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The current risk management system for community mental health patients in England is based around the Care Programme Approach (CPA). This system is not responsive to changes in risk for community patients. This paper aims to introduce a practical system to manage risk that has been developed for an Early Intervention Service in East London on the basis of need.

Coding of red, amber and green is associated with specific criteria agreed by all disciplines in the team. The change of a code leads to a rapid change in risk level and management. An agreed clinical and non‐clinical action plan leads to a whole team response. The limitation of use is dependent on the size of the case load and the number of clinical staff attending a daily clinical briefing.

Zoning according to the traffic lights system could complement the CPA system and support a clinical governance structure utilising a whole team response.

The risk management system described has not been tested empirically. Currently it has been used in early intervention mental health teams but will need to be adapted for other teams with bigger case loads.

This practical risk management system is aligned with the statuary CPA requirements. A dynamic and flexible management of risk is central to early intervention in psychosis teams but the risk management system described can suit any community mental health team and fits well with the distributed responsibility model of functionalised teams according to new ways of working.

Due to the unknown location, size and timing of disasters, the rapid response required by humanitarian operations (HO) faces high uncertainty and limited time to raise funds. These harsh realities make HO challenging. This study aims to systematically capture the complex dynamic relationships between operations in humanitarian settings.

To achieve this goal, the authors undertook a systematic review of the extant academic literature linking HO to system dynamics (SD) simulation.

The research reviews 88 papers to propose a taxonomy of different topics covered in the literature; a framework represented through a causal loop diagram (CLD) to summarise the taxonomy, offering a view of operational activities and their linkages before and after disasters; and a research agenda for future research avenues.

As the authors provide an adequate representation of reality, the findings can help decision makers understand the problems faced in HO and make more effective decisions.

While other reviews on the application of SD in HO have focused on specific subjects, the current research presents a broad view, summarising the main results of a comprehensive CLD.

This paper aims to examine Cameroon’s health service resilience in the first five months (March–July 2020) of the coronavirus (COVID-19) outbreak. The motive is to diagnose sub-optimal performance in sustaining health-care services during the pandemic to identify areas for improvement and draw lessons for the future.

This is principally qualitative, exploratory, analytical and descriptive research that involves the collation of empirical, primary and secondary data. A conceptual framework [health systems resilience for emerging infectious diseases (HSREID)] provides structure to the study and an anchor for interpreting the findings. The research validity has been established by analysing the aims/objectives from multiple perspectives in the research tradition of triangulation.

Cameroon has exerted much effort to combat the COVID-19 pandemic. Yet, several constraints and gaps exist. The findings reveal limitations in Cameroon’s response to the COVID-19 pandemic in the provision of fundamental health-care services under contextual themes of health infrastructure/medical supplies, human capital, communication/sensitisation/health education, governance and trust/confidence. Analysis of the identified impediments demonstrates that Cameroon’s health-care system is not resilient enough to cope with the COVID-19 pandemic and provides several insights for an enhanced response as the pandemic accelerates in the country.

This is one of the first scholarly articles to examine how Cameroon’s health-care system is faring in COVID-19 combat. Underscored by the novel HSREID model, this study provides initial insights into Cameroon’s resilience to COVID-19 with a view to enhancing the health system’s response as the pandemic unfolds and strengthens readiness for subsequent health crises.

This paper aims to analyze the socioeconomic impacts of infectious diseases based on uncertain behaviors of social and effective subsystems in the countries. The economic impacts of infectious diseases in comparison with predicted gross domestic product (GDP) in future years could be beneficial for this aim along with predicted social impacts of infectious diseases in countries.

The proposed uncertain SEIAR (susceptible, exposed, infectious, asymptomatic and removed) model evaluates the impacts of variables on different trends using scenario base analysis. This model considers different subsystems including healthcare systems, transportation, contacts and capacities of food and pharmaceutical networks for sensitivity analysis. Besides, an adaptive neuro-fuzzy inference system (ANFIS) is designed to predict the GDP of countries and determine the economic impacts of infectious diseases. These proposed models can predict the future socioeconomic trends of infectious diseases in each country based on the available information to guide the decisions of government planners and policymakers.

The proposed uncertain SEIAR model predicts social impacts according to uncertain parameters and different coefficients appropriate to the scenarios. It analyzes the sensitivity and the effects of various parameters. A case study is designed in this paper about COVID-19 in a country. Its results show that the effect of transportation on COVID-19 is most sensitive and the contacts have a significant effect on infection. Besides, the future annual costs of COVID-19 are evaluated in different situations. Private transportation, contact behaviors and public transportation have significant impacts on infection, especially in the determined case study, due to its circumstance. Therefore, it is necessary to consider changes in society using flexible behaviors and laws based on the latest status in facing the COVID-19 epidemic.

The proposed methods can be applied to conduct infectious diseases impacts analysis.

In this paper, a proposed uncertain SEIAR system dynamics model, related sensitivity analysis and ANFIS model are utilized to support different programs regarding policymaking and economic issues to face infectious diseases. The results could support the analysis of sensitivities, policies and economic activities.

• A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;

• Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;

• Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;

• An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;

• A real case study is considered to evaluate the performances of the proposed models.

A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;

Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;

Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;

An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;

A real case study is considered to evaluate the performances of the proposed models.

This paper aims to explore the relationship between integrity, reputation, trust on awqaf institution and intention to endow cash waqf.

Quantitative research using survey questionnaire was conducted. A total of 377 completed survey questionnaires were received and analyzed using structural equation modeling.

Findings show that integrity and reputation of awqaf institutions have direct impact on endowers’ trust on awqaf institution, which consequently lead to endowers’ intention to endow cash waqf.

This study provides practical information on how awqaf institutions could develop endowers’ trust on awqaf institutions, which will consequently increase the intention of endowers to endow cash waqf.