Disease surveillance is an information-based activity involving the collection, analysis and interpretation of large volumes of disease outbreak data from a variety of sources in order to inform and drive objective and informed intervention. The Disease Surveillance and Response Unit (DSRU) is the entity mandated (in Kenya) to monitor and undertake response and mitigation measures in the event of a notifiable disease outbreak; a notifiable disease refers to any disease in a country or community whose occurrence must be reported to the authorities (WHO, 2006). Each time a notifiable disease is reported, the DSRU undertakes the necessary response activities (CDC, 2012; DSRU, 2014).
In Kenya, disease outbreaks are mostly tackled from two perspectives; reactive measures - in the event a notifiable disease outbreak is reported, mitigating steps are only undertaken in response to the particular incident(s) to minimize the potential consequent adverse effects; not much is learned or information utilized in the aftermath that could meaningfully, incrementally and objectively inform future outbreaks and; proactive measures – anticipatory measures are put into play such that should an outbreak occur or recur, its adverse effects are greatly minimized with health personnel taking informed, premeditated and experience-driven steps as a better approach to empower the health personnel be better prepared to cope with every subsequent outbreak.
The infectious diseases of the past have been known to have included some of the most contagious and feared plagues of the past, with new strains continuing to emerge over time; this warrants a widely and greatly co-operative and proactive approach even when the disease outbreak responses and intervention efforts remain the prerogative of the concerned national government. Global partners (such as the Centre for Disease Control and Prevention [CDC], the United Stated Agency for International Development [USAID], the World Health Organization [WHO] among others) have also been seen to play a great role by working in close collaboration to offer the much needed medico-technical and social support from its battery of experienced and seasoned teams cutting across numerous medical specialities and vast geopolitical backgrounds (Brownstein et al, 2009; Martinez, 2000).
To enable each country’s concerned teams better manage its disease outbreaks more efficiently, a notifiable disease list and its epidemiological week (Epi-Week) must be defined; an Epi-week is a weekly period in a country within which notifiable disease outbreak data must be recorded and reported to the relevant health authorities. Kenya’s epi-week runs from Monday through Sunday (DSRU, 2014; WHO, 2006).
The efforts to manage disease outbreaks have become a very complex endeavour; historically, it was easier due to smaller populations and the limited, minimized yet localized cross-border and cross-territorial movements and interactions that curtailed the cross-pollination or dissemination of infectious diseases the concerned population may have been harbouring - this has greatly changed in the advent of globalization (Wagner, 2001).
The effects of globalization have brought forth new dynamic risk factors in disease spread and management. Such factors include: faster and easy cross-border movements of people and animals, making diseases spread faster - for instance, urbanization remains one of the greatest factors of disease spread: new urban settlements and availability of a huge community of commuting skilled and readily available labour across geopolitical boundaries having the ability to create some infection epicentres that if not well-managed, could easily become incubators for new epidemics, and zoonotic diseases, which can spread in a more rapid manner, quickly elevating them to global levels of interest and concern (Nsubuga et al, 2010).
Next comes means of transporting goods or parcels. The efficient and rapid movement of goods also presents a possibility of enabling and enhancing the spread of diseases since the goods may be harbouring and transporting whatever existing disease strain to wherever they are transported or delivered (Mack, et al, 2010). Additionally, there is also the new, modern practice of families frequently eating out where they get more exposed to different infectious disease strains, among other exposures (Zhong et. al., 2021). Suddenly, one nation’s (seemingly localized) epidemic challenges quickly become other nations’, regions’ and partners’ health concerns – pathogens are not known to commonly follow or respect geopolitical and human boundaries.
Additionally, in economic and industrial competitive terms, other factors could also kick in - for instance, the economic empowerment or disempowerment of the notifiable disease-affected populations when skilled, experienced and knowledgeable working personnel get grossly affected by a disease (Kulldorff, 2001; Morse, 2001; Neiderud, 2015; Pillai et al, 2014). The push and pull factors for disease surveillance also touch on the socio-economic activities of a nation; disease outbreaks have been known to decimate the knowledgeable, skilled and able-bodied working populations of any nation to a point of economic near-standstill if not total collapse (Roser, 2015).
Further, it is has been observed that the progression or retrogression of the economic well-being of a community can now be greatly tied to proper disease outbreak management; if the adverse effects slow down economic activity, then all measures, (including the improvement of the health infrastructure and the response and mitigation apparatus of a country) must be called upon to prevent or deal with the adversity of the disease outbreaks (Baker et al, 2002; Roser, 2015). To combat such disease strains, concerted efforts and clear-cut strategies need to be employed; the enhanced use of ICT software and tools has been seen as a great driver and catalyst to enable the quick aggregation, packaging and dissemination of disease data through to the relevant personnel for easier, faster and better-informed interventions (Weinberg et al, 2003).
The disease outbreak data used here is subjected to AI’s machine learning theory. Machine learning is a technique that provides systems with the ability to automatically learn and improve from experience (Neiderud, 2015; Roser, 2015). Whilst traditional disease outbreak management assumes the method of relying on past disease data that is seen to point towards what infectious disease strains manifest, this research looks to dig deeper. Using AI, the researcher hopes to drive a different perspective to notifiable disease outbreak management.
Of the two disease outbreak management perspectives outlined earlier, the researcher looks to build on the proactive disease outbreak measures. The main driving question or hypothesis here is whether a different approach could be employed to the processing and packaging of notifiable disease data in order to better inform and drive proactivity in the disease surveillance and response practice.