Disease Dynamics Research Articles

Genetic differentiation of wild boar populations in a region affected by African swine fever

In the European Union, African swine fever (ASF) affects wild boar (Sus scrofa) populations in several Member States. Knowledge of population connectivity is important for the implementation of control measures, in particular the establishment of effective barriers. Population genetic comparisons of neighbouring populations can be very helpful in this respect. The present study investigated the genetic differentiation of wild boar in eastern Germany. This region has been affected by ASF since September 2020. A total of 1,262 wild boars from 31 hunting grounds (populations) in ASF-affected and ASF-free districts were sampled over a total area of almost 100,000 km². The study area encompassed a network of geographical factors that promote (roads, rivers, cities) or inhibit (natural areas, habitat corridors) genetic differentiation between wild boar populations. The genetic differentiation of the areas was based on 12 microsatellite markers. Three different Bayesian algorithms were used to analyse the data. The results were combined into a common approach with 9 clusters. Based on the cluster distribution in each population, the connectivity between the areas was quantified. The strongest differentiation was found along an imaginary line along the lower Elbe valley through Berlin and the A11 freeway to the Szczecin Lagoon. In contrast, the Mecklenburg Lake District and the south-east of the study area showed strong connectivity between areas. The special features of the landscapes along the lower Elbe valley, which was assessed as highly connective, and the high barrier effect of the A11 freeway in contrast to the other freeways in the study area show that barrier effects cannot be generalised in principle, but are actually determined by the circumstances of individual structures. The results of the connectivity analysis were compared with the distribution of viral lineages and variants. The genotypes of the wild boar populations and the ASFV lineages and variants showed a good explanatory approach for the observed disease dynamics in the study area. The newly gained knowledge on barriers and regionally different connectivity between wild boar populations can support considerations and measures for the containment of ASF in the affected areas by improving the understanding of wild boar dispersal dynamics.

Abnormal EEG microstates in Alzheimer's disease: predictors of β-amyloid deposition degree and disease classification.

Electroencephalography (EEG) microstates are used to study cognitive processes and brain disease-related changes. However, dysfunctional patterns of microstate dynamics in Alzheimer's disease (AD) remain uncertain. To investigate microstate changes in AD using EEG and assess their association with cognitive function and pathological changes in cerebrospinal fluid (CSF). We enrolled 56 patients with AD and 38 age- and sex-matched healthy controls (HC). All participants underwent various neuropsychological assessments and resting-state EEG recordings. Patients with AD also underwent CSF examinations to assess biomarkers related to the disease. Stepwise regression was used to analyze the relationship between changes in microstate patterns and CSF biomarkers. Receiver operating characteristics analysis was used to assess the potential of these microstate patterns as diagnostic predictors for AD. Compared with HC, patients with AD exhibited longer durations of microstates C and D, along with a decreased occurrence of microstate B. These microstate pattern changes were associated with Stroop Color Word Test and Activities of Daily Living scale scores (all P < 0.05). Mean duration, occurrences of microstate B, and mean occurrence were correlated with CSF Aβ 1-42 levels, while duration of microstate C was correlated with CSF Aβ 1-40 levels in AD (all P < 0.05). EEG microstates are used to predict AD classification with moderate accuracy. Changes in EEG microstate patterns in patients with AD correlate with cognition and disease severity, relate to Aβ deposition, and may be useful predictors for disease classification.

Prevalence and risk factors for Anaplasma marginale seropositivity in cattle in California

Bovine anaplasmosis, a tick-borne disease caused by Anaplasma marginale, imposes considerable economic bur­den on cattle industries throughout the world. Clinical signs of bovine anaplasmosis include fever, weight loss, lethargy, jaundice, abortion and death. The severity of the disease in­creases with age, and calves under 1 year of age generally do not progress to clinical disease or develop mild disease. Once infected, cattle remain persistently infected carriers for life. Climate change and drought conditions may affect the distri­bution and prevalence of tick populations and tick-borne dis­eases and re-evaluating the seroprevalence of Anaplasma would provide a better understanding of the disease dynamics in California. To our knowledge, there has been no updated study regarding the distribution of Anaplasma infection status in California since 2008, and the information on weather factors related to the disease is also limited. There is an urgent need to evaluate the prevalence of Anaplasma infection in different regions of California. The goal of this study is to estimate the seroprevalence of Anaplasma infection in California cattle over time, and the specific goal is to identify potential risk factors for infection including animal demographics, region, Anaplasma seroprevalence of wild ruminants, Dermacentor tick species presence, and weather effects.

