract Background Wilms’ tumor, also called nephroblastoma, is the most common pediatric renal malignancy. The pathogenesis of Wilms’ tumor has been attributed to several genetic and epigenetic factors. However, the most pervasive internal mRNA modification that affects almost every process of RNA metabolism, RNA N6-Methyladenosine (m6A) methylation, has not been characterized in Wilms’ tumor. Methods Wilms’ tumor (WT) and adjacent non-cancerous (NC) tissue samples were obtained from 23 children with nephroblastoma, and the global m6A levels were measured by mass spectrometry. Analyses by m6A-mRNA epitranscriptomic microarray and mRNA microarray were performed, and m6A-related mRNAs were validated by quantitative real-time PCR for input and m6A-immunoprecipitated RNA samples from WT and NC tissues. Gene ontology analysis and KEGG pathway analysis were performed for differentially expressed genes, and expression of RNA methylation-related factors was measured by quantitative real-time PCR. Results The total m6A methylation levels in total RNA of WT samples and NC samples were (0.21 ± 0.01)% and (0.22 ± 0.01)%, respectively, with no statistically significant difference. Fifty-nine transcripts were differentially m6A-methylated between the WT and NC groups, which showed distinct m6A modification patterns. Gene ontology analysis indicated that m6A-modified genes were enriched in cancer-associated pathways, including the mTOR pathway, and conjoint analysis of the unique methylation and gene expression patterns in WT samples suggested an association with metabolic pathways.The mRNA levels of the m6A-related “reader” genes, YTHDF1, YTHDF2 and IGF2BP3, were statistically higher in WT samples than in NC samples. Conclusion This is the first study to determine the m6A modification profiles in Wilms’ tumor. Our data provide novel information regarding patterns of m6A modification that correlate with carcinogenesis in Wilms’ tumor.

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An empirical test of the role of small-scale transmission in large-scale disease dynamics

10.1101/285080 ◽

2018 ◽

Author(s):

Joseph R. Mihaljevic ◽

Carlos M. Polivka ◽

Constance J. Mehmel ◽

Chentong Li ◽

Vanja Dukic ◽

...

Keyword(s):

Infectious Disease ◽

Disease Transmission ◽

Large Scale ◽

Empirical Test ◽

Disease Model ◽

Individual Variability ◽

Host Susceptibility ◽

Small Scale ◽

Unconstrained Model

AbstractA key assumption of models of infectious disease is that population-scale spread is driven by transmission between host individuals at small scales. This assumption, however, is rarely tested, likely because observing disease transmission between host individuals is non-trivial in many infectious diseases. Quantifying the transmission of insect baculoviruses at a small scale is in contrast straightforward. We fit a disease model to data from baculovirus epizootics (= epidemics in animals) at the scale of whole forests, while using prior parameter distributions constructed from branch-scale experiments. Our experimentally-constrained model fits the large-scale data very well, supporting the role of small-scale transmission mechanisms in baculovirus epizootics. We further compared our experimentally-based model to an unconstrained model that ignores our experimental data, serving as a proxy for models that include large-scale mechanisms. This analysis supports our hypothesis that small-scale mechanisms are important, especially individual variability in host susceptibility to the virus. Comparison of transmission rates in the two models, however, suggests that large-scale mechanisms increase transmission compared to our experimental estimates. Our study shows that small-scale and large-scale mechanisms drive forest-wide epizootics of baculoviruses, and that synthesizing mathematical models with data collected across scales is key to understanding the spread of infectious disease.

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Superspreading k-cores at the center of COVID-19 pandemic persistence

10.1101/2020.08.12.20173476 ◽

2020 ◽

Author(s):

Matteo Serafino ◽

Higor S. Monteiro ◽

Shaojun Luo ◽

Saulo D. S. Reis ◽

Carles Igual ◽

...

