Disease Dynamics Research Articles

Effect of EV71 Vaccination on Transmission Dynamics of Hand, Foot, and Mouth Disease and Its Epidemic Prevention Threshold

Objective: To investigate the effect of Enterovirus A71 (EV71) vaccination on the transmissibility of different enterovirus serotypes of hand, foot, and mouth disease (HFMD) in Zhejiang, China. Methods: Daily surveillance data of HFMD and EV71 vaccination from August 2016 to December 2019 were collected. Epidemic periods for each HFMD type were defined, and the time-varying effective reproduction number (Rt) was estimated, which could provide more direct evidence of disease epidemics than case number. General additive models (GAMs) were employed to analyze associations between EV71 vaccination quantity and rate and HFMD transmissibility. The epidemic prevention threshold, represented by required vaccination numbers and rates, was also estimated. Results: Vaccinating every 100,000 children ≤ 5 years could lead to a decrease in the Rt of EV71-associated HFMD by 14.44% (95%CI: 6.76%, 21.42%). Additionally, a positive correlation was observed between vaccinations among children ≤ 5 years old (per 100,000) and the increased transmissibility of other HFMD types (caused by enteroviruses other than EV71 and CA16) at 1.82% (95%CI: 0.80%, 2.84%). It was estimated that an additional 362,381 vaccinations, corresponding to increased vaccine coverage to 54.51% among children ≤ 5 years could effectively prevent EV71 epidemics in Zhejiang. Conclusions: Our findings highlight the importance of enhancing EV71 vaccine coverage for controlling the epidemic of EV71-HFMD and assisting government officials in developing strategies to prevent HFMD.

Time delayed fractional diabetes mellitus model and consistent numerical algorithm

The diabetes mellitus model (DMM) is explored in this study. Many health issues are caused by this disease. For this reason, the integer order DMM is converted into the time delayed fractional order model by fitting the fractional order Caputo differential operator and delay factor in the model. It is proved that the generalized model has the advantage of a unique solution for every time t. Moreover, every solution of the system is positive and bounded. Two equilibrium states of the fractional model are worked out i.e. disease free equilibrium state and the endemic equilibrium state. The risk factor indicator, R0 is computed for the system. The stability analysis is carried out for the underlying system at both the equilibrium states. The key role of R0 is investigated for the disease dynamics and stability of the system. The hybridized finite difference numerical method is formulated for obtaining the numerical solutions of the delayed fractional DMM. The physical features of the numerical method are examined. Simulated graphs are presented to assess the biological behavior of the numerical method. Lastly, the outcomes of the study are furnished in the conclusion section.

Mathematical Modeling and Analysis of Ebola Virus Disease Dynamics: Implications for Intervention Strategies and Healthcare Resource Optimization

Mathematical Modeling and Analysis of Ebola Virus Disease Dynamics: Implications for Intervention Strategies and Healthcare Resource Optimization

Modeling Ebola Dynamics with a Φ-Piecewise Hybrid Fractional Derivative Approach

Ebola virus disease (EVD) is a severe and often fatal illness posing significant public health challenges. This study investigates EVD transmission dynamics using a novel fractional mathematical model with five distinct compartments: individuals with low susceptibility (S1), individuals with high susceptibility (S2), infected individuals (I), exposed individuals (E), and recovered individuals (R). To capture the complex dynamics of EVD, we employ a Φ-piecewise hybrid fractional derivative approach. We investigate the crossover effect and its impact on disease dynamics by dividing the study interval into two subintervals and utilize the Φ-Caputo derivative in the first interval and the Φ-ABC derivative in the second interval. The study determines the basic reproduction number R0, analyzes the stability of the disease-free equilibrium and investigates the sensitivity of the parameters to understand how variations affect the system’s behavior and outcomes. Numerical simulations support the model and demonstrate consistent results with the theoretical analysis, highlighting the importance of fractional calculus in modeling infectious diseases. This research provides valuable information for developing effective control strategies to combat EVD.

Comprehending symmetry in epidemiology: A review of analytical methods and insights from models of COVID-19, Ebola, Dengue, and Monkeypox.