Dynamics analysis and optimal control of delayed SEIR model in COVID-19 epidemic

The coronavirus disease 2019 (COVID-19) remains serious around the world and causes huge deaths and economic losses. Understanding the transmission dynamics of diseases and providing effective control strategies play important roles in the prevention of epidemic diseases. In this paper, to investigate the effect of delays on the transmission of COVID-19, we propose a delayed SEIR model to describe COVID-19 virus transmission, where two delays indicating the incubation and recovery periods are introduced. For this system, we prove its solutions are nonnegative and ultimately bounded with the nonnegative initial conditions. Furthermore, we calculate the disease-free and endemic equilibrium points and analyze the asymptotical stability and the existence of Hopf bifurcations at these equilibrium points. Then, by taking the weighted sum of the opposite number of recovered individuals at the terminal time, the number of exposed and infected individuals during the time horizon, and the system cost of control measures as the cost function, we present a delay optimal control problem, where two controls represent the social contact and the pharmaceutical intervention. Necessary optimality conditions of this optimal control problem are exploited to characterize the optimal control strategies. Finally, numerical simulations are performed to verify the theoretical analysis of the stability and Hopf bifurcations at the equilibrium points and to illustrate the effectiveness of the obtained optimal strategies in controlling the COVID-19 epidemic.

How do host population dynamics impact Lyme disease risk dynamics in theoretical models?

Lyme disease is the most common wildlife-to-human transmitted disease reported in North America. The study of this disease requires an understanding of the ecology of the complex communities of ticks and host species involved in harboring and transmitting this disease. Much of the ecology of this system is well understood, such as the life cycle of ticks, and how hosts are able to support tick populations and serve as disease reservoirs, but there is much to be explored about how the population dynamics of different host species and communities impact disease risk to humans. In this study, we construct a stage-structured, empirically-informed model with host dynamics to investigate how host population dynamics can affect disease risk to humans. The model describes a tick population and a simplified community of three host species, where primary nymph host populations are made to fluctuate on an annual basis, as commonly observed in host populations. We tested the model under different environmental conditions to examine the effect of environment on the interactions of host dynamics and disease risk. Results show that allowing for host dynamics in the model reduces mean nymphal infection prevalence and increases the maximum annual prevalence of nymphal infection and the density of infected nymphs. Effects of host dynamics on disease measures of nymphal infection prevalence were nonlinear and patterns in the effect of dynamics on amplitude in nymphal infection prevalence varied across environmental conditions. These results highlight the importance of further study of the effect of community dynamics on disease risk. This will involve the construction of further theoretical models and collection of robust field data to inform these models. With a more complete understanding of disease dynamics we can begin to better determine how to predict and manage disease risk using these models.

Micromobility systems and disease transmission: a spatially explicit agent-based approach

We propose a novel micro-level and spatially explicit agent-based modeling framework dubbed TranEpiSim to model the spread of infectious diseases through micromobility systems and a baseline population in an urban area. A case study is conducted on the Chicago City public bikesharing system to demonstrate the ability of the model to isolate the role of transportation as a vector and assess the efficacy of the model. Results show that the emergence of viral disease through micromobility transportation is possible, but the overall impact of the system on the disease dynamics in a worst-case scenario, especially with the current size of the system, is rather small. The proposed model offers a comprehensive approach to estimate the impact of micromobility (and more generally, specific transportation modes) on disease spread by considering infections that occur both at destinations and in/on transportation vehicles and infrastructure. The spatial pattern for the risk of exposure shows a higher risk in the central business district and north of it, where most of the shared bike transportation occurs. Because of its intrinsic features, the proposed framework is uniquely placed to assess the efficacy of interventions and make trade-offs between competing scenarios when dealing with epidemics.

