In recent years, especially in 2014, Africa, as well as the whole world, has faced an Ebola epidemic. The facts have demonstrated the weakness of the global crisis management and limitation of existing diseases prediction, prevention, monitoring, and surveillance systems and policies. From 2015 until today, many studies have been carried out and systems have been implemented to improve the global infectious diseases monitoring. Most proposed monitoring systems consist of using wearable sensors for the remote sensing vital parameter in an individual. These monitoring systems are, however, limited. This chapter proposes a novel infection monitoring and prevention system using a hybrid crowdsensing paradigm to overcome the limitation of existing systems. The proposed system uses large-distance optical sensors (e.g., fiber Bragg grating sensors) for sensing bio-signals in individuals within (ad-hoc) crowds to anticipate any risks of emerging infectious diseases spreading or epidemics.
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Infectious diseases can quickly spread to a crowd at a market or supermarket, festivities, dancing clubs, schools, universities, etc. Therefore, it matters to monitor any (ad-hoc) crowds, especially in regions and eventual in season at high-risk for infectious diseases.
The tendency today in diseases surveillance consists of collecting epidemiological data on emerging infectious diseases through social media, wearable sensors systems, or mobile applications and carrying out the analysis by examining the collected epidemiological data. Screening for diseases is one of the oldest traditional diseases surveillance methods. It consists of asking and medically examine a patient or an asymptomatic individual in order to early detect diseases. This method presents certain limitations in early detecting the pathology. In much of cases, diseases are quite late detected and can only be treated than prevented, people do not adhere enough to the screening programs. Additionally, the results of screening for diseases can be wrong or biased.
In the age of information and communication technology, many social web-platforms are used to collect epidemiological data to predict and prevent (infectious emerging) diseases and health conditions. For example, Foodborne Chicago1 and Flu Near You2 are social media application used to collect diseases and health condition related data (Christaki, 2015). Though, collecting data through social media applications is limited in its “participatory” and/or voluntary aspect. Furthermore, they present a geographical surveillance gap due to limitations in communication infrastructures in low- and middle-income countries. To overcome this limitation, mobile phone application is using to collect epidemiological data on infectious diseases, since the mobile phone is widely distributed in these areas (Christaki, 2015). In (Brownstein, Freifeld, Reis, & Mandl, 2008) have discussed the limitations of the internet based diseases surveillance using the example of the Health Map system. The authors summarize the limitations of the HealthMap as follow:
… While Internet-based online media sources are becoming a critical tool for global infectious disease surveillance, important challenges still need to be addressed. Since regions with the least advanced communication infrastructure also tend to carry the greatest infectious disease burden and risk, system development must be aimed at closing the gaps in these critical areas…
Influenza is endemic in Europe. Several Influenza monitoring and surveillance systems exist at national level. The authors in (Paolotti et al., 2014) have discussed the limitations of existing Influenza monitoring and surveillance systems and propose an innovative web 2.0 based monitoring system that is an improvement compared to the existing system. However, the authors conclude that “Internet surveillance of healthcare usage can be used to complement traditional surveillance”. This conclusion reveals another limitation of internet-based diseases surveillance.
In (Steinhubl, Marriott, & Wegerich, 2015), the authors present a system consisted of wearable sensors to remote sensing vital parameter in the patient. The system can detect any change in patient’s status quickly than conventional monitoring. The tests conducted are conclusive. However, this system can only monitor a unique individual, thus, it is limited in monitoring a crowd.
The few, but representative, Internet-based monitoring systems presented above have demonstrated the limitations of such systems, which are not applicable everywhere. Most of them are more focused on diseases monitoring than surveillance. Monitoring is when an individual is suffering from an affection and healthcare professionals are sensing changes occurring in his medical status. Diseases surveillance is when a population at given areas are screening to detect any abnormal indices pointing to any infection.
Collecting epidemiological data through internet based, social media, or wearable sensors applications requires that the user should have access to the internet, be literate (can read and write) and is willing to provide the requested information. Further, it possible that the provided information can be biased or fanciful. To our best knowledge, there exists no study that analyzes the accuracy and scientific soundness of data containing in such diseases surveillance and monitoring systems.