Epidemiological Model Research Articles

A Century of Mathematical Epidemiology: A Bibliometric Analysis and Visualization of Research Trends.

Defined as the application of mathematical models and methods for the study of disease spread and control, Mathematical epidemiology has now emerged as a very important area for understanding public health dynamics. The paper presents an overall bibliometric analysis of research in mathematical epidemiology using the Scopus database. This overview comprises 1,787 documents: journal articles, book chapters, and conference papers from 819 sources. From 1916 to 2024, it has been possible to identify key trends, influential authors, and central themes through the application of the PRISMA methodology. The results reflect that since 2000, there has been a significant growth in research production; most of it was during the period of the COVID-19 pandemic. The study also determined trends in international collaboration, leading funding sponsors, and the dynamics underlying major research topics. According to this study, the role of mathematical models in epidemiology is becoming increasingly prominent, driven by the need to address complex global health challenges and an expanding influence on public health strategies.

Development of a Formula for Calculating the Probability of the Spread of African Swine Fever

Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives forecasting epidemics. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. A formula has been developed for calculating the probability of spreading african swine fever from infected animals of a threatened pig farm to other farms. The formula allows you to calculate the probability of an outbreak of infection for pigs of each of the pig farms located in the analyzed area. This takes into account the possible ways of spreading the pathogen in the presence of mechanisms and factors of transmission of african swine fever in real time.

8702 Age-Sex Differences in Global Burden of Ischemic Heart Disease Attributable to High LDL in the Last 3 Decades:Global, Regional, National Variations, Age Dynamics, and Implications for Global Health

Abstract Disclosure: V. Adedara: None. K. Maddineni: None. A. Dekhne: None. M. Chinthapalli: None. G. Potluri: None. A. Parisapogu: None. K. Markandu: None. H. Desai: None. Background: Ischemic Heart Disease (IHD) stands as the foremost cause of mortality globally, contributing to 9.6 million deaths. Elevated levels of low-density lipoprotein cholesterol (LDL-C) consistently feature among the risk factors associated with IHD. Methods: Employing the Global Burden of Disease framework, we computed mortality and DALYs linked to IHD attributable to LDL-C, stratified by age-sex, year, and covering 204 countries and territories during the period from 1990 to 2019. Non-fatal health outcomes were estimated using DISMOD MR 2.1, Bayesian meta-regression tool for epidemiological modelling. Results were expressed in absolute counts and age-standardized rates (ASR) per 100,000 person-years. Results: The total number of deaths attributed to IHD due to elevated LDL-C increased from 2.5 million (95% Uncertainty Interval: 2-3.1 million) in 1990 to 3.7 million (95% UI: 2.8-4.8 million) in 2019. Concurrently, DALYs increased from 60.4 million (95% UI: 50.4-70.7 million) in 1990 to 84.9 million (95% UI: 69.5-101.3 million) in 2019. In terms of ASR, the annual percentage change (APC) in death rates decreased by 36%, and DALY rates decreased by 32% from 1990 to 2019. Regionally, East Asia exhibited the highest increase in APC in death counts by 191%, followed by Commonwealth Low-Income countries at 164%, and Oceania at 160% during the same period. Conversely, the Commonwealth High-Income region experienced the highest decrease at 41%. Males displayed higher APC in death (48% vs. 44%) and DALYs (42% vs. 38%) compared to females over the last three decades. In terms of ASR, males observed a higher decrease compared to females. In 2019, the age group 80-84 years had the highest number of deaths at 534,079 (264,177-813,898), while DALYs were most pronounced in the 55-59 year age group, reaching 10.6 million (8.9-12.3 million). Conclusion: IHD remains a significant global health challenge, with high LDL-C levels being a crucial modifiable risk factor. Age-sex disparities in the burden of IHD are evident, driven by variations in LDL-C metabolism, hormonal changes, and lifestyle factors. Recognizing and addressing these disparities is essential for developing effective prevention and treatment strategies tailored to different populations and age groups. Presentation: 6/2/2024

Simulation of Epidemic Dynamics Using a Multi-Agent Model: Analysis of Social Distancing Strategies and Their Impacts on Public Health and Economy

