Public health officials using computers to manage outbreaks

Digital Disease Detection, commonly referred to as digital epidemiology provided strategies and methods for allowing digital-technology users to monitor infectious disease and conduct surveillance. These strategies help in the understanding of concerns and attitudes regarding infectious diseases. The process begins with the basics, such as the availability of internet access, online sharing platforms and other digital devices. These sources offer huge amounts of data. It is important to note that while these sources collect data, they do not, do so, with public health objectives in focus (Denecke, 2017).

The past few decades have seen tremendous changes in the world. There have been many and varied threats; from bioterrorism, influenza pandemics and the emergence of infectious diseases. There is also the issue of unforeseen population mobility which is among the reasons that triggered the development of public health surveillance systems. Such systems are invaluable tools in the detection and response to infectious disease outbreaks. However, the systems frequently fail to avail the required lead time promptly to enable response for the best outcome (Signorini, 2014).

It is important to conduct monitoring of emerging infectious disease in order to detect health threats to the public in good time. Emergence of new infections is connected to the upsurge in human population density, trade, travel and ecological issues such as change of climate and agricultural practice. New molecular technologies are now available for identifying pathogens. They also help in accurately monitoring activity of infectious disease. Surveillance tools that are web based along with other epidemic intelligence approaches applied by most big hospitals are meant to provide assessment of risk and detection of outbreak in a timely way (Christaki, 2015).

The rising concerns regarding the spread of influenza of pandemic proportion, bioterrorism and an upsurge of new infectious diseases have triggered efforts to improve surveillance and increase capacity for detecting infections early and controlling them to prevent outbreaks. Significant resources have been directed at developing advanced electronic reporting infrastructure. These are guided by non-specific syndromic symptoms. There is still debate on the effectiveness of these systems. Also, it has been established that informal digital systems have the ability to run several activities and processing information including mining, filtering, categorizing and visualizing information online with regard to epidemics. Some common examples of such resources include ProMED-mail, BioCaster and HealthMap (Digital Resources for Disease Detection, 2013).

The use of digital resources has become common. They have been used to monitor infectious diseases using both informal and informal methods. One of the driving forces that informed the evolution of such technologies was the need to reduce the time taken to detect outbreaks of infectious diseases. There is scarce evidence to demonstrate that the resources indeed help in detecting outbreaks faster than traditional methods. Nevertheless, it is evident that the resources help in availing important information for managing these outbreaks through an increased level of situational awareness. The resources help in boosting risk communication information (Digital Resources for Disease Detection, 2013).

Although computer technologies also come with their problems, they have added an important element in the development of public health monitoring. For instance, in the US in 1991, the National Electronics Telecommunications Systems for Surveillance had connected all state health facilities and departments for regular collection, analysis and dissemination of processed data on conditions categorized as notifiable. The country’s CDC started implementing the National Electronic Disease Surveillance Systems in 2001, to manage the huge number of the existing systems for surveillance, and enable the Public Health Services to respond in a faster way to emerging threats. By 2007, 35 states had integrated their public health monitoring systems following the vision by NEDDS. Once NEDDS is implemented fully in the USA, officials from the Public Health section and other professionals in related disciplines will recognize and respond to public health threats faster (Choi, 2012).

France’s Minitel system has sown that office based surveillance is effective in public health matters. Monitoring of public health depends on information systems that include a range of data sources important to influence public health action (Choi, 2012).

According to Chowell et al (2016) news reports from the internet and health bulletins should be used to gather information on cohorts of patients and reconstruct transmission trees and reproduction figures by making use the lessons learnt from Middle East respiratory syndrome as well as the Ebola outbreak. Sources data from the news media can be a fast way to come up with accurate and timely assessment of the chains of transmission; an essential aspect, given the absence of surveillance data with sufficient details. This is the scenario that was witnessed in the Ebola outbreak of 2014. Although the authors used a manual search method, to extract and model information that is relevant, they state that the method can be expanded by scanning internet news and the creation of tools for language processing meant to point out transmission that is sequential. It is evident that the method that uses HealthMap (Harvard School of Public Health, 2016) approaches along with others on the internet could be the lead towards a productive channel for surveillance purposes in middle income and low-income countries. This is particularly true and most warranted in locations with scarce studies on transmission or when there is limited time to act. The apparent undeterred antimicrobial resistance and failure by the global medical experts to monitor the issue in good time has been cited by McFadden et al. The authors talk of Resistance Open, which is an online bacterial drug resistance monitoring platform. It is based on aggregation, analysis and dissemination of local and regional index reports for resistance. The method is a direct offshoot of the earlier efforts to tract infectious diseases across the globe through curating and analyzing of a range of data sources from the internet (Harvard School of Public Health, 2016).

