Epidemiological Model Research Articles

Cardio Policy Optimiser: an integrated public health approach to enhancing cardiovascular health

Abstract Background Cardiovascular diseases (CVDs) remain a leading cause of mortality in Europe, with atherosclerotic-related conditions accounting for 80% of CVD deaths - a third of them premature. Despite extensive efforts over the past five decades, the decline in these deaths over the last 20-25 years has plateaued, highlighting the need for innovative approaches. Additionally, the co-occurrence of CVDs with other chronic conditions such as diabetes, hypertension, and renal and hepatic diseases, adds complexity to their management and prevention. Methods In Portugal, the pilot phase implemented a novel integrated approach combining a Bayesian epidemiological model - which incorporates genetic, sociodemographic, comorbidity, and lifestyle data - with an adaptive conjoint analysis model focusing on atherosclerosis policy intervention. These models are part of a comprehensive response framework that incorporates health outcomes and associated costs resulting from better prevention and diagnosis. Results Currently in the development and calibration stage, the model is expected to yield initial results by early September. Conclusions This modeling approach offers new opportunities for public health to intervene at early stages in the natural history of CVDs. It emphasizes enhancing prevention and early diagnosis to potentially reduce the incidence of acute episodes and associated mortalities. Moreover, the model facilitates rapid evaluation of the economic benefits derived from preventive policies, thereby supporting more informed public health decision-making. Key messages • The CPO model combines epidemiological and policy models to address cardiovascular morbidity and mortality. • It boosts early diagnosis and prevention, and assesses economic benefits from policies.

Modelling Of The Weed Invasion And Control Strategies For Mesquite Plants

The new study presents an innovative model for examining the spread of the mesquite species, which is well-known for its extensive presence around the world thanks to water dispersion in various areas. The goal is to assess the stability of the system locally and globally using epidemiological modeling techniques. The approach involved analyzing the basic reproduction number (R₀) using the next-generation matrix to find the coefficients for the Lyapunov function. The results indicated that, the absence of mesquite remains stable on both local and global scale when R₀ is less than 1, but when R₀ exceeds 1, an endemic equilibrium is observed. Numerical simulations using Runge Kutta ode45 algorithm implemented in MATLAB. The study demonstrated that, an increase in the number of water bodies flowing on land, leads to a faster spread and invasion of mesquite. The study suggests the use of this model in areas where the mesquite species is native to raise awareness, inform decision-making, and educate communities, ultimately aiding efforts to manage and eliminate the species.

Dynamics for advective-cooperative system with free boundaries in a nondegenerate epidemiological model

Dynamics for advective-cooperative system with free boundaries in a nondegenerate epidemiological model

Numerical Simulation of SEIR Model for COVID-19 Infection

One of the most basic models in epidemiology, the SEIR (Susceptible-Exposed-Infectious-Recovered) model explains how viral infections spread through communities. This study analyzes the SEIR model's differential equations to learn more about its dynamic behaviors. This study discusses the 2019 corona virus disease (COVID-19) epidemic model using numerical methods of susceptible exposed infected recovered (SEIR). A numerical description of the notion is provided using two numerical approaches, including forward Euler and RK-4 approaches. The subplots of SEIR model are drawn by ode 45 commands. The stability of disease free equilibrium and Endemic equilibrium points are depicted. The significance of comprehending the interaction between epidemiological variables and population dynamics in developing efficient public health treatments is brought to light in this study, which investigates the consequences of these equations on the transmission and management of infectious diseases. Insights into the dynamics of illness transmission and potential measures for mitigating its effects are presented through the use of mathematical analysis and computational simulations. Jagannath University Journal of Science, Volume 11, Number 1, June 2024, pp. 172−185

A Comprehensive Review of Statistical Methods in Microbiology and epidemiology: A Data-Driven Approach

The rapid growth of computational power and big data analytics has transformed the landscape of statistical methods in microbiology and epidemiology. Advanced techniques such as machine learning, Bayesian inference, and network modeling are now being used alongside traditional statistical approaches. These methods offer unprecedented precision in identifying microbial patterns, tracking infectious diseases, and predicting outbreaks. This review highlights recent advances in statistical tools used in microbiology and epidemiology, particularly in analyzing antibiotic resistance, viral mutation rates, and epidemiological forecasting models for global pandemics such as COVID-19. The integration of novel statistical methodologies has improved our understanding of complex microbial interactions, resistance evolution, and the dynamics of infectious disease spread, enabling more informed public health interventions.