The challenge of developing comprehensive mathematical models for guiding public health initiatives in disease control is varied. Creating complex models is essential to understanding the mechanics of the spread of infectious diseases. We reviewed papers that synthesized various mathematical models and analytical methods applied in epidemiological studies with a focus on infectious diseases such as Severe Acute Respiratory Syndrome Coronavirus-2, Ebola, Dengue, and Monkeypox. We address past shortcomings, including difficulties in simulating population growth, treatment efficacy and data collection dependability. We recently came up with highly specific and cost-effective diagnostic techniques for early virus detection. This research includes stability analysis, geographical modeling, fractional calculus, new techniques, and validated solvers such as validating solver for parametric ordinary differential equation. The study examines the consequences of different models, equilibrium points, and stability through a thorough qualitative analysis, highlighting the reliability of fractional order derivatives in representing the dynamics of infectious diseases. Unlike standard integer-order approaches, fractional calculus captures the memory and hereditary aspects of disease processes, resulting in a more complex and realistic representation of disease dynamics. This study underlines the impact of public health measures and the critical importance of spatial modeling in detecting transmission zones and informing targeted interventions. The results highlight the need for ongoing financing for research, especially beyond the coronavirus, and address the difficulties in converting analytically complicated findings into practical public health recommendations. Overall, this review emphasizes that further research and innovation in these areas are crucial for addressing ongoing and future public health challenges.

Cycles of susceptibility: Immunity debt explains altered infectious disease dynamics post -pandemic.

The concept of immunity debt is a phenomenon resulting from the suppression of endemic pathogens during the COVID-19 pandemic due to non-pharmaceutical interventions (NPIs). The reduced circulation of various pathogens during the pandemic, particularly respiratory syncytial virus (RSV), altered typical infectious disease dynamics by reducing levels of population immunity usually acquired through exposure to infection. This concept is demonstrated through post-pandemic resurgence of diseases such as RSV and Group A Streptococcus, and highlights the interplay between reduced pathogen exposure and increased susceptibility in populations. The complexities and non-linear dynamics of seasonal transmission are observed in differences in pathogen resurgence across regions. These issues highlight the importance of comprehensive disease surveillance and public health strategies in mitigating these long-term epidemiological impacts.

Interplay between a polerovirus and a closterovirus decreases aphid transmission of the polerovirus.

Multi-infection of plants by viruses is very common and can change drastically infection parameters such as virus accumulation, distribution, and vector transmission. Sugar beet is an important crop that is frequently co-infected by the polerovirus beet chlorosis virus (BChV) and the closterovirus beet yellows virus (BYV), both vectored by the green peach aphid (Myzus persicae). These phloem-limited viruses are acquired while aphids ingest phloem sap from infected plants. Here we found that co-infection decreased transmission of BChV by ~50% but had no impact on BYV transmission. The drastic reduction of BChV transmission was due to neither lower accumulation of BChV in co-infected plants nor reduced phloem sap ingestion by aphids from these plants. Using the signal amplification by exchange reaction fluorescent in situ hybridization technique on plants, we observed that 40% of the infected phloem cells were co-infected and that co-infection caused redistribution of BYV in these cells. The BYV accumulation pattern changed from distinct intracellular spherical inclusions in mono-infected cells to a diffuse form in co-infected cells. There, BYV co-localized with BChV throughout the cytoplasm, indicative of virus-virus interactions. We propose that BYV-BChV interactions could restrict BChV access to the sieve tubes and reduce its accessibility for aphids and present a model of how co-infection could alter BChV intracellular movement and/or phloem loading and reduce BChV transmission.IMPORTANCEMixed viral infections in plants are understudied yet can have significant influences on disease dynamics and virus transmission. We investigated how co-infection with two unrelated viruses, BChV and BYV, affects aphid transmission of the viruses in sugar beet plants. We show that co-infection reduced BChV transmission by about 50% without affecting BYV transmission, despite similar virus accumulation rates in co-infected and mono-infected plants. Follow-up experiments examined the localization and intracellular distribution of the viruses, leading to the discovery that co-infection caused a redistribution of BYV in the phloem vessels and altered its repartition pattern within plant cells, suggesting virus-virus interactions. In conclusion, the interplay between BChV and BYV affects the transmission of BChV but not BYV, possibly through direct or indirect virus-virus interactions at the cellular level. Understanding these interactions could be crucial for managing virus propagation in crops and preventing yield losses.

