Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance

Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models

The COVID-19 pandemic led to unparalleled pressure on healthcare services. Improved healthcare planning in relation to diseases affecting the respiratory system has consequently become a key concern. We investigated the value of integrating sales of non-prescription medications commonly bought for managing respiratory symptoms, to improve forecasting of weekly registered deaths from respiratory disease at local levels across England, by using over 2 billion transactions logged by a UK high street retailer from March 2016 to March 2020. We report the results from the novel AI (Artificial Intelligence) explainability variable importance tool Model Class Reliance implemented on the PADRUS model (Prediction of Amount of Deaths by Respiratory disease Using Sales). PADRUS is a machine learning model optimised to predict registered deaths from respiratory disease in 314 local authority areas across England through the integration of shopping sales data and focused on purchases of non-prescription medications. We found strong evidence that models incorporating sales data significantly out-perform other models that solely use variables traditionally associated with respiratory disease (e.g. sociodemographics and weather data). Accuracy gains are highest (increases in R2 (coefficient of determination) between 0.09 to 0.11) in periods of maximum risk to the general public. Results demonstrate the potential to utilise sales data to monitor population health with information at a high level of geographic granularity.

A scoping review on data integration in the field of infectious diseases, 2009-2018

Background and Aim: Little is known about data integration in public health research and its impact. This study aimed to summarize known collaboration information, the characteristics of the datasets used, the methods of data integration, and knowledge gaps. Materials and Methods: We reviewed papers on infectious diseases from two or more datasets published during 2009- 2018, before the coronavirus disease pandemic. Two independent researchers searched the Medline and Global Health databases using predetermined criteria. Results: Of the 2375 items retrieved, 2272 titles and abstracts were reviewed. Of these, 164 were secondary reviews. Full-text reviews identified 153 relevant articles; we excluded 11 papers that did not meet our inclusion criteria. Of the 153 papers, 150 were single-country studies. Most papers were from North America (n=47). Viral diseases were the most commonly researched diseases (n=66), and many studies sought to define infection rates (n=62). Data integration usually employed unique national identifiers (n=37) or address-based identifiers (n=30). Two data sources were combined (n=121), and at least one data source typically included routine surveillance information. Conclusion: We found a growing usage of data integration in infectious diseases, emphasizing the advantages of data integration and linkage analysis, and reiterating its importance in public health emergency preparedness and response.

Spread of Epidemic Disease on Edge-Weighted Graphs from a Database: A Case Study of COVID-19

The understanding of infectious diseases is a priority in the field of public health. This has generated the inclusion of several disciplines and tools that allow for analyzing the dissemination of infectious diseases. The aim of this manuscript is to model the spreading of a disease in a population that is registered in a database. From this database, we obtain an edge-weighted graph. The spreading was modeled with the classic SIR model. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics. Moreover, a deterministic approximation is provided. With database COVID-19 from a city in Chile, we analyzed our model with relationship variables between people. We obtained a graph with 3866 vertices and 6,841,470 edges. We fitted the curve of the real data and we have done some simulations on the obtained graph. Our model is adjusted to the spread of the disease. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics, in this case with real data of COVID-19. This valuable information allows us to also include/understand the networks of dissemination of epidemics diseases as well as the implementation of preventive measures of public health. These findings are important in COVID-19's pandemic context.

Implementation System of a Biosurveillance System in the Republic of Korea and Its Legal Ramifications

Laws are fundamental tools that regulate and manage various issues to protect the rights of the people in a society. Legislation on disease surveillance enables agencies to regulate and manage public health, including preventing the spread of infectious diseases. We assessed the Infectious Disease Prevention and Control Act of Korea (IDPCA) through the lens of biosurveillance to understand its effectiveness in protecting public health. In addition, the relevant legislation and regulations of the United States and the World Health Organization were examined. The evaluation concludes that the current IDPCA is limited in terms of providing guidance for early detection of and response to hazards using integrated data and an information-sharing system. Further revision of the laws is needed to enable early detection and warning of potential threats to public health.

The authors assessed the Infectious Disease Prevention and Control Act of Korea (IDPCA) through the lens of biosurveillance to understand its effectiveness in protecting public health. The relevant legislation and regulations of the United States and the World Health Organization also were examined. They concluded that the current IDPCA is limited in terms of providing guidance for early detection of and response to hazards using integrated data and an information-sharing system.

An extensible framework and database of infectious disease for biosurveillance

Biosurveillance, a relatively young field, has recently increased in importance because of increasing emphasis on global health. Databases and tools describing particular subsets of disease are becoming increasingly common in the field. Here, we present an infectious disease database that includes diseases of biosurveillance relevance and an extensible framework for the easy expansion of the database.

Surveillance for Emerging Diseases with Multiplexed Point-of-Care Diagnostics

We present an analysis of the diagnostic technologies that were used to identify historical outbreaks of Ebola virus disease and consider systematic surveillance strategies that may greatly reduce the peak size of future epidemics. We observe that clinical signs and symptoms alone are often insufficient to recognize index cases of diseases of global concern against the considerable background infectious disease burden that is present throughout the developing world. We propose a simple sampling strategy to enrich in especially dangerous pathogens with a low background for molecular diagnostics by targeting blood-borne pathogens in the healthiest age groups. With existing multiplexed diagnostic technologies, such a system could be combined with existing public health screening and reference laboratory systems for malaria, dengue, and common bacteremia or be used to develop such an infrastructure in less-developed locations. Because the needs for valid samples and accurate recording of patient attributes are aligned with needs for global biosurveillance, local public health needs, and improving patient care, co-development of these capabilities appears to be quite natural, flexible, and extensible as capabilities, technologies, and needs evolve over time. Moreover, implementation of multiplexed diagnostic technologies to enhance fundamental clinical lab capacity will increase public health monitoring and biosurveillance as a natural extension.

