Mind the Scales: Harnessing Spatial Big Data for Infectious Disease Surveillance and Inference

Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic

The last decade has seen major advances and growth in internet-based surveillance for infectious diseases through advanced computational capacity, growing adoption of smart devices, increased availability of Artificial Intelligence (AI), alongside environmental pressures including climate and land use change contributing to increased threat and spread of pandemics and emerging infectious diseases. With the increasing burden of infectious diseases and the COVID-19 pandemic, the need for developing novel technologies and integrating internet-based data approaches to improving infectious disease surveillance is greater than ever. In this systematic review, we searched the scientific literature for research on internet-based or digital surveillance for influenza, dengue fever and COVID-19 from 2013 to 2023. We have provided an overview of recent internet-based surveillance research for emerging infectious diseases (EID), describing changes in the digital landscape, with recommendations for future research directed at public health policymakers, healthcare providers, and government health departments to enhance traditional surveillance for detecting, monitoring, reporting, and responding to influenza, dengue, and COVID-19.

Innovations in genomic antimicrobial resistance surveillance

Whole-genome sequencing of antimicrobial-resistant pathogens is increasingly being used for antimicrobial resistance (AMR) surveillance, particularly in high-income countries. Innovations in genome sequencing and analysis technologies promise to revolutionise AMR surveillance and epidemiology; however, routine adoption of these technologies is challenging, particularly in low-income and middle-income countries. As part of a wider series of workshops and online consultations, a group of experts in AMR pathogen genomics and computational tool development conducted a situational analysis, identifying the following under-used innovations in genomic AMR surveillance: clinical metagenomics, environmental metagenomics, gene or plasmid tracking, and machine learning. The group recommended developing cost-effective use cases for each approach and mapping data outputs to clinical outcomes of interest to justify additional investment in capacity, training, and staff required to implement these technologies. Harmonisation and standardisation of methods, and the creation of equitable data sharing and governance frameworks, will facilitate successful implementation of these innovations.

Spatial distribution and determinants of childhood vaccination refusal in the United States

Parental refusal and delay of childhood vaccination has increased in recent years in the United States. This phenomenon challenges maintenance of herd immunity and increases the risk of outbreaks of vaccine-preventable diseases. We examine US county-level vaccine refusal for patients under five years of age collected during the period 2012-2015 from an administrative healthcare dataset. We model these data with a Bayesian zero-inflated negative binomial regression model to capture social and political processes that are associated with vaccine refusal, as well as factors that affect our measurement of vaccine refusal. Our work highlights fine-scale socio-demographic characteristics associated with vaccine refusal nationally, finds that spatial clustering in refusal can be explained by such factors, and has the potential to aid in the development of targeted public health strategies for optimizing vaccine uptake.

Using geospatial social media data for infectious disease studies: a systematic review

Geospatial social media (GSM) data has been increasingly used in public health due to its rich, timely, and accessible spatial information, particularly in infectious disease research. This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022. These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood, county, state, and country. We categorized these studies into three major infectious disease research domains: surveillance, explanation, and prediction. With the assistance of advanced statistical and spatial methods, GSM data has been widely and deeply applied to these domains, particularly in surveillance and explanation domains. We further identified four knowledge gaps in terms of contextual information use, application scopes, spatiotemporal dimension, and data limitations and proposed innovation opportunities for future research. Our findings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.

A novel textual track-data-based approach for estimating individual infection risk of COVID-19

With the recurrence of infectious diseases caused by coronaviruses, which pose a significant threat to human health, there is an unprecedented urgency to devise an effective method to identify and assess who is most at risk of contracting these diseases. China has successfully controlled the spread of COVID-19 through the disclosure of track data belonging to diagnosed patients. This paper proposes a novel textual track-data-based approach for individual infection risk measurement. The proposed approach is divided into three steps. First, track features are extracted from track data to build a general portrait of COVID-19 patients. Then, based on the extracted track features, we construct an infection risk indicator system to calculate the infection risk index (IRI). Finally, individuals are divided into different infection risk categories based on the IRI values. By doing so, the proposed approach can determine the risk of an individual contracting COVID-19, which facilitates the identification of high-risk populations. Thus, the proposed approach can be used for risk prevention and control of COVID-19. In the empirical analysis, we comprehensively collected 9455 pieces of track data from 20 January 2020 to 30 July 2020, covering 32 provinces/provincial municipalities in China. The empirical results show that the Chinese COVID-19 patients have six key features that indicate infection risk: place, region, close-contact person, contact manner, travel mode, and symptom. The IRI values for all 9455 patients vary from 0 to 43.19. Individuals are classified into the following five infection risk categories: low, moderate-low, moderate, moderate-high, and high risk.

Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives

With the rapid development of the internet, the application of internet search data has been seen as a novel data source to offer timely infectious disease surveillance intelligence. Moreover, the advancements in internet search data, which include rich information at both space and time scales, enable investigators to sufficiently consider the spatiotemporal uncertainty, which can benefit researchers to better monitor infectious diseases and epidemics. In the present study, we present the necessary groundwork and critical appraisal of the use of internet search data and spatiotemporal analysis approaches in infectious disease surveillance by updating the current stage of knowledge on them. The study also provides future directions for researchers to investigate the combination of internet search data with the spatiotemporal analysis in infectious disease surveillance. Internet search data demonstrate a promising potential to offer timely epidemic intelligence, which can be seen as the prerequisite for improving infectious disease surveillance.

Spatial aggregation choice in the era of digital and administrative surveillance data

Traditional disease surveillance is increasingly being complemented by data from non-traditional sources like medical claims, electronic health records, and participatory syndromic data platforms. As non-traditional data are often collected at the individual-level and are convenience samples from a population, choices must be made on the aggregation of these data for epidemiological inference. Our study seeks to understand the influence of spatial aggregation choice on our understanding of disease spread with a case study of influenza-like illness in the United States. Using U.S. medical claims data from 2002 to 2009, we examined the epidemic source location, onset and peak season timing, and epidemic duration of influenza seasons for data aggregated to the county and state scales. We also compared spatial autocorrelation and tested the relative magnitude of spatial aggregation differences between onset and peak measures of disease burden. We found discrepancies in the inferred epidemic source locations and estimated influenza season onsets and peaks when comparing county and state-level data. Spatial autocorrelation was detected across more expansive geographic ranges during the peak season as compared to the early flu season, and there were greater spatial aggregation differences in early season measures as well. Epidemiological inferences are more sensitive to spatial scale early on during U.S. influenza seasons, when there is greater heterogeneity in timing, intensity, and geographic spread of the epidemics. Users of non-traditional disease surveillance should carefully consider how to extract accurate disease signals from finer-scaled data for early use in disease outbreaks.

Exploring the Relationship among Human Activities, COVID-19 Morbidity, and At-Risk Areas Using Location-Based Social Media Data: Knowledge about the Early Pandemic Stage in Wuhan

It is significant to explore the morbidity patterns and at-risk areas of the COVID-19 outbreak in megacities. In this paper, we studied the relationship among human activities, morbidity patterns, and at-risk areas in Wuhan City. First, we excavated the activity patterns from Sina Weibo check-in data during the early COVID-19 pandemic stage (December 2019~January 2020) in Wuhan. We considered human-activity patterns and related demographic information as the COVID-19 influencing determinants, and we used spatial regression models to evaluate the relationships between COVID-19 morbidity and the related factors. Furthermore, we traced Weibo users’ check-in trajectories to characterize the spatial interaction between high-morbidity residential areas and activity venues with POI (point of interest) sites, and we located a series of potential at-risk places in Wuhan. The results provide statistical evidence regarding the utility of human activity and demographic factors for the determination of COVID-19 morbidity patterns in the early pandemic stage in Wuhan. The spatial interaction revealed a general transmission pattern in Wuhan and determined the high-risk areas of COVID-19 transmission. This article explores the human-activity characteristics from social media check-in data and studies how human activities played a role in COVID-19 transmission in Wuhan. From that, we provide new insights for scientific prevention and control of COVID-19.

Challenges in the development of digital public health interventions and mapped solutions: Findings from a scoping review

Background

“Digital public health” has emerged from an interest in integrating digital technologies into public health. However, significant challenges which limit the scale and extent of this digital integration in various public health domains have been described. We summarized the literature about these challenges and identified strategies to overcome them.

Methods

We adopted Arksey and O’Malley's framework (2005) integrating adaptations by Levac et al. (2010). OVID Medline, Embase, Google Scholar, and 14 government and intergovernmental agency websites were searched using terms related to “digital” and “public health.” We included conceptual and explicit descriptions of digital technologies in public health published in English between 2000 and June 2020. We excluded primary research articles about digital health interventions. Data were extracted using a codebook created using the European Public Health Association's conceptual framework for digital public health.

