Modeling Studies Research Articles

Respiratory system mechanics during noninvasive proportional assist ventilation: A model study

AbstractPurposeTo assess the accuracies of airway resistance (Raw) and compliance (Crs) calculations using the expiratory time constant (RCexp) method as well as the accuracy of Pmus estimation in obstructive lung models.MethodsA Respironics V60 ventilator was connected to an active lung simulator. The driving pressure was maintained at 5–10 cmH2O and positive end-expiration pressure (PEEP) was 5 cmH2O. Maximal Pmus, estimated based on equations of motion and respiratory mechanical properties, was calculated by the RCexp method to derive respiratory system compliance (Crs) and inspiratory (Rinsp) and expiratory (Rexp) resistance.ResultsDuring PAV, the assist proportion was adjusted to 55% and 40% with Pmus of 5 and 10 cmH2O, respectively. Pmus measurement errors were <20% of the preset values in most lung conditions. In the active lung model with PAV, an overestimation of Raw was found in the normal resistance condition, and Rinsp was underestimated in the severe obstructive model (P < 0.01). Crs was overestimated significantly except in the severe obstructive model at a Pmus of 10 cmH2O (all P < 0.01). Using the RCexp method, the target of ≤20% between the calculated and preset values in airway resistance was achieved in most obstructive models at a Pmus of 5 cmH2O.ConclusionsThe RCexp method might provide real-time assessments of respiratory mechanics (elastance and resistance) in the PAV mode. With low inspiratory effort, the estimation error was acceptable (<20%) in most obstructive lung models.

Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence

The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like healthcare, finance, natural language processing, and robotics. However, challenges such as computational demands, data dependency, and model interpretability remain. This paper discusses the benefits, limitations, and future potential of ML and DL and also provides a review study of a hybrid model makes use of both techniques (machine learning & deep learning) advantages to solve complicated problems more successfully than one could on its own. To boost performance, increase efficiency, or address scenarios where either ML or DL alone would not be able to manage, this approach combines deep learning structures with conventional machine learning techniques.

Bioinformatics analysis to design a multi-epitope mRNA vaccine againstS. agalactiaeexploiting pathogenic proteins.

Antibiotic resistance in bacterial pathogen infections is a growing global issue that occurs due to their adaptation to changing environmental conditions. Therefore, producing an efficient vaccine as an alternative approach can improve the immune system, eradicate related pathogens, and overcome this growing problem.Streptococcus agalactiaebelongs to group B Streptococcus (GBS). Colonization of GBS during pregnancy is a significant risk factor for infants and young children. S. agalactiae infected population exhibits resistance to beta-lactams, including penicillin and the second-line antibiotics erythromycin and clindamycin.On the other hand, there are currently no commercial vaccines against this pathogen. Vaccination of pregnant women is a highly effective method to protect newborns and infants from S. agalactiaeinfection, and it has been identified as an urgent demand by the World Health Organization. This study employed various immunoinformatic tools to develop an effective vaccine that could trigger both humoral and cell-mediated immunity and prevent disease. For this purpose, three conserved antigenic proteins of the main pathogenic strains ofS. agalactiaewere utilized to predict CTL, HTL, and B-cell epitopes for producing an mRNA vaccine against different strains ofS. agalactiae. The selected epitopes were fused using proper linkers. The Resuscitation promoting factor E (RpfE) sequence was incorporated in the designed vaccine construct as an adjuvant to boost its immune response. Different physicochemical characteristics of the final designed vaccine, modeling of the three-dimensional structure, molecular docking, molecular dynamics simulation, and immunological response simulation were screened following vaccine administration in an in vivo model.Computational immune simulation data identified that IgG1, IgM, INF γ, IL-2, T helper, and B-cell populations increased significantly after vaccination. These findings suggested that the vaccine candidate may provide good protection againstS. agalactiaeinfection. However,experimental and animal model studies are required for additional validation and implementation in human vaccination programs.

A Comparative Study of Swin Transformer and CNN Models for Crowd Counting

Abstract. Crowd counting, a critical component in the management and safety planning of large gatherings and public spaces, is essential for ensuring smooth event operations and preventing potential overcrowding issues. While the standard convolutional neural network (CNN) based model performs well in head counting tasks, it has certain drawbacks when applied to complex scenarios. With the rapid development of artificial intelligence, Transformer models that rely on self-attention mechanisms, as Swin Transformer, have demonstrated exceptional performance in visual tasks, such as image classification, and segmentation in recent times. This study examines the experimental findings of Swin Transformer's head counting tasks and contrasts them with the CNN-based model. Mean Absolute Error (MAE) and Mean Square Error (MSE) evaluation indicators show that the Transformer model outperforms the classic CNN model in terms of generalization abilities when dealing with complicated scenarios. Future research work will increase the diversity of data sets and focus on optimizing model structure and improving training efficiency.

