Global Infectious Disease Surveillance Research Articles

Surveillance and risk assessment for early detection of emerging infectious diseases in livestock.

Those who work in the area of surveillance and prevention of emerging infectious diseases (EIDs) face a challenge in accurately predicting where infection will occur and who (or what) it will affect. Establishing surveillance and control programmes for EIDs requires substantial and long-term commitment of resources that are limited in nature. This contrasts with the unquantifiable number of possible zoonotic and non-zoonotic infectious diseases that may emerge, even when the focus is restricted to diseases involving livestock. Such diseases may emerge from many combinations of, and changes in, host species, production systems, environments/habitats and pathogen types. Given these multiple elements, risk prioritisation frameworks should be used more widely to support decision-making and resource allocation for surveillance. In this paper, the authors use recent examples of EID events in livestock to review surveillance approaches for the early detection of EIDs, and highlight the need for surveillance programmes to be informed and prioritised by regularly updated risk assessment frameworks. They conclude by discussing some unmet needs in risk assessment practices for EIDs, and the need for improved coordination in global infectious disease surveillance.

What we can learn from the exported cases in detecting disease outbreaks – a case study of the COVID-19 epidemic

Purpose: Early warning in the travel origins is crucial to prevent disease spreading. When travel origins have delays in reporting disease outbreaks, the exported cases could be used to estimate the epidemic.Methods: We developed a Bayesian model to jointly estimate the epidemic prevalence and detection delay using the exported cases and their arrival and detection dates. We used simulation studies to discuss potential biases generated by the exported cases. We proposed a hypothesis testing framework to determine the epidemic severity.Results: We applied the method to the early phase of the COVID-19 epidemic of Wuhan, United States, Italy, and Iran and found that the indicators estimated from the exported cases were consistent with the domestic data under certain scenarios. The exported cases could generate various biases if not modeled properly. We presented the required number of exported cases for determining different severity levels of the outbreak.Conclusions: The exported case data is a good addition to the domestic data but also has its drawbacks. Utilizing the diagnosis resources from all countries, we advocate that countries work collaboratively to strengthen the global infectious disease surveillance system.

Use of Epidemic Intelligence from Open Sources for global event-based surveillance of infectious diseases for the Tokyo 2020 Olympic and Paralympic Games.

The establishment of enhanced surveillance systems for mass gatherings to detect infectious diseases that may be imported during an event is recommended. The World Health Organization Regional Office for the Western Pacific contributed to enhanced event-based surveillance for the Tokyo 2020 Olympic and Paralympic Games (the Games) by using Epidemic Intelligence from Open Sources (EIOS) to detect potential imported diseases and report them to the National Institute of Infectious Diseases (NIID), Japan. Daily screening of media articles on global infectious diseases was conducted using EIOS, which were systematically assessed to determine the likelihood of disease importation, spread and significant impact to Japan during the Games. Over 81 days of surveillance, 103 830 articles were screened by EIOS, of which 5441 (5.2%) met the selection criteria for initial assessment, with 587 (0.6%) assessed as signals and reported to NIID. None of the signals were considered to pose a significant risk to the Games based on three risk assessment criteria. While EIOS successfully captured media articles on infectious diseases with a likelihood of importation to and spread in Japan, a significant manual effort was required to assess the articles for duplicates and against the risk assessment criteria. Continued improvement of artificial intelligence is recommended to reduce this effort.

Prevalence of arbovirus antibodies in young healthy adult population in Brazil

BackgroundThe emergence and re-emergence of infectious diseases are a cause for worldwide concern. The introduction of Zika and Chikungunya diseases in the Americas has exposed unforeseen medical and logistical challenges for public health systems. Moreover, the lack of preventive measures and vaccination against known and emerging mosquito-transmitted pathogens, and the occurrence of unanticipated clinical complications, has had an enormous social and economic impact on the affected populations. In this study, we aimed to measure the seroprevalence of endemic and emerging viral pathogens in military personnel stationed in Manaus, Amazonas state.MethodsWe measured the seropositivity of antibodies against 19 endemic and emerging viruses in a healthy military personnel group using a hemagglutination inhibition assay (HIA).ResultsOverall, DENV positivity was 60.4%, and 30.9% of the individuals reacted against ZIKV. Also, 46.6%, 54.7%, 51.3% and 48.7% individuals reacted against West Nile virus (WNV), Saint Louis encephalitis virus (SLEV), Ilheus virus (ILHV) and Rocio virus (ROCV), respectively. Individuals with high DENV HIA titer reacted more frequently with ZIKV or WNV compared to those with low HIA titers. Observed cross-reactivity between Flaviviruses varied depending on the virus serogroup. Additionally, 0.6% and 0.3% individuals were seropositive for Oropouche virus (OROV) and Catu virus (CATUV) from the family Peribunyaviridae, respectively. All samples were negative for Eastern Equine Encephalitis virus (EEEV), Western Equine Encephalomyelitis virus (WEEV), Mayaro virus (MAYV), Mucambo virus (MUCV) and CHIKV from the family Togaviridae.ConclusionsA high proportion of individuals in our high-risk population (~ 60%) lacked antibodies against major endemic and emerging viruses, which makes them susceptible for further infections. Military personnel serving in the Amazon region could serve as sentinels to strengthen global infectious disease surveillance, particularly in remote areas.Graphical abstract

