We formulate and analyze a mathematical model for malaria with treatment and the well-known three levels of resistance in humans. The model incorporates both sensitive and resistant strains of the parasites. Analytical results reveal that the model exhibits the phenomenon of backward bifurcation (co-existence of a stable disease-free equilibrium with a stable endemic equilibrium), an epidemiological situation where although necessary, having the basic reproduction number less than unity, it is not sufficient for disease elimination. Through quantitative analysis, we show the effects of varying treatment levels in a high transmission area with different levels of resistance. Increasing treatment has limited benefits in a population with resistant strains, especially in high transmission settings. Thus, in a cost–benefit analysis, the rate of treatment and percentage to be treated become difficult questions to address.

This study explores the mental health challenges encountered by Sri Lankan camp refugees, a population often marginalized in mental health research, and analyzes a range of factors including socio-demographic characteristics, living conditions in camps, and psychological variables. In quantitative mental health research, linear regression serves as a conventional approach for assessing the influence of diverse factors on mental health outcomes. However, this method fails to accommodate non-linear relationships between mental health variables and predictors and relies on stringent model assumptions that often do not align with real-world conditions. This study introduces a model-agnostic, advanced machine learning/artificial intelligence (ML/AI) technique, glmboost, as a viable alternative to linear regression. The glmboost algorithm is capable of fitting non-linear prediction models while also conducting variable selection. Moreover, the coefficients obtained from the glmboost model retain the same interpretability as those derived from linear regression. While the glmboost model identifies several key factors including post-migration living difficulties, post-traumatic stress disorder, difficulty in sleeping, poor family functioning, and lower informal support from families as markers of declining mental well-being among the Sri Lankan refugees, the linear regression overlooks vital predictors such as family functioning and family support, highlighting the importance of utilizing advanced ML/AI techniques like glmboost to comprehensively capture complex relationships between predictor variables and mental health outcomes among refugee populations. Thus, by introducing a novel, data-driven approach to mental health risk assessment, this study paves the way for more precise and efficient analyses and interventions in refugee settings, with the potential for improved resource allocation and personalized support, thus revolutionizing mental health service delivery in challenging environments. Additionally, it contributes to the academic discussion on refugee mental health while emphasizing the pivotal role of advanced data analytics in addressing complex health issues within humanitarian contexts.

Barrier islands provide a first line of defense against ocean flooding and storm surge. Biogeomorphic interactions are recognized as important in coastal system processes, but current barrier island models are primarily dominated by physical processes. Recent research has demonstrated different biogeomorphic states that influence response to sea level rise and other disturbance. Building on this understanding, we present a cellular model utilizing biotic and abiotic processes and their interactions for barrier island evolution. Using the literature and field derived parameters, we model barrier island evolution and compare to three decades of change for Smith Island, a Virginia Coast Reserve barrier island. We conduct simulations that show the impact of biogeomorphic states on island migration under different sea level rise scenarios. We find that migration is highest in areas with low topography and light vegetation cover (i.e. disturbance reinforcing) compared to areas with greater topographic complexity and high cover of woody vegetation i.e. disturbance resisting). This study demonstrates the importance of biogeomorphic interactions for barrier island evolution with sea level rise and will aid future predictions for these important ecosystems with climate change.

Allelochemicals produced by plants may be ingested by herbivorous insects and transferred to higher trophic levels with potentially deleterious effects. We develop a system of differential equations to investigate the effect of the transfer of allelochemicals, such as nicotine, on the population dynamics of a system of hosts, parasitoids, and two competing hyperparasitoids that attack different life stages of the parasitoids. We find both somewhat deleterious effects of nicotine on the larvae‐attacking hyperparasitoids and increased attack rates for the pupae‐attacking hyperparasitoids can promote coexistence. We also use an evolutionary game‐theoretic approach to determine the optimal distribution of hyperparasitoid attacks among nicotine‐producing and nicotine‐free plants. With strong deleterious effects of nicotine and increased attack rates for the pupae‐attacking hyperparasitoid, we find both species attack parasitoids on the nicotine‐free plant but only pupae‐attacking hyperparasitoids attack parasitoids on the nicotine‐producing plant. Recommendations for Resource Managers: In the absence of nicotine and with equal attack rates, the larvae‐attacking hyperparasitoids will exclude pupae‐attacking hyperparasitoids due to the advantage of attacking an earlier life‐stage of the parasitoid. Coexistence of larvae‐ and pupae‐attacking hyperparasitoid species can occur when the pupae‐attacking species has the advantage of a higher oviposition rate and/or when the larvae‐attacking species suffers increased mortality due to nicotine. If mortality rates of larvae‐attacking hyperparasitoids due to nicotine become too large, they are unable to persist. The population dynamics of systems incorporating multiple trophic levels are complex and intertwined. The presence of allelochemicals such as nicotine in the first trophic level may impact the observed diversity of hyperparasitoid species in the fourth trophic level.

Sri Lankan Tamil refugees have endured over four decades of protracted displacement in southern India. This paper synthesizes findings from four studies conducted between 2005 and 2018 among refugees residing in the Gummidipoondi and Trichy refugee camps in the state of Tamil Nadu, India. Framed by a Community Based Participatory Research, in collaboration with the Organization for Eelam Refugee Rehabilitation (OfERR) and Community Advisory Board, these studies aimed to investigate the challenges related to migration stressors, mental health, family dynamics, and resource utilization of the Sri Lankan refugees living in India. The association of Psychological Distress and Migration Stress (PDMS) study examined migration stressors influencing psychological distress among refugees. The intergenerational Conflict and Community Readiness (ICCR study) assessed community readiness for repatriation and intergenerational conflict employing semi-structured qualitative interviews. The Family Dynamics (FD) mixed-method study investigated longitudinal associations between migration stressors, resource utilization, family dynamics, and health outcomes among Sri Lankan Tamil refugees. The Parenting Processes and Intervention Development (PPID) Study incorporated perspectives of community health workers through qualitative exploratory research. Integrating these findings, the research developed (1) Conceptual Model of Refugee Family Dynamics and (2) Framework for Refugee Health Intervention. These models provide a culturally sensitive framework for future interventions to improve family well-being among refugee populations.