Use of IoT in Prediction of Disease Outbreaks in Kenya.

  IntroductionThere are mixed challenges with core and support Integrated Disease Surveillance and Response (IDSR) functions in Kenya. Globally, the main challenges of IDSR are systemic. These issues include financial burden of IDSR, poor co-ordination, erratic feedback, inadequate supervision from the next level, weak laboratory capacities coupled with unavailability of job aids (case definitions/reporting formats), and pervasively poor communication and transport systems particularly in remote setting. Apart from human resources and the health care system structures, technical and technological issues of IDSR determine its output. The current Kenyan IDSR is technologically wanting; it is not timely and accurate, there is minimal information that could influence budget for investment in IDSR, there is inadequate feedback, paper based inefficient and ineffective tools of data collection and analysis, inadequate capacity for analytical and predictive epidemiology, inadequate data managers, epidemiologist and monitoring & evaluation, lack of operational research evidence, poor quality of outbreak investigation and reporting, challenge in capturing of data from private sector clinicians, inadequate epidemiological data analysis and utilization at all levels untimely information from IDSR to guide public health interventions. Therefore, IDSR in Kenya can largely benefit from leveraging on IoT platforms to support surveillance functions alongside health care infrastructures at all levels. Healthcare and largely IDSR in Kenya and globally can potentially be improved if treatments were personalized, clinical data were collected beyond the occasional patient-doctor visit, disease progression were detected earlier and proactively treated (at the individual and community levels), and more effective cures were found for an array of obdurate conditions. Big data and IoT have many applications in healthcare in these respects. For example, clinical trial design can be improved by applying statistical tools and algorithms by mining patient data and recommending better protocol designs. Also, mapping data can help support response to disease outbreaks. For instance, during Cholera outbreak in Haiti following earthquake disaster of 2010, the Ministry of Health used mapping data applications to facilitate decision-making on the allocation of medicine and mobilization of health teams.How IoT WorksThe Internet of Things (IoT) is a scenario in which objects, there are unique identifiers for almost any phenomena and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Typically, IoT is expected to offer advanced connectivity of devices, systems, and services that goes beyond machine-to-machine communications and covers a variety of protocols, domains, and applications. The interconnection of these embedded devices (including smart objects), has ushered in automation in nearly all fields. Things, in the IoT, can refer to a wide variety of devices such as heart monitoring implants, biochip transponders on farm. Thus the Internet of Things (IoT) can help people do virtually anything. While all the IoT enabled tasks are important for making life more convenient, the IoT has the potential to do so much more, including stopping or minimizing the spread of infectious diseases. IoT in reducing diagnosis timeSometimes, people are not aware that they are carrying an infectious disease because they either have no symptoms or they did not recognize the symptoms as something indicating an infection. This can lead them to spread the disease to their communities by merely going about their daily lives as usual. One way in which IoT may help reduce this unintentional spread is by facilitating earlier diagnosis of the condition.In Uganda, providers using an over-the-air system that works with an IoT SIM card and global IoT communications platform have diagnosed cases of tuberculosis in only three days. Previous methods required patients to wait about two months before getting their results. This improvement does not prevent the disease in the diagnosed person, but it can stop the individual from potentially spreading it to others while they are unaware that they are infectious. As it were, detection is as important as control as early detection limits the time lapse which can be very costly to the healthcare system. IoT in predicting diseasePreparation for seasonal infections by healthcare systems around the world is typically unnerving. Some new IoT devices may be able to provide insights about these diseases that are otherwise not as easily available. IoT smart gadgets like thermometers may link to symptom tracking apps. These can be used to transmit relevant data, such as a person’s daily changes in temperature and or symptoms suggestive of a seasonal condition like flu, malaria and rift Valley Fever to a user’s doctor. The doctor can use the data to gauge the likelihood that the patient has caught the disease and whether or not a medication might be helpful. In addition, when this type of data is aggregated, public health professionals can us it forecasting occurrence of seasonal infectious conditions in various regions of Kenya. This type of data may provide more advanced notice of outbreaks than traditional methods. Some studies with seasonal flu using smart thermometers gave forecasts with a three weeks lead time than past methods. In a nutshell, IoT can give IDSR programs an advantage in helping public health specialists to predict outbreaks with a better lead time for preparations before the actual outbreak. Enabling environmental detection There are some microbes whose growth is fuelled by environmental changes. Effective control of such microorganisms depends on knowing the conducive environmental conditions for their proliferation. An example of such microbes is the Legionnaires Bacteria that is fatal when affecting other body organs apart from the lungs. This bacterium is more common in hot humid seasons and is found in water cooling towers and bathtubs. IoT can combine weatherman reports, water pH, pollution monitors and demographics to predict its outbreak. Equally, IoT can measure the efficacy of measures to control the disease like water purification, and zapping of the bacterium with electric current. They can track the overall efficiency of the cooling towers, verify that the bacteria-zapping technology is working as it should. It can also warn tower operators of any unusual conditions.Since the data gathered by IoT equipment provides ongoing information, it’s more likely for people to notice problems quickly. This reduces the chance that the first sign of a such health problems is a sick patient. Therefore, environmental controls can be optimised by use of IoT as the monitoring mechanism. IoT in enhancing multi and inter disciplinary collaboration After an outbreak of an infectious disease occurs, the situation becomes a race against the time to control the progression. However, understanding