Study of the Spectral Energy Distribution Model of the Protoplanetary Disk Geometric around the Brown Dwarf CFHT-BD-Tau 4

The accretion circumstellar disk of young stars and the Brown dwarf plays an essential role in the formation and evaluation of the planet. Our main work in this paper is to investigate the geometrical shape model for the protoplanetary disk around one of the Brown Dwarfs. The photometric measurements for the brown dwarf CFHT-BD-Tau 4 were extracted from the Vizier archive. We used a numerical simulation to build a model of the spectral energy distribution of our target CFHT-BD-Tau 4. The spectral energy distribution model was fitted with observational data for the brown dwarf CFHT-BD-Tau 4. A transitional disk has been assumed around CFHT-BD-Tau 4. We obtained physical properties of the two disks and the size of the gap between them by fitting the SED. The gap in the protoplanetary disk proves that a planet formation process occurred around the Brown dwarf.

Statistical Inference of Uncertain Autoregressive Model via the Principle of Least Squares

In the study of uncertain autoregressive models, how to estimate the unknown parameters and uncertain disturbance term in the models is always a key problem. In view of this, this paper proposes a statistical inference method based on the principle of least squares to determine the unknown parameters and uncertain disturbance term in an uncertain autoregressive model, and designs a numerical algorithm to calculate the numerical solutions of the corresponding estimators. Then, the uncertain hypothesis test is used to verify the applicability of the estimated uncertain autoregressive model, and point forecast and interval forecast are also made for the time series of future moments. Finally, a case study of the Consumer Price Index for all items in U.S. cities is provided to illustrate the effectiveness of the approach proposed in this paper.

New Pharmacological Insight into Etanercept and Pregabalin in Allodynia and Nociception: Behavioral Studies in a Murine Neuropathic Pain Model

Background/Objectives: Neuropathic pain is challenging to treat, often resistant to current therapies, and associated with significant side effects. Pregabalin, an anticonvulsant that modulates calcium channels, is effective but can impair mental and motor functions, especially in older patients. To improve patient outcomes, reducing the doses of pregabalin and combining it with other drugs targeting different neuropathic pain mechanisms may be beneficial. TNF-α blockers such as etanercept have shown potential in addressing neuropathic pain by affecting sodium channels, synaptic transmission, and neuroinflammation. This study evaluates the efficacy and safety of combining low doses of etanercept and pregabalin in allodynia and nociceptive tests. Materials and Methods: Male C57/BL6 mice underwent chronic constriction injury (CCI) of the sciatic nerve to induce neuropathic pain. They were divided into seven groups: sham control, CCI control, low and high doses of pregabalin, low and high doses of etanercept, and a combination of low doses of both drugs. Behavioral tests, including von Frey, hot-plate, and rotarod tests, were used to assess pain responses and motor activity. Results: The results indicated that a high dose of pregabalin significantly reduced mechanical allodynia and thermal hyperalgesia but impaired motor function. Conversely, low doses of etanercept alone had no significant effect. However, the combination of low doses of etanercept (20 mg/kg) and pregabalin (5 mg/kg) effectively alleviated pain without compromising locomotor activity. Conclusions: These results suggest a novel therapeutic strategy for neuropathic pain, enhancing analgesic efficacy while minimizing adverse effects.