Insight into a new perspective on the complex propagation processes in networks: dynamic link equations

Insight into the spread of epidemics under different transmission mechanisms in networks has long been an important research question in the field of complex network dynamics. Currently, under simple transmission mechanisms, our analysis of the dynamic processes in networks starts only from the node level, considering the scale of infected nodes in the network. However, the information provided by this lowest-order approach to considering dynamic processes in networks is very limited. Most importantly, it is not applicable to the analysis of dynamic processes in networks under more common complex transmission mechanisms, as it neglects the interactions between nodes. Therefore, in this article, we propose a set of closed link dynamic equations to gain insight into complex propagation processes from a microscopic perspective. Fundamentally, we have developed a set of analytical tools for analyzing complex dynamic behaviors at the link level, enabling us to reexamine the complex dynamic processes on networks from a higher-order perspective. Additionally, we apply the proposed analytical framework to complex SIS epidemiological models on two real and synthetic networks, and extensive numerical simulation results demonstrate the feasibility and effectiveness of the proposed method.

Exponential series approximation of the SIR epidemiological model

IntroductionThe SIR (Susceptible-Infected-Recovered) model is one of the simplest and most widely used frameworks for understanding epidemic outbreaks.MethodsA second-order dynamical system for the R variable is formulated using an infinite exponential series expansion, and a recursion relation is established between the series coefficients. A numerical approximation scheme for the R variable is also developed.ResultsThe proposed numerical method is compared to a double exponential (DE) nonlinear approximate analytic solution, which reveals two coupled timescales: a relaxation timescale, determined by the ratio of the model’s time constants, and an excitation timescale, dictated by the population size. The DE solution is applied to estimate model parameters for a well-known epidemiological dataset—the boarding school flu outbreak.DiscussionFrom a theoretical standpoint, the primary contribution of this work is the derivation of an infinite exponential, Dirichlet, series for the model variables. Truncating the series yields a finite approximation, known as a Prony series, which can be interpreted as a sequence of coupled exponential relaxation processes, each with a distinct timescale. This apparent complexity can be approximated well by the DE solution, which appears to be of main practical interest.

Computational analysis of the Covid-19 model using the continuous Galerkin–Petrov scheme

Abstract Epidemiological models feature reliable and valuable insights into the prevention and transmission of life-threatening illnesses. In this study, a novel SIR mathematical model for COVID-19 is formulated and examined. The newly developed model has been thoroughly explored through theoretical analysis and computational methods, specifically the continuous Galerkin–Petrov (cGP) scheme. The next-generation matrix approach was used to calculate the reproduction number ( R 0 ) ({R}_{0}) . Both disease-free equilibrium (DFE) ( E 0 ) ({E}^{0}) and the endemic equilibrium ( E ⁎ ) ({E}^{\ast }) points are derived for the proposed model. The stability analysis of the equilibrium points reveals that ( E 0 ) ({E}^{0}) is locally asymptotically stable when R 0 < 1 {R}_{0}\lt 1 , while E ⁎ {E}^{\ast } is globally asymptotically stable when R 0 > 1 {R}_{0}\gt 1 . We have examined the model’s local stability (LS) and global stability (GS) for endemic equilibrium \text{ } and DFE based on the number ( R 0 ) ({R}_{0}) . To ascertain the dominance of the parameters, we examined the sensitivity of the number ( R 0 ) ({R}_{0}) to parameters and computed sensitivity indices. Additionally, using the fourth-order Runge–Kutta (RK4) and Runge–Kutta–Fehlberg (RK45) techniques implemented in MATLAB, we determined the numerical solutions. Furthermore, the model was solved using the continuous cGP time discretization technique. We implemented a variety of schemes like cGP(2), RK4, and RK45 for the COVID-19 model and presented the numerical and graphical solutions of the model. Furthermore, we compared the results obtained using the above-mentioned schemes and observed that all results overlap with each other. The significant properties of several physical parameters under consideration were discussed. In the end, the computational analysis shows a clear image of the rise and fall in the spread of this disease over time in a specific location.