Results and analysis

Overall, 163 publications were included from 6953 retrieved articles with the majority (64%, n = 105) published between 2015 and June 2020. Nontechnical challenges to digital integration in public health concerned ethics, policy and governance, health equity, resource gaps, and quality of evidence. Technical challenges included fragmented and unsustainable systems, lack of clear standards, unreliability of available data, infrastructure gaps, and workforce capacity gaps. Identified strategies included securing political commitment, intersectoral collaboration, economic investments, standardized ethical, legal, and regulatory frameworks, adaptive research and evaluation, health workforce capacity building, and transparent communication and public engagement.

Conclusion

Developing and implementing digital public health interventions requires efforts that leverage identified strategies to overcome diverse challenges encountered in integrating digital technologies in public health.

Using big data for evaluating development outcomes: A systematic map

BACKGROUND

Policy makers need access to reliable data to monitor and evaluate the progress of development outcomes and targets such as sustainable development outcomes (SDGs). However, significant data and evidence gaps remain. Lack of resources, limited capacity within governments and logistical difficulties in collecting data are some of the reasons for the data gaps. Big data-that is digitally generated, passively produced and automatically collected-offers a great potential for answering some of the data needs. Satellite and sensors, mobile phone call detail records, online transactions and search data, and social media are some of the examples of big data. Integrating big data with the traditional household surveys and administrative data can complement data availability, quality, granularity, accuracy and frequency, and help measure development outcomes temporally and spatially in a number of new ways.The study maps different sources of big data onto development outcomes (based on SDGs) to identify current evidence base, use and the gaps. The map provides a visual overview of existing and ongoing studies. This study also discusses the risks, biases and ethical challenges in using big data for measuring and evaluating development outcomes. The study is a valuable resource for evaluators, researchers, funders, policymakers and practitioners in their effort to contributing to evidence informed policy making and in achieving the SDGs.

OBJECTIVES

Identify and appraise rigorous impact evaluations (IEs), systematic reviews and the studies that have innovatively used big data to measure any development outcomes with special reference to difficult contexts.

SEARCH METHODS

A number of general and specialised data bases and reporsitories of organisations were searched using keywords related to big data by an information specialist.

SELECTION CRITERIA

The studies were selected on basis of whether they used big data sources to measure or evaluate development outcomes.

DATA COLLECTION AND ANALYSIS

Data collection was conducted using a data extraction tool and all extracted data was entered into excel and then analysed using Stata. The data analysis involved looking at trends and descriptive statistics only.

MAIN RESULTS

The search yielded over 17,000 records, which we then screened down to 437 studies which became the foundation of our systematic map. We found that overall, there is a sizable and rapidly growing number of measurement studies using big data but a much smaller number of IEs. We also see that the bulk of the big data sources are machine-generated (mostly satellites) represented in the light blue. We find that satellite data was used in over 70% of the measurement studies and in over 80% of the IEs.

AUTHORS' CONCLUSIONS

This map gives us a sense that there is a lot of work being done to develop appropriate measures using big data which could subsequently be used in IEs. Information on costs, ethics, transparency is lacking in the studies and more work is needed in this area to understand the efficacies related to the use of big data. There are a number of outcomes which are not being studied using big data, either due to the lack to applicability such as education or due to lack of awareness about the new methods and data sources. The map points to a number of gaps as well as opportunities where future researchers can conduct research.

The US COVID Atlas: A dynamic cyberinfrastructure surveillance system for interactive exploration of the pandemic

Distributed spatial infrastructures leveraging cloud computing technologies can tackle issues of disparate data sources and address the need for data-driven knowledge discovery and more sophisticated spatial analysis central to the COVID-19 pandemic. We implement a new, open source spatial middleware component (libgeoda) and system design to scale development quickly to effectively meet the need for surveilling county-level metrics in a rapidly changing pandemic landscape. We incorporate, wrangle, and analyze multiple data streams from volunteered and crowdsourced environments to leverage multiple data perspectives. We integrate explorative spatial data analysis (ESDA) and statistical hotspot standards to detect infectious disease clusters in real time, building on decades of research in GIScience and spatial statistics. We scale the computational infrastructure to provide equitable access to data and insights across the entire USA, demanding a basic but high-quality standard of ESDA techniques. Finally, we engage a research coalition and incorporate principles of user-centered design to ground the direction and design of Atlas application development.

