Power your Surveillance Network with Qolty

Monitor patients remotely for symptoms

Introduction

Disease surveillance is a global concern. In a world where international trade, migration of humans and animals, and ecological changes take place at a rapid pace, numerous infectious diseases and vectors have pandemic aptitudes. With life-threatening diseases spreading beyond borders, global health security is at risk, especially in developing countries. For instance, infectious viruses like Zika, SARS, and H5N1 have become a pandemic threat, revealing that adequate disease surveillance networks can save lives and economies. To set an example, it’s been documented that an effective surveillance system can decrease the magnitude of a SARS outbreak by one-third and its duration by one month (Mirza et al., 2013).

Disease surveillance networks have the potential to collect vital medical and ecological information and detect diseases in their early stages. A systematic review conducted by Choi and colleagues (2016) revealed that technologies could boost the capacities of standard disease surveillance systems. By integrating digital solutions, local and international networks can enhance rapid communication, engagement, early warning, and research, which will result in decreased disability, mortality, and poverty rates. To answer the newest demands of global health security, platforms like Qolty can help experts build effective surveillance networks and implement technological advancements in both practice and research.

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The Core of Disease Surveillance and Digital Solutions

Disease surveillance is essential for human-animal-ecosystem dynamics. Disease surveillance is defined as the ongoing task of collecting, analyzing and reporting any data related to emerging infection outbreaks. From the accurate prediction of epidemics to the effective development of action plans, information can be utilized across different areas. One of the main applications surveillance systems have is an early warning, which is the process of informing and connecting individuals, institutions, and governments (Yang, 2017).

By implementing technological solutions into disease surveillance networks, experts can foster detection, prevention, and communication. Also, there’s growing evidence that effective surveillance systems can mitigate costs (Mirza et al., 2013). Note that originally, disease surveillance networks relied on printed reports managed by experts and volunteers, which made the data prone to errors and delays. New digital approaches – such as mobiles apps, wearables, sensors, web platforms, and syndromic surveillance systems – on the other hand, can only improve health outcomes (Global Infectious Disease Surveillance and Detection: Assessing the Challenges – Finding Solutions, Workshop Summary.). Therefore, Qolty enables the successful integration of health technologies to support visibility, coordination, recovery, and prevention of health crises:

Collection of data: The abundance of medical data worldwide can challenge data collection. Via ongoing monitoring of multiple sources, Qolty enables noise reduction and the automatic extraction of relevant information across web content, RSS feeds, media news, social media platforms (e.g., Twitter), contact rosters, email lists (e.g., ProMed), flight manifests, science records (e.g. OpenMRS), and other sources. Consequently, analytics modules support the analysis of big data and its hidden patterns. In addition, since the human input is essential for the verification of information, users can modify the collaboration settings of their network, invite specialists and witnesses, and allow new entries and feedback. Note that the increased use of mobile phones and apps facilitates data collection and validation across borders – with data being processed in real time and validated at the source. For instance, collaborators can enter data (e.g., number of deaths) via their devices (e.g., in a simple table with a user-friendly calendar).

Search optimization: Information should be processed efficiently. Since most surveillance systems are disease-specific (e.g., swine flu), Qolty can help parties explore a disease, population or region of interest. The implementation of data mining techniques leads to structured and optimized searches. Both symptom and quarantine monitoring are targeted. To set an example, sophisticated analytics, and data mining solutions can improve the information filtering of relevant quests (e.g., bioterrorism). Note that learning algorithms are robust to past processing errors, which improves the future identification of early symptoms, geocoding, and routes of transmission.

Data integration: Data integration is another challenge which effective disease surveillance systems should overcome. Since digital solutions benefit data integration, Qolty can help experts take advantage of the newest information advancements. The system supports the screening, filtering, and integration of relevant information across various users, applications, and sources. The implementation of standard codes, on the other hand, allows users to track, synchronize, and change data from multiple databases (e.g., Excel tables, Google spreadsheets, and JavaScript) in order to support healthcare data exchange and interoperability.

