Epidemiological Model Research Articles

2024 Mpox outbreak: A rapidly evolving public health emergency of international concern

The declaration of a second Public Health Emergency of International Concern (PHEIC) for mpox in August 2024 underscores the urgent need for a comprehensive understanding of the evolving epidemiology [1] clinical manifestations, and zoonotic potential of this emerging threat [2]. This work delves into the intricate interplay between human and animal mpox infections, with a specific focus on the unique characteristics of various viral clades and their implications for individual and public health.There is a critical need to elucidate the factors driving multiple spillover events and the subsequent emergence of new clades better adapted to human-to-human transmission. We hypothesize that anthropogenic changes, including deforestation, urbanization, and climate change are facilitating increased human-to-animal contact, leading to more frequent zoonotic transmissions and viral adaptations. Our conceptual framework integrates One Health principles, evolutionary virology, and epidemiological modeling to investigate the demographic, clinical, and treatment differences among mpox clades in both humans and animals. We employ a mixed-methods approach, combining genomic analysis, clinical data review, and ecological surveys to construct a comprehensive picture of mpox's changing dynamics. The research questions explore the differences in epidemiological and clinical profiles among mpox clades and the factors that likely contribute to successful cross-species transmission and human adaptation.This manuscript introduces an updated Identify, Isolate, Inform (3I) Tool meticulously redesigned to significantly improve the early detection, containment, and reporting of mpox cases across diverse settings. By integrating clinical, virological, and ecological data, this work aims to lay the groundwork for enhanced risk assessment, targeted interventions, and global preparedness strategies in the face of this evolving zoonotic threat.

Hepatitis C Virus RNA and Genotype in Semen of Primitively Diagnosed Patients Infected with HCV-RNA Infection Amongst the Community of Lahore Cantt

Objective: To determine whether Hepatitis C virus is present in the seminal fluid of primitively diagnosed patients and subsequent genotyping of semen-positive samples. Study Design: Cross-sectional study. Place and Duration of Study: Darul Shifa Clinic and the PCR Lab, Lahore Pakistan, from Apr 2021 to May 2022. Methodology: We studied 100 patients with diagnosed Hepatitis C infection via Polymerase Chain Reaction. The reverse transcriptase Polymerase Chain Reaction was used to detect the presence of the Hepatitis C virus in the semen of these sample patients. Results: Mean age of enrolled 100 male patients was 33.86±7.9 years. Only 4% of semen samples tested HCV RNA positive, while 96% were negative. The results of multiplex Polymerase Chain Reaction revealed that 50% of cases showed genotype 2a and 50% cases showed genotype 3a out of 4 positive cases of HCV RNA. Conclusion: The observed viral prevalence was relatively low; nevertheless, the potential for pathogen dissemination remains significant. These findings contribute to our understanding of disease transmission dynamics and may have implications for epidemiological modeling and public health interventions.

Detection and Transmission Dynamics of Intestinal Schistosomiasis among Primary School Pupils in Tharaka Nithi County in the Mt. Kenya Highlands

Emerging infections cause considerable public health problems to humanity worldwide. The presence of Biomphalaria spp vector snails, the compatibility of the snails with schistosomes, and the prevalence of intestinal schistosomiasis infection in school-going children were determined. The study sought to determine the presence and transmission dynamics of schistosomiasis in Tharaka Nithi, an emerging schistosomiasis transmission focus in the Mt. Kenya highlands. The Epidemiological Triangle Model (ETM), which sheds light on the transmission of infectious diseases, served as the foundation for this study. A cross-sectional study design was used. The study targeted 178 students in grades 1–8 from the two primary schools in Tharaka Nithi located along the catchment area of Mukothima and Thanantu rivers. The number of study participants was determined using the Cochran formula. Study participants were selected randomly from the class register while ensuring equal numbers of boys and girls to avoid bias in results. The stool samples collected from the participants were processed using Kato Katz's quantitative screening technique for helminths and formal ether concentration technique for intestinal protozoa. Biomphalaria snails were collected and identified based on morphology. Snails were also bred and experimentally infected with a laboratory strain of Schistosoma mansoni and passed to the F5 generation to check the compatibility with the Schistosoma mansoni parasite. The overall mean snail parasite infection rate was 23.51% with a positivity of 19.38% in Mukothima River compared to 27.64% in Thanantu River. Although none of the field sampled snails were found infected with S. mansoni, pupils tested were found to be infected with the parasite with a positivity rate of 15.5%. Other parasites detected included Ascaris lumbricoides with a prevalence rate of 9.23% and Entamoeba histolytica with a prevalence of 8.48%. Results from the snail susceptibility experiment showed that the snails supported the development of S. mansoni to full patency, indicating the potential to support the schistosomiasis transmission cycle. Despite not finding any field-sampled snails infected with S. mansoni, the demonstrated compatibility of the snails with the parasite and the finding of infection in school children suggests that transmission is a possibility. Schistosomiasis prevention strategies including deworming programs, school and community environment sanitation, and latrines provision are highly recommended to prevent further spread of the disease.