Infectious disease epidemics have played a crucial role in shaping public health responses, particularly in global health crises. This study emerges as part of the efforts to prepare effective responses to potential future pandemics, leveraging lessons learned during the COVID-19 crisis. The research uses an adapted compartmental epidemiological model and a synthetic multi-agent community to investigate how social variables influence epidemic forecasts in socioeconomically vulnerable regions. Focusing on the simulation of epidemic dynamics in the socio-economically disadvantaged neighbourhood of Ilha Joana Bezerra in Recife, this study examines the impacts of social distancing strategies and other control measures, such as face masks and moderate social isolation. Through the adapted SEPAI3R3O model, which includes compartments for pre-symptomatic and asymptomatic states, this study provides a detailed analysis of disease dynamics in contexts characterised by high social vulnerability. The results underscore the importance of public health policies adapted to socio-economic factors, emphasising the need for continuous preparedness to manage future epidemic threats in vulnerable communities effectively.

When are predictions useful? A new method for evaluating epidemic forecasts

BackgroundCOVID-19 will not be the last pandemic of the twenty-first century. To better prepare for the next one, it is essential that we make honest appraisals of the utility of different responses to COVID. In this paper, we focus specifically on epidemiologic forecasting. Characterizing forecast efficacy over the history of the pandemic is challenging, especially given its significant spatial, temporal, and contextual variability. In this light, we introduce the Weighted Contextual Interval Score (WCIS), a new method for retrospective interval forecast evaluation.MethodsThe central tenet of the WCIS is a direct incorporation of contextual utility into the evaluation. This necessitates a specific characterization of forecast efficacy depending on the use case for predictions, accomplished via defining a utility threshold parameter. This idea is generalized to probabilistic interval-form forecasts, which are the preferred prediction format for epidemiological modeling, as an extension of the existing Weighted Interval Score (WIS).ResultsWe apply the WCIS to two forecasting scenarios: facility-level hospitalizations for a single state, and state-level hospitalizations for the whole of the United States. We observe that an appropriately parameterized application of the WCIS captures both the relative quality and the overall frequency of useful forecasts. Since the WCIS represents the utility of predictions using contextual normalization, it is easily comparable across highly variable pandemic scenarios while remaining intuitively representative of the in-situ quality of individual forecasts.ConclusionsThe WCIS provides a pragmatic utility-based characterization of probabilistic predictions. This method is expressly intended to enable practitioners and policymakers who may not have expertise in forecasting but are nevertheless essential partners in epidemic response to use and provide insightful analysis of predictions. We note that the WCIS is intended specifically for retrospective forecast evaluation and should not be used as a minimized penalty in a competitive context as it lacks statistical propriety. Code and data used for our analysis are available at https://github.com/maximilian-marshall/wcis.

Updates to the wild bird abundance and movement models for the early warning system for avian influenza in the EU

Abstract Highly pathogenic avian influenza (HPAI) viruses pose a significant threat to both poultry and wild bird populations. Migratory wild birds play a key role in the intercontinental spread of avian influenza (AI), introducing the virus into poultry populations. In response to frequent AI outbreaks in Europe, the European Food Safety Authority (EFSA), at the request of the European Commission (EC), produces quarterly and annual epidemiological reports to monitor and analyse AI trends. A key component of this surveillance includes the integration of outbreak data from Member States and contributions from non‐governmental ornithological organisations like the European Bird Census Council (EBCC) and the European Union for Bird Ringing (EURING) together in a predictive spatio‐temporal risk assessment model. Previous data integration and modelling efforts led to the development of an early warning system for predicting HPAI outbreaks accessible through a publicly available online user interface: the Bird Flu Radar. This report presents an improvement of the system by expanding the species coverage and refining the existing base models behind the epidemiological model. Specifically, this report details the exploration to incorporate 12 additional wild bird species into the models, and the changes made to the base models predicting the distribution and movements of wild birds. We demonstrate the improvements respecting the existing base models while at the same time enhancing the effectiveness in predicting HPAI outbreaks and possibly mitigating negative effects in Europe by providing more accurate predictions to different stakeholders.