Accuracy and accessibility: Technologies facilitate the collection and the analysis of information. Qolty provides accurate, integrated and validated information, which is monitored and analyzed by specialists 24/7. Data is not only accurate but accessible. The collection of data occurs in real time and can be processed in different languages translated into English, which makes information accessible beyond borders. Daily summary reports are also available. In addition, experts can train applications to process upcoming messages with possible mistakes (e.g., no commas or full stops) without changing user behaviors. Since many people report cases or symptoms via text messages or social media posts, the capability to analyze reports with syntax structures prone to errors becomes vital.

Visualization: Health technologies provide an interactive and engaging interface and software programs. Augmented reality improves visual experience. In fact, visualization, such as maps and reports, is another fundamental aspect Qolty supports. By producing regular graphs and extracting visuals, experts will be able to understand the particular disease of interest and its impact on global health. To be more precise, after collecting and integrating information about disease instances, experts can visualize that data on maps or graphs (according to location, time, duration, etc.). Visuals help parties spot and understand patterns at a glance.

Effective communication: Surveillance systems aim to improve global health. Therefore, communication and transparency become essential. From eyewitness reports to lab tests, Qolty brings people together. Real-time public health surveillance and availability of data (stored electronically and transmitted to the cloud) facilitate global health data exchange. The use of available Internet sources may lead to access to information, which hasn’t been censored by governments. Effective disease surveillance systems enhance communication via social networks, app notifications, emails, and free subscriptions. In addition, open-source tools can overcome literacy, language, and technological barriers. Experts can create apps (based either on built-in tools or new codes) that interact via voice and support VoIP. Note that confidentiality, one of the main principles of research, is guaranteed.

Engagement: Research shows that digital tools fuel engagement. In fact, crowdsourcing – or the process of obtaining information via a big section from the public – support point-of-care data collection. Qolty can help experts create tools that set up reminders, lists, and subscriptions. Such features can support immunization reminders, which can prevent illnesses later in life. By setting a time interval, for instance, notifications become tailored instead of invasive. Note that reminders can be sent to any text messaging-enabled mobile device or smartphone. On top of that, since communication is a two-way street, people’s updates and opinions are also being considered. Experts can conduct polls (including surveys with short answers) which eliminates the need for paper surveys. Results can be extracted as a spreadsheet or any other easy to use formats and utilized to evaluate intervention and prevention programs.

Action steps: Tech solutions support not only data collection and analysis but real action steps, such as early warning, evaluation of current practices, and vaccines. By enhancing the continuous monitoring of datasets and overcoming potential disease surveillance challenges, Qolty can help experts create a disease surveillance network which supports an active investigation and effective prevention. The process of early detection is achieved through access to data and analysis of abundant demographic and geographic information (including visuals and maps of outbreaks). Proactive monitoring is essential. For example, if a contact shows any symptoms of an infection, findings are tracked to minimize continued disease spread. Analysists can flag events and set alerts to boost actions, as well as provide support and feedback. By using voice, GPS, web platforms, and SMS, experts can create an effective network, improve team communication, and build a response team to support public safety. Such features, including the use of simulations, can benefit activists and human rights organizations.

Digital perspectives: Digital solutions are reshaping global health. Note that, as explained above, the increased use of mobile phones and apps helps people send information, get notifications, and participate in surveys related to disease outbreaks at low infrastructure costs. Low infrastructure costs can benefit regions and economies in need. As a result, Qolty can help experts create a disease surveillance network which follows the One Health approach and embraces the future of digital health (Mackenzie et al., 2013). Note that apps are no longer reserved for programmers. For instance, anyone can build a unique message-based application with a user-friendly API and simple protocol (e.g., HTTP) to respond to crises or outbreaks. Apps are configurable for changing technologies and locations, while at the same time, data can be throttled to minimize potential tension with mobile companies.

Conventionally, these assessments were administered through paper and pen method used in combination with pagers or electronic wristwatches (Delespaul et al. 1995). With the advancement in technology, electronic devices (PDA’s), and smartphone apps such as Qolty surpassed the traditional pen and paper technique. The surveys are usually short and completed within 1 to 2 minutes. The items are designed for prompt and easy data collection which usually comprise of open-ended questions, checklists or self-report Likert scales, and visual analog scales (Csikszentmihalyi et al. 2013).

