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During the COVID-19 pandemic, prisons faced a unique challenge of preventing and managing outbreaks with minimal adverse impact. This study aims to describe the epidemiology of COVID-19 in prisons, identify lessons learnt and make recommendations.

The authors used the PubMed advanced search function using MeSH terms; (coronavirus, sars) AND (prisons) AND (disease outbreaks). The authors included original research reporting COVID-19 outbreaks in prisons. All other types and non-English publications were excluded. The authors used a structured data abstraction template to extract data systematically, and a second author independently abstracted data from 10% of the papers for quality assurance.

The search yielded 96 hits. The authors included 15 studies meeting the inclusion criteria. These studies were from four countries. Seven studies reported individual outbreaks. The mean and median number of inmates and staff were 1,765, 1,126 and 575, 510. The mean and median number of cases among inmates and staff were 584, 464, and 72, 77. The number of reported deaths varied from 0 to 11. The authors present the prison-specific hazards grouped under human factors, healthcare factors and environmental factors. The authors also summarise interventions deployed as either primary prevention interventions, such as vaccinations, or secondary prevention interventions, including screening and contact tracing.

This narrative review summarises the prison-specific hazards, which include movement of people in and out of the person, moving in new prisoners from other prisons, mixing of prisoners when transporting to courts, limited medical and isolation resources, crowded dormitories, shared lavatories, small communal facilities, poor ventilation and overcrowding. The interventions included limiting non-medical transfers into and out of the persons, assigning staff members to specific areas, encouraging face coverings among prisoners and staff and social isolation measures within the constraints of the prison setting. The interventions were adopted by prison authorities to contain and manage the outbreaks. Public Health and prison authorities need to be aware of the risk of further outbreaks of COVID-19 and other infectious diseases in these settings and implement key measures identified in this review to minimise adverse outcomes.

Information sharing enhances the supply chain profitability significantly, but it may result in adverse impacts also (e.g. leakages of secret information to competitors, sharing of wrong information that result into losses). So, it is important to understand the various risk factors that lead to distortion in information sharing and results in negative consequences. Information risk identification and assessment in supply chain would help in choosing right mitigation strategies. The purpose of this paper is to identify various information risks that could impact a supply chain, and develop a conceptual framework to quantify them.

Bayesian belief network (BBN) modeling will be used to provide a framework for information risk analysis in a supply chain. Bayesian methodology provides the reasoning in causal relationship among various risk factors and incorporates both objective and subjective data.

This paper presents a causal relationship among various information risks in a supply chain. Three important risk factors, namely, information security, information leakages and reluctance toward information sharing showed influence on a company’s revenue.

Capability of Bayesian networks while modeling in uncertain conditions, provides a prefect platform for analyzing the risk factors. BBN provides a more robust method for studying the impact or predicting various risk factors.

The major contribution of this paper is to develop a quantitative model for information risks in supply chain. This model can be updated when a new data arrives.