Prediction models for ischemic stroke and bleeding in dialysis patients: a systematic review and meta-analysis

Abstract Background Patients with kidney failure on maintenance dialysis have a high stroke and bleeding risk. Multivariable prediction models can be used to estimate the risk of ischemic stroke and bleeding. A systematic review and meta-analysis was performed to determine the performance of the existing models in patients on dialysis. Methods MEDLINE and Embase databases were searched, from inception through January 12, 2024, for studies of prediction models for stroke or bleeding, derived or validated in dialysis cohorts. Discrimination measures for models with c-statistic data from ≥ 3 cohorts were pooled by random effects meta-analysis and a 95% prediction interval (PI) was calculated. Risk of bias was assessed using PROBAST. The review was conducted according to the PRISMA statement and the CHARMS checklist. Results Eight studies were included in this systematic review. All the included studies validated pre-existing models that were derived in cohorts from the general population. None of the identified studies reported the development of a new dialysis specific prediction model for stroke, while dialysis specific risk scores for bleeding were proposed by two studies. In meta-analysis of c-statistics, the CHA2DS2-VASc, CHADS2, ATRIA, HEMORR(2)HAGES and HAS-BLED scores showed very poor discriminative ability in the dialysis population. Six of the eight included studies were at low or unclear risk of bias and certainty of evidence was moderate. Conclusions The existing prediction models for stroke and bleeding have very poor performance in the dialysis population. New dialysis-specific risk scores should be developed to guide clinical decision making in these patients.

A tetraspecific engager armed with a non-alpha IL-2 variant harnesses natural killer cells against B cell non-Hodgkin lymphoma.

NK cells offer a promising alternative to T cell therapies in cancer. We evaluated IPH6501, a clinical-stage, tetraspecific NK cell engager (NKCE) armed with a non-alpha IL-2 variant (IL-2v), which targets CD20 and was developed for treating B cell non-Hodgkin lymphoma (B-NHL). CD20-NKCE-IL2v boosts NK cell proliferation and cytotoxicity, showing activity against a range of B-NHL cell lines, including those with low CD20 density. Whereas it presented reduced toxicities compared with those commonly associated with T cell therapies, CD20-NKCE-IL2v showed greater killing efficacy over a T cell engager targeting CD20 in in vitro preclinical models. CD20-NKCE-IL2v also increased the cell surface expression of NK cell-activating receptors, leading to activity against CD20-negative tumor cells. In vivo studies in nonhuman primates and tumor mouse models further validated its efficacy and revealed that CD20-NKCE-IL2v induces peripheral NK cell homing at the tumor site. CD20-NKCE-IL2v emerges as a potential alternative in the treatment landscape of B-NHL.

Predicting the Impact of Parameter Uncertainty on Model-Guided Li-O2 Cathode Design

Abstract Despite their high theoretical capacity, actual performance in lithium-oxygen (Li-O2) batteries is limited by sluggish transport and kinetic processes. Cathodes must provide ample surface area to host solid reduction products, but must also support species transport to access these surfaces. Numerous modeling studies therefore attempt to optimize cathode design by identifying microstructures to balance these two needs. However, model validation has historically relied on literature-sourced transport properties, which vary greatly between studies. In this work, we develop an open source, 1-D, continuum-scale Li-O2 battery model to examine the impact of electrolyte properties on predicted Li-O2 battery performance and design. Results demonstrate that O2 diffusion and solubility have the greatest impact on optimal design. Varying O2 diffusivity within the range of literature values surveyed led to maximum energy density variations of nearly 400%. These variations have a meaningful impact on the associated design conclusions: the optimal cathode porosity varied between 55 and 75%, depending on the O2 diffusivity. Moreover, the impact of advanced micrsotructures, such as graded cathode porosity, varies greatly with changes in electrolyte transport parameter estimates. As such, fundamental studies are required to accurately measure key electrolyte properties to enable numerical simulation as a guide to Li-O2 cathode design.

Atlantic Meridional Overturning Response to Increased Southern Ocean Wind Stress in a Climate Model with an Eddy-Rich Ocean

Abstract The Southern Hemisphere westerly winds experienced significant changes over recent decades and are projected to further strengthen, altering ocean hydrography and dynamics. While anomalies of Southern Hemisphere origin are hypothesized to impact the Atlantic meridional overturning circulation (AMOC), many details of this relationship remain unknown as previous modeling studies are limited by the application of forced, coarse-resolution ocean models, or a short integration length. Here, a coupled, nested climate model configuration, covering the Atlantic Ocean at an eddy-rich 1/10° resolution is applied to study the adjustment of the large-scale circulation to a 30% increase in the Southern Ocean wind stress. The AMOC responds to the stronger wind stress with a strengthening of 0.6–1.4 Sv (1 Sv ≡ 106 m3 s−1) across the entire Atlantic after approximately 80 years. At that time, anomalous watermass transformation mainly occurs at the entry into the Nordic seas. A density anomaly in the overflow water then induces anomalous sinking in the eastern subpolar gyre, providing a link between AMOC changes in density and depth coordinates. Our study suggests that these watermass changes are caused by northward-propagating anomalies and provides a detailed hypothesis for the link between the Indian Ocean inflow via Agulhas leakage and the AMOC. Nevertheless, due to coupled ocean–atmosphere adjustments, anomalies do not simply follow the main volume transport pathways along the western boundary. Mixing between advectively transported and locally forced anomalies leads to an increasingly complex evolution of watermass anomalies and less certainty in the relative contributions of involved mechanisms toward subpolar latitudes.