Randomness suppress backward bifurcation in an epidemic model with limited medical resources

This paper delves into the dynamic features of a stochastic SIR epidemic model featuring a perturbed transmission rate influenced by white noise. Our primary aim is to unravel the intricate interplay between restricted medical resources, their supply efficiency, and environmental stochasticity, shedding light on their collective impact on the transmission dynamics of infectious diseases. Our findings bring to light a notable distinction from the deterministic counterpart of the model. Specifically, under varying scenarios of medical resource availability and supply efficiency, the stochastic model exhibits a departure from bifurcation phenomena. This stands in contrast to the deterministic model, which is characterized by the presence of both backward bifurcation and Hopf bifurcation phenomena. To complement and validate our theoretical findings, numerical simulations are employed, providing concrete illustrations of the dynamical phenomena discussed in the paper. This research contributes to a nuanced understanding of the intricate interplay between stochastic environmental factors, medical resource constraints, and disease transmission dynamics, offering valuable insights for public health management and epidemic control strategies.

Investigating oral microbiome dynamics in chronic kidney disease and post-transplantation in continuous culture.

This study investigates dynamic changes in the oral microbiome associated with changes in salivary urea concentration, an important factor in chronic kidney disease (CKD). The in vitro system modeled increased urea concentrations and subsequent reductions post-transplantation. The study provides insight into the oral microbial shifts during different simulated clinical phases. Understanding these dynamics is crucial for advancing our comprehension of CKD-associated oral microbiome variations and their potential impact on patient well-being and recovery.

Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics

Epidemiological parameters such as the reproduction number, latent period, and infectious period provide crucial information about the spread of infectious diseases and directly inform intervention strategies. These parameters have generally been estimated by mathematical models that involve an unrealistic assumption of history-independent dynamics for simplicity. This assumes that the chance of becoming infectious during the latent period or recovering during the infectious period remains constant, whereas in reality, these chances vary over time. Here, we find that conventional approaches with this assumption cause serious bias in epidemiological parameter estimation. To address this bias, we developed a Bayesian inference method by adopting more realistic history-dependent disease dynamics. Our method more accurately and precisely estimates the reproduction number than the conventional approaches solely from confirmed cases data, which are easy to obtain through testing. It also revealed how the infectious period distribution changed throughout the COVID-19 pandemic during 2020 in South Korea. We also provide a user-friendly package, IONISE, that automates this method.

How does anthropogenic food influence the trophic ecology of Rocky Mountain Red Fox?

Abstract Providing novel food subsidies to wildlife can influence behavior, resource use, disease dynamics, and food web interactions. Studies examining impacts of anthropogenic food have primarily occurred in urban landscapes where anthropogenic resources are consistently accessible. National parks, however, experience similar issues regarding wildlife exploiting anthropogenic food, but access to anthropogenic food is contingent on human visitation, which can fluctuate dramatically between seasons. We evaluated how fluctuating availability of anthropogenic food and demographic factors influenced the isotopic niche of Rocky Mountain Red Fox (Vulpes vulpes macroura) at the population and individual level in Grand Teton National Park, Wyoming. From 2016 to 2021, we assessed diet-related questions by collecting 55 whisker samples and 52 hair samples from 40 unique individuals and analyzed them using stable isotope analysis (δ13C and δ15N). We discovered that Rocky Mountain red foxes have a broad isotopic niche, with seasonal differences in their consumption of anthropogenic food. Contrary to our expectations, foxes demonstrated increased reliance on anthropogenic food (higher δ13C values) during winter months, whereas natural food consumption or dietary mixing was more prominent in summer months. Natural food scarcity could be driving foxes to seek out more anthropogenic food in the winter, whereas the increased abundance of natural food items in the summer could result in a lower need for foxes to supplement their diet with anthropogenic food. Adults had consistently higher δ13C values relative to juveniles, indicating either intraspecific variation in boldness or dominance leading to increased exploitation of anthropogenic food, which could be a learned behavior coinciding with habituation. Finally, we observed that increasing densities of human features within the home range of a fox was a strong predictor of anthropogenic food reliance at the individual level. Our work provided a deeper understanding of the trophic ecology of Rocky Mountain Red Fox in relation to human activity in a nature preserve, and equipped managers with insight into potential patterns associated with food-conditioned foxes.