Types of Disease Surveillance and Benefits of Active Surveillance

Since the health outcomes of humans, animals, and ecosystems are interconnected, disease surveillance systems can prevent pandemics. The global efforts to improve health outcomes in humans, animals, and ecosystems is known as One Health (Mackenzie et al., 2013). Note that many infectious diseases can spread across species, with 70% being zoonotic. Therefore, disease surveillance systems are designed to support the aims of the One Health approach. Note that surveillance can be classified in different groups according to four criteria. First of all, surveillance systems can vary according to their scope: systems can be divided into death surveillance, event-based surveillance, and syndromic surveillance. Targets also influence surveillance. For instance, surveillance can be community-based, lab-based or health facility-based. Although surveillance supports global health, coverage should be considered. Systems can monitor the whole population, high-risk population, or sentinels. Perhaps one of the most tech-relevant divisions is according to data collection methods: there’s active and passive surveillance. While passive systems rely on clinicians, who may notice a case, in active systems agencies, make outreach and actively search for cases (Yang, 2017).

Although there are different types of surveillance systems with various purposes (e.g., early warning and evaluation of measures across countries), most traditional systems are hierarchical. In any hierarchical system, a clinician spots and reports a case, authorities conduct epidemiological and lab investigation, and governments take actions steps (Morse, 2014), which often is time-consuming and prone to errors. Therefore, advanced platforms like Qolty implement technologies to foster active outreach and improve active surveillance. It’s interesting to mention that both traditional and digital networks rely on four crucial aspects: data collection, characterization, analysis, and dissemination. When it comes to dissemination, information can be a double-edged sword: while it can benefit travelers and locals, governments and economies may be affected by unrestricted access of inaccurate information (Global Infectious Disease Surveillance and Detection: Assessing the Challenges – Finding Solutions, Workshop Summary.).

Interestingly, the tech revolutions in healthcare and surveillance have led to various newly coined terms, as revealed by a comprehensive review conducted by Choi and colleagues (2016). Syndromic surveillance is one of the recent terms which refers to systems that detect disease outbreaks in a timely manner. Biosurveillance relies on complex algorithms to identify threats before actual diagnoses. Infodemiology epidemiology, on the other hand, supports the distribution of information to inform public health. Infoveillance refers to longitudinal tracking and analyses of vital disease-related metrics. Digital surveillance opts to provide knowledge through the accurate analysis and distribution of digital data. Last but not least, real-time surveillance refers to the identification of any early signs, which can support measures and treatments.

The Applications of Disease Surveillance Systems: Practice and Research

The experience sampling method has countless applications, broadly classified into four categories. These four categories are individual differences, natural history, temporal sequences, and contextual associations.

Individual Differences

The ecological momentary assessment data is aggregated to measure the individual subject’s response over the specified period of time. For instance, in the case of the pain experienced by the patient, the data could be gathered before and after the intervention to quantify the subject’s quality of life. The aggregated ESM data is projected to provide reliable (because of aggregation) and valid (because of the absence of recall bias, representative sampling, and ecological validity) assessments.

Natural History

To elaborate natural history, ecological momentary assessment measures are analyzed for trajectories over time. The time factor serves as an independent variable, whereas the subject’s intrinsic variation over time is taken the dependent variable. For instance, McCarthy et al. 2006 demonstrated the trends of various withdrawal symptoms experienced by the ex-smokers after quitting. The ESM data revealed that some symptoms, although intense in the early phase gradually faded away with time, while others increased and lingered, and still others increased only progressively over time. These trajectories declined the widely held beliefs about the progression of the withdrawal syndrome and were linked with differences in treatment outcomes. Therefore, the basic descriptive evidence about the natural history of the symptoms over time can pave path for the better understanding of clinical disorders and consequences.

Temporal Sequences

The longitudinal nature of experience sampling method data is employed to probe events or experiences in the closest time possible, whether to document antecedents or outcomes of events or behaviors or to examine the cascades of events. In these assessments, the sequence of events is the main focus.

Curry and Marlatt 1987 hypothesized that the psychological response to lapses plays an integral part towards the development of relapse. Later, Shiffman et al. 1997b investigation on smoking cessation assessed smoker’s affect and self-efficacy before and after lapses to smoking, and their effects on consequent development toward relapse. Marlatt’s hypothesis was validated by Shiffman et al. 1997b by comparing assessments before and after lapses, stating that lapses would result in increased negative affect and decreased self-efficacy. However, later comparisons with EMA data depicted that retrospective reports of relapse episodes were erroneous and biased. The subjects recalled their mood as worse than it actually had been, and those who started smoking again at the time of recall exaggerated the demoralizing nature of the initial lapse. Therefore, a valid and robust understanding of behavior could be accomplished with prospective assessments of the flow of behavior and experience.

