Illicit opioid overdose continues to rise in North America and is a leading cause of death. Mathematical modeling is a valuable tool to investigate the epidemiology of this public health issue, as it can characterize key features of population outcomes and quantify the broader effect of structural and interventional changes on overdose mortality. The aim of this study is to quantify and predict the impact of key harm reduction strategies at differing levels of scale-up on fatal and nonfatal overdose among a population of people engaging in unregulated opioid use in Toronto.
An individual-based model for opioid overdose was built featuring demographic and behavioural variation among members of the population. Key individual attributes known to scale the risk of fatal and nonfatal overdose were identified and incorporated into a dynamic modeling framework, wherein every member of the simulated population encompasses a set of distinct characteristics that govern demographics, intervention usage, and overdose incidence. The model was parametrized to fatal and nonfatal overdose events reported in Toronto in 2019. The interventions considered were opioid agonist therapy (OAT), supervised consumption sites (SCS), take-home naloxone (THN), drug-checking, and reducing fentanyl in the drug supply. Harm reduction scenarios were explored relative to a baseline model to examine the impact of each intervention being scaled from 0% use to 100% use on overdose events.
Model simulations resulted in 3690.6 nonfatal and 295.4 fatal overdoses, coinciding with 2019 data from Toronto. From this baseline, at full scale-up, 290 deaths were averted by THN, 248 from eliminating fentanyl from the drug supply, 124 from SCS use, 173 from OAT, and 100 by drug-checking services. Drug-checking and reducing fentanyl in the drug supply were the only harm reduction strategies that reduced the number of nonfatal overdoses.
Within a multi-faceted harm reduction approach, scaling up take-home naloxone, and reducing fentanyl in the drug supply led to the largest reduction in opioid overdose fatality in Toronto. Detailed model simulation studies provide an additional tool to assess and inform public health policy on harm reduction.

Outbreaks of corallivorous Crown of Thorns Starfish (Acanthaster spp.; CoTS) cause substantial coral mortality throughout the Indo-Pacific, particularly on the Great Barrier Reef (GBR). Refining CoTS population density modelling and understanding the disparities between real-world observations and model predictions is crucial for developing effective control strategies. Using a spatially explicit ecosystem model of the GBR, we compared CoTS density model predictions to observations and incorporated a new zone-specific mortality rate to account for differences in predation of CoTS between fished and protected reefs. We found high congruence between predictions and observations: ∼81 % of categorical reef level CoTS densities matched or only differed by one category. However, underpredictions increased with higher observed densities. Zone-specific CoTS mortality reduced severe underpredictions from 7.1 % to 5.6 %, which is critical for managers as underpredictions indicate missing outbreaks where targeted culling is necessary, but also lead to underestimated coral loss attributed to CoTS outbreaks. Reef protection status affected prediction accuracy, highlighting the importance of further research on in situ CoTS mortality rates. The location of a reef inside or outside the \"initiation box\", a speculative area of primary outbreaks (i.e., initial abrupt population increases) on the GBR, also influenced accuracy, with exact predictions more likely outside. Accurately modelling initiation box dynamics is challenging due to limited empirical data on CoTS outbreaks, highlighting the need for focussed research on outbreak dynamics to enhance predictive accuracy. Spatial factors, such as region and shelf position, contributed to the variance between observations and predictions, underscoring the importance of the spatial-temporal context of each observation. Observations of CoTS can help refine model predictions, guide targeted control measures, and contribute to effective ecosystem management for the long-term resilience of the GBR and other reefs targeted by CoTS throughout the Indo-Pacific.

This study explores the food transport efficiency (E) in a termite tunnel consisting of a main tunnel and a 2-segment loop tunnel through a model simulation. Simulated termites navigate between the main and loop tunnels through branching nodes (a, b, c, d) with associated probabilities (P1, P2, P3, P4). The loop tunnel locations (δ) are considered: near the nest (δ = 1), at the center of the main tunnel (δ = 2), and close to the food site (δ = 3). The results reveal that for δ = 1, paths such as a → d → b → c and c → d → b → a exhibited high E values. Conversely, for δ = 2, P3 and P4 demonstrate elevated E values ranging from 0.4 to 0.6. For δ = 3, paths like c → d or c → b display high E values, emphasizing the significance of in-loop separation tunnels (characterized by P3 and P4) in alleviating traffic congestion. Partial rank correlation validates that P1 and P2 minimally influence E, while P3 and P4 significantly negatively impact E, regardless of δ. However, for δ = 2, the influence of P3 and P4 is notably reduced due to the positional symmetry of the loop tunnel. In the discussion, we address model limitations and propose strategies to overcome them. Additionally, we outline potential experimental validations to ensure a comprehensive understanding of the dynamics governing termite food transport within tunnels.