Global Infectious Disease Surveillance and Case Tracking System for COVID-19: Development Study.

BackgroundCOVID-19 has affected more than 180 countries and is the first known pandemic to be caused by a new virus. COVID-19’s emergence and rapid spread is a global public health and economic crisis. However, investigations into the disease, patient-tracking mechanisms, and case report transmissions are both labor-intensive and slow.ObjectiveThe pandemic has overwhelmed health care systems, forcing hospitals and medical facilities to find effective ways to share data. This study aims to design a global infectious disease surveillance and case tracking system that can facilitate the detection and control of COVID-19.MethodsThe International Patient Summary (IPS; an electronic health record that contains essential health care information about a patient) was used. The IPS was designed to support the used case scenario for unplanned cross-border care. The design, scope, utility, and potential for reuse of the IPS for unplanned cross-border care make it suitable for situations like COVID-19. The Fast Healthcare Interoperability Resources confirmed that IPS data, which includes symptoms, therapies, medications, and laboratory data, can be efficiently transferred and exchanged on the system for easy access by physicians. To protect privacy, patient data are deidentified. All systems are protected by blockchain architecture, including data encryption, validation, and exchange of records.ResultsTo achieve worldwide COVID-19 surveillance, a global infectious disease information exchange must be enacted. The COVID-19 surveillance system was designed based on blockchain architecture. The IPS was used to exchange case study information among physicians. After being verified, physicians can upload IPS files and receive IPS data from other global cases. The system includes a daily IPS uploading and enhancement plan, which covers real-time uploading through the interoperation of the clinic system, with the module based on the Open Application Programming Interface architecture. Through the treatment of different cases, drug treatments, and the exchange of treatment results, the disease spread can be controlled, and treatment methods can be funded. In the Infectious Disease Case Tracking module, we can track the moving paths of infectious disease cases. The location information recorded in the blockchain is used to check the locations of different cases. The Case Tracking module was established for the Centers for Disease Control and Prevention to track cases and prevent disease spread.ConclusionsWe created the IPS of infectious diseases for physicians treating patients with COVID-19. Our system can help health authorities respond quickly to the transmission and spread of unknown diseases, and provides a system for information retrieval on disease transmission. In addition, this system can help researchers form trials and analyze data from different countries. A common forum to facilitate the mutual sharing of experiences, best practices, therapies, useful medications, and clinical intervention outcomes from research in various countries could help control an unknown virus. This system could be an effective tool for global collaboration in evidence-based efforts to fight COVID-19.

What Can We Learn from the Exported Cases in Detecting Disease Outbreaks: A Case Study of the COVID-19 Epidemic

Background: Travel is a potent force in the emergence of disease. Early warning in the travel origins is crucial to prevent the disease from spreading. However, many travel origins have delays in reporting the disease outbreak. Under this circumstance, those exported cases (travelers tested positive outside their travel origins) became valuable data for detecting the disease outbreaks. Methods: We estimated some key indicators (the exponential growth rate, the doubling time, and the basic reproduction number) using the exported cases in the early phase of the COVID-19 epidemic of four travel origins, Wuhan (China), U.S., Italy, and Iran. We compared the estimates from the exported cases to the ones from domestic data, and quantitatively evaluated how different features in the exported cases influenced the estimation. We used hypothesis testing to determine if an indicator has exceeded a certain threshold.Findings: We found that the dates that the exported cases rang an alarm about the disease outbreak were all before or shortly after some emergency measures were taken in the travel origins. If the assumed epidemic start date was relatively accurate and the traveler samples were representative of the general population, the indicators estimated from the exported cases were consistent with the domestic data. We provided the minimum number of exported cases to determine whether the epidemic growth rate was above a certain level. Flexible scenarios were provided in a Shiny App. Interpretation: Rapid detection of a disease outbreak is crucial for government intervention and raising public awareness. Utilizing the diagnosis resources from all countries, the exported case data is a good addition to the domestic data. To strengthen the global infectious disease surveillance system, we advocate that countries work in a collaborative way by sharing detailed travel data in a timely manner.Funding: NIH/NIAID 5R01AI136664Declaration of Interests: The authors declare no competing interests.

