Establishing the connection between successful disposal of public assets and sustainable public procurement practice

The disposal of assets after their end-of-use is often considered the end-stage in the procurement cycle. Assets disposal in a public organization is its capacity to attach reusable value to its assets at their end of life. It is an area where the failure of the public procurement process is most attributed yet has the most potential for sustainable procurement practice. This paper examines the factors for the successful disposal of public assets and the public purchasers' perceptions of how these factors contribute to a better understanding of sustainable procurement practice. Using a cross-sectional survey among key actors in the public procurement process, three key success factors, namely strategic assets management, strategic planning for assets disposal, and assets disposal mechanismsare identified; their effect on successful asset disposal is examined. The results indicate that the three broad factors studied are all important aspects for effective assets disposal. Yet, their effect on the success of the disposal of public assets process is somewhat varied. Based on the three factors' statistical significance, we conclude that strategic assets’ planning is perhaps the most dominant factor for a successful assets disposal process and provides the most promise for sustainable procurement in public organizations. This study shows that strategic assets management, strategic planning for assets disposal, and the choice of assets disposal mechanisms are all critical enablers of successful disposal of public assets in public organizations and a precursor for sustainable procurement practice. But at the microlevel, disposal planning is by far the most significant enabler for successful assets’ disposal, and therefore a direct driver for sustainable procurement practice

Human-centered artificial intelligence for the public sector: The gate keeping role of the public procurement professional

The increasing deployment of artificial intelligence (AI) powered solutions for the public sector is hoped to change how developing countries deliver services in key sectors such as agriculture, healthcare, education, and social sectors. And yet AI has a high potential for abuse and creates risks, which if not managed and monitored will jeopardize respect and dignity of the most vulnerable in society. In this study, we argue for delineating public procurements’ role in the human-centred AI (HCAI) discourses, focusing on the developing countries. The study is based on an exploratory inquiry and gathered data among procurement practitioners in Uganda and Kenya, which have similar country procurement regimes: where traditional forms of competition in procurement apply compared to more recent pre-commercial procurement mechanisms that suit AI procurement. We found limited customization in AI technologies, a lack of developed governance frameworks, and little knowledge and distinction between AI procurement and other typical technology procurement processes. We proposed a framework, which in absence of good legal frameworks can allow procurement professionals to embed HCAI principles in AI procurement processes

Dynamics of forest cover conversion in and around Bwindi impenetrable forest, Southwestern Uganda

Forest cover has been converted to agricultural land use in and around the protected areas of Uganda. The objectives of this study were; to examine the dynamics of forest cover change in and around Bwindi impenetrable forest between 1973 and 2010 and to identify the drivers of forest cover change. The trend in forest cover change was assessed by analyzing a series of orthorectified landsat imageries of 1973, 1987 and 2001 using unsupervised and supervised classification. Land use/cover map for 2010 was reconstructed by analyzing 2001 image, validated and/or reconstructed by ground truthing, use of secondary data and key informant interviews. A series of focused group discussions and key informant interviews were also used to identify drivers of land use/cover change. Policies and institutional arrangements that could have affected forest cover change for the studied time period were also identified. Results showed that protected forest and woodlot in unprotected area had declined by 7.8% and 70.7% respectively as small scale farming and tea plantations had increased by 13.9% and 78.3% respectively between 1973 and 2010. The conversions were attributed to land use pressure due to population growth, change in socio-economic conditions and institutional arrangements. The severe loss of woodlot outside the protected area not only poses a potential threat to the protected forest but also calls for intervention measures if efforts to mitigate climate change impacts are to be realize

Optimisation and Validation of a conventional ELISA and cut-offs for detecting and quantifying anti-SARS-CoV-2 Spike, RBD, and Nucleoprotein IgG, IgM, and IgA antibodies in Uganda

There is an urgent need for better immunoassays to measure antibody responses as part of immune-surveillance activities and to profile immunological responses to emerging SARS-CoV-2 variants. We optimised and validated an in-house conventional ELISA to identify and quantify SARS-CoV-2 spike- (S-), receptor binding domain- (RBD-), and nucleoprotein- (N-) directed IgG, IgM, and IgA binding antibodies in the Ugandan population and similar settings. Pre- and post-pandemic specimens were used to compare the utility of mean ± 2SD, mean ± 3SD, 4-fold above blanks, bootstrapping, and receiver operating characteristic (ROC) analyses in determining optimal cut-off optical densities at 450 nm (OD) for discriminating between antibody positives and negatives. “Limits of detection” (LOD) and “limits of quantitation” (LOQ) were validated alongside the assay’s uniformity, accuracy, inter-assay and inter-operator precision, and parallelism. With spike-directed sensitivity and specificity of 95.33 and 94.15%, respectively, and nucleoprotein sensitivity and specificity of 82.69 and 79.71%, ROC was chosen as the best method for determining cutoffs. Accuracy measurements were within the expected CV range of 25%. Serum and plasma OD values were highly correlated (r = 0.93, p=0.0001). ROC-derived cut-offs for S-, RBD-, and N-directed IgG, IgM, and IgA were 0.432, 0.356, 0.201 (S), 0.214, 0.350, 0.303 (RBD), and 0.395, 0.229, 0.188 (N). The sensitivity and specificity of the S-IgG cut-off were equivalent to the WHO 20/B770-02 S-IgG reference standard at 100% level. Spike negative IgG, IgM, and IgA ODs corresponded to median antibody concentrations of 1.49, 3.16, and 0 BAU/mL, respectively, consistent with WHO low titre estimates. Anti-spike IgG, IgM, and IgA cut-offs were equivalent to 18.94, 20.06, and 55.08 BAU/mL. For the first time, we provide validated parameters and cut-off criteria for the in-house detection of subclinical SARS-CoV-2 infection and vaccine-elicited binding antibodies in the context of Sub-Saharan Africa and populations with comparable risk factors

The subdued post-boost spike-directed secondary IgG antibody response in Ugandan recipients of the Pfizer-BioNTech BNT162b2 vaccine has implications for local vaccination policies

IntroductionThis study aimed to delineate longitudinal antibody responses to the Pfizer-BioNTech BNT162b2 COVID-19 vaccine within the Ugandan subset of the Sub-Saharan African (SSA) demographic, filling a significant gap in global datasets.MethodsWe enrolled 48 participants and collected 320 specimens over 12 months after the primary vaccination dose. A validated enzyme-linked immunosorbent assay (ELISA) was used to quantify SARS-CoV-2-specific IgG, IgM, and IgA antibody concentrations (ng/ml) and optical densities (ODs). Statistical analyses included box plots, diverging bar graphs, and the Wilcoxon test with Bonferroni correction.ResultsWe noted a robust S-IgG response within 14 days of the primary vaccine dose, which was consistent with global data. There was no significant surge in S-IgG levels after the booster dose, contrasting trends in other global populations. The S-IgM response was transient and predominantly below established thresholds for this population, which reflects its typical early emergence and rapid decline. S-IgA levels rose after the initial dose then decreased after six months, aligning with the temporal patterns of mucosal immunity. Eleven breakthrough infections were noted, and all were asymptomatic, regardless of the participants’ initial S-IgG serostatus, which suggests a protective effect from vaccination.DiscussionThe Pfizer-BioNTech BNT162b2 COVID-19 vaccine elicited strong S-IgG responses in the SSA demographic. The antibody dynamics distinctly differed from global data highlighting the significance of region-specific research and the necessity for customised vaccination strategies