The effect of visibility on forecast and inventory management performance during the COVID-19 pandemic

During the COVID-19 pandemic, healthcare facilities faced significant shortages of critical supplies like personal protective equipment, with dire repercussions. This study evaluates the potential role of decreased data visibility on these shortages, analysing different forecasting methods integrated with a periodic review inventory system (i.e. a base stock policy) on semi-simulated data encompassing several visibility issues. The forecasting methods chosen pertain to different data types: Holt and naïve methods are used as demand-based predictors, while a modified epidemiological model utilises pandemic data for demand forecasting. We scrutinise three prevalent data visibility issues through specifically crafted scenarios examining the effects of delayed, temporally aggregated, and erroneous data on system performance. Generally, our research illustrates that data visibility issues have a detrimental impact on the healthcare supply chain's efficacy. Interestingly, the system performance sees an uptick when these issues spur significantly oversized over-forecasts, e.g. when employing an epidemiological compartmental model for predictions.

Optimization of testing protocols to screen for COVID-19: a multi-objective model.

In this paper we develop a new multi-objective simulated annealing (MOSA) algorithm to generate optimal testing protocols for infectious diseases, using the COVID-19 pandemic as our context. A SEIR (susceptible-exposed-infected-recovered) epidemiological model is embedded as the computational platform for our MOSA algorithm to optimize testing protocols for screening across three joint objectives: minimum cost of test materials, minimum total infections over the testing horizon, and minimum number of false negatives over the horizon. We demonstrate the application of this optimization tool to recommend screening protocols for K-12 school districts in the U.S.State of North Carolina. Our approach is scalable by population coverage and can be employed at the level of individual school districts or regional collections of districts, individual schools or collections of schools across a district, business sites, or nursing homes, among other congregate settings where individuals may be screened prior to gaining entry to the site. The algorithm can be solved two ways, generating either independent optimal protocols across individual testing locations, or a common protocol covering all locations in the collection of testing sites. Our findings can be used to inform policy decisions to guide the development of effective testing strategies for controlling the spread of COVID-19 or other pandemic diseases in a wide range of congregate settings across various geographic regions.

Investigating Respiratory Disease Transmission Patterns Around the Figuil Cement Works

The objective of this research is to examine the dissemination of respiratory illnesses, exacerbated by the airborne pollutants emitted by the Figuil cement works, among the local population residing in the vicinity. The primary objective is to examine the impact of pollution, particularly the emission of fine particles and noxious gases, on the transmission of respiratory diseases such as asthma, chronic bronchitis and other lung disorders. The modified SEIR (Susceptible-Exposed-Infected-Recovered) epidemiological model is employed for the analysis of transmission dynamics within the community. This model incorporates environmental, health and demographic variables, thereby enabling the simulation of disease transmission as a function of varying pollution levels. Particular emphasis is placed on vulnerable groups, such as children and the elderly, due to immunosenescence, who are more likely to suffer from the adverse effects of pollution. The results will facilitate the formulation of efficacious strategies, including the implementation of awareness-raising campaigns and the introduction of sophisticated systems for the filtration and capture of pollutants at their source, such as fine particle filters or devices for the reduction of nitrogen oxides (NOx), with the objective of limiting the spread of respiratory diseases in at-risk areas and the formulation of suitable control measures.