Big Data, Algorithmic Governmentality and the Regulation of Pandemic Risk

This article investigates the rise of algorithmic disease surveillance systems as novel technologies of risk analysis utilised to regulate pandemic outbreaks in an era of big data. Critically, the article demonstrates how intensified efforts towards harnessing big data and the application of algorithmic processing techniques to enhance the real-time surveillance and regulation infectious disease outbreaks significantly transform practices of global infectious disease surveillance; observed through the advent of novel risk rationalities which underpin the deployment of intensifying algorithmic practices to increasingly colonise and patrol emergent topographies of data in order to identify and govern the emergence of exceptional pathogenic risks. Conceptually, this article asserts further howthe rise of these novel risk regulating technologies within a context of big data transforms the government and forecasting of epidemics and pandemics: illustrated by the rise of emergent algorithmic governmentalties of risk within contemporary contexts of big data, disease surveillance and the regulation of pandemic.

Metagenomic analysis of viruses in toilet waste from long distance flights-A new procedure for global infectious disease surveillance.

Human viral pathogens are a major public health threat. Reliable information that accurately describes and characterizes the global occurrence and transmission of human viruses is essential to support national and global priority setting, public health actions, and treatment decisions. However, large areas of the globe are currently without surveillance due to limited health care infrastructure and lack of international cooperation. We propose a novel surveillance strategy, using metagenomic analysis of toilet material from international air flights as a method for worldwide viral disease surveillance. The aim of this study was to design, implement, and evaluate a method for viral analysis of airplane toilet waste enabling simultaneous detection and quantification of a wide range of human viral pathogens. Toilet waste from 19 international airplanes was analyzed for viral content, using viral capture probes followed by high-throughput sequencing. Numerous human pathogens were detected including enteric and respiratory viruses. Several geographic trends were observed with samples originating from South Asia having significantly higher viral species richness as well as higher abundances of salivirus A, aichivirus A and enterovirus B, compared to samples originating from North Asia and North America. In addition, certain city specific trends were observed, including high numbers of rotaviruses in airplanes departing from Islamabad. Based on this study we believe that central sampling and analysis at international airports could be a useful supplement for global viral surveillance, valuable for outbreak detection and for guiding public health resources.

Advances and Scope in Big Data Analytics in Healthcare

Global healthcare industry is under significant pressure to reduce costs and more efficiently manage resources while improving patient care. Rising costs, chronic illness, an aging population and a shortage of professionals are forcing massive changes in the healthcare industry. To gain insight into how they can improve service while reducing costs, healthcare payers and providers are turning to data and analytics. Today Big data in healthcare is a hot issue. Therefore, in this communication, it is attempted to briefly present definitions, sources of big data, characteristics of Big Data, the architectural framework of big data analytics required in healthcare application. Big data analytics in healthcare reported in various literature are highlighted particularly on clinical data, Pharmaceutical data, Patient behavior, sentiment data, viral and Global Infectious Disease Surveillance. Lastly, the challenges are identified followed by future directions and advantages of big data analytics in healthcare.

Biosurveillance: a systematic review of global infectious disease surveillance systems from 1900 to 2016.

Biosurveillance is crucial to detect, identify and minimise the negative consequences of infectious disease. Its value to society and importance to global public health and global health security are growing. Despite the long history and global importance of biosurveillance, a systematic review of all existing biosurveillance systems across the 'One Health' spectrum has not yet been published. This study conducted a systematic review to identify all extant and defunct biosurveillance systems from 1900 to 2016. Of the 815 systems examined, the majority surveyed human, animal or plant data discretely. Some 105 collected human and animal data, whereas only 31 collected data on all three categories. The authors found a large increase in the number of global biosurveillance systems between 1900 and 2008, but a reduction in the number of biosurveillance systems from 2008 to the present. The number of syndromic systems created, versus laboratory-based biosurveillance systems, increased rapidly after 1980 across the globe.