One-size measures do not fit all areas: Evaluation of area-specific control of foot and mouth disease in Thailand using bioeconomic modelling

Information on the epidemiological and economic consequences of control measures is fundamental to design effective foot and mouth disease (FMD) control measures. One approach to obtaining this information is through bioeconomic modelling. In this study, a bioeconomic model was used to evaluate FMD control in two different study areas in Thailand: a high farm density area predominantly consisting of dairy farms and a low farm density area with mixed farm types. The bioeconomic model consists of an epidemiological part and an economic part. For the epidemiological part, a stochastic between-farm transmission model was constructed with transmission parameters estimated from FMD outbreaks in Thailand. The outputs from the epidemiological model, i.e. the number of infected farms, the number of affected farms and the outbreak duration, are used as inputs for economic model to calculate the economic consequences. We applied the simulation model with four FMD control measures: culling the animals of infected farms, ring vaccination, animal movement restrictions and isolation of infected farms. Furthermore, we included effect of farmers' compliance to asses its effect on control measures. The simulated FMD outbreaks in the low farm density area were small, thus control measures did not greatly affect the size of outbreaks and, therefore, did not have a positive economic return. In contrast, in the high farm density area, FMD outbreaks were large without control measures. All measures reduced the size of the outbreaks but resulted in different total costs. In terms of outbreak control, culling infectious farms was the best option, but its total cost was higher than ring vaccination or isolation of infected farms. In terms of cost-effectiveness, ring vaccination was the best measure. If farmers' compliance were low, all control measures would be ineffective, resulting in high total cost of the outbreak. The cost distribution between compliant and non-compliant farms showed that non-compliant farms paid more than compliant farms, except for the ring vaccination scenario. The results emphasize the economic significance to customize control measures specific to the area's conditions and highlight the importance of farmers' compliance when designing control measures.

Why aphid virus retention needs more attention: Modelling aphid behaviour and virus manipulation in non-persistent plant virus transmission.

Plant viruses threaten food security and are often transmitted by insect vectors. Non-persistently transmitted (NPT) plant viruses are transmitted almost exclusively by aphids. Because virions attach to the aphid's stylet (mouthparts) and are acquired and inoculated via brief epidermal probes, the aphid-virus interaction is highly transient, with a very short aphid virus retention time. Many NPT viruses manipulate their host plant's phenotype to change aphid behaviour to optimise virus transmission. Epidemiological models of this have overlooked a key feature of aphid NPT virus retention: probing or feeding on a plant causes aphids to lose the virus. Furthermore, experimental studies suggest aphids could possibly inoculate multiple healthy plants within one infective period if they do not feed. Consequences of this for virus manipulation of host plant phenotype have not been explored. Our new compartmental epidemiological model includes both behaviour-based aphid dispersal and infectivity loss rates, and the ability of infective aphids to probe multiple plants before virus loss. We use our model to explore how NPT virus-induced host phenotypes affect epidemic outcomes, comparing these results to representative previous models. We find that previous models behave fundamentally differently and underestimate the benefit of an 'attract-and-deter' phenotype, where the virus induces increased aphid attraction to infected plants but deters them from prolonged feeding. Our results also highlight the importance of characterising NPT virus retention upon the aphid during probing. Allowing for multiple infective probes increases disease incidence and the effectiveness of virus manipulation, with implications for epidemic prediction and control.

When Do Tumours Develop? Neoplastic Processes Across Different Timescales: Age, Season and Round the Circadian Clock.