Welcome to the revolution: COVID-19 and the democratization of spatial-temporal data

On January 22, 2020, Johns Hopkins University launched its online COVID-19 dashboard to track in real time what began in December as the regional outbreak of a novel coronavirus first identified in Wuhan, China. The dashboard and its format were quickly adopted by other organizations, making global, national, and regional data on the pandemic available to all. The wealth of data freely offered in this way was collected by syndromic programs whose precise algorithms search official and popular sources for data on COVID-19 and other diseases. The dashboard signals a new phase in the maturation of the "digital revolution" from paper resources and, in their popular employ, a "democratizion" of data and their presentation. This perspective thus uses the COVID-19 experience as an example of the effect of this digital revolution on both expert and popular audiences. Understanding it permits a broader perspective on not simply the pandemic but also the cultural and socioeconomic context in which it has occurred.

Spatiotemporal data mining: a survey on challenges and open problems

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

Harnessing Social Media in the Modelling of Pandemics-Challenges and Opportunities

As COVID-19 spreads throughout the world without a straightforward treatment or widespread vaccine coverage in the near future, mathematical models of disease spread and of the potential impact of mitigation measures have been thrust into the limelight. With their popularity and ability to disseminate information relatively freely and rapidly, information from social media platforms offers a user-generated, spontaneous insight into users' minds that may capture beliefs, opinions, attitudes, intentions and behaviour towards outbreaks of infectious disease not obtainable elsewhere. The interactive, immersive nature of social media may reveal emergent behaviour that does not occur in engagement with traditional mass media or conventional surveys. In recognition of the dramatic shift to life online during the COVID-19 pandemic to mitigate disease spread and the increasing threat of further pandemics, we examine the challenges and opportunities inherent in the use of social media data in infectious disease modelling with particular focus on their inclusion in compartmental models.

Transmission and control pressure analysis of the COVID-19 epidemic situation using multisource spatio-temporal big data

Taking the Guangdong-Hong Kong-Macao Greater Bay Area as the research area, this paper used OD cluster analysis based on Baidu migration data from January 11 to January 25 (before the sealing-off of Wuhan) and concluded that there is a significant correlation 1the migration level from Wuhan to the GBA and the epidemic severity index. This paper also analyzed the migration levels and diffusivity of the outer and inner cities of the GBA. Lastly, four evaluation indexes were selected to research the possibility of work resumption and the rating of epidemic prevention and control through kernel density estimation. According to the study, the amount of migration depends on the geographical proximity, relationship and economic development of the source region, and the severity of the epidemic depends mainly on the migration volume and the severity of the epidemic in the source region. The epidemic risk is related not only to the severity of the epidemic in the source region but also to the degree of urban traffic development and the degree of urban openness. After the resumption of work, the pressure of epidemic prevention and control has been concentrated mainly in Shenzhen and Canton; the further away a region is from the core cities, the lower the pressure in that region. The mass migration of the population makes it difficult to control the epidemic effectively. The study of the relationship between migration volume, epidemic severity and epidemic risk is helpful to further analyze transmission types and predict the trends of the epidemic.

Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making

The biggest change brought about by the “era of big data” to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex “variety” dimension of data in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.

The duration of travel impacts the spatial dynamics of infectious diseases

Humans can impact the spatial transmission dynamics of infectious diseases by introducing pathogens into susceptible environments. The rate at which this occurs depends in part on human-mobility patterns. Increasingly, mobile-phone usage data are used to quantify human mobility and investigate the impact on disease dynamics. Although the number of trips between locations and the duration of those trips could both affect infectious-disease dynamics, there has been limited work to quantify and model the duration of travel in the context of disease transmission. Using mobility data inferred from mobile-phone calling records in Namibia, we calculated both the number of trips between districts and the duration of these trips from 2010 to 2014. We fit hierarchical Bayesian models to these data to describe both the mean trip number and duration. Results indicate that trip duration is positively related to trip distance, but negatively related to the destination population density. The highest volume of trips and shortest trip durations were among high-density districts, whereas trips among low-density districts had lower volume with longer duration. We also analyzed the impact of including trip duration in spatial-transmission models for a range of pathogens and introduction locations. We found that inclusion of trip duration generally delays the rate of introduction, regardless of pathogen, and that the variance and uncertainty around spatial spread increases proportionally with pathogen-generation time. These results enhance our understanding of disease-dispersal dynamics driven by human mobility, which has potential to elucidate optimal spatial and temporal scales for epidemic interventions.