Syndromic Surveillance System for Korea-US Joint Biosurveillance Portal: Design and Lessons Learned

Driven by the growing importance of situational awareness of bioterrorism threats, the Republic of Korea (ROK) and the United States have constructed a joint military capability, called the Biosurveillance Portal (BSP), to enhance biosecurity. As one component of the BSP, we developed the Military Active Real-time Syndromic Surveillance (MARSS) system to detect and track natural and deliberate disease outbreaks. This article describes the ROK military health data infrastructure and explains how syndromic data are derived and made available to epidemiologists. Queries corresponding to 8 syndromes, based on published clinical effects of weaponized pathogens, were used to classify military hospital patient records to form aggregated daily syndromic counts. A set of ICD-10 codes for each syndrome was defined through literature review and expert panel discussion. A study set of time series of national daily counts for each syndrome was extracted from the Defense Medical Statistical Information System between January 1, 2011, and May 31, 2014. A stratified, adjusted cumulative summation algorithm was implemented for each syndrome group to signal alerts prompting investigation. The algorithm was developed by calculating sensitivity to sets of 1,000 artificial outbreak signals randomly injected in the dataset, with each signal injected in a separate trial. Queries and visualizations were adapted from the Suite for Automated Global bioSurveillance. Findings indicated that early warning of outbreaks affecting fewer than 50 patients will require analysis at subnational levels, especially for common syndrome groups. Developing MARSS to improve sensitivity will require modification of underlying syndromic diagnosis codes, engineering to coordinate alerts among subdivisions, and enhanced algorithms. The bioterrorist threat in the Korean peninsula mandates these efforts.

The Biosurveillance Analytics Resource Directory (BARD): Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance

Epidemiological modeling for infectious disease is important for disease management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. A key need is a universal framework to facilitate model description and understanding of its features. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. The framework was developed through a consensus among a panel of subject matter experts. In this paper, we describe the framework, its application to model characterization, and the development of the Biosurveillance Analytics Resource Directory (BARD; http://brd.bsvgateway.org/brd/), to facilitate the rapid selection of operational models for specific infectious/communicable diseases. We offer this framework and associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models.

Bioterrorism and the Role of the Clinical Microbiology Laboratory

Regular review of the management of bioterrorism is essential for maintaining readiness for these sporadically occurring events. This review provides an overview of the history of biological disasters and bioterrorism. I also discuss the recent recategorization of tier 1 agents by the U.S. Department of Health and Human Services, the Laboratory Response Network (LRN), and specific training and readiness processes and programs, such as the College of American Pathologists (CAP) Laboratory Preparedness Exercise (LPX). LPX examined the management of cultivable bacterial vaccine and attenuated strains of tier 1 agents or close mimics. In the LPX program, participating laboratories showed improvement in the level of diagnosis required and referral of isolates to an appropriate reference laboratory. Agents which proved difficult to manage in sentinel laboratories included the more fastidious Gram-negative organisms, especially Francisella tularensis and Burkholderia spp. The recent Ebola hemorrhagic fever epidemic provided a check on LRN safety processes. Specific guidelines and recommendations for laboratory safety and risk assessment in the clinical microbiology are explored so that sentinel laboratories can better prepare for the next biological disaster.

Coincident polio and Ebola crises expose similar fault lines in the current global health regime

BACKGROUND

In 2014, the World Health Organization (WHO) declared two "public health emergencies of international concern", in response to the worldwide polio situation and the Ebola epidemic in West Africa respectively. Both emergencies can be seen as testing moments, challenging the current model of epidemic governance, where two worldviews co-exist: global health security and humanitarian biomedicine.

DISCUSSION

The resurgence of polio and the spread of Ebola in 2014 have not only exposed the weaknesses of national health systems, but also the shortcomings of the current global health regime in dealing with transnational epidemic threats. These shortcomings are of three sorts. Firstly, the global health regime is fragmented and dominated by the domestic security priorities of industrialised nations. Secondly, the WHO has been constrained by constitutional country allegiances, crippling reforms and the limited impact of the (2005) International Health Regulations (IHR) framework. Thirdly, the securitization of infectious diseases and the militarization of humanitarian aid undermine the establishment of credible public health surveillance networks and the capacity to control epidemic threats.

SUMMARY

The securitization of communicable diseases has so far led foreign aid policies to sideline health systems. It has also been the source of ongoing misperceptions over the aims of global health initiatives. With its strict allegiance to Member States, the WHO mandate is problematic, particularly when it comes to controlling epidemic diseases. In this context, humanitarian medical organizations are expected to palliate the absence of public health services in the most destitute areas, particularly in conflict zones. The militarization of humanitarian aid itself threatens this fragile and imperfect equilibrium. None of the reforms announced by the WHO in the wake of the 68(th) World Health Assembly address these fundamental issues.

Disease prediction models and operational readiness

The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness Level definitions.

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

The National Strategy for Biosurveillance defines 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". The question 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.