Strategic nucleic acid detection approaches for diagnosing African swine fever (ASF): navigating disease dynamics.

African swine fever (ASF) is a devastating disease caused by African swine fever virus (ASFV) and leads to significant economic losses in the pig farming industry. Given the absence of an effective vaccine or treatment, the mortality rate of ASF is alarmingly close to 100%. Consequently, the ability to rapidly and accurately detect ASFV on site and promptly identify infected pigs is critical for controlling the spread of this pandemic. The dynamics of the ASF virus load and antibody response necessitate the adoption of various detection strategies at different stages of infection, a topic that has received limited attention to date. This review offers detailed guidance for choosing appropriate ASF diagnostic techniques tailored to the clinical manifestations observed from the acute to chronic phases, including asymptomatic cases. We comprehensively summarize and evaluate the latest advancements in ASFV detection methods, such as CRISPR-based diagnostics, biosensors, and microfluidics. Additionally, we address the challenges of false negatives or positives due to ASF variants or the use of injected live attenuated vaccines. This review provides an exhaustive list of diagnostic tests suitable for detecting each stage of symptoms and potential target genes for developing new detection methods. In conclusion, we highlight the current challenges and future directions in ASFV detection, underscoring the need for continued research and innovation in this field.

Optimal control strategies for infectious disease management: Integrating differential game theory with the SEIR model

The rapid spread of infectious diseases poses a critical threat to global public health. Traditional frameworks, such as the Susceptible–Exposed–Infectious–Recovered (SEIR) model, have been crucial in elucidating disease dynamics. Nonetheless, these models frequently overlook the strategic interactions between public health authorities and individuals. This research extends the classic SEIR model by incorporating differential game theory to analyze optimal control strategies. By modeling the conflicting objectives of public health authorities aiming to minimize infection rates and intervention costs, and individuals seeking to reduce their infection risk and inconvenience, we derive a Nash equilibrium that provides a balanced approach to disease management. Using Picard’s iterative method, we solve the extended model to determine dynamic, optimal control strategies, revealing oscillatory behavior in public health interventions and individual preventive measures. This comprehensive approach offers valuable insights into the dynamic interactions essential for effective infectious disease control.

Stochastic Optimal Control Analysis for HBV Epidemic Model with Vaccination

In this study, we explore the concept of symmetry as it applies to the dynamics of the Hepatitis B Virus (HBV) epidemic model. By incorporating symmetric principles in the stochastic model, we ensure that the control strategies derived are not only effective but also consistent across varying conditions, and ensure the reliability of our predictions. This paper presents a stochastic optimal control analysis of an HBV epidemic model, incorporating vaccination as a pivotal control measure. We formulate a stochastic model to capture the complex dynamics of HBV transmission and its progression to acute and chronic stages. By leveraging stochastic differential equations, we examine the model’s stationary distribution and asymptotic behavior, elucidating the impact of random perturbations on disease dynamics. Optimal control theory is employed to derive control strategies aimed at minimizing the disease burden and vaccination costs. Through rigorous numerical simulations using the fourth-order Runge–Kutta method, we demonstrate the efficacy of the proposed control measures. Our findings highlight the critical role of vaccination in controlling HBV spread and provide insights into the optimization of vaccination strategies under stochastic conditions. The symmetry within the proposed model equations allows for a balanced approach to analyzing both acute and chronic stages of HBV.