Infectious Disease Research in the Era of Big Data

Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.

Exploring Urban Spatial Features of COVID-19 Transmission in Wuhan Based on Social Media Data

During the early stage of the COVID-19 outbreak in Wuhan, there was a short run of medical resources, and Sina Weibo, a social media platform in China, built a channel for novel coronavirus pneumonia patients to seek help. Based on the geo-tagging Sina Weibo data from February 3rd to 12th, 2020, this paper analyzes the spatiotemporal distribution of COVID-19 cases in the main urban area of Wuhan and explores the urban spatial features of COVID-19 transmission in Wuhan. The results show that the elderly population accounts for more than half of the total number of Weibo help seekers, and a close correlation between them has also been found in terms of spatial distribution features, which confirms that the elderly population is the group of high-risk and high-prevalence in the COVID-19 outbreak, needing more attention of public health and epidemic prevention policies. On the other hand, the early transmission of COVID-19 in Wuhan could be divide into three phrases: Scattered infection, community spread, and full-scale outbreak. This paper can help to understand the spatial transmission of COVID-19 in Wuhan, so as to propose an effective public health preventive strategy for urban space optimization.

Big data analytics in medical engineering and healthcare: methods, advances and challenges

Big data analytics are gaining popularity in medical engineering and healthcare use cases. Stakeholders are finding big data analytics reduce medical costs and personalise medical services for each individual patient. Big data analytics can be used in large-scale genetics studies, public health, personalised and precision medicine, new drug development, etc. The introduction of the types, sources, and features of big data in healthcare as well as the applications and benefits of big data and big data analytics in healthcare is key to understanding healthcare big data and will be discussed in this article. Major methods, platforms and tools of big data analytics in medical engineering and healthcare are also presented. Advances and technology progress of big data analytics in healthcare are introduced, which includes artificial intelligence (AI) with big data, infrastructure and cloud computing, advanced computation and data processing, privacy and cybersecurity, health economic outcomes and technology management, and smart healthcare with sensing, wearable devices and Internet of things (IoT). Current challenges of dealing with big data and big data analytics in medical engineering and healthcare as well as future work are also presented.

A call for an ethical framework when using social media data for artificial intelligence applications in public health research

Advancements in artificial intelligence (AI), more precisely the subfield of machine learning, and their applications to open-source internet data, such as social media, are growing faster than the management of ethical issues for use in society. An ethical framework helps scientists and policy makers consider ethics in their fields of practice, legitimize their work and protect members of the data-generating public. A central question for advancing the ethical framework is whether or not Tweets, Facebook posts and other open-source social media data generated by the public represent a human or not. The objective of this paper is to highlight ethical issues that the public health sector will be or is already confronting when using social media data in practice. The issues include informed consent, privacy, anonymization and balancing these issues with the benefits of using social media data for the common good. Current ethical frameworks need to provide guidance for addressing issues arising from the use of social media data in the public health sector. Discussions in this area should occur while the application of open-source data is still relatively new, and they should also keep pace as other problems arise from ongoing technological change.

Epidemiologic Survey of Legionella Urine Antigen Testing Within a Large Wisconsin-Based Health Care System

Purpose

Legionella pneumophila pneumonia is a life-threatening, environmentally acquired infection identifiable via Legionella urine antigen tests (LUAT). We aimed to identify cumulative incidence, demographic distribution, and undetected disease outbreaks of Legionella pneumonia via positive LUAT in a single eastern Wisconsin health system, with a focus on urban Milwaukee County.

Methods

A multilevel descriptive ecologic study was conducted utilizing electronic medical record data from a large integrated health care system of patients who underwent LUAT from 2013 to 2017. A random sample inclusive of all positive tests was reviewed to investigate geodemographic differences among patients testing positive versus negative. Statistical comparisons used chi-squared or 2-sample t-tests; stepwise regression followed by binary logistic regression was used for multivariable analysis. Positive cases identified by LUAT were mapped to locate hotspots; positive cases versus total tests performed also were mapped by zip code.

