Browsing by Author "Nyambo, Devotha G."

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Characteristics of smallholder dairy farms by association rules mining based on apriori algorithm
(International Journal of Society Systems Science (IJSSS), 2019-06-03) Nyambo, Devotha G.; Luhanga, Edith T.; Yonah, Zaipuna O.
Characteristics of smallholder dairy farmers across regions are highly similar. However, introduction of improved farm management practices and extension support can be effective if specific constraints are identified for each farm typology. So far, approaches used to formulate farm types and characterise farming systems are not tailored to studying hidden patterns from farm datasets. Using the apriori association rules mining algorithm, characteristics of four smallholder dairy farm types are studied. Applying the power of the ArulesViz package, frequent items were visualised. These visuals which display some hidden attributes, solidified understanding on the key determinants for change in the studied farm types. The hidden smallholder farm characteristics were identified in addition to those given by cluster analysis in preliminary studies. Characterising smallholder farm data by using association rules mining is recommended in order to understand such systems in terms of what/how the majority practice rather than basing on cluster averages.
A Framework for Timely and More Informative Epidemic Diseases Surveillance: The Case of Tanzania
(Journal of Health Informatics in Developing Countries, 2018-12-12) Rutatola, Edger P.; Yonah, Zaipuna O.; Nyambo, Devotha G.; Mchau, Geofrey J.; Musabila, Albogast K.
Background: A number of health facilities in the United Republic of Tanzania use different Hospital Management Information Systems (HoMISs) for capturing and managing clinical and administrative information for report generation. Despite the potentials of the data in the systems for use in epidemic diseases surveillance, timely extraction of the data for integrated data mining and analysis to produce more informative reports is still a challenge. This paper identifies the candidate data attributes for epidemic diseases surveillance to be extracted and analyzed from the Government of Tanzania Hospital Management Information System (GoT-HoMIS). It also examines the current reporting setup for epidemic diseases surveillance in Tanzania from the health facilities to the district, regional, and national levels. Methods: The study was conducted at the Ministry of Health, Community Development, Gender, Elderly, and Children (MoHCDGEC), Tumbi Designated Regional Referral Hospital (TDRRH), Muhimbili University of Health and Allied Sciences (MUHAS), and Mzumbe Health Centre, all in the United Republic of Tanzania. A total of 10 key informants (medical doctors, epidemiologists, and focal persons for various health information systems in Tanzania) were interviewed to obtain primary data. Data entry process in the GoT-HoMIS was also observed. Documents were reviewed to broaden understanding on several aspects. Results: All the respondents (100%) suggested patients’ gender, age, and residence as suitable attributes for epidemic diseases surveillance. Other suggested attributes were occupation (85.71%), diagnosis (57.14%), catchment area population (57.14%), vital status (57.14%), date of onset (57.14%), tribe (42.86%), marital status (42.86%), and religion (14.29%). Timeliness, insufficient immediate particulars on an epidemic-prone case(s), aggregated data limiting extensive analytics, missing community data and ways to analyze rumors, and poor data quality were also identified as challenges in the current reporting setup. Conclusion: A framework is proposed to guide researchers in integrating data from health facilities with those from social media and other sources for enhanced epidemic disease surveillance. Data entrants in the systems should also be informed on the essence and applications of data they feed, as quality data are the roots of quality reports.
Leveraging peer-to-peer farmer learning to facilitate better strategies in smallholder dairy husbandry
(SAGE journal, 2020-12-02) Nyambo, Devotha G.; Luhanga, Edith T.; Yonah, Zaipuna O.; Mujibi, Fidalis D. N; Clemen, Thomas
Peer-to-peer learning paradigm is seldom used in studying how farmers can increase yield. In this article, agent-based modelling has been applied to study the chances of dairy farmers increasing annual milk yield by learning better farming strategies from each other. Two sets of strategies were considered; in one set (S), each farmer agent would possess a number of farming strategies based on their knowledge, and in a second set (S'), farmer agents would possess farming strategies that they have adopted from their peers. Regression models were used to determine litres of milk that could be produced whenever new strategies were applied. By using data from Ethiopia and Tanzania, 28 and 25 determinants for increase in milk yield were fitted for the two countries, respectively. There was a significant increase in average milk yield as the farmer agents interacted and updated their S'– from baseline data, average milk yield of 12.7 ± 4.89 and 13.62 ± 4.47 to simulated milk yield average of 17.57 ± 0.72 and 20.34 ± 1.16 for Tanzania and Ethiopia, respectively. The peer-to-peer learning approach details an inexpensive method manageable by the farmers themselves. Its implementation could range from physical farmer groups to online interactions.