A 14-year longitudinal study of neurofilament light chain dynamics in premanifest and transitional Huntington's disease.

Growing evidence supports the value of neurofilament light (NfL) as a prognostic biomarker in premanifest Huntington's disease (HD). To date, however, there has been no longitudinal study exceeding 3years examining either its serial dynamics or predictive power in HD. We aimed to conduct the first such study. Serum NfL was sampled using ultrasensitive immunoassay at four timepoints across a 14-year period in a cohort of HD gene carriers (n = 21) and controls (n = 14). Gene carriers were premanifest at baseline. Clinical features of HD were evaluated by Unified Huntington's Disease Rating Scale (UHDRS TMS), Montreal Cognitive Assessment (MoCA), Trail A/B task, Symbol Digit Modalities Task and semantic/phonemic fluency tasks. 14/21 HD gene carriers converted to prodromal or manifest disease by the final timepoint ("converters"). At baseline and each subsequent timepoint, NfL levels were higher in converters than in non-converters and controls (p = < 0.001-0.03, ηp2 = 0.25-0.66). The estimated rate of change in NfL was higher in converters than in non-converters (p = 0.03) and controls (p = 0.001). Baseline NfL was able to discriminate converters from non-converters (area under curve = 1.000, p = 0.003). A higher rate of change in NfL was predictive of more severe motor (UHDRS-TMS p = 0.007, β = 0.711, R2 = 0.468) and cognitive deficits (MoCA p = 0.007, β = -0.798, R2 = 0.604; Trail B, p = 0.007, β = 0.772, R2 = 0.567; phonemic fluency p = 0.035, β = -0.632, R2 = 0.345). Our data suggest that (1) NfL longitudinal dynamics in premanifest/transitional HD are non-constant; rising faster in those closer to disease onset, and (2) NfL can identify individuals at risk of conversion to manifest disease and predict clinical trajectory, > 10years from disease onset.

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.

Exploring pathogen population density as a metric for understanding post-COVID infectious disease surges.

After the easing of COVID-19 restrictions, peaks of common infectious diseases surpassed pre-pandemic levels, raising questions about causes and ways to monitor these changes. A proposed measure, the Pathogen Population Density (PPD) score, could help track these shifts. PPD refers to the concentration of infectious agents within a population at a given time and location, serving as a potential indicator of infection levels in susceptible individuals at the population level. It is likely that PPD remains relatively stable within a specific community, as an equilibrium forms between infections and susceptibility. During the pandemic, nonpharmaceutical interventions (NPIs) led to a reduction in infectious diseases, possibly lowering population immunity and decreasing the PPD score. Once NPIs were lifted, the PPD score likely increased sharply due to a larger pool of susceptible individuals, causing more primary infections and stronger recurrent infections, faster transmission, and more severe pathogenic outcomes at the individual level. Monitoring the PPD score over time could help predict when infection peaks will occur. PPD is influenced by factors such as public health strategies, vaccination programs, and the behavior of high-risk individuals. As a quantitative measure, PPD has the potential to serve as a valuable predictive and monitoring tool, helping public health officials anticipate and track changes in infectious disease dynamics. It could be an effective tool for managing future outbreaks or pandemics and serve as a communication tool between scientists and the public to understand the emergence of new disease peaks.

Estimating the impact of sarcoptic mange epidemic on the population size of wild raccoon dogs (Nyctereutes procyonoides) from wildlife rescue data

The impact of infectious diseases on host populations is often not quantified because it is difficult to observe the host population and infectious disease dynamics. To address this problem, we developed a state-space model to simultaneously estimate host population and disease dynamics using wildlife rescue data. Using this model, we aimed to quantify the impact of sarcoptic mange on a Japanese raccoon dog population by estimating the change in their relative population size. We classified the status of rescued raccoon dogs into four categories: i) rescued due to infection with mange, ii) rescued due to traffic accidents without mange, iii) rescued due to traffic accidents with mange, and iv) rescued due to causes other than traffic accidents or mange. We modelled the observation process for each category and fitted the model to the reported number of raccoon dogs rescued between 1990 and 2010 at three wildlife rescue facilities in Kanagawa Prefecture, Japan. The mortality rate induced by mange was estimated to be 1.09 (95% credible interval (CI): 0.47–1.72) per year. The estimated prevalence of sarcoptic mange ranged between 4–80% in the study period. When a substantial prevalence of mange was observed (1995 to 2002), the host population size decreased by 91.2% (95% credible intervals: 86.3–94.7). We show that the impact of infectious disease outbreak on the wildlife population can be estimated from the time-series data of wildlife rescue events due to multiple causes. Our estimates suggest that sarcoptic mange triggered a substantial decrease in the Japanese wild raccoon dog populations.