Testing and results of an open-source radiation epidemiology model using the Goiânia accident

In the event of dispersed radioactive materials, whether from accidental orphan sources or deliberate use of radiological dispersal devices (RDD) or radiological exposure devices (RED), free open-source modelling codes can greatly assist in forecasting the dispersion of the radiation following the event. Several codes are currently available to quickly calculate the progression of radiological dispersion. However, most of these codes can only simulate the evolution of the threat for limited times after the event and over relatively short distances from the location. In order to predict the transport of radioactive material over long distances and for long times, and thus prevent its expected effects on the exposed population, specific epidemiological codes can be used, taking into account the characteristic of the radiation. If it is considered that radioactive material can be deposited on unsuspecting people who continue their daily activities after exposure, it can be assumed that these people unintentionally carry this radioactive material over long distances. This scenario is comparable to viral vectors of a hypothetical virus designed to mimic the physical characteristics of radiation. In this work, the free open-source spatio-temporal epidemiological modeller (STEM) tool is used to simulate the spread of a chimeric viral agent with specific characteristics of Ebola and COVID-19, designed to replicate the biological conditions caused by exposure to a Cs-137 source for an individual unaware of the risk. The goal is to predict the territorial spread of radioactive material caused by a CBRNe event, such as orphan sources or the use of a RDD or a RED, and its possible effects on the affected population. This supports decision-makers in forecasting the consequences of radioactive material spread and thus helps in reducing the risk. The code was tested comparing its results with the real case of the famous 1987 Goiânia radiological accident. The results show that the developed code was indeed able to accurately represent the number of contaminated individuals and the number of casualties within a month of the initial exposure, with a distribution of radioactive material in the territory similar to that actually caused by the Goiânia accident.

The Impact of Exposure Dosage and Host Genetics on the Shedding Kinetics of Flavobacterium psychrophilum in Rainbow Trout.

Flavobacterium psychrophilum, the causative agent of bacterial cold water disease (BCWD), is one of the leading pathogens in rainbow trout (Oncorhynchus mykiss) aquaculture. To date, there is little knowledge of the transmission kinetics of F. psychrophilum over the course of infection. In particular, how transmission is affected by host genotype and pathogen exposure dosage are not well studied. In order to fill in these knowledge gaps, we exposed two divergently selected lines of rainbow trout (ARS-Fp-R and ARS-Fp-S) to a range of dosages of F. psychrophilum (strain CSF117-10). We then measured mortality and bacterial shedding to estimate transmission risk at multiple time points since initial infection. As dosage increased, the number of fish shedding and the amount of bacteria shed increased ranging from 0% to 100% and 103 to 108 cells fish-1 h-1, respectively. In addition, we found that disease resistance (survival) was not correlated with transmission risk blocking, in that 67% of fish which shed bacteria experienced no clinical disease. In general, fish mortality began on Day 3, peaked between Days 5-7 and was higher in the ARS-Fp-R line. Results from this study could be used to develop epidemiological models and improve disease management, particularly in the context of aquaculture and selective breeding.

An immuno-epidemiological model with non-exponentially distributed disease stage on complex networks

Most of epidemic models assume that duration of the disease phase is distributed exponentially for the simplification of model formulation and analysis. Actually, the exponentially distributed assumption on the description of disease stages is hard to accurately approximate the interplay of drug concentration and viral load within host. In this article, we formulate an immuno-epidemiological epidemic model on complex networks, which is composed of ordinary differential equations and integral equations. The linkage of within- and between-host is connected by setting that the death caused by the disease is an increasing function in viral load within host. Mathematical analysis of the model includes the existence of the solution to the epidemiological model on complex networks, the existence and stability of equilibrium, which are completely determined by the basic reproduction number of the between-host system. Numerical analysis are shown that the non-exponentially distributions and the topology of networks have significant roles in the prediction of epidemic patterns.

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.