Global stability analysis and modelling onchocerciasis transmission dynamics with control measures

ABSTRACT Background: Onchocerciasis infection is one of the neglected tropical diseases targeted for eradication by 2030. The disease is usually transmitted to humans through the bites of black flies. These black flies mostly breed near well-oxygenated fast-running water bodies. The disease is common in mostly remote agricultural villages near rivers and streams. Objective: In this study, a deterministic model describing the infection dynamics of human onchocerciasis disease with control measures is presented. Methods: We derived the model’s reproductive number and used a stability theorem of a Metzler matrix to show that disease-free equilibrium is both locally and globally asymptotically stable whenever the reproductive number is less than one. Parameter contribution was conducted using sensitivity analysis. The model endemic equation is shown to be a cubic polynomial in the presence of infected immigrants and a quadratic form in their absence. Results: When the inflow of infected immigrants is null, the model endemic equation may admit a unique equilibrium if the reproductive number is greater than one, or admits multiple endemic equilibria if the reproductive number is less than unity. We carried out a sensitivity analysis to identify the significant parameters that contribute to onchocerciasis spread. Conclusion: Onchocerciasis disease can be eradicated if the importation of infected immigrants is properly monitored. The integration of the One Health concept in the public health system is key in tackling the emergence and spread of diseases.

Modelling the impact of precaution on disease dynamics and its evolution.

In this paper, we introduce the notion of practically susceptible population, which is a fraction of the biologically susceptible population. Assuming that the fraction depends on the severity of the epidemic and the public's level of precaution (as a response of the public to the epidemic), we propose a general framework model with the response level evolving with the epidemic. We firstly verify the well-posedness and confirm the disease's eventual vanishing for the framework model under the assumption that the basic reproduction number . For , we study how the behavioural response evolves with epidemics and how such an evolution impacts the disease dynamics. More specifically, when the precaution level is taken to be the instantaneous best response function in literature, we show that the endemic dynamic is convergence to the endemic equilibrium; while when the precaution level is the delayed best response, the endemic dynamic can be either convergence to the endemic equilibrium, or convergence to a positive periodic solution. Our derivation offers a justification/explanation for the best response used in some literature. By replacing "adopting the best response" with "adapting toward the best response", we also explore the adaptive long-term dynamics.

Herbivores can benefit both plants and their pathogens through selective herbivory on diseased tissue

Abstract Infectious disease can shape community structure, particularly when pathogens affect foundation species. Seagrasses are foundation species that form meadows along coasts worldwide, controlling sediment deposition and biogeochemical cycling while supporting a diverse community of fish and invertebrates. These plants are hosts to wasting diseases that cause necrotic tissue lesions, which could alter seagrass value as food, habitat and mediators of ecosystem processes. However, such biotic interactions and influences on disease dynamics are still not well understood. We investigated whether a common herbivore affected the development of wasting disease on eelgrass. We measured the severity and prevalence of eelgrass wasting disease in a meadow across two summers through repeated field surveys. We assessed the role of the eelgrass herbivore on disease spread and growth using microcosm and mesocosm experiments. We further explored herbivore feeding preferences in a choice trial, which was paired with chemical analysis of plant tissue and analysed using a structural equation model. While the herbivore facilitates the growth of new disease lesions among isolated leaves, on balance they reduce lesion severity by more than 50% in comparison with no‐herbivore controls in field‐realistic settings. This was likely because the herbivore strongly prefers to eat diseased rather than healthy tissue, consuming nearly twice as much lesion area in choice trials. This preference arises from pathogen‐driven changes in the host plant; lesioned tissue requires less force to penetrate than non‐lesioned tissue. Additionally, as lesions increase in size, their polyphenolic concentrations drop, which further increases the magnitude of preference for lesioned tissue. Synthesis: These results suggest that these herbivores could help maintain disease in this system at a high prevalence (by facilitating disease development) but low severity (through preferential consumption), which is consistent with our field observations of nearly 100% prevalence and low severity in a natural bed where herbivore density is high. Describing such multi‐species interactions in marine systems will advance our predictions of future disease states, as current understandings focus primarily on how environmental change contributes to pathogen outbreaks.

Qualitative analysis of solutions for a degenerate partial differential equations model of epidemic spread dynamics

Compartmental models are widely used in mathematical epidemiology to describe the dynamics of infectious diseases or in mathematical models of population genetics. In this study, we study a time-dependent Susceptible-Infectious-Susceptible (SIS) Partial Differential Equation (PDE) model that is based on a diffusion-drift approximation of a probability density from a well-known discrete-time Markov chain model. This SIS-PDE model is conservative due to the degeneracy of the diffusion term at the origin. The main results of this article are the qualitative behavior of weak solutions, the dependence of the local asymptotic property of these solutions on initial data, and the existence of Dirac delta function type solutions. Moreover, we study the long-term behavior of solutions and confirm our analysis with numerical computations.