Global infectious disease surveillance: getting back to basics.

Global infectious disease surveillance: getting back to basics.

Sharing Data for Global Infectious Disease Surveillance and Outbreak Detection

Rapid global sharing and comparison of epidemiological and genomic data on infectious diseases would enable more rapid and efficient global outbreak control and tracking of diseases. Several barriers for global sharing exist but, in our opinion, the presumed magnitude of the problems appears larger than they are, and solutions can be found.

The Politics of Global Health Security: Problemetizing a Social Evidence

Why have states, in a somewhat short period of time (1995-2005), suddenly decided to “cooperate” regarding global infectious disease surveillance? What kind of “cooperation” is it? Why did states apparently surrender part of their sovereign power to the WHO by giving it the power to declare pandemic at the global scale without state consent? These questions appear especially relevant in the context where issues of health and diseases at the global scale have been explicitly linked with the concepts of “risk”, “security”, “emergency”, “crisis”, “intelligence”, and “terrorism”. The objective of this article is to start answering these questions by first of all looking at the problems and paradoxes of the practices of Global Health Security through an analysis of the microbial space, capitalistic cooperation, and the production of information and data about health security. Secondly, the article draws the attention to the politics behind the structuration of Global Health Security as a social evidence by looking at contested concepts that represent promising research avenues.

Meta-genomic analysis of toilet waste from long distance flights; a step towards global surveillance of infectious diseases and antimicrobial resistance.

Human populations worldwide are increasingly confronted with infectious diseases and antimicrobial resistance spreading faster and appearing more frequently. Knowledge regarding their occurrence and worldwide transmission is important to control outbreaks and prevent epidemics. Here, we performed shotgun sequencing of toilet waste from 18 international airplanes arriving in Copenhagen, Denmark, from nine cities in three world regions. An average of 18.6 Gb (14.8 to 25.7 Gb) of raw Illumina paired end sequence data was generated, cleaned, trimmed and mapped against reference sequence databases for bacteria and antimicrobial resistance genes. An average of 106,839 (0.06%) reads were assigned to resistance genes with genes encoding resistance to tetracycline, macrolide and beta-lactam resistance genes as the most abundant in all samples. We found significantly higher abundance and diversity of genes encoding antimicrobial resistance, including critical important resistance (e.g. blaCTX-M) carried on airplanes from South Asia compared to North America. Presence of Salmonella enterica and norovirus were also detected in higher amounts from South Asia, whereas Clostridium difficile was most abundant in samples from North America. Our study provides a first step towards a potential novel strategy for global surveillance enabling simultaneous detection of multiple human health threatening genetic elements, infectious agents and resistance genes.

Integrated quantum dot barcode smartphone optical device for wireless multiplexed diagnosis of infected patients.

Inorganic nanoparticles are ideal precursors for engineering barcodes for rapidly detecting diseases. Despite advances in the chemical design of these barcodes, they have not advanced to clinical use because they lack sensitivity and are not cost-effective due to requirement of a large read-out system. Here we combined recent advances in quantum dot barcode technology with smartphones and isothermal amplification to engineer a simple and low-cost chip-based wireless multiplex diagnostic device. We characterized the analytical performance of this device and demonstrated that the device is capable of detecting down to 1000 viral genetic copies per milliliter, and this enabled the diagnosis of patients infected with HIV or hepatitis B. More importantly, the barcoding enabled us to detect multiple infectious pathogens simultaneously, in a single test, in less than 1 h. This multiplexing capability of the device enables the diagnosis of infections that are difficult to differentiate clinically due to common symptoms such as a fever or rash. The integration of quantum dot barcoding technology with a smartphone reader provides a capacity for global surveillance of infectious diseases and the potential to accelerate knowledge exchange transfer of emerging or exigent disease threats with healthcare and military organizations in real time.

Selecting Essential Information for Biosurveillance - A Multi-Criteria Decision Analysis

This paper proposes the use of Multi-Attribute Utility Theory to address the issue of identifying and selecting essential information for inclusion into a biosurveillance system or process. We developed a decision support framework that can facilitate identifying data streams for use in biosurveillance systems or processes and demonstrated utility by applying the framework to the problem of evaluating data streams for use in an global infectious disease surveillance system.

Selecting essential information for biosurveillance--a multi-criteria decision analysis.