Results

Of all LUAT performed (n=21,599), 0.68% were positive. Among those in the random sample (n=11,652), positive cases by LUAT were more prevalent in the June-November time period (86.2%) and younger patients (59.4 vs 67.7 years) and were disproportionately male (70.3% vs 29.7%) (P<0.0001 for each). Cumulative incidence was higher among nonwhite race/ethnicity (1.91% vs 1.01%, P<0.0001) but did not remain significant on multivariable analysis. Overall, 5507 tests were performed in Milwaukee County zip codes, yielding 82 positive cases by LUAT (60.7% of all positive cases in the random sample). A potential small 2016 outbreak was identified.

Conclusions

Cumulative incidence of a positive LUAT was less than 1%. LUAT testing, if done in real time by cooperative health systems, may complement public health detection of Legionella pneumonia outbreaks.

Genomic Epidemiology as a Public Health Tool to Combat Mosquito-Borne Virus Outbreaks

Next-generation sequencing technologies, exponential increases in the availability of virus genomic data, and ongoing advances in phylogenomic methods have made genomic epidemiology an increasingly powerful tool for public health response to a range of mosquito-borne virus outbreaks. In this review, we offer a brief primer on the scope and methods of phylogenomic analyses that can answer key epidemiological questions during mosquito-borne virus public health emergencies. We then focus on case examples of outbreaks, including those caused by dengue, Zika, yellow fever, West Nile, and chikungunya viruses, to demonstrate the utility of genomic epidemiology to support the prevention and control of mosquito-borne virus threats. We extend these case studies with operational perspectives on how to best incorporate genomic epidemiology into structured surveillance and response programs for mosquito-borne virus control. Many tools for genomic epidemiology already exist, but so do technical and nontechnical challenges to advancing their use. Frameworks to support the rapid sharing of multidimensional data and increased cross-sector partnerships, networks, and collaborations can support advancement on all scales, from research and development to implementation by public health agencies.

Spatiotemporal signal detection using continuous shrinkage priors

Periodontal disease (PD) is a chronic inflammatory disease that affects the gum tissue and bone supporting the teeth. Although tooth-site level PD progression is believed to be spatio-temporally referenced, the whole-mouth average periodontal pocket depth (PPD) has been commonly used as an indicator of the current/active status of PD. This leads to imminent loss of information, and imprecise parameter estimates. Despite availability of statistical methods that accommodates spatiotemporal information for responses collected at the tooth-site level, the enormity of longitudinal databases derived from oral health practice-based settings render them unscalable for application. To mitigate this, we introduce a Bayesian spatiotemporal model to detect problematic/diseased tooth-sites dynamically inside the mouth for any subject obtained from large databases. This is achieved via a spatial continuous sparsity-inducing shrinkage prior on spatially varying linear-trend regression coefficients. A low-rank representation captures the nonstationary covariance structure of the PPD outcomes, and facilitates the relevant Markov chain Monte Carlo computing steps applicable to thousands of study subjects. Application of our method to both simulated data and to a rich database of electronic dental records from the HealthPartners® Institute reveal improved prediction performances, compared with alternative models with usual Gaussian priors for regression parameters and conditionally autoregressive specification of the covariance structure.

Communicable Disease Surveillance Ethics in the Age of Big Data and New Technology

Surveillance is essential for communicable disease prevention and control. Traditional notification of demographic and clinical information, about individuals with selected (notifiable) infectious diseases, allows appropriate public health action and is protected by public health and privacy legislation, but is slow and insensitive. Big data-based electronic surveillance, by commercial bodies and government agencies (for profit or population control), which draws on a plethora of internet- and mobile device-based sources, has been widely accepted, if not universally welcomed. Similar anonymous digital sources also contain syndromic information, which can be analysed, using customised algorithms, to rapidly predict infectious disease outbreaks, but the data are nonspecific and predictions sometimes misleading. However, public health authorities could use these online sources, in combination with de-identified personal health data, to provide more accurate and earlier warning of infectious disease events-including exotic or emerging infections-even before the cause is confirmed, and allow more timely public health intervention. Achieving optimal benefits would require access to selected data from personal electronic health and laboratory (including pathogen genomic) records and the potential to (confidentially) re-identify individuals found to be involved in outbreaks, to ensure appropriate care and infection control. Despite existing widespread digital surveillance and major potential community benefits of extending its use to communicable disease control, there is considerable public disquiet about allowing public health authorities access to personal health data. Informed public discussion, greater transparency and an ethical framework will be essential to build public trust in the use of new technology for communicable disease control.