Keyword(s):

Disease Transmission ◽

Large Scale ◽

Mobile Apps ◽

Human Mobility ◽

Contact Tracing ◽

Weak Links ◽

Contact Network ◽

Transmission Network ◽

Before And After ◽

High Betweenness

The spread of COVID-19 caused by the recently discovered SARS-CoV-2 virus has become a worldwide problem with devastating consequences. To slow down the spread of the pandemic, mass quarantines have been implemented globally, provoking further social and economic disruptions. Here, we address this problem by implementing a large-scale contact tracing network analysis to find the optimal quarantine protocol to dismantle the chain of transmission of coronavirus with minimal disruptions to society. We track billions of anonymized GPS human mobility datapoints from a compilation of hundreds of mobile apps deployed in Latin America to monitor the evolution of the contact network of disease transmission before and after the confinements. As a consequence of the lockdowns, people's mobility across the region decreases by ~53%, which results in a drastic disintegration of the transmission network by ~90%. However, this disintegration did not halt the spreading of the disease. Our analysis indicates that superspreading k-core structures persist in the transmission network to prolong the pandemic. Once the k-cores are identified, the optimal strategy to break the chain of transmission is to quarantine a minimal number of 'weak links' with high betweenness centrality connecting the large k-cores. Our results demonstrate the effectiveness of an optimal tracing strategy to halt the pandemic. As countries race to build and deploy contact tracing apps, our results could turn into a valuable resource to help deploy protocols with minimized disruptions.

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Searching for the Source of Infection: A Website to Help Teach the Principles of Infectious Disease

The American Biology Teacher ◽

10.1525/abt.2020.82.1.37 ◽

2020 ◽

Vol 82 (1) ◽

pp. 37-42

Author(s):

Jie Shen ◽

Zhu Xiang ◽

Yang Peijing ◽

Zhou Zixuan

Keyword(s):

Infectious Disease ◽

Infectious Diseases ◽

Real Time ◽

Disease Transmission ◽

Exchange Process ◽

Multiple Infection ◽

Major Threat ◽

Source Of Infection ◽

Sources Of Infection ◽

Test Use

Infectious diseases are a major threat to humans, and finding sources of infection is therefore an important task. We designed a website to help teachers communicate the relevant principles of infectious diseases, deepen students' understanding of disease transmission, and equip students with the ability to trace the origin of infections caused by microorganisms. The website enables multi-person online use, with real-time recording of the exchange process and real-time viewing of infection results. Additionally, the website is able to preserve data permanently by setting multiple infection sources, providing a better simulation of real-world scenarios. Test use of the website by 120 students demonstrated that it has no significant bugs.

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The secondary transmission pattern of COVID-19 based on contact tracing in Rwanda

BMJ Global Health ◽

10.1136/bmjgh-2020-004885 ◽

2021 ◽

Vol 6 (6) ◽

pp. e004885

Author(s):

Muhammed Semakula ◽

FranÇois Niragire ◽

Angela Umutoni ◽

Sabin Nsanzimana ◽

Vedaste Ndahindwa ◽

...

Keyword(s):

Risk Factors ◽

High Risk ◽

Disease Transmission ◽

Index Case ◽

Conditional Logistic Regression ◽

Contact Tracing ◽

African Countries ◽

Targeted Interventions ◽

The Impact ◽

Mapping Models

IntroductionCOVID-19 has shown an exceptionally high spread rate across and within countries worldwide. Understanding the dynamics of such an infectious disease transmission is critical for devising strategies to control its spread. In particular, Rwanda was one of the African countries that started COVID-19 preparedness early in January 2020, and a total lockdown was imposed when the country had only 18 COVID-19 confirmed cases known. Using intensive contact tracing, several infections were identified, with the majority of them being returning travellers and their close contacts. We used the contact tracing data in Rwanda for understanding the geographic patterns of COVID-19 to inform targeted interventions.MethodsWe estimated the attack rates and identified risk factors associated to COVID-19 spread. We used Bayesian disease mapping models to assess the spatial pattern of COVID-19 and to identify areas characterised by unusually high or low relative risk. In addition, we used multiple variable conditional logistic regression to assess the impact of the risk factors.ResultsThe results showed that COVID-19 cases in Rwanda are localised mainly in the central regions and in the southwest of Rwanda and that some clusters occurred in the northeast of Rwanda. Relationship to the index case, being male and coworkers are the important risk factors for COVID-19 transmission in Rwanda.ConclusionThe analysis of contact tracing data using spatial modelling allowed us to identify high-risk areas at subnational level in Rwanda. Estimating risk factors for infection with SARS-CoV-2 is vital in identifying the clusters in low spread of SARS-CoV-2 subnational level. It is imperative to understand the interactions between the index case and contacts to identify superspreaders, risk factors and high-risk places. The findings recommend that self-isolation at home in Rwanda should be reviewed to limit secondary cases from the same households and spatiotemporal analysis should be introduced in routine monitoring of COVID-19 in Rwanda for policy making decision on real time.