While it is recognised that most, if not all, multicellular organisms harbour neoplastic processes within their bodies, the timing of when these undesirable cell proliferations are most likely to occur and progress throughout the organism's lifetime remains only partially documented. Due to the different mechanisms implicated in tumourigenesis, it is highly unlikely that this probability remains constant at all times and stages of life. In this article, we summarise what is known about this variation, considering the roles of age, season and circadian rhythm. While most studies requiring that level of detail be done on humans, we also review available evidence in other animal species. For each of these timescales, we identify mechanisms or biological functions shaping the variation. When possible, we show that evolutionary processes likely played a role, either directly to regulate the cancer risk or indirectly through trade-offs. We find that neoplastic risk varies with age in a more complex way than predicted by early epidemiological models: rather than resulting from mutations alone, tumour development is dictated by tissue- and age-specific processes. Similarly, the seasonal cycle can be associated with risk variation in some species with life-history events such as sexual competition or mating being timed according to the season. Lastly, we show that the circadian cycle influences tumourigenesis in physiological, pathological and therapeutic contexts. We also highlight two biological functions at the core of these variations across our three timescales: immunity and metabolism. Finally, we show that our understanding of the entanglement between tumourigenic processes and biological cycles is constrained by the limited number of species for which we have extensive data. Improving our knowledge of the periods of vulnerability to the onset and/or progression of (malignant) tumours is a key issue that deserves further investigation, as it is key to successful cancer prevention strategies.

Influenza vaccine allocation in tropical settings under constrained resources.

Influenza virus seasonality, synchronicity, and vaccine supply differ substantially between temperate and tropical settings, and optimal vaccination strategy may differ on this basis. Many national vaccine recommendations focus on high-risk groups, elderly populations, and healthcare workers despite previous analyses demonstrating broad benefits to vaccinating younger high-contact age groups. In this study, we parameterized an age-structured nonseasonal asynchronous epidemiological model of influenza virus transmission for a tropical low-income setting. We evaluated timing and age allocation of vaccines across vaccine supplies ranging from 10 to 90% using decade-based age groups. Year-round vaccination was beneficial when compared with more concentrated annual vaccine distribution. When targeting a single age group for vaccine prioritization, maximum vaccine allocation to the 10-19 high-contact age group minimized annual influenza mortality for all but one vaccine supply. When evaluating across all possible age allocations, optimal strategies always allocated a plurality of vaccines to school-age children (10-19). The converse, however, was not true as not all strategies allocating a plurality to children aged 10-19 minimized mortality. Allocating a high proportion of vaccine supply to the 10-19 age group is necessary but not sufficient to minimize annual mortality as distribution of remaining vaccine doses to other age groups also needs to be optimized. Strategies focusing on indirect benefits (vaccinating children) showed higher variance in mortality outcomes than strategies focusing on direct benefits (vaccinating the elderly). However, the indirect benefit approaches showed a lower mean mortality and a lower minimum mortality than vaccination focused on the elderly.

Harnessing uncertainty: A deep mechanistic approach for cautious diagnostic and forecast of Bovine Respiratory Disease

Bovine Respiratory Disease (BRD) is a prevalent infectious disease of respiratory tract in cattle, presenting challenges in accurate diagnosis and forecasting due to the complex interactions of multiple risk factors. Common methods, including mathematical epidemiological models and data-driven approaches such as machine learning models, face limitations such as difficult parameter estimation or the need for data across all potential outcomes, which is challenging given the scarcity and noise in observing BRD processes. In response to these challenges, we introduce a novel approach known as the Bayesian Deep Mechanistic method. This method couples a data-driven model with a mathematical epidemiological model while accounting for uncertainties within the processes. By utilising 265 lung ultrasound videos as sensor data from 163 animals across 9 farms in France, we trained a Bayesian deep learning model to predict the infection status (infected or non-infected) of an entire batch of 12 animals, also providing associated confidence levels. These predictions, coupled with their confidence levels, were used to filter out highly uncertain diagnoses and diffuse uncertainties into the parameterisation of a mathematical epidemiological model to forecast the progression of infected animals. Our findings highlight that considering the confidence levels (or uncertainties) of predictions enhances both the diagnosis and forecasting of BRD. Uncertainty-aware diagnosis reduced errors to 32 %, outperforming traditional automatic diagnosis. Forecast relying on veterinarian diagnoses, considered the most confident, had a 23 % error, whilst forecast taking into account diagnosis uncertainties had a close 27.2 % error. Building upon uncertainty-awareness, our future research could explore integrating multiple types of sensor data, such as video surveillance, audio recordings, and environmental parameters, to provide a comprehensive evaluation of animal health, employing multi-modal methods for processing this diverse data.

