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Recent socio-economic trends have made Artificial intelligence (AI) a vital institutional force driving development among countries with optimal opportunities and costs. Among researchers in this domain, the benefit of AI is highly espoused, having been underexplored in Africa. However, the outbreak of the COVID-19 pandemic has highlighted the need to strengthen the education sector, given that many schools have migrated their teaching and learning online. While face-to-face teaching was the norm, the transition to online teaching has brought about the need to rethink the use of Information Technology to strengthen teaching and learning. To proffer solutions for the implementation of AI in Africa, there is the need to understand the challenges. Therefore, in this chapter, we explore the possible challenges that hinder the implementation of AI in Africa. Further, we propose solutions for the implementation of AI in the education sector, especially in this era of the COVID-19 pandemic. The solutions stem from rethinking the role of AI in the education sector. Finally, a conceptual framework that synthesises the problems and the proposed solution is developed. We envision that the proffered solutions can mitigate the deepening misconceptions and challenges bedevilling AI implementation in Africa.

Traditional epidemic models, like the classical SIR, are fitted to real data using deterministic optimization techniques. As a consequence, their performances cannot be properly assessed and, more importantly, the estimates of the critical epidemic parameters (which are of dramatic importance in monitoring the epidemic evolution) cannot be complemented with the calculation of confidence intervals. The aim of the present work is to remove such limitations and to compare the results obtained using two stochastic versions of deterministic SIR models. We describe the two alternatives and the associated estimation procedures, and we apply the two methodologies to a set of COVID-19 data observed in Italy in the 2020 pandemic wave. Our estimates of the basic reproduction number are comparable with the official sources, but using our methods uncertainty can also be properly assessed.

The growing turbulence of the external environment has progressively led to the necessity by organizations of exploiting new opportunities provided by data-driven approaches for supporting the even more complex decision-making processes. The new digital environment has led to the development and adoption of innovative approaches; also in the urban context which has always been characterized by different, interconnected, and dynamic dimensions. Urban governance models have been enhanced by smart technologies, which act as enablers of advanced services and foster connections between citizens, public and private organizations, and decision-makers. In this context, the objective of this chapter is to examine the role of data-driven approaches in the urban context during the chaotic and high variable circumstances related to the diffusion of the Coronavirus disease 2019 (Covid-19). Thanks to the adoption of the co-evolutionary perspective, a cycle in urban governance decision-making approach based on digital technologies is depicted and its contribution for managing the ongoing Covid-19 is traced. The results of the analysis highlight how the data-driven approach supports urban decision-making process and shed light on the co-evolutionary perspective as heuristic device to map the interactions settled in the networks between local governments, data-driven technologies, and citizens. In this sense, this chapter offers interesting insights, potentially capable of generating useful implications for both researchers and professionals in the public sector.

This chapter develops a conceptual taxonomy of five emerging digital citizenship regimes: (1) the globalised and generalisable regime called pandemic citizenship that clarifies how post-COVID-19 datafication processes have amplified the emergence of four intertwined, non-mutually exclusive and non-generalisable new technopoliticalised and city-regionalised digital citizenship regimes in certain European nation-states’ urban areas; (2) algorithmic citizenship, which is driven by blockchain and has allowed the implementation of an e-Residency programme in Tallinn; (3) liquid citizenship, driven by dataism – the deterministic ideology of big data – and contested through claims for digital rights in Barcelona and Amsterdam; (4) metropolitan citizenship, as revindicated in reaction to Brexit and reshuffled through data co-operatives in Cardiff; and (5) stateless citizenship, driven by devolution and reinvigorated through data sovereignty in Barcelona, Glasgow and Bilbao. This chapter challenges the existing interpretation of how these emerging digital citizenship regimes together are ubiquitously rescaling the associated spaces/practices of European nation-states.

Kalecki's theory of the business cycle is rightly renowned for various reasons: in particular, besides itself providing an original contribution, it set the framework for Kalecki's ideas on effective demand, for his anticipation of a number of Keynesian elements, and for the development of Kalecki's related themes such as income determination and distribution. Although the secondary literature (both technical and descriptive) on this subject is immense, a specific aspect seems to deserve further reflection.