Social information is predicted to enhance the quality of animals\' migratory decisions in dynamic ecosystems, but the relative benefits of social information in the long-range movements of marine megafauna are unknown. In particular, whether and how migrants use nonlocal information gained through social communication at the large spatial scale of oceanic ecosystems remains unclear. Here we test hypotheses about the cues underlying timing of blue whales\' breeding migration in the Northeast Pacific via individual-based models parameterized by empirical behavioral data. Comparing emergent patterns from individual-based models to individual and population-level empirical metrics of migration timing, we find that individual whales likely rely on both personal and social sources of information about forage availability in deciding when to depart from their vast and dynamic foraging habitat and initiate breeding migration. Empirical patterns of migratory phenology can only be reproduced by models in which individuals use long-distance social information about conspecifics\' behavioral state, which is known to be encoded in the patterning of their widely propagating songs. Further, social communication improves pre-migration seasonal foraging performance by over 60% relative to asocial movement mechanisms. Our results suggest that long-range communication enhances the perceptual ranges of migrating whales beyond that of any individual, resulting in increased foraging performance and more collective migration timing. These findings indicate the value of nonlocal social information in an oceanic migrant and suggest the importance of long-distance acoustic communication in the collective migration of wide-ranging marine megafauna.

Here, employing computer simulation tools, we present a study on the development of a bacterial biofilm from a single starter cell on a flat inert surface overlaid by an aqueous solution containing nutrients. In our simulations, surface colonization involves an initial stage of two-dimensional cell proliferation to eventually transition to three-dimensional growth leading to the formation of biofilm colonies with characteristic three-dimensional semi-ellipsoids shapes. Thus, we have introduced the influence of the nutrient concentration on bacterial growth, and calculated the cell growth rate as a function of nutrient uptake, which in turn depends on local nutrient concentration in the vicinity of each bacterial cell. Our results show that the combination of cell growth and nutrient uptake and diffusion leads to the formation of stratified colonies containing an inner core in which nutrients are depleted and cells cannot grow or divide, surrounded by an outer, shallow crust in which cells have access to nutrients from the bulk medium and continue growing. This phenomenon is more apparent at high uptake rates that enable fast nutrient depletion. Our simulations also predict that the shape and internal structure of the biofilm are largely conditioned by the balance between nutrient diffusion and uptake.

Pulmonary artery stenosis (PAS) often presents in children with congenital heart disease, altering blood flow and pressure during critical periods of growth and development. Variability in stenosis onset, duration, and severity result in variable growth and remodeling of the pulmonary vasculature. Computational fluid dynamics (CFD) models enable investigation into the hemodynamic impact and altered mechanics associated with PAS. In this study, a one-dimensional (1D) fluid dynamics model was used to simulate hemodynamics throughout the pulmonary arteries of individual animals. The geometry of the large pulmonary arteries was prescribed by animal-specific imaging, whereas the distal vasculature was simulated by a three-element Windkessel model at each terminal vessel outlet. Remodeling of the pulmonary vasculature, which cannot be measured in vivo, was estimated via model-fitted parameters. The large artery stiffness was significantly higher on the left side of the vasculature in the left pulmonary artery (LPA) stenosis group, but neither side differed from the sham group. The sham group exhibited a balanced distribution of total distal vascular resistance, whereas the left side was generally larger in the LPA stenosis group, with no significant differences between groups. In contrast, the peripheral compliance on the right side of the LPA stenosis group was significantly greater than the corresponding side of the sham group. Further analysis indicated the underperfused distal vasculature likely moderately decreased in radius with little change in stiffness given the increase in thickness observed with histology. Ultimately, our model enables greater understanding of pulmonary arterial adaptation due to LPA stenosis and has potential for use as a tool to noninvasively estimate remodeling of the pulmonary vasculature.

Mitomycin-C (MMC) chemotherapy is a well-established anti-cancer treatment for non-muscle-invasive bladder cancer (NMIBC). However, despite comprehensive biological research, the complete mechanism of action and an ideal regimen of MMC have not been elucidated. In this study, we present a theoretical investigation of NMIBC growth and its treatment by continuous administration of MMC chemotherapy. Using temporal ordinary differential equations (ODEs) to describe cell populations and drug molecules, we formulated the first mathematical model of tumor-immune interactions in the treatment of MMC for NMIBC, based on biological sources. Several hypothetical scenarios for NMIBC under the assumption that tumor size correlates with cell count are presented, depicting the evolution of tumors classified as small, medium, and large. These scenarios align qualitatively with clinical observations of lower recurrence rates for tumor size ≤ 30[mm] with MMC treatment, demonstrating that cure appears up to a theoretical x[mm] tumor size threshold, given specific parameters within a feasible biological range. The unique use of mole units allows to introduce a new method for theoretical pre-treatment assessments by determining MMC drug doses required for a cure. In this way, our approach provides initial steps toward personalized MMC chemotherapy for NMIBC patients, offering the possibility of new insights and potentially holding the key to unlocking some of its mysteries.