Dynamics of minimal residual disease and its clinical implications in multiple myeloma: A retrospective real-life analysis

BackgroundMinimal residual disease (MRD) testing is a promising approach to tailor the treatment of multiple myeloma (MM). However, several major concerns remain to be addressed before moving it into daily practice, most of which stem from the dynamic nature of the MRD status. Thus, it is crucial to understand the MRD dynamics and propose its clinical implications. MethodsWe retrospectively analysed the data of patients with newly diagnosed MM (NDMM) who had flow cytometry-based MRD tests at multiple time points after initiation of therapy. The impact of undetectable MRD (including attainment, duration and loss) on clinical outcomes was analysed. ResultsIn a cohort of 220 patients with NDMM, attainment of MRD− offered favourable outcomes (P < 0.0001 for both progression-free survival (PFS) and overall survival (OS)), regardless of baseline risk factors. Notably, MRD− duration ≥12 months was associated with an 83 % (95 % confidence interval (CI), 0.09−0.34; P < 0.0001) or 69 % (95 % CI, 0.13−0.76; P = 0.0098) reduction in risk of progression/death or death, while the longer MRD− was sustained, the better the outcome was. Loss of MRD− led to poor PFS (hazard ratio (HR) 0.01, 95 % CI 0–0.06, P < 0.0001) and OS (HR 0.03, 95 % CI 0–0.24, P = 0.0008). Most patients (70 %) who lost MRD− status carried high-risk cytogenetic abnormalities (HRCAs). While MRD− was temporally inconsistent with conventional therapeutic responses (eg ≥ complete remission or very good partial response), it predicted disease progression or recurrence more robustly than the latter. Last, the predictive value of the MRD status was independent of baseline risk factors (eg high-risk cytogenetic abnormality, International Staging System (ISS) or Revised (R-)ISS staging). ConclusionsLongitudinal assessment of MRD during the treatment course and follow-up is required for monitoring disease progression or relapse, to guide treatment decisions. Accordingly, a prospective study is currently ongoing to investigate the feasibility and benefit of the MRD-tailored therapy according to the longitudinal changes of the MRD status.

Predictive modelling of the effectiveness of vaccines against COVID-19 in Bogotá: Methodological innovation involving different variants and computational optimisation efficiency

The uncertainty associated with the future of viruses such as SARS-CoV-2 poses a challenge to public health officials because of its implications for welfare, economics and population health. In this document, we develop an age-stratified epidemiological-mathematical model to predict various health outcomes, considering the effectiveness of COVID-19 vaccines. The analytical model proposed and developed for this research is based on the approach constructed by the COVID-19 International Modelling Consortium. Following this approach, this paper innovates at the frontier of knowledge by including the various variants of SARS-CoV-2 in the Consortium model. Furthermore, for the first time in international literature, a complete compilation of the formal mathematical development of this entire quantitative model is presented. Our model accurately fits the observed historical data of new infections, cumulative mortality, symptomatic infections, hospitalisations, and Intensive Care Units admissions, capturing the waves of contagion that have occurred in Bogotá, Colombia. In turn, the prognosis obtained indicates a considerable decrease in the incidence and lethality caused by SARS-CoV-2 under current conditions, thus evidencing the effectiveness of vaccines against infection, hospitalisation, and death. This model enables the evaluation of different scenarios in response to changes in the dynamics of this infectious disease, providing information to policymakers for real-world evidence-based decision-making.