Evaluating the Application of the SIR Model to a Localized Population

Objective Using data collected from the Johns Hopkins COVID-19 Repository, I investigated the reliability of the SIR (Susceptible-Infected-Removed) model. Summary of Background Data Modern pandemic responses have evolved into effective defenses when implemented correctly but present issues in epidemiological modeling efforts. Intervention strategies such as quarantining and masking limit population size (N), which affects the accuracy of modeling population-based rate systems especially in highly transmissible diseases. Methods I begin by reviewing the SIR model retroactively to the initial SARS-CoV-2 Wuhan strain. I compared the parameters available in the published literature (N = 2,717,000, β = 2, γ = 1/14) to the best-fitting SIR-yielded values by minimizing the root mean squared error function. Subsequently, I evaluated its predictive capabilities on the Delta variant using early surge data, which was later compared against a retroactive analysis. Results Using a least-squares error best-fit analysis allowed me to retroactively define remarkably accurate model parameters for the Wuhan waves. Parameters including N = 730, β = 0.46, γ = 0.043 in the first wave and N = 11,200, β = 0.198, γ = 0.07 in the second reflected effective intervention strategies. I show it is an effective predictive tool regarding the Delta variant, yielding parameters N = 50,900, β = 0.87, γ = 1/3.7 that proved accurate when compared with parameters from a full retroactive analysis (N = 60,000, β = 0.94, γ = 1/3.6). Conclusion The similarity of the yielded parameters in my results supports the SIR model’s utility in epidemiological monitoring of high-transmissibility, low-mortality outbreaks vis-à-vis various containment measures.

Analyzing steady-state equilibria and bifurcations in a time-delayed SIR epidemic model featuring Crowley-Martin incidence and Holling type II treatment rates

This article presents a time-delayed SIR epidemiological model that has been quantitatively examined. The model incorporates a logistic growth function for the susceptible population, a Crowley-Martin type incidence, and Holling type II treatment rates. We investigated two separate time delays. The first delay refers to the rate at which new infections occur, allowing us to evaluate the impact of the latent period. The second delay relates to the rate of treatment for those who have contracted the infection, which allows us to examine the consequences of postponed access to therapy. The investigation of the steady-state behavior of the model emphasizes two equilibria, namely, the infection-free equilibrium and the endemic equilibrium. The determination of critical values involves the use of the fundamental reproduction number, denoted R0, which serves as a predictive measure to determine the potential elimination of a disease within a specific population. Using the fundamental reproduction number, it can be shown that infection-free equilibrium exhibits local asymptotic stability when the value of R0 is less than 1. In contrast, when R0 exceeds 1, the infection-free equilibrium becomes unstable in the context of the time-delayed system. Furthermore, an analysis of the steady-state dynamics of the endemic equilibrium indicates the appearance of oscillations and periodic solutions with the Hopf bifurcation for all feasible combinations of two-time delays as the bifurcated parameter. In sensitivity analysis, a sensitivity index is utilized to evaluate the relative modification in the fundamental reproduction number caused by each parameter. In summary, numerical simulations are employed to offer empirical evidence for the theoretical findings.

Unbiased temperature-related mortality estimates using weekly and monthly health data: a new method for environmental epidemiology and climate impact studies

Exposure to environmental factors has a high burden on human health, with millions of premature annual deaths associated with the short-term health effects of ambient temperatures and air pollution. However, direct estimations of exposure-related mortality from real data are still not available in most parts of the world, especially in low-resource settings, due to the unavailability of daily health records to calibrate epidemiological models. In this study, we have filled the crucial gap in available direct estimations by developing a method to make valid inference for the relationship between exposure and response data that uses only exposure and temporally aggregated response data. We provided the mathematical derivation of the method, and compared the results by using simulations applied to daily temperature and daily, weekly, and monthly mortality data. The method was then applied to the newly created database of the EARLY-ADAPT project. The daily and weekly models produced similar and unbiased estimates of the temperature-related relative risks and attributable mortality, with only slightly more imprecision in the weekly model. Even the estimates of the monthly model were unbiased when using enough data, although at the expense of a substantial increase in variability. The real data analysis showed that the similarity between the regional values of two aggregation models increased with the number of years and regions of the dataset, and decreased with the difference in their degree of temporal aggregation. Our method opens the door to conducting epidemiological studies in low-resource settings, where access to daily health data is not possible. Moreover, it allows accurate estimation of the short-term health effects of environmental exposures in near-real time, when daily health data are still not available, such as in the estimation of the mortality burden of recent record-breaking heat episodes. Overall, our method represents an important new approach to how the public health community can use data to create new evidence for research, translation and policy making. European Research Council (ERC).