Large-scale loyalty card data in health research

Objective

To study the characteristics of large-scale loyalty card data obtained in Finland, and to evaluate their potential and challenges in health research.

Methods

We contacted the holders of a certain loyalty card living in a specific region in Finland via email, and requested their electronic informed consent to obtain their basic background characteristics and grocery expenditure data from 2016 for health research purposes. Non-participation and the characteristics and expenditure of the participants were mainly analysed using summary statistics and figures.

Results

The data on expenditure came from 14,595 (5.6% of those contacted) consenting loyalty card holders. A total of 68.5% of the participants were women, with an average age of 46 years. Women and residents of Helsinki were more likely to participate. Both young and old participants were underrepresented in the sample. We observed that annual expenditure represented roughly two-thirds of the nationally estimated annual averages. Customers and personnel differed in their characteristics and expenditure, but not so much in their most frequently bought items.

Conclusions

Loyalty card data from a major retailer enabled us to reach a large, heterogeneous sample with fewer resources than conventional surveys of the same magnitude. The potential of the data was great because of their size, coverage, objectivity, and long periods of dynamic data collection, which enables timely investigations. The challenges included bias due to non-participation, purchases in other stores, the level of detail in product grouping, and the knowledge gaps in what is being consumed and by whom. Loyalty card data are an underutilised resource in research, and could be used not only in retailers’ activities, but also for societal benefit.

Deploying digital health data to optimize influenza surveillance at national and local scales

The surveillance of influenza activity is critical to early detection of epidemics and pandemics and the design of disease control strategies. Case reporting through a voluntary network of sentinel physicians is a commonly used method of passive surveillance for monitoring rates of influenza-like illness (ILI) worldwide. Despite its ubiquity, little attention has been given to the processes underlying the observation, collection, and spatial aggregation of sentinel surveillance data, and its subsequent effects on epidemiological understanding. We harnessed the high specificity of diagnosis codes in medical claims from a database that represented 2.5 billion visits from upwards of 120,000 United States healthcare providers each year. Among influenza seasons from 2002-2009 and the 2009 pandemic, we simulated limitations of sentinel surveillance systems such as low coverage and coarse spatial resolution, and performed Bayesian inference to probe the robustness of ecological inference and spatial prediction of disease burden. Our models suggest that a number of socio-environmental factors, in addition to local population interactions, state-specific health policies, as well as sampling effort may be responsible for the spatial patterns in U.S. sentinel ILI surveillance. In addition, we find that biases related to spatial aggregation were accentuated among areas with more heterogeneous disease risk, and sentinel systems designed with fixed reporting locations across seasons provided robust inference and prediction. With the growing availability of health-associated big data worldwide, our results suggest mechanisms for optimizing digital data streams to complement traditional surveillance in developed settings and enhance surveillance opportunities in developing countries.

Influenza contributes substantially to global morbidity and mortality each year, and epidemiological surveillance for influenza is typically conducted by sentinel physicians and health care providers recruited to report cases of influenza-like illness. While population coverage and representativeness, and geographic distribution are considered during sentinel provider recruitment, systems cannot always achieve these standards due to the administrative burdens of data collection. We present spatial estimates of influenza disease burden across United States counties by leveraging the volume and fine spatial resolution of medical claims data, and existing socio-environmental hypotheses about the determinants of influenza disease disease burden. Using medical claims as a testbed, this study adds to literature on the optimization of surveillance system design by considering conditions of limited reporting and spatial aggregation. We highlight the importance of considering sampling biases and reporting locations when interpreting surveillance data, and suggest that local mobility and regional policies may be critical to understanding the spatial distribution of reported influenza-like illness.

Data distribution in public veterinary service: health and safety challenges push for context-aware systems

Background

Today’s globalised and interconnected world is characterized by intertwined and quickly evolving relationships between animals, humans and their environment and by an escalating number of accessible data for public health. The public veterinary services must exploit new modeling and decision strategies to face these changes. The organization and control of data flows have become crucial to effectively evaluate the evolution and safety concerns of a given situation in the territory. This paper discusses what is needed to develop modern strategies to optimize data distribution to the stakeholders.

