No abstract

No abstract

The Southern African Development Community is lagging behind in terms of knowledge economy relative to other regions worldwide. This dramatically reduces the chances of keeping up with their economically established counterparts in terms of sustainable development. This paper therefore, applies multivariate panel data analysis which is predicted on the Cobb–Douglas production function to analyze the affiliation flanked by knowledge-based economy pillars and economic growth from 1998–2018. The World Bank knowledge-based economy framework is employed. To achieve the study goal, the long-run effect regarding proxies of each pillar in the knowledge-based economy on economic growth is first estimated. Afterwards, the average impact of each pillar is examined using the average impact index (AII). Employment of both conventional unit root and co-integration tests showed all observed series are stationary and co-integrated. Further estimation of the long-run relationship using both static and dynamic models (fixed effect and generalized method of moment) portrayed that government effectiveness, adjusted savings on education expenditure, tertiary enrollment, scientific and technical journals, and mobile cellular subscriptions have significant positive impact on economic growth. Finally, the AII estimation unveiled that the innovation pillar is the most impactful aspect on economic growth followed by education and skills with the least being information and communication technology infrastructure. Feasible policy recommendations are further suggested.

No abstract

Malaria is the leading cause of morbidity in Ghana representing 40-60% of outpatient hospital attendance with about 10% ending up on admission. Microscopic examination of peripheral blood film remains the most preferred and reliable method for malaria diagnosis worldwide. But the level of skills required for microscopic examination of peripheral blood film is often lacking in Ghana. This study looked at determining the extent to which haematological parameters and demographic characteristics of patients could be used to predict malaria infection using logistic regression. The overall prevalence of malaria in the study area was determined to be 25.96%; nonetheless, 45.30% of children between the ages of 5 and 14 tested positive. The binary logistic model developed for this study identified age, haemoglobin, platelet, and lymphocyte as the most significant predictors. The sensitivity and specificity of the model were 77.4% and 75.7%, respectively, with a PPV and NPV of 52.72% and 90.51%, respectively. Similar to RDT this logistic model when used will reduce the waiting time and improve the diagnosis of malaria.