Spatial distribution of poultry farms using point pattern modelling: A method to address livestock environmental impacts and disease transmission risks.

The distribution of farm locations and sizes is paramount to characterize patterns of disease spread. With some regions undergoing rapid intensification of livestock production, resulting in increased clustering of farms in peri-urban areas, measuring changes in the spatial distribution of farms is crucial to design effective interventions. However, those data are not available in many countries, their generation being resource-intensive. Here, we develop a farm distribution model (FDM), which allows the prediction of locations and sizes of poultry farms in countries with scarce data. The model combines (i) a Log-Gaussian Cox process model to simulate the farm distribution as a spatial Poisson point process, and (ii) a random forest model to simulate farm sizes (i.e. the number of animals per farm). Spatial predictors were used to calibrate the FDM on intensive broiler and layer farm distributions in Bangladesh, Gujarat (Indian state) and Thailand. The FDM yielded realistic farm distributions in terms of spatial clustering, farm locations and sizes, while providing insights on the factors influencing these distributions. Finally, we illustrate the relevance of modelling realistic farm distributions in the context of epidemic spread by simulating pathogen transmission on an array of spatial distributions of farms. We found that farm distributions generated from the FDM yielded spreading patterns consistent with simulations using observed data, while random point patterns underestimated the probability of large outbreaks. Indeed, spatial clustering increases vulnerability to epidemics, highlighting the need to account for it in epidemiological modelling studies. As the FDM maintains a realistic distribution of farm location and sizes, its use to inform mathematical models of disease transmission is particularly relevant for regions where these data are not available.

Data-driven decision-making in public health: The role of advanced statistical models in epidemiology

This paper critically examines the transformative role of data-driven decision-making in public health, focusing on the integration of advanced statistical models in epidemiology. As the volume and complexity of health data increase, leveraging predictive analytics, machine learning, and real-time data integration has become essential for improving public health outcomes. The study explores how these technologies have shifted public health strategies from reactive to proactive approaches, particularly in areas such as disease surveillance, chronic disease management, and health equity. Through comprehensive analysis, the paper identifies key advancements, such as hybrid models that combine traditional epidemiological frameworks with AI, and the integration of multi-modal data sources that enhance predictive accuracy. The findings emphasize the potential of these models to optimize resource allocation, address health disparities, and provide timely interventions. However, challenges such as data quality, algorithmic bias, and the ethical implications of model transparency are highlighted as critical issues requiring ongoing research. The study concludes that for these models to be effectively adopted, there must be a balance between technological innovation and ethical considerations. Recommendations include the need for interdisciplinary collaboration, improved data governance frameworks, and the development of more inclusive models that are generalizable across diverse populations. This research underscores the necessity of combining robust analytical tools with ethical frameworks to enhance the reliability and equity of public health interventions.

Assessing vaccine strategies for mpox outbreak in New York City using an age-structure model

BackgroundSince May 7 2022, mpox has been endemic in many countries which has attracted the attention of health authorities in various countries and made control decisions, in which vaccination is the mainstream strategy. However, the shortage of vaccine doses and the reduction of protective efficacy have led to unresolved issues such as vaccine allocation decisions and evaluation of transmission scale.MethodsWe developed an epidemiological model to describe the prevalence of the mpox virus in New York City and calibrated the model to match surveillance data from May 19 to November 3, 2022. Finally, we adjusted the model to simulate and compare several scenarios of non-vaccination and pre-pandemic vaccination.ResultsRelative to the status quo, if vaccination is not carried out, the number of new infections increases to about 385%, and the transmission time will be extended to about 350%, while if vaccinated before the epidemic, the number of new infections decreases to 94.2-96%.ConclusionsThe mpox outbreak in New York City may be linked to the Pride event. However, with current vaccine coverage, there will be no more large-scale outbreaks of mpox, even if there is another similar activity. For areas with limited vaccines, priority is given to high-risk groups in the age group [34–45] years as soon as possible.

