http://hdl.handle.net/123456789/1245 2026-04-08T17:52:31Z http://hdl.handle.net/123456789/1760 STRUCTURE OF CONVERSATIONS OF HEALTHCARE PRACTITIONERS ON EBOLA VIRUS DISEASE IN THE MEDSCAPE NETWORK, 2014-2018 IGWE, Ebelechukwu Gloria The outbreak of Ebola Virus Disease (EVD) during 2014-2015 generated discussions and exchange of information among Healthcare Practitioners (HCPs) on social media and other platforms. These exchanges improved understanding of the disease. Previous studies have examined the medical aspects of the disease, with little attention paid to the nature and structure of conversations on the disease among HCPs on social media networks. This study was, therefore, designed to investigate the characteristics, trends, knowledge content, and relevance of conversations on EVD on the Medscape network and the participation behaviours and roles of different categories of HCPs. Conversation and Social Network theories guided the study, while content analysis was adopted as the research design. Data on EVD topics and corresponding HCPs‘ posts on the topics from March 26, 2014 to April 27, 2018 were extracted from the Medscape network website. The data were analysed thematically, while a 1-mode network was developed to determine the centrality measures of the nodes representing the participating HCPs. There were 391 EVD news topics and associated contents, and the HCPs responded to 234 of the topics which generated 7,343 conversations, while 157 topics received no responses. The trend of EVD conversations among HCPs showed a high conversation frequency of 6,479 (88.2%) at the peak period of EVD outbreak in 2014, but declined thereafter. The 234 news posts that received responses were on six main themes - management (106, 45.3%), risks (33, 14.1%), resources (29, 12.4%), treatment (25, 10.7%), transmission (19, 8.1%) and others (22, 9.4%), but the ensuing conversations by HCPs on the topics focused mainly on EVD risks (4,679, 63.7%). Almost all the conversations (7,230, 98.6%) were adjudged by medical experts to be relevant to the EVD topics and contents posted on the network. A total of 3,310 HCPs participated in the conversations, but only 95 were assessed active. Participation in the conversations by HCPs showed that medical doctors contributed 57.4%, followed by nurses (27.5%), pharmacists (2.2%), health/business administrators (2.2%), medical students (1.9%), and nursing students (1.1%), while miscellaneous other categories accounted for 7.6%. Conversations by the healthcare practitioners focused mainly on Ebola virus disease management and risks, and were mostly relevant to the posted topics. Health agencies at national and global levels should recognise conversations among healthcare practitioners on social media networks as important sources of information on Ebola virus disease and other emerging diseases. 2021-12-01T00:00:00Z http://hdl.handle.net/123456789/1247 MODELLING NIGERIA’S PRESIDENTIAL ELECTION DATA USING BENFORD’S LAW AND MONTE CARLO SIMULATION TUNMIBI, SUNDAY OLAPADE Allegations of fraudulent practices and bogus results led to application of election forensic tools in the analysis of election data. Previous studies examined the digital distributional patterns of electoral data using Zipfian and agent-based modelling, while neglecting sensitivity check that could reveal other anomalies. This study, therefore, was designed to analyse Nigeria’s presidential election data between 2007 and 2015 by applying Benford’s Law and Monte Carlo Simulation which can indicate voter distribution and reveal any anomalies in the election results, with a view to assessingthe integrity of the election process and results. Benford’s Law and Monte Carlo Simulation models were used as framework, while Modelling and Simulation, which compares the observed patterns against the expected patterns, were adopted as design. Purposive sampling was used to select 2007, 2011 and 2015 presidential election results.Data wasobtained from the website of Independent National Electoral Commission. Political parties included in the analyses were those with at least four digits vote counts: 24 parties for 2007 election; PDP, CPC, ACN and ANPP for 2011 election; and APC and PDP for 2015 election. Also included were voters’ turnout for 2011 and 2015 elections (data was not available for 2007). Data were analysed using descriptive statistics and Spearman rank correlation test at 0.05 level of significance, while R programming was used for the Monte Carlo Simulation. Whereas the 2007 election result contains only vote counts of the 24 political parties, collated at national level only, the 2011 and 2015 election results contain voters’ turnout and vote counts for each political party per state.The distribution of last digits of vote counts of 2007, 2011 and 2015 elections and voters’ turnouts of 2011 and 2015 elections did not follow the expected uniform distribution of last digits for fraud-free data. The distributional pattern of vote counts for 2011 and 2015 elections deviated from distributional pattern of Monte Carlo simulated vote counts. The first digits of vote counts in 2007 elections of the 24 political parties (r=0.68); in 2011 elections of ACN (r=0.96), PDP (r=0.93), CPC (r=0.75) and ANPP (r=0.73); and in 2015 elections of APC (r=0.96) and PDP (r=0.74) significantly correlate with Benford’s Law. The occurrence of first digits in voters turnouts of 2011 (r=0.07) and 2015 (r=0.37) elections did not follow Benford’s Law. The occurrence of second digits in vote counts of the 2007 elections (r=0.36), 2011 elections [PDP (r=0.51), ACN (r=0.51), CPC (r=0.20) and ANPP (r=0.17)] and 2015 elections [APC (r=-0.61) and PDP (r=0.02)] did not follow Benford’s Law. This was also the case in voters’ turnout of 2011 (r=-0.17) and 2015 (r=-0.09) elections. The application of Benford’s Law and Monte Carlo Simulation on Nigerian presidential election data of selected years reveals that the election results are not error-free. Nigeria’s electoral process should apply these forensic analyses on electoral data and adjust the electoral process in line with findings. 2021-01-01T00:00:00Z