These cases represent the utilization of EMA data to assess hypothesis with respect to the dynamic connections among procedures over time. Data provided by EMA studies might be compared to a motion picture, in which dynamic correlations emerge over time, whereas worldwide or recall based assessments are analogous to a still photograph, a solitary static preview of time. By providing temporal resolution, experience sampling methods enable investigators to scrutinize sequences of events and experiences, and empower them to analyze and break down the cascade of events in specific periods of time for better understanding.

Human behaviors are intricate; therefore, insight into micro-processes can give a better understanding of the overall process. Many theories of psychopathology and treatment focus on how disease process unfolds over time. In addition, splitting the events into micro-processes can help develop more efficacious interventions. The ability of the Experience Sampling methods to focus on dynamic processes and situational influences is potentially the most stellar contribution in the field of clinical psychology.

Contextual Associations

Contextual association studies usually investigate the association between two or more events or experiences occurring simultaneously. Although the data is collected longitudinally, the analysis of contextual association is cross-sectional, and it focuses on the co-occurrence of events or experiences rather than their sequence; timeframe is not represented explicitly. For instance, Myin-Germeys et al. 2001 probed emotions associated with stressful events to scrutinize a diathesis-stress model of schizophrenia. They concluded that susceptibility to schizophrenia would be reflected as excessive emotional outburst accompanying stress.

Schizophrenics, their first-degree relatives (who are hereditarily susceptible), and controls were surveyed 10 times daily about distressing occasions and mood. An examination of individual differences in average demonstrated that schizophrenics reported more negative effect and more stressful occasions, whereas susceptible people and controls did not vary. The contextual association between the stressor and mood uncovered that the first degree relatives responded more unequivocally than did controls. This contextual association was helpful in determining the genetic predisposition due to schizophrenia.

Understanding the momentary cross-sectional relationship between various aspects of experience has, likewise, been essential for foundational investigations of the structure of behavior and experience. To address whether positive and negative feelings are inverses or are autonomous dimensions and can be experienced simultaneously: Feldman-Barrett and Russell 1998 utilized EMA data to address the contention that albeit one could be both happy and distressed over some period, but in a specific moment, these opposite forces cannot co-exist together.

Although most contextual association studies are conducted between different variables within the same individual, a fascinating variation discussed the impact of one individual on the other in a relationship (Bolger et al. 2005). In accordance with the same line of thought, Larson et al. 1994 instructed the couples to track their experience in parallel and record how the mood of each affected the other. They concluded that a husband’s mood when he comes home from work greatly influences his wife’s mood, but not vice versa.

Applications of Experience Sampling Method in Treatment and Intervention

The experience sampling method/ecological momentary assessment can also help in designing effective treatment and intervention plans.

Applications in Treatment

Besides the applications in the research data, the EMA studies can also be employed for ongoing assessment during treatment. A properly structured EMA data can provide revealing opportunities for the treatment plan. As change is expected during treatment, ongoing assessments can prove to be informative. EMA data can also capture the processes and mediators of psychotherapy-induced change.

Kramer et al. 2014 demonstrated that the supplemental use of ecological momentary assessment along with the standard antidepressant treatment might prove to be an effective tool. They concluded that the EMA data complement the anti-depressive treatment significantly, and EMA-derived positive feedback was correlated with the linear decrease in HDRS depressive symptoms over time that lingered until the previous follow-up six months later.

Applications in Intervention

The implementation of EMA methods in real-time interventions can revolutionize the clinical treatment plans. Newman et al. 2003 discussed a variety of electronic assessment methods for the treatment of psychological disorders. Earlier, Newman et al. 1997 reported that a brief Electronic momentary assessment for panic disorder was comparable in efficacy to a longer therapist administered treatment. This depicts the incremental benefits of EMA intervention. Momentary interventions have also been evaluated in addictive disorders (Riley et al. 2002) and eating disorders (Norton et al. 2003).