Abstract PO5-15-06: NVIGEN X® Comprehensive Liquid Biopsy for Sensitive ctDNA, Circulating Tumor Cells (CTC), and Protein Detection in Breast Cancer: Next-Generation Sequencing, Patient Case Studies, and Clinical Implications

Abstract Background Breast cancer (BC) represents the most prevalent cancer and is a leading cause of cancer related mortality among women worldwide. Despite notable therapeutic advancements, concern for metastatic recurrence remains a significant challenge. Liquid biopsy has emerged as a promising method for monitoring cancer treatment efficacy and disease progression. However, existing technologies predominantly focus on ctDNA, and their clinical utility is hindered by limited detection sensitivity and accuracy. To address these challenges, NVIGEN has developed NVIGEN X® comprehensive liquid biopsy, a novel minimally invasive approach that integrates ctDNA, CTC, and protein biomarkers. This comprehensive method could enable longitudinal monitoring of tumor dynamics, offering valuable insights into cancer biology, treatment responses, and emerging therapeutic targets. Here, we showcase compelling patient case studies, and the potential clinical implications of our technology for an exciting and evolving field in cancer diagnostics. Method Patients with advanced breast cancer were prospectively identified and consented. 10mL blood per timepoint was collected in Streck tubes. ctDNA, CTC and proteins were captured from a single tube of blood using NVIGEN magnetic nanoparticles according to established protocols. Following DNA extraction, ctDNA and CTC samples underwent NVIGEN’s highly sensitive sample workflow, optimized for NGS data accuracy and efficiency, employing hybridization capture with a 180 gene customizable cancer panel. Sequenced data were processed with NVIGEN’s proprietary high-sensitivity data analysis pipeline. Gene alterations identified from both ctDNA and CTC samples were analyzed and integrated to provide a comprehensive view of the tumor profile. Protein biomarkers of interest were detected using NVIGEN’s on-beads ELISA assay. Correlation analysis was performed to examine the relationship between the detected gene alterations, protein biomarkers, ctDNA/CTC data, and clinicopathologic features. Treatment response and identification of potential new treatment targets were evaluated based on the integrated analysis of ctDNA, CTC, protein biomarkers, and patient information. Result We collected 94 blood samples from 85 patients. Here we will present data from initial 36 patient samples. Of these 36 patients, 18 were tested with ctDNA/protein biomarkers (duo assays), and 18 were tested with ctDNA/CTC/protein biomarkers (trio assays). Comparison between duo and trio samples revealed that trio samples provided 2.5 times the number of detected variants compared to duo samples, with the median of variant numbers in duo assays being 52 (range 22-442), and in trio assays being 130 (range 73-703). Over 50% of the detected variants fell within the 0.1-0.5% variant allele frequency (VAF) range (number 2599) with total variant number being 5123. Several patient case studies were conducted using serial blood samples. These case studies revealed notable patterns of important gene alterations, such as ESR1, ERBB2 and PIK3CA, and demonstrated trends in the presence of these genes in ctDNA, CTC, or their coexistence. Protein biomarker information was correlated with ctDNA, CTC NGS data, and patient clinical status. Conclusions The analysis of the presented data from 36 patient samples with advanced breast cancer highlight the clinical relevance and potential significance of utilizing ctDNA, CTC and protein biomarkers in monitoring disease dynamics, treatment response, and guiding personalized treatment approaches. Our results suggest that individual protein biomarker analysis may be crucial in supporting personalized diagnostics and enabling more effective personalized medicine strategies. Citation Format: Aihua Fu, Minh Ton, Kevan Wang, Weiwei Gu, Henry Jin, Nuzhat Shaikh, Sasha Madan, Shreya Perepa, Tianhong Li, Minetta Liu, Heather Parsons, George Sledge, Fauzia Riaz. NVIGEN X® Comprehensive Liquid Biopsy for Sensitive ctDNA, Circulating Tumor Cells (CTC), and Protein Detection in Breast Cancer: Next-Generation Sequencing, Patient Case Studies, and Clinical Implications [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-15-06.

