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The purpose of this paper is to map anti-money laundering policy and its impact on money laundering. The AML system is discussed from the perspective of the compliance officer, who is responsible for translating AML law into practice in Belgian banks.

Literature review, based largely on a PhD study (2009) that involved a survey and interviews. Additionally, 12 compliance officers were interviewed in 2015.

The global AML system impacts significantly on issues of privacy and due process but has not yet been evaluated. The system’s preventive effect is difficult to measure because of a lack of (cross-border) information. The way in which Risks are currently managed in diverse ways.

Results from the first study in 2009 (based on interviews in 2007-2008) were potentially outdated. This recent update (2015) confirms that compliance officers are still dealing with the same issues.

The study clarifies the ways in which compliance and AML is dealt with and mapped, providing insights into an often closed setting.

The battle against money laundering is very costly and intrusive, making the need for stringent evaluation more pressing.

The study is both original and valuable because compliance officers have rarely been the subject of research. The study discloses useful information about their role.

The purpose of this paper is to argue for an improved conceptualisation of health service research, using Stengers' (2018) metaphor of “slow science” as a critical yardstick.

The paper is structured in three parts. It first reviews the field of health services research and the approaches that dominate it. It then considers the healthcare research approaches whose principles and methodologies are more aligned with “slow science” before presenting a description of a “slow science” project in which the authors are currently engaged.

Current approaches to health service research struggle to offer adequate resources for resolving frontline complexity, principally because they set more store by knowledge generalisation, disciplinary continuity and integrity and the consolidation of expertise, than by engaging with frontline complexity on its terms, negotiating issues with frontline staff and patients on their terms and framing findings and solutions in ways that key in to the in situ dynamics and complexities that define health service delivery.

There is a need to engage in a paradigm shift that engages health services as co-researchers, prioritising practical change and local involvement over knowledge production. Economics is a research field where the products are of natural appeal to powerful health service managers. A “slow science” approach adopted by the embedded Economist Program with its emphasis on pre-implementation, knowledge mobilisation and parallel site capacity development sets out how research can be flexibly produced to improve health services.

As of July 30, 2020, more than 17 million novel coronavirus disease 2019 (COVID-19) cases were registered including 671,500 deaths. Yet, there is no immediate medicine or vaccination for control this dangerous pandemic and researchers are trying to implement mathematical or time series epidemic models to predict the disease severity with national wide data.

In this study, the authors considered COVID-19 daily infection data four most COVID-19 affected nations (such as the USA, Brazil, India and Russia) to conduct 60-day forecasting of total infections. To do that, the authors adopted a machine learning (ML) model called Fb-Prophet and the results confirmed that the total number of confirmed cases in four countries till the end of July were collected and projections were made by employing Prophet logistic growth model.

Results highlighted that by late September, the estimated outbreak can reach 7.56, 4.65, 3.01 and 1.22 million cases in the USA, Brazil, India and Russia, respectively. The authors found some underestimation and overestimation of daily cases, and the linear model of actual vs predicted cases found a p-value (<2.2e-16) lower than the R2 value of 0.995.

In this paper, the authors adopted the Fb-Prophet ML model because it can predict the epidemic trend and derive an epidemic curve.