Early and Late Microvascular Inflammation Have Differing Etiological Causes and Clinical Expression.

Microvascular inflammation (MVI) is an important pathological feature of antibody-mediated rejection (AMR). How posttransplant time affects its clinicopathological expression is little understood. This retrospective, single-center study screened 3398 kidney transplant biopsies and dichotomized 202 MVI ≥ 2 (Banff glomerulitis + peritubular capillaritis ≥ 2) samples by 9-mo median incidence time for comparison. The prevalence of MVI ≥ 2 was 12.4% in transplant kidneys, which failed more frequently than propensity-matched normal controls (n = 202; P < 0.001). Epidemiological risk factors for early MVI ≥ 2 were delayed graft function, prior AMR, and circulating donor-specific antibodies (DSAs+). Prior recipient sensitization occurred in 72.3%. Early MVI ≥ 2 was classified AMR in 65.3% and cellular rejection in 34.7%, and demonstrated excellent functional recovery and graft survival comparable to normal control kidneys. Late MVI ≥ 2 was predicted by younger (18 = 29 y) age, female recipient, living-donation, prior methylprednisolone, cyclosporine (versus tacrolimus, levels <5 ng/mL), absent antiproliferative therapy, and DSA+ using multivariable epidemiological modeling. Nonadherence caused 49.5%, with iatrogenic minimization responsible for 47.5%, usually for recipient infection. Late MVI ≥ 2 was because of AMR in 93.1%, and characterized by greater interstitial fibrosis, tubular atrophy, complement degradation split-product 4d (C4d) staining of peritubular capillaries+, endothelial C4d staining of glomerular capillaries+, transplant glomerulopathy and vasculopathy scores, DSA strength, and graft failure than early MVI ≥ 2 or normal transplant kidneys. Death-censored graft survival in 149 unique MVI ≥ 2 kidneys was independently determined by nonadherence, serum creatinine, proteinuria, DSA+, Banff C4d staining of peritubular capillaries+, and chronic interstitial fibrosis scores. MVI score and time lost significance using multivariable Cox regression. The changing expression of MVI ≥ 2 over time is best explained by differences in underimmunosuppression and microvascular injury from AMR impacting allograft function and survival.

Analysis and Simulation of Misinformation Spread in Social Networks: A Hybrid Stochastic-Deterministic Approach with NEAB Model

This paper examined and simulated the dynamics of misinformation spread within social networks using the NEAB model [32]. In this model, (N) represents non-believers, (E) denotes individuals exposed to the information but undecided, (A) refers to those who accept the information and may propagate it, and (B) are the believers. Similar to epidemiological models like SEIR (Susceptible-Exposed-Infected-Recovered), the NEAB model adapts to study the spread of various types of information and behaviors within a population [20]. By combining elements of network theory [28] and epidemiology, the model investigates how behaviors and network structures influence information transmission. Our simulations, particularly when applied to real-world scenarios like misinformation spread during events such as COVID-19 or natural disasters, highlight key factors influencing dissemination [7]. Rates of exposure to misinformation, the transition of individuals from exposed to active spreaders, and the rate of recovery all play crucial roles in shaping how misinformation spreads [33]. Misinformation propagates more easily when exposure rates are high, while higher recovery rates help limit its spread. Sensitivity analysis shows that variations in (β) and (δ) significantly impact the spread, emphasizing the importance of targeted interventions during these critical stages [22]. The objective of this study is to evaluate strategies for reducing the effects of misinformation on public discourse and health outcomes while offering insights into the key factors that drive its dissemination based on model simulations.

Dynamic behaviors for fractional epidemiological model featuring vaccination and quarantine compartments

Dynamic behaviors for fractional epidemiological model featuring vaccination and quarantine compartments