Dynamic response characteristics of concrete gravity dam induced by underwater explosion: scaled model study

Dynamic response characteristics of concrete gravity dam induced by underwater explosion: scaled model study

Study of isotropic stellar models via durgapal-lake solutions in rastall system

Abstract This manuscript is dealt with the influences of Rastall factor on the physical aspects of isotropic celestial models. In this scenario, both the ideal fluid distribution and static spherically symmetry are taken into consideration. In specifically, the Durgapal-Lake solutions are taken into consideration to analyze the different characteristics of several specific compact star models like Her X-1, Vela X-1, LMC X-4 and RXJ 1856-37. Due to its innovative combination of two methodologies, this solution is a significant advancement on Durgapal-Fuloria and Lake's previous ansatz in enormous crucial eras. Using observed estimates of radii and masses of certain specific star objects, the undefined parameters in Durgapal-Lake ansatz are derived by matching conditions. The consistency of the adopted solutions is examined through the visual interpretation of matter constituents, equation of state factor, energy conditions, mass function and stability criteria corresponding to distinct choices of Rastall factor. The radially symmetric graphs of matter variables as well as the mass function are also displayed. Moreover, We present the graphical analysis for vanishing Rastall factor. It is concluded that in the context of Rastall theory, the stars under examination exhibit stable compositions with Durgapal-Lake solution, while in the context of general relativity, they exhibit instability.

Abstract I008: Antisense Oligonucleotides as Therapeutics for Difficult-to-Drug Targets in Oncology

Abstract Many potential oncology targets are difficult to modulate by conventional classes of agents. Interfering with these targets at the level of RNA is a novel approach for difficult-to target proteins, as well as for non-coding RNAs, emerging targets that are part of the “dark matter’ of the genome. Antisense oligonucleotide (ASO) therapeutics are short sequences of 12- 30 DNA/RNA residues, designed to bind the cognate transcribed RNA. The ASO is chemically modified to increase stability, improve binding affinity, optimize pharmacokinetic properties and reduce immunostimulatory potential. The hybridization of ASO to cognate RNA results in cleavage of bound RNA by RNAse H1 and subsequent degradation. Several ASOs are marketed for treatment of rare diseases, but to date none have been approved for oncology indications. The long-noncoding RNA (lncRNA) MALAT1 is an example of an oncology target uniquely suited for an ASO therapeutic. This lncRNA is not translated and resides mostly in the nucleus, limiting access for most therapeutic approaches. MALAT1 is highly upregulated in various cancers, appears to play a role in epithelial to mesenchymal transition, and is associated with disease progression in patients. Inhibition of MALAT1 in preclinical breast cancer models, either genetic or pharmacologic using a murine ASO, leads to changes in tumor architecture and reduction in metastasis. FLM-7523 is a 16-mer ASO designed to bind to human MALAT1. It is a generation 2.5 design based on the constrained ethyl (cEt) modification to the 3 residues on the 3’ and 5’ ends of the ASO, and administered in a saline solution without any delivery vehicle. Preclinical studies have shown that FLM-7523 effectively inhibits MALAT1 expression in in vitro and in vivo models, and GLP toxicology studies have been completed to support a First-in-Human clinical trial. The signal transduction molecule STAT3 is another oncology target of interest that has, to date, been difficult to modulate. Danvatirsen is a 16-mer generation 2.5 ASO targeting human STAT3 mRNA and is currently in clinical trials. Preclinical in vivo studies in murine tumor models have demonstrated a reduction in STAT3 mRNA and protein following treatment with a STAT3 targeting ASO, along with tumor growth inhibition. Combination treatments with immune checkpoint inhibitors, including anti-PD-1 and anti-PD-L1, demonstrate improved tumor growth inhibition relative to monotherapy. In clinical trials, tumor uptake of danvatirsen is observed in post-treatment biopsies, with corresponding reduction in STAT3 protein. Danvatirsen is generally well tolerated with the most common adverse events being increased liver function tests and decreased myeloid hematologic parameters, both of which are easily monitored, manageable, and reversible. Clinical responses have been observed with the most robust being in combination with a PD-L1 inhibitor in recurrent/metastatic head and neck squamous cell carcinoma. In conclusion, ASOs are novel therapies that can inhibit oncology targets not tractable by traditional approaches. Citation Format: Andrew Denker. Antisense Oligonucleotides as Therapeutics for Difficult-to-Drug Targets in Oncology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr I008.