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A sub-exponential branching process to study early epidemic dynamics with application to Ebola

10.1101/797878 ◽

2019 ◽

Author(s):

Alexander E. Zarebski ◽

Robert Moss ◽

James M. McCaw

Keyword(s):

Infectious Disease ◽

Time Series ◽

West Africa ◽

Real Time ◽

Exponential Growth ◽

Branching Process ◽

Ebola Virus ◽

Time Series Data ◽

Contact Tracing ◽

Series Data

AbstractExponential growth is a mathematically convenient model for the early stages of an outbreak of an infectious disease. However, for many pathogens (such as Ebola virus) the initial rate of transmission may be sub-exponential, even before transmission is affected by depletion of susceptible individuals.We present a stochastic multi-scale model capable of representing sub-exponential transmission: an in-homogeneous branching process extending the generalised growth model. To validate the model, we fit it to data from the Ebola epidemic in West Africa (2014–2016). We demonstrate how a branching process can be fit to both time series of confirmed cases and chains of infection derived from contact tracing. Our estimates of the parameters suggest transmission of Ebola virus was sub-exponential during this epidemic. Both the time series data and the chains of infections lead to consistent parameter estimates. Differences in the data sets meant consistent estimates were not a foregone conclusion. Finally, we use a simulation study to investigate the properties of our methodology. In particular, we examine the extent to which the estimates obtained from time series data and those obtained from chains of infection data agree.Our method, based on a simple branching process, is well suited to real-time analysis of data collected during contact tracing. Identifying the characteristic early growth dynamics (exponential or sub-exponential), including an estimate of uncertainty, during the first phase of an epidemic should prove a useful tool for preliminary outbreak investigations.Author SummaryEpidemic forecasts have the potential to support public health decision making in outbreak scenarios for diseases such as Ebola and influenza. In particular, reliable predictions of future incidence data may guide surveillance and intervention responses. Existing methods for producing forecasts, based upon mechanistic transmission models, often make an implicit assumption that growth is exponential, at least while susceptible depletion remains negligible. However, empirical studies suggest that many infectious disease outbreaks display sub-exponential growth early in the epidemic. Here we introduce a mechanistic model of early epidemic growth that allows for sub-exponential growth in incidence. We demonstrate how the model can be applied to the types of data that are typically available in (near) real-time, including time series data on incidence as well as individual-level case series and chains of transmission data. We apply our methods to publically available data from the 2014–2016 West Africa Ebola epidemic and demonstrate that early epidemic growth was sub-exponential. We also investigate the statistical properties of our model through a simulation re-estimation study to identify it performance characteristics and avenues for further methodological research.

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Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada

F1000Research ◽

10.12688/f1000research.75891.1 ◽

2021 ◽

Vol 10 ◽

pp. 1312

Author(s):

Eliseos J. Mucaki ◽

Ben C. Shirley ◽

Peter K. Rogan

Keyword(s):

Public Health ◽

Real Time ◽

Disease Transmission ◽

Time Integration ◽

Community Level ◽

Moran’S I ◽

Contact Tracing ◽

Term Care ◽

Moran's I ◽

Community Transmission

Introduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. Methods: COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran’s I spatial autocorrelation, and Local Moran’s I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources. Results: This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Conclusions: Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.

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Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology (Preprint)

10.2196/preprints.9876 ◽

2018 ◽

Author(s):

Lindsay Meyers ◽

Christine C Ginocchio ◽

Aimie N Faucett ◽

Frederick S Nolte ◽

Per H Gesteland ◽

...

Keyword(s):

Infectious Disease ◽

United States ◽

Real Time ◽

Large Scale ◽

The United States ◽

Molecular Diagnostic ◽

Respiratory Pathogens ◽

Test Results ◽

Patient Privacy ◽

Clinical Laboratories

BACKGROUND Health care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy. OBJECTIVE The aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems. METHODS We describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States. RESULTS The majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present. CONCLUSIONS Syndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.

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