There is a lot of hope and interest over the possibility that computer technologies will enhance the effectiveness, quality and the capacity of public systems for disease outbreak surveillance. The use of the interactive information technology system in health called eHealth has drawn a lot of interest among practitioners and other health service stake holders. It is a system supported by electronic communication processes (Choi, 2012). The need for surveillance cannot be overemphasized in public health service provision. It is the backbone of data that informs the health fraternity and warns of the impending danger of an outbreak. Health tams respond based on the warning data available. They also use surveillance to monitor trends and conduct case management exercises including reporting (Mack, Choffnes, Sparling, Hamburg, & Lemon, 2007). It is critical to detect an outbreak early. It is the only way to stop the spread of such an epidemic (Aziz, 2017).

Managing a viral disease outbreak is complex and challenging. It calls for high levels of visibility and an efficient coordination plan across the levels of crisis. The action is focused on protection, prevention and recovery for people at risk. Therefore, management of technology cannot be ignored. It is an essential tool for fighting against outbreak of disease. Maven is a market leader in disease outbreak management and surveillance systems. It is used by a wide range of medical experts to support and share information in crisis situations. The system effectively and securely tracks patients who are at risk of infection, those infected with such communicable diseases as Zika, measles, STDs, tuberculosis, HIV, and influenza. The system supports sharing of data, integration and even coordination among health professionals working on the frontline in war against communicable outbreaks. Maven can easily be configured to handle a wide range of disease outbreak types and other complex technologies. It comes with an intuitive integrated portal to allow several stakeholders to record quickly and share information (Conduent, n.d). The system provides a proactive monitoring tweak which helps in the monitoring of disease. If a contact starts to show signs of infection, the results are tracked electronically to reduce chances of spreading the disease. Maven is a system that can be relied on to provide quality data so that the health experts can make informed decisions regarding specific cases and situations. The system takes note of all interactions because no interaction can be ignored in outbreak situations. The system can be used to leverage against outbreaks and to stop them from translating into epidemics (Conduent, n.d).

Participatory systems come with the advantage of reporting from communities and populations that would ordinarily not seek medical attention for ailments that they regard as minor. This tendency is common with influenza infected people. Infectious disease surveillance is a clear game changer in the way outbreaks are detected and managed. Apart from rapid detection and transmission of relevant data, these systems reach out to hidden groups. Furthermore, the systems can be used to monitor vaccine and drug response outcomes. Big data streams must, however, be used with caution to for the most benefit (Bansal et al, 2016).

The history of disease surveillance is given, briefly, by Simonsen et al. The authors point out the gaps in the systems in use in the present day, and make a case for using big data to enhance syndromic surveillance. They use influenza as a case study and show how using high volume surveillance at international level in the classification of disease collected by large private sector warehouses highlights how pandemics spread and gives remarkable spatial detail about the same. They show how the data streams are underutilized, partly due to concerns about privacy and common barriers to do with access to e-health data in government and the academic fraternity (Bansal et al, 2016).

Research based on the internet and social media in the area of health care and epidemiology poses several functional, technical and formal challenges. The first challenge has to do with ethics in public health. The Helsinki Declaration (Bourne 2015), is the oldest framework that provides guidelines on medical health ethics. The declaration insists that the patient must first be consulted before involving them or their data in a study. Legal and ethical concerns connected to online and social media collection of data, in particular have been discussed in a few articles and legal suits (Zimmer 2010). The ethical issues that researchers should explore were summarized by Bond et al (2013). The authors highlight the pertinent ethical issues that researchers should bear in mind when researching on social media platforms. The bottom-line is that researching on social media requires prior privacy and security permissions from subjects.

New technologies will continue to be used to enhance approaches to investigation of epidemiologic outbreaks. Diagnostic precision and its timeliness have been provided by fast laboratory inventions and discoveries based on these technologies. Evidently, the increased application of electronic health records will allow timely collection and accurate use of data. It will equally facilitate fast transmission of the recommended measures to control epidemics among healthcare staff. New statistical approaches are developed by statisticians focused on the improvement of data analysis. These efforts are also aimed at improving control measures and to cut down the numbers of epidemic related cases (Choi, 2012). Big data promises a rich reservoir of information and a host of analytical tools which will help health teams to demystify the complex and intricate details of transmission of infectious diseases that have been obscured due to lack of clear data.


References
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