Abstract
Timely prediction of Cholera epidemics is essential for preventing and controlling the size of an outbreak. Over the past years, there have been great initiatives in the development of Cholera epidemic models using mathematical techniques, which are believed to be the most powerful tools in developing mechanistic understanding of epidemics. Despite the existence of these initiatives, the timely prediction of Cholera is still a great challenge. Recently, the World Health Organization reported that “the global burdens of waterborne epidemics from environmental factors are expected to increase over-time with an increase of epidemic size.” Due to these challenges, this paper reviewed existing Cholera mathematical models and observe that they have limitations/complexities, especially when working with many variables. The use of how machine learning (ML) can be used to overcome the limitations/complexities, such as lack of effective integration of environmental factors, such as weather are investigated. Hence, the study developed an ML reference model and its development procedures, which can be used to overcome the existing complexities. The results indicate at an average of 87% that the developed measures can integrate a large number of datasets, including environmental factors for the timely prediction of Cholera epidemics in Tanzania.
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Appendix
Appendix
1.1 Focus Group Discussion and Interviewer-Administered Questionnaire
I am Dr. Judith Leo, a Lecturer at the Nelson Mandela African Institution of Science and Technology (NM-AIST)—Arusha. I am currently doing a research on proposing measures to overcome challenges in the existing initiative of timely Cholera prediction by proposing ARPM and its development procedure through the use of ML techniques. This questionnaire is aimed at assessing the perspectives of healthcare, environmental workers, cholera and diarrhea patients, ICT, Maths and ML experts on its feasibility, user acceptance, complexity, and impact of using an ARPM to enhance timely cholera epidemics analysis and prediction in Tanzania. The following are some of the sample questions.
Questionnaire Number:
Date:
Name of the working location:
A. Social-Economic Profile of the Respondent
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1.
Full name of the respondent: … .
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2.
Gender (Mark only one)
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(a)
Male
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(b)
Female
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(a)
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3.
Age (Mark only one)
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(a)
Below 19 years
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(b)
19 up to 30 years
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(c)
Above 30 years
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(a)
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4.
What is your level of education?
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5.
Choose techniques/initiatives that have been done to timely predict Cholera epidemics?
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(a)
ICT-based techniques through the use of Machine learning techniques
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(b)
ICT-based techniques through the use of non-Machine learning techniques such as Mathematics, mobile app etc.
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(c)
Non-ICT-based techniques such as policies and guidelines
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(a)
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6.
Is there any linkage between the occurrence and transmission of Cholera disease with the environmental factors?
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(a)
Yes; there is a linkage.
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(b)
No; there is no linkage.
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(c)
None of the above.
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(a)
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7.
Are ML models capable of integrating many factors? (Tick one–either Yes or No).
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(a)
Yes.
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(b)
No.
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(c)
None of the above.
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(a)
B. Information about the Validation of the Proposed Measures (Please Answer Questions that Concerns you as Regards to Expert Levels)
Please indicate the level of agreement by ticking in the box, corresponding to the row and column | |||||
|---|---|---|---|---|---|
Strongly Agree | Agree | Neither Agree nor Disagree | Disagree | Strongly Disagree | |
I found it easy to learn and follow the development procedure of ARPM | |||||
The proposed ARPM can solve the existing challenges of big data and timely prediction | |||||
It is very important to integrate environmental factors in the prediction of cholera epidemics | |||||
The proposed ARPM and its procedure is useful towards the development and implementation of ML cholera epidemic models | |||||
I would prefer these techniques (ARPM and its procedure) in the development of prediction models for different diseases | |||||
The proposed measures and developed model can be easily applied in the developing countries’ settings towards complementing the limitations and complexity of existing cholera epidemic models. | |||||
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Leo, J. (2022). Complexity of Epidemics Models: A Case-Study of Cholera in Tanzania. In: Marx Gómez, J., Lorini, M.R. (eds) Digital Transformation for Sustainability. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-031-15420-1_18
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