Main text

If traditionally the system manager and knowledge engineer have been concerned with the increase of speed of data flow and the improvement of data quality, nowadays they need to worry about data overflow as well. To avoid this risk an information system should be capable of selecting the data which need to be shown to the human operator. In this perspective, two aspects need to be distinguished: data classification vs data distribution. Data classification is the problem of organizing data depending on what they refer to and on the way they are obtained; data distribution is the problem of selecting which data is accessible to which stakeholder. Data classification can be established and implemented via ontological analysis and formal logic but we claim that a context-based selection of data should be integrated in the data distribution application. Data distribution should provide these new features: (a) the organization of situation types distinguishing at least ordinary vs extraordinary scenarios (contextualization of scenarios); (b) the possibility to focus on the data that are really important in a given scenario (data contextualization by scenarios); and (c) the classification of which data is relevant to which stakeholder (data contextualization by users).

Short conclusion

Public veterinary services, to efficaciously and efficiently manage the information needed for today’s health and safety challenges, should contextualize and filter the continuous and growing flow of data by setting suitable frameworks to classify data, users’ roles and possible situations.

Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas

Social media data is increasingly used as a proxy for human activity in different environments, including protected areas, where collecting visitor information is often laborious and expensive, but important for management and marketing. Here, we compared data from Instagram, Twitter and Flickr, and assessed systematically how park popularity and temporal visitor counts derived from social media data perform against high-precision visitor statistics in 56 national parks in Finland and South Africa in 2014. We show that social media activity is highly associated with park popularity, and social media-based monthly visitation patterns match relatively well with the official visitor counts. However, there were considerable differences between platforms as Instagram clearly outperformed Twitter and Flickr. Furthermore, we show that social media data tend to perform better in more visited parks, and should always be used with caution. Based on stakeholder discussions we identified potential reasons why social media data and visitor statistics might not match: the geography and profile of the park, the visitor profile, and sudden events. Overall the results are encouraging in broader terms: Over 60% of the national parks globally have Twitter or Instagram activity, which could potentially inform global nature conservation.

Big Data for Infectious Disease Surveillance and Modeling

We devote a special issue of the Journal of Infectious Diseases to review the recent advances of big data in strengthening disease surveillance, monitoring medical adverse events, informing transmission models, and tracking patient sentiments and mobility. We consider a broad definition of big data for public health, one encompassing patient information gathered from high-volume electronic health records and participatory surveillance systems, as well as mining of digital traces such as social media, Internet searches, and cell-phone logs. We introduce nine independent contributions to this special issue and highlight several cross-cutting areas that require further research, including representativeness, biases, volatility, and validation, and the need for robust statistical and hypotheses-driven analyses. Overall, we are optimistic that the big-data revolution will vastly improve the granularity and timeliness of available epidemiological information, with hybrid systems augmenting rather than supplanting traditional surveillance systems, and better prospects for accurate infectious diseases models and forecasts.

Big Data Analytics, Infectious Diseases and Associated Ethical Impacts

The exponential accumulation, processing and accrual of big data in healthcare are only possible through an equally rapidly evolving field of big data analytics. The latter offers the capacity to rationalize, understand and use big data to serve many different purposes, from improved services modelling to prediction of treatment outcomes, to greater patient and disease stratification. In the area of infectious diseases, the application of big data analytics has introduced a number of changes in the information accumulation models. These are discussed by comparing the traditional and new models of data accumulation. Big data analytics is fast becoming a crucial component for the modelling of transmission-aiding infection control measures and policies-emergency response analyses required during local or international outbreaks. However, the application of big data analytics in infectious diseases is coupled with a number of ethical impacts. Four key areas are discussed in this paper: (i) automation and algorithmic reliance impacting freedom of choice, (ii) big data analytics complexity impacting informed consent, (iii) reliance on profiling impacting individual and group identities and justice/fair access and (iv) increased surveillance and population intervention capabilities impacting behavioural norms and practices. Furthermore, the extension of big data analytics to include information derived from personal devices, such as mobile phones and wearables as part of infectious disease frameworks in the near future and their potential ethical impacts are discussed. Considered together, the need for a constructive and transparent inclusion of ethical questioning in this rapidly evolving field becomes an increasing necessity in order to provide a moral foundation for the societal acceptance and responsible development of the technological advancement.