Unraveling Glioblastoma: TME Implication and Gene Therapy Advances.

Glioblastoma is a malignant manifestation of a solid brain tumour with a very dismal prognosis due to an overall median survival of 14 months. The currently administered Standard treatment plan, the STUPP regimen, is not very effective in tackling this neoplasia. A major concern that affects the development of new drug formulations, specifically for Glioma, is the inherent sub-clonal heterogeneity, which includes the dynamic and intricate nature of the Tumour Microenvironment (TME). Targeting the cellular niche using personalized medication for glioma specifically gene therapy, seems to be promising, with most studies in preclinical models yielding optimistic results. This paper analyses the great headways made in glioma gene therapy in the last 10 years while looking into different therapeutic strategies. That said, certain challenges do plague the clinical use of gene therapy which have been highlighted in the hopes that future researchers will address these concerns and further propel gene therapy in its journey from the Lab to the bedside.

Study in murine model: Is there a relationship between presbycusis and frailty?

Age-related hearing loss (ARHL) or Presbycusis is the most frequent sensory deficiency in older adults and is associated with comorbidities, such as falls, cognitive decline, and frailty. Frailty is related to poor health outcomes in old age. Recent research suggested that ARHL may be a potentially modifiable risk factor for frailty, although inconclusive. The use of animal models to study the correlation between ARHL and frailty is important to test future interventions to be translated into clinical practice. The aim of this study was to determine if there is an association between ARHL and frailty in experimental animals based on the human frailty phenotype. This research studied male and female C57Bl/6J mice, at different ages (6, 14, and 22 months). Auditory steady-state responses (ASSR) threshold shifts were measured at different frequencies. To assess frailty status, we were based on the "Valence Score" which consists of measuring: weakness, weight loss, low level of activity, slowness, and little resistance. We found that hearing is significantly lower in older age groups. The mice become frail as they age. The worsening in ASSR threshold shifts with age correlates significantly with an increasing frailty. No significant differences were found between both sexes. Our research is, to our knowledge, the first carried out in experimental animals to establish the association between ARHL and frailty, which would provide a useful tool to evaluate future interventions in mice before translating them into clinical practice.

Influenza Vaccination, Household Composition, and Race-Based Differences in Influenza Incidence: An Agent-Based Modeling Study.

Objectives. To estimate the effect of influenza vaccination disparities. Methods. We compared symptomatic influenza cases between Black and White races in 2 scenarios: (1) race- and age-specific vaccination coverage and (2) equal vaccination coverage. We also compared differences in household composition between races. We used the Framework for Reconstructing Epidemiological Dynamics, an agent-based model that assigns US Census‒based age, race, households, and geographic location to agents (individual people), in US counties of varying racial and age composition. Results. Influenza cases were highest in counties with higher proportions of children. Cases were up to 30% higher in Black agents with both race-based and race-equal vaccination coverage. Compared with corresponding categories of White households, cases in Black households without children were lower and with children were higher. Conclusions. Racial disparities in influenza cases persisted after equalizing vaccination coverage. The proportion of children in the population contributed to the number of influenza cases regardless of race. Differences in household composition may provide insight into racial differences and offer an opportunity to improve vaccination coverage to reduce influenza burden for both races. (Am J Public Health. Published online ahead of print November 14, 2024:e1-e8. https://doi.org/10.2105/AJPH.2024.307878).