The idea of delivering intervention immediately on the spot can address behaviors at crucial moments in patient’s life. The individual patient’s history may prove to be helpful in designing effective interventions for others. Additionally, the screening of patients over time could be beneficial in making predictive algorithms. For instance, by noticing the increasing stress levels, and intervening in the early phase before the symptoms get worse.

Practice across critical regions across Asia and Africa reveals that surveillance systems can save lives. With all the benefits of infectious disease surveillance networks, the need for sophisticated surveillance networks and digital solutions is eminent. Big data can be utilized to explore the distribution of diseases, animal reservoirs, habitats, drug resistance of pathogens, natural disasters, and political factors (Yang, 2017). Accurate information can save lives and prevent catastrophes. It can also benefit economies, especially in developing countries and rural areas, and force legal regulations.

Disease surveillance and action steps cannot be analyzed separately. By providing critical information, disease surveillance systems support early warning and action programs. Surveillance networks can be used to:

  • Collect data and explore trends of a disease
  • Identify patterns, risks, and vectors, including additional factors, such as drug sales and the number of children absent from school
  • Improve early warning. Interestingly, an analysis conducted by Yang and colleagues (2017) revealed that the majority of early warning models include temporal, spatial, and regression algorithms. Note that modern technologies and methods, such as mathematical analyses and Monte Carlo techniques, can improve conventional early warning models.
  • Evaluate measures and policies, as well as guide the use of vaccines (Yang, 2017)
  • Support the implementation of control and prevention programs, at local, national, and international levels – at low infrastructure costs
  • Benefit ecosystems and One Health approaches by connecting a wide variety of specialists (e.g., scientists, politicians, and analysts) and people (Mackenzie, 2013)
  • Develop regulated protocols to answer the demands of international health regulations and data dissemination
  • Foster digital health practices, including bioinformatics, AI, coding, systems engineering, and statistics
  • Benefit research and pathogen discovery

As there are numerous applications of disease surveillance, which is an integrated factor for global health, Qolty can help experts concentrate their research on major aspects, such as:

Mass gatherings: Disease surveillance systems, including anticipatory and enhanced surveillance, are necessary to regulate mass gatherings, such as religious celebrations and sports events (Nsoesie et al., 2015). Note that researchers show that factors like close proximity may lead to outbreaks of both vaccine-preventable and non-vaccine-preventable conditions.

Global travel: Lower costs and access to travel may lead to emerging outbreaks. For instance, MERS, which was introduced to South Korea and spread across China, was brought by a traveler. Therefore, all data collected from mobile apps and wireless sensors is essential to support early warning and surveillance.

Food safety: Food safety is another global concern. Since humans, animals, and ecosystems are interconnected, bacteria, viruses, and parasites can enter species – with the foodborne route being the most common way of contamination (Mackenzie, 2013). A surveillance system can support the development of regulations and safety practices.

Wildlife: From hunting to farming, people are in close contact with animals. To set an example, the gray squirrels introduced from the US to the UK harmed the native red squirrels and became a pest to farmers, foresters, and conversationalists. Therefore, wildlife must be considered, given the increased pathogens that can infect humans and domestic animals.

Regions: Insufficient resources may challenge global health and disease surveillance. Therefore, any comprehensive system should support the mapping of poor and underrepresented areas and improve health outcomes worldwide. Digital tools can benefit text messaging, data exchange, and visualization. For instance, users can send messages, enter data or upload an existing file, which can be updated, visualized, and analyzed from everywhere across the globe. Note that live map and geospatial information can benefit information about resource allocations.

Disease Surveillance Networks and Future Perspectives

Technological solutions and healthcare practices are interconnected. The recent advancements in technology allow Qolty to support experts in the development of sophisticated disease surveillance systems. Data collection and integration across sources reveal impressive capabilities. Accurate information and visualization support the effective monitoring and analysis of infectious outbreaks, action steps, and resource allocations. As a result, disease surveillance systems can benefit early warning, evaluation of measures, and prevention of outbreaks.

In the end, the increased use of mobile phones allows people and governments from all over the globe to send information, report cases, and access data – all at decreased infrastructure costs. Big data, crowd-sourced information, and SMS become essential tools in surveillance. Online networks and apps enhance communication beyond borders and improve practices and research. Note that technology can boost laboratory advancements and pathogen discovery as well. In a nutshell, disease surveillance systems with their numerous capabilities and applications have the potential to improve global health and people’s well-being.

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