the extent of the disease often requires multidisciplinary collaborations; pathologist, microbiologist, entomologist, epidemiologist, data manager, statistician and many other specialists. If an outbreak happens in a remote area, the time it takes to get a well thought multidisciplinary team response can be quite an uphill task. The turnaround time could lead to devastating consequences. However, IoT-enabled digital pathology microscopes, conferencing facilities, large data analytics, GIS and many more capabilities enable consultation, collaboration of different disciplines to happen substantially faster than it would without that technology. Therefore, IoT bridges the gap between the field pathologist and physician, data analyst and policy maker, clinical carer and ethics committee, patient and stewardship teams and many others. All these professionals maybe in different parts of the world but can be brought together by IoT. This enables real-time response to disease outbreaks. IoT in efficiency of actionDetecting an infectious disease is an after-the-fact-activity, and preventing its spread requires real-time information and analytics. Acting quickly with accurate information can have a profound impact on the lives of people profoundly, socially and economically. Therefore, the use of IoT to collect sensory data in real-time is key in detecting and response to outbreaks. This involves tracking people, health systems, and environments. With the emergence of IoT and big data analytics in healthcare, data can be collected from different places in real time where manual data collection could beat the essence of early detection. This can be very vital tracking of diseases in real-time and in predictive analytics to prevent the spread of the diseases.With the support of City and town planners, public health scientists can collect samples of microbes and bacteria from bus and railway stations across the city and town as part of the global Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) international consortium. Once the samples are processed the results can be made available to city the planners, public health officials and scientists who can use the data to help officials predict and prepare for future disease outbreaks and discover new species and biological systems. IoT in enhancing leverage on patient experience A case of how IoT is transforming lives is the Johns Hopkins Center for Clinical Global Health Education that uses connected devices to increase patient engagement and help clinicians and nurses at the point of care share information on clinical studies in real-time. The software-enabled tablet interface allows the researchers to holistically view patients’ clinical data and have insights into social determinants of health and health care outcomes. These systems also enable patients to access their health data and tracks the progress of their health. This system definitely enhances patient’s interest in health issues. Public health organizations can benefit from such organizational platforms based on improved patient experience and free participation to leverage IoT-based connected devices to detect and manage public health crises. In the case of a rising suspicion of an outbreak, health systems can use a network of IoT devices to procure more focused data to pinpoint the source of an epidemic. In the event of a confirmed outbreak, the same network can be used or enhanced to provide the necessary supply of drugs, medical devices and other diagnostic tools.IoT in enhancing public and health care provider safety in highly infectious outbreaksThrough enhanced patient participation in health by IoT enabled devices, lay people can know how certain diseases of public health concern are transmitted and other vital control measures. Although caregivers receive training before coming in contact with patients, they still make mistakes. However, IoT technology could send alerts to a health worker who inadvertently exposes themselves to harm through wearable devices. Smart wearables could maintain safeguards while improving the quality of care, too. For example, in highly infectious diseases, its challenging for the healthcare providers to monitor disease using traditional methods like thermometers and stethoscopes. Wearable IoT device can monitor a patient by taking their baseline readings of the heart, temperature, and oxygen saturation and informs care provider of deviations from those initial statistics through data transmission mechanism to central point such as at a control centre near an infectious zone. The device lets caregivers see the conditions of all patients in a predetermined area.IoT and researchIoT with the enhancement of big data analytics can be applied in medical research. For instance, use of freely available cancer registry clinical big data can be made more accessible to potential researchers thus ease of fulfilling cancer funding requirements. This could lead to potential unimaginable benefits in cancer care in Kenya. Using anonymous, unstructured data provided by the National Cancer Registry in Nairobi there can be development of cognitive algorithms to automate the inference of national cancer statistics in Kenya. This can reduce a time lag in cancer statistics, and analytics reporting to real-time. This can thus help in mapping of cancer in Kenya and appropriate equitable cancer resource deployment. Conclusion The promise of IoT to address healthcare must balance the privacy, confidentiality and safety of patients that are currently the gold-standard aspects of quality of clinical care. Confidential health records disclosed to third-parties could potentially impact insurance policies on cover and capitation or even future employment prospects. There should policy redress of appropriate regulatory frameworks, as well as assurance of professional and organizational fidelity to Good Clinical Practice standards. Considering the present situation in Kenya where there is a need for an efficient and timely disease surveillance system, this Internet of Things based disease surveillance system can be a major Public health and technology breakthrough. The current situation where surveys are conducted on a weekly basis and are heavily dependent on the state health centres for disease case reporting can change drastically. IoT based disease detection systems can potentially lead to timely and accurate disease reporting and reduced burden for the district and the sub-counties. The possible outcomes of IoT based disease detection are; timely case reporting and thus significantly reducing under reporting, accurate analysis and trends about the diseases, timely preventive measures, real-time national audit on adequacy of medicines to tackle any outbreaks, ability to predict a disease by the collective trending symptoms in an area, elimination of delayed reporting and timely alerts from the Ministry of Health on impending and inevitable outbreaks.

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