Computer Modeling Study of the Effects of Radio Frequency EM Radiation on Humans

Computer Modeling Study of the Effects of Radio Frequency EM Radiation on Humans

Analysis of Synergistic Changes in PM2.5 and O3 Concentrations Based on Structural Equation Model Study

Given the increasing importance of effectively identifying synergistic changes between PM2.5 and O3 and comprehensively analyzing their impact on air quality management in China, we employ the Sen+Mann–Kendall (Sen+M-K) trend test in this study to examine the temporal and spatial variation trends of PM2.5 and O3 in the Yangtze River Delta (YRD), from 2003 to 2020. We identified the regions where these pollutants exhibited synergistic changes and established the pathways between the pollutants and their potential drivers, using geographically weighted random forest algorithms and structural equation modeling. The study results revealed as follows: (1) Overall, the PM2.5 concentrations show a decreasing trend, while the O3 concentrations exhibit an increasing trend, in the YRD. Analysis of the combined trends indicates that approximately 95% of the area displays opposing trends for PM2.5 and O3, with only about 4% in the southern region showing synergistic trends for both pollutants. (2) Drought and the average temperature are the main drivers of the changes in PM2.5 and O3 concentrations in areas experiencing synergistic changes. Their combined effects alleviate the aggregation of PM2.5 and reduce the formation of VOCs, indirectly reducing the generation of pollutants. The negative effect of the average temperature on the O3 concentration may indicate the existence of nonlinear effects and complex interaction effects between the drivers. NOx and VOCs play important dual roles in the generation and conversion of pollutants, although their overall impact is smaller than meteorological factors. They produce significant indirect effects through their interaction with meteorological and other human factors, further affecting the concentrations of PM2.5 and O3. In areas without coordinated changes, the main impact of meteorological factors remains unchanged, and the relationship between the two anthropogenic emission sources and their effects on PM2.5 and O3 are complex, with different directions and levels involved. This study provides detailed insights into the drivers of air quality changes in the YRD and offers a scientific basis for environmental management authorities to develop more comprehensive and targeted strategies for balancing the control of PM2.5 and O3 pollution.

Estimates of resource use in the public-sector health-care system and the effect of strengthening health-care services in Malawi during 2015-19: a modelling study (Thanzi La Onse).

In all health-care systems, decisions need to be made regarding allocation of available resources. Evidence is needed for these decisions, especially in low-income countries. We aimed to estimate how health-care resources provided by the public sector were used in Malawi during 2015-19 and to estimate the effects of strengthening health-care services. For this modelling study, we used the Thanzi La Onse model, an individual-based simulation model. The scope of the model was health care provided by the public sector in Malawi during 2015-19. Health-care services were delivered during health-care system interaction (HSI) events, which we characterised as occurring at a particular facility level and requiring a particular number of appointments. We developed mechanistic models for the causes of death and disability that were estimated to account for approximately 81% of deaths and approximately 72% of disability-adjusted life-years (DALYs) in Malawi during 2015-19, according to the Global Burden of Disease (GBD) estimates; we computed DALYs incurred in the population as the sum of years of life lost and years lived with disability. The disease models could interact with one another and with the underlying properties of each person. Each person in the Thanzi La Onse model had specific properties (eg, sex, district of residence, wealth percentile, smoking status, and BMI, among others), for which we measured distribution and evolution over time using demographic and health survey data. We also estimated the effect of different types of health-care system improvement. We estimated that the public-sector health-care system in Malawi averted 41·2 million DALYs (95% UI 38·6-43·8) during 2015-19, approximately half of the 84·3 million DALYs (81·5-86·9) that the population would otherwise have incurred. DALYs averted were heavily skewed to children aged 0-4 years due to services averting DALYs that would be caused by acute lower respiratory tract infection, HIV or AIDS, malaria, or neonatal disorders. DALYs averted among adults were mostly attributed to HIV or AIDS and tuberculosis. Under a scenario whereby each appointment took the time expected and health-care workers did not work for longer than contracted, the health-care system in Malawi during 2015-19 would have averted only 19·1 million DALYs (95% UI 17·1-22·4), suggesting that approximately 21·3 million DALYS (20·0-23·6) of total effect were derived through overwork of health-care workers. If people becoming ill immediately accessed care, all referrals were successfully completed, diagnostic accuracy of health-care workers was as good as possible, and consumables (ie, medicines) were always available, 28·2% (95% UI 25·7-30·9) more DALYS (ie, 12·2 million DALYs [95% UI 10·9-13·8]) could be averted. The health-care system in Malawi provides substantial health gains with scarce resources. Strengthening interventions could potentially increase these gains, so should be a priority for investigation and investment. An individual-based simulation model of health-care service delivery is valuable for health-care system planning and strengthening. The Wellcome Trust, UK Research and Innovation, the UK Medical Research Council, and Community Jameel.