To clarify the wind-driven post-bloom dispersion range of Microcystis, which originally clustered on the water surface, an Individual-Based Model (IBM) of Microcystis movement considering the combined effects of wind and light was developed based on actual hydrodynamic data and Microcystis biomass. After calibrating the effects of hydrodynamics and light, 66 cases of short-term (within a week) post-bloom with satellite images from 2011 to 2017 were simulated. The results showed that there were three short-term post-bloom types: vertical reduction (VR), horizontal reduction (HR) and mixed reduction (MR). For VR type, the cyanobacterial bloom reduction rate was rapid (>160 km2/day), but the dispersion range of Microcystis was limited (<2 km/day), and a larger bloom area was likely to form in the original location when wind speed decreased. For HR type, the cyanobacterial bloom reduction rate was slow (<10 km2/day), but Microcystis exhibited a broad dispersion range (>4 km/day), often leading to smaller, thicker, and longer-lasting cyanobacterial blooms downwind, albeit with a lower probability of occurrence. The characteristics of MR lay between the two aforementioned types.

Trait polymorphisms are widespread in nature, and explaining their stable co-existence is a central problem in ecology and evolution. Alternative reproductive tactics, in which individuals of one or more sex exhibit discrete, discontinuous traits in response to reproductive competition, represent a special case of trait polymorphism in which the traits are often complex, behavioural, and dynamic. Thus, studying how alternative reproductive tactics are maintained may provide general insights into how complex trait polymorphisms are maintained in populations. We construct a spatially explicit individual-based model inspired from extensively collected empirical data to address the mechanisms behind the co-existence of three behavioural alternative reproductive tactics in males of a tree cricket (Oecanthus henryi). Our results show that the co-existence of these tactics over ecological time scales is facilitated by the spatial structure of the landscape they inhabit, which serves to equalise the otherwise unequal mating benefits of the three tactics. We also show that this co-existence is unlikely if spatial aspects of the system are not considered. Our findings highlight the importance of spatial dynamics in understanding ecological and evolutionary processes and underscore the power of integrative approaches that combine models with empirical data.

BACKGROUND: Prior to September 2021, 55,000-90,000 hospital inpatients in England were identified as having a potentially nosocomial SARS-CoV-2 infection. This includes cases that were likely missed due to pauci- or asymptomatic infection. Further, high numbers of healthcare workers (HCWs) are thought to have been infected, and there is evidence that some of these cases may also have been nosocomially linked, with both HCW to HCW and patient to HCW transmission being reported. From the start of the SARS-CoV-2 pandemic interventions in hospitals such as testing patients on admission and universal mask wearing were introduced to stop spread within and between patient and HCW populations, the effectiveness of which are largely unknown.
METHODS: Using an individual-based model of within-hospital transmission, we estimated the contribution of individual interventions (together and in combination) to the effectiveness of the overall package of interventions implemented in English hospitals during the COVID-19 pandemic. A panel of experts in infection prevention and control informed intervention choice and helped ensure the model reflected implementation in practice. Model parameters and associated uncertainty were derived using national and local data, literature review and formal elicitation of expert opinion. We simulated scenarios to explore how many nosocomial infections might have been seen in patients and HCWs if interventions had not been implemented. We simulated the time period from March-2020 to July-2022 encompassing different strains and multiple doses of vaccination.
RESULTS: Modelling results suggest that in a scenario without inpatient testing, infection prevention and control measures, and reductions in occupancy and visitors, the number of patients developing a nosocomial SARS-CoV-2 infection could have been twice as high over the course of the pandemic, and over 600,000 HCWs could have been infected in the first wave alone. Isolation of symptomatic HCWs and universal masking by HCWs were the most effective interventions for preventing infections in both patient and HCW populations. Model findings suggest that collectively the interventions introduced over the SARS-CoV-2 pandemic in England averted 400,000 (240,000 - 500,000) infections in inpatients and 410,000 (370,000 - 450,000) HCW infections.
CONCLUSIONS: Interventions to reduce the spread of nosocomial infections have varying impact, but the package of interventions implemented in England significantly reduced nosocomial transmission to both patients and HCWs over the SARS-CoV-2 pandemic.