The National Strategy for Biosurveillancedefines biosurveillance as “the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels.” However, the strategy does not specify how “essential information” is to be identified and integrated into the current biosurveillance enterprise, or what the metrics qualify information as being “essential”. Thequestion of data stream identification and selection requires a structured methodology that can systematically evaluate the tradeoffs between the many criteria that need to be taken in account. Multi-Attribute Utility Theory, a type of multi-criteria decision analysis, can provide a well-defined, structured approach that can offer solutions to this problem. While the use of Multi-Attribute Utility Theoryas a practical method to apply formal scientific decision theoretical approaches to complex, multi-criteria problems has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance.We have developed a formalized decision support analytic framework that can facilitate identification of “essential information” for use in biosurveillance systems or processes and we offer this framework to the global BSV community as a tool for optimizing the BSV enterprise. To demonstrate utility, we applied the framework to the problem of evaluating data streams for use in an integrated global infectious disease surveillance system.

Internet and free press are associated with reduced lags in global outbreak reporting.

Background: Global outbreak detection and reporting have generally improved for a variety of infectious diseases and geographic regions in recent decades. Nevertheless, lags in outbreak reporting remain a threat to the global human health and economy. In the time between first occurrence of a novel disease incident and public notification of an outbreak, infected individuals have a greater possibility of traveling and spreading the pathogen to other nations. Shortening outbreak reporting lags has the potential to improve global health by preventing local outbreaks from escalating into global epidemics. Methods: Reporting lags between the first record and the first public report of an event were calculated for 318 outbreaks occurring 1996-2009. The influence of freedom of the press, Internet usage, per capita health expenditure, and cell phone subscriptions, on the timeliness of outbreak reporting was evaluated. Results: Freer presses and increasing Internet usage correlate with reduced time between the first record of an outbreak and the public report. Increasing Internet usage reduced the expected reporting lag from more than one month in nations without Internet users to one day in those where 75 of 100 people use the Internet. Conclusion: Advances in technology and the emergence of more open and free governments are associated with to improved global infectious disease surveillance.

How can we improve global infectious disease surveillance and prevent the next outbreak?

Despite a significant amount of progress in the past decade, global infectious disease surveillance still often falters, as in the case of the emerging novel coronavirus that has killed at least 17 people in Saudi Arabia. This article argues that we must continuously re-evaluate global infectious disease surveillance systems. It takes stock of problems in various countries' infectious disease surveillance systems and offers recommendations for how to improve surveillance and ensure more rapid reporting. Chief among the recommendations are strategies for reducing fragmentation in global surveillance systems and methods for making these systems less disease-specific. Suggestions are also offered for ways to improve infectious disease surveillance strategies in resource-limited settings.

Using High-Throughput Sequencing to Leverage Surveillance of Genetic Diversity and Oseltamivir Resistance: A Pilot Study during the 2009 Influenza A(H1N1) Pandemic

BackgroundInfluenza viruses display a high mutation rate and complex evolutionary patterns. Next-generation sequencing (NGS) has been widely used for qualitative and semi-quantitative assessment of genetic diversity in complex biological samples. The “deep sequencing” approach, enabled by the enormous throughput of current NGS platforms, allows the identification of rare genetic viral variants in targeted genetic regions, but is usually limited to a small number of samples.Methodology and Principal FindingsWe designed a proof-of-principle study to test whether redistributing sequencing throughput from a high depth-small sample number towards a low depth-large sample number approach is feasible and contributes to influenza epidemiological surveillance. Using 454-Roche sequencing, we sequenced at a rather low depth, a 307 bp amplicon of the neuraminidase gene of the Influenza A(H1N1) pandemic (A(H1N1)pdm) virus from cDNA amplicons pooled in 48 barcoded libraries obtained from nasal swab samples of infected patients (n = 299) taken from May to November, 2009 pandemic period in Mexico. This approach revealed that during the transition from the first (May-July) to second wave (September-November) of the pandemic, the initial genetic variants were replaced by the N248D mutation in the NA gene, and enabled the establishment of temporal and geographic associations with genetic diversity and the identification of mutations associated with oseltamivir resistance.ConclusionsNGS sequencing of a short amplicon from the NA gene at low sequencing depth allowed genetic screening of a large number of samples, providing insights to viral genetic diversity dynamics and the identification of genetic variants associated with oseltamivir resistance. Further research is needed to explain the observed replacement of the genetic variants seen during the second wave. As sequencing throughput rises and library multiplexing and automation improves, we foresee that the approach presented here can be scaled up for global genetic surveillance of influenza and other infectious diseases.