Fractional-Order Modeling of COVID-19 Transmission Dynamics: A Study on Vaccine Immunization Failure

COVID-19 is an enveloped virus with a single-stranded RNA genome. The surface of the virus contains spike proteins, which enable the virus to attach to host cells and enter the interior of the cells. After entering the cell, the virus exploits the host cell’s mechanisms for replication and dissemination. Since the end of 2019, COVID-19 has spread rapidly around the world, leading to a large-scale epidemic. In response to the COVID-19 pandemic, the global scientific community quickly launched vaccine research and development. Vaccination is regarded as a crucial strategy for controlling viral transmission and mitigating severe cases. In this paper, we propose a novel mathematical model for COVID-19 infection incorporating vaccine-induced immunization failure. As a cornerstone of infectious disease prevention measures, vaccination stands as the most effective and efficient strategy for curtailing disease transmission. Nevertheless, even with vaccination, the occurrence of vaccine immunization failure is not uncommon. This necessitates a comprehensive understanding and consideration of vaccine effectiveness in epidemiological models and public health strategies. In this paper, the basic regeneration number is calculated by the next generation matrix method, and the local and global asymptotic stability of disease-free equilibrium point and endemic equilibrium point are proven by methods such as the Routh–Hurwitz criterion and Lyapunov functions. Additionally, we conduct fractional-order numerical simulations to verify that order 0.86 provides the best fit with COVID-19 data. This study sheds light on the roles of immunization failure and fractional-order control.

Mortality burden attributed to anthropogenic warming during Europe’s 2022 record-breaking summer

The record-breaking temperatures in Europe during the 2022 summer were associated with over 60,000 heat-related deaths. By combining epidemiological models with detection and attribution techniques, we attribute half of this mortality burden (~56% [95% CI 39–77%]) to anthropogenic warming. Likewise, this applies to all sexes, ages, and heat-related mortality burdens during previous years (2015–2021). Our results urgently call for increasing ambition in adaptation and mitigation.

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

Epidemiological model can forecast COVID-19 outbreaks from wastewater-based surveillance in rural communities

Wastewater has emerged as a crucial tool for infectious disease surveillance, offering a valuable means to bridge the equity gap between underserved communities and larger urban municipalities. However, using wastewater surveillance in a predictive manner remains a challenge. In this study, we tested if detecting SARS-CoV-2 in wastewater can forecast outbreaks in rural communities.Under the CDC National Wastewater Surveillance program, we monitored the SARS-CoV-2 in the wastewater of five rural communities and a small city in Idaho (USA). We then used a particle filter method coupled with a stochastic susceptible-exposed-infectious-recovered (SEIR) model to infer active case numbers using quantities of SARS-CoV-2 in wastewater.Our findings revealed that while high daily variations in wastewater viral load made real-time interpretation difficult, the SEIR model successfully factored out this noise, enabling accurate forecasts of the Omicron outbreak in five of the six towns shortly after initial increases in SARS-CoV-2 concentrations were detected in wastewater. The model predicted outbreaks with a lead time of 0 to 11 days (average of 6 days +/- 4) before the surge in reported clinical cases.This study not only underscores the viability of wastewater-based epidemiology (WBE) in rural communities—a demographic often overlooked in WBE research—but also demonstrates the potential of advanced epidemiological modeling to enhance the predictive power of wastewater data. Our work paves the way for more reliable and timely public health guidance, addressing a critical gap in the surveillance of infectious diseases in rural populations.

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.