Browsing by Author "Mwamnyange, Mdoe"

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    Big Data Analytics Framework for Childhood Infectious Disease Surveillance and Response System using Modified MapReduce Algorithm
    (International Journal of Advanced Computer Science and Applications, 2021) Mwamnyange, Mdoe; Luhanga, Edith T.; Thodge, Sanket R.
    Tanzania, like most East African countries, faces a great burden from the spread of preventable infectious childhood diseases. Diarrhea, acute respiratory infections (ARI), pneumonia, malnutrition, hepatitis, and measles are responsible for the majority of deaths amongst children aged 0-5 years. Infectious disease surveillance and response is the foundation of public healthcare practices, and it is increasingly being undertaken using information technology. Tanzania however, due to challenges in information technology infrastructure and public health resources, still relies on paper-based disease surveillance. Thus, only traditional clinical patient data is used. Nontraditional and pre-diagnostic infectious disease report case data are excluded. In this paper, the development of the Big Data Analytics Framework for Childhood Infectious Disease Surveillance and Response System is presented. The framework was designed to guide healthcare professionals to track, monitor, and analyze infectious disease report cases from sources such as social media for prevention and control of infectious diseases affecting children. The proposed framework was validated through use-cases scenario and performance-based comparison.
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    Big data analytics framework for childhood infectious disease surveillance system using modified mapreduce algorithm: a case study of Tanzania
    (NM-AIST, 2021-11) Mwamnyange, Mdoe
    Tanzania has been affected with a potential emerging and re-emerging of infectious diseases such as diarrhea, acute respiratory infections, pneumonia, hepatitis, and measles. There is an increasing trend for the occurrences of new emerging pandemic diseases such as the coronavirus (Covid-19) in 2020 as well as re-occurrence of old infectious diseases such as cholera epidemic in 2015-2017, chikungunya and dengue fever outbreak in 2010, 2012, 2014, 2018, and 2019 which affected different regions in Tanzania. These diseases by far are the main causes of the high mortality rate for women and children of 0-5 years of age. The traditional disease surveillance system as the foundation of the public healthcare practices has been facing challenges in data collection and analysis using health big data sources to prevent and control infectious diseases. Health big data sources on infectious diseases have been recognized as the potential supplement for the provision of evidence-based decision-making worldwide. Tanzania as one of the resource-limited setting countries has lagged because of the challenges in information technology infrastructure and public healthcare resources. The traditional disease surveillance system is still paper-based, semi-automated, and limited in scope which relies on clinical-oriented patient data sources and leaving out nontraditional and pre-diagnostic unstructured big data sources. This research study aimed to improve the traditional infectious disease surveillance system to employ big data analytics technology in healthcare data collection and analysis to improve decision-making. Big data analytics framework for the childhood infectious disease surveillance system was developed which guides healthcare professionals to streamline the collection and analysis of health big data for infectious disease surveillance. The framework was then fairly compared with the existing framework in its performance using infrastructures, data size and transformation, and running-time execution of the systems. The experimental results indicate the efficiency of the framework system performance with the highest running time execution of about 56% quicker over the traditional system. Also, it has the best performance in processing multiple data structures using additional processing units. In particular, the proposed framework can be adopted to improve the prenatal and postnatal healthcare system in Tanzania.