The Problem: Refugees represent a population that is at a high risk of mental health problems be-cause of the accumulation of stressful and traumatic life events. This study aimed to objectively describe the knowledge domain and emerging trends of refugees’ mental health.

The Approach: The author conducted co-occurrence network analyses to graphically depict the relationships between the extracted words. 81 journal articles published on mental health of refugees and asylum seekers in post-covid area have been reviewed. The co-occurrence matrix was used by the UCInet software.

Findings: 82 nodes(words) and 266 ties (co-occurrence frequency) has been identified. Depression, anxiety, PTSD and Post-migratory difficulties had the highest degree centrality, while social work, equality and diversity, cultural competency and post-migratory trauma had the lowest degree centrality. Moreover, Depression, PTSD, Discrimination, and anxiety had the highest betweenness centrality, identified as the vital points that provide important bridging connections between two research interests.

Implications: The presentation of the thematic map of the articles will make the researchers more aware of the status of the research conducted and the subject's gap. It could help readers broaden innovative ideas and discover new research area opportunities and served as important indicators for host health system governance, especially LMICs.

Among people living with HIV (PLH), high levels of internalized stigma are related to reduced adherence to ART, increased mental health and substance use issues, poorer health and diminished quality of life. Research has shown that enacted stigma from members of PLH’s networks increased internalized stigma, while greater trust from members of PLH’s networks decreased internalized stigma. Using data from the control arm of a randomized controlled trial of the Game Changers HIV intervention, we explore these findings further, using multi-level dyadic analyses to explore the relationship and network member characteristics that predict enacted stigma in PLH-network member dyads. Data were collected using Network Canvas and follow a personal network survey design. Enacted stigma was assessed using a 4-item scale covering expressions of stigma against PLH such as: A person with HIV/AIDS must have done something wrong and deserves to be punished. Network member enacted stigma will be predicted using alter characteristics such as age, gender, role in the relationship, network member degree, support provision, and HIV status. Relationship characteristics such as frequency of contact, trust, and emotional closeness will also be assessed. Index characteristics will be controlled for. We will present results of dyadic analyses conducted as a set of lagged regressions across four waves of data collected at baseline and 6, 12, and 18 months. Results of these analyses will help refine social network interventions aimed at enacted and internalized stigma reduction, leading to improved care adherence and better quality of life for PLH in Uganda and elsewhere.

Animal mobility is essential to Nigeria's economy and pastoral culture but facilitates the spread of highly contagious diseases like Peste des petits ruminants (PPR). Despite this risk, there is no adequate surveillance system or proper tracking of animal movements. Instead, data is gathered through ad-hoc mobility surveys, which are often limited in scope, making it challenging to identify key areas for disease monitoring. Using mobility data from three Nigerian states and a simulation framework, we evaluated the impact of missing movement links (unrecorded by surveys) on PPR spread in Nigeria.

The small ruminant mobility network was reconstructed using market survey data from three Nigerian states, comprising 233 villages and 335 movement links. Using the COCLEA algorithm, contagion clusters were identified, and a stochastic SIR model was used to simulate PPR spread using 10,000 transmission and recovery probability combinations. Sentinel nodes, crucial in early outbreaks, were determined based on structural, socio-economic, and environmental factors. An uncertainty analysis assessed the impact of missing movement links, predicted with a Hierarchical Random Graph (HRG), by gradually adding probable links to the network.

The livestock mobility network identified seven geographically dispersed communities. Epidemic simulations were categorized into four groups based on final epidemic sizes. While the number of sentinel nodes varied, their structural characteristics, especially in-degree, in-closeness, in-neighborhood, and eigenvector, remained stable and more important than socio-economic factors. The uncertainty analysis showed that epidemic size remained stable with 1% of missing links but fluctuated beyond 3%, stabilizing after adding 50% of probable links. Less probable links required 90% inclusion for stability. The study highlighted the importance of in-closeness and in-neighborhood in node vulnerability.

Predicting missing links presents a promising method for enhancing the reliability of sentinel node identification, ultimately contributing to more effective disease surveillance and control strategies in Nigeria.