Towards bioinformatics assisted infectious disease control

Next-Generation Sequencing Reveals the Progression of COVID-19

The novel coronavirus SARS-CoV-2 (causing the disease COVID-19) has caused a highly transmissible and ongoing pandemic worldwide. Due to its rapid development, next-generation sequencing plays vital roles in many aspects. Here, we summarize the current knowledge on the origin and human transmission of SARS-CoV-2 based on NGS analysis. The ACE2 expression levels in various human tissues and relevant cells were compared to provide insights into the mechanism of SAS-CoV-2 infection. Gut microbiota dysbiosis observed by metagenome sequencing and the immunogenetics of COVID-19 patients according to single-cell sequencing analysis were also highlighted. Overall, the application of these sequencing techniques could be meaningful for finding novel intermediate SARS-CoV-2 hosts to block interspecies transmission. This information will further benefit SARS-CoV-2 diagnostic development and new therapeutic target discovery. The extensive application of NGS will provide powerful support for our fight against future public health emergencies.

Revealing COVID-19 transmission in Australia by SARS-CoV-2 genome sequencing and agent-based modeling

In January 2020, a novel betacoronavirus (family Coronaviridae), named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified as the etiological agent of a cluster of pneumonia cases occurring in Wuhan City, Hubei Province, China^1,2. The disease arising from SARS-CoV-2 infection, coronavirus disease 2019 (COVID-19), subsequently spread rapidly causing a worldwide pandemic. Here we examine the added value of near real-time genome sequencing of SARS-CoV-2 in a subpopulation of infected patients during the first 10 weeks of COVID-19 containment in Australia and compare findings from genomic surveillance with predictions of a computational agent-based model (ABM). Using the Australian census data, the ABM generates over 24 million software agents representing the population of Australia, each with demographic attributes of an anonymous individual. It then simulates transmission of the disease over time, spreading from specific infection sources, using contact rates of individuals within different social contexts. We report that the prospective sequencing of SARS-CoV-2 clarified the probable source of infection in cases where epidemiological links could not be determined, significantly decreased the proportion of COVID-19 cases with contentious links, documented genomically similar cases associated with concurrent transmission in several institutions and identified previously unsuspected links. Only a quarter of sequenced cases appeared to be locally acquired and were concordant with predictions from the ABM. These high-resolution genomic data are crucial to track cases with locally acquired COVID-19 and for timely recognition of independent importations once border restrictions are lifted and trade and travel resume.

Systematic review of electronic surveillance of infectious diseases with emphasis on antimicrobial resistance surveillance in resource-limited settings

BACKGROUND

Electronic surveillance of infectious diseases involves rapidly collecting, collating, and analyzing vast amounts of data from interrelated multiple databases. Although many developed countries have invested in electronic surveillance for infectious diseases, the system still presents a challenge for resource-limited health care settings.

METHODS

We conducted a systematic review by performing a comprehensive literature search on MEDLINE (January 2000-December 2015) to identify studies relevant to electronic surveillance of infectious diseases. Study characteristics and results were extracted and systematically reviewed by 3 infectious disease physicians.

RESULTS

A total of 110 studies were included. Most surveillance systems were developed and implemented in high-income countries; less than one-quarter were conducted in low-or middle-income countries. Information technologies can be used to facilitate the process of obtaining laboratory, clinical, and pharmacologic data for the surveillance of infectious diseases, including antimicrobial resistance (AMR) infections. These novel systems require greater resources; however, we found that using electronic surveillance systems could result in shorter times to detect targeted infectious diseases and improvement of data collection.

CONCLUSIONS

This study highlights a lack of resources in areas where an effective, rapid surveillance system is most needed. The availability of information technology for the electronic surveillance of infectious diseases, including AMR infections, will facilitate the prevention and containment of such emerging infectious diseases.

Overview of Knowledge Discovery in Databases Process and Data Mining for Surveillance Technologies and EWS

Development of more effective early warning systems (EWSs) for various applications have been possible during the past decade due to advancements in information, detection, data mining (DM) and surveillance technologies. These application areas include economy, banking, finance, health care, bioinformatics, production and service delivery, hazard and crime prevention and minimization of other social risks involving the environment, administrations, politics and human rights. This chapter aims to define knowledge discovery in databases (KDD) process in five steps: Data preparation, data preprocessing, DM, evaluation and interpretation, and implementation. DM is further explained in descriptive and predictive mining categories with their functions and methods used or likely to be used in EWSs. In addition to well-known structured data types, mining of advanced data types such as spatial, temporal, sequence, images, multimedia and hypertexts is also introduced. Moreover, it presents a brief survey of overview and application papers and software in the EWS literature.

Innovation in observation: a vision for early outbreak detection

The emergence of new infections and resurgence of old onesFhealth threats stemming from environmental contamination or purposeful acts of bioterrorismFcall for a worldwide effort in improving early outbreak detection, with the goal of ameliorating current and future risks. In some cases, the problem of outbreak detection is logistically straightforward and mathematically easy: a single case of a disease of great concern can constitute an outbreak. However, for the vast majority of maladies, a simple analytical solution does not exist. Furthermore, each step in developing reliable, sensitive, effective surveillance systems demonstrates enormous complexities in the transmission, manifestation, detection, and control of emerging health threats. In this communication, we explore potential future innovations in early outbreak detection systems that can overcome the pitfalls of current surveillance. We believe that modern advances in assembling data, techniques for collating and processing information, and technology that enables integrated analysis will facilitate a new paradigm in outbreak definition and detection. We anticipate that moving forward in this direction will provide the highly desired sensitivity and specificity in early detection required to meet the emerging challenges of global disease surveillance.

Informatics for Infectious Disease Research and Control

The goal of infectious disease informatics is to optimize the clinical and public health management of infectious diseases through improvements in the development and use of antimicrobials, the design of more effective vaccines, the identification of biomarkers for life-threatening infections, a better understanding of host-pathogen interactions, and biosurveillance and clinical decision support. Infectious disease informatics can lead to more targeted and effective approaches for the prevention, diagnosis and treatment of infections through a comprehensive review of the genetic repertoire and metabolic profiles of a pathogen. The developments in informatics have been critical in boosting the translational science and in supporting both reductionist and integrative research paradigms.