SARS-CoV-2 wastewater surveillance at two university campuses: lessons learned and insights on intervention strategies for public health guidance

ABSTRACT Wastewater surveillance has been a tool for public health officials throughout the COVID-19 pandemic. Universities established pandemic response committees to facilitate safe learning for students, faculty, and staff. These committees met to analyze both wastewater and clinical data to propose mitigation strategies to limit the spread of COVID-19. This paper reviews the initial efforts of utilizing campus data inclusive of wastewater surveillance for SARS-CoV-2 RNA concentrations, clinical case data from university response teams, and mitigation strategies from Grand Valley State University in West Michigan (population 21,648 students) and Oakland University in East Michigan (population 18,552 students) from November 2020 to April 2022. Wastewater positivity rates for both universities ranged from 32.8 to 46.8%. Peak viral signals for both universities directly corresponded to variant points of entry within the campus populations from 2021 to 2022. It was found that the organization of clinical case data and variability of wastewater testing data were large barriers for both universities to effectively understand disease dynamics within the university population. We review the initial efforts of onboarding wastewater surveillance and provide direction for structuring ongoing surveillance workflows and future epidemic response strategies based on those that led to reduced viral signals in campus wastewater.

A switching state-space transmission model for tracking epidemics and assessing interventions

The effective control of infectious diseases relies on accurate assessment of the impact of interventions, which is often hindered by the complex dynamics of the spread of disease. A Beta-Dirichlet switching state-space transmission model is proposed to track underlying dynamics of disease and evaluate the effectiveness of interventions simultaneously. As time evolves, the switching mechanism introduced in the susceptible-exposed-infected-recovered (SEIR) model is able to capture the timing and magnitude of changes in the transmission rate due to the effectiveness of control measures. The implementation of this model is based on a particle Markov Chain Monte Carlo algorithm, which can estimate the time evolution of SEIR states, switching states, and high-dimensional parameters efficiently. The efficacy of the proposed model and estimation procedure are demonstrated through simulation studies. With a real-world application to British Columbia's COVID-19 outbreak, the proposed switching state-space transmission model quantifies the reduction of transmission rate following interventions. The proposed model provides a promising tool to inform public health policies aimed at studying the underlying dynamics and evaluating the effectiveness of interventions during the spread of the disease.

Considerations and Opportunities for Probe Capture Enrichment Sequencing of Emerging Viruses from Wastewater.

Until recently, wastewater-based monitoring for pathogens of public health concern primarily used PCR-based quantification methods and targeted sequencing for specific pathogens (e.g., SARS-CoV-2). In the past three years, researchers have expanded sequencing to monitor a broad range of pathogens, applying probe capture enrichment to wastewater. The goals of those studies included (1) monitoring and expanding fundamental knowledge of disease dynamics for known pathogens and (2) evaluating the potential for early detection of emerging diseases resulting from zoonotic spillover or novel viral variants. Several studies using off-the-shelf probe panels designed for clinical and environmental surveillance reported that enrichment increased virus relative abundance but did not recover complete genomes for most nonenteric viruses. Based on our experience and recent results reported by others using these panels for wastewater, clinical, and synthetic samples, we discuss challenges and technical factors that affect the rates of false positive and false negative results. We identify trade-offs and opportunities throughout the workflow, including in wastewater sample processing, probe panel design, and bioinformatic analysis. We suggest tailored methods of virus concentration and background removal, carefully designed probe panels, and multithresholded bioinformatics analysis.

Trends in imported infections among migrants and travellers to Spain: a decade of analysis through the +Redivi network (2012-2022).

High-speed global travel, increased trade, world population growth, migration, urbanisation and climate change have favoured the emergence and spread of pathogens. We aimed to analyse the evolution of imported infections in Spain during 2012-2022 and the potential impact of some of the abovementioned factors on differential morbidity patterns. In this retrospective study (January/2012 to December/2022), we analysed data collected by the +Redivi network across 25 health centres. The network's standardised database records new cases of imported infections, including patient demographics, travel history, pre-travel advice and diagnostic information. To assess outcome rates over time and potential interactions, we constructed penalised weighted models to reduce the bias related to a low event rate and used weighted logistic regression for morbidity outcomes. We recorded 25 632 episodes, comprising 13 913 migrants, 4047 visiting friends and relatives (VFR) immigrants, 392 VFR travellers and 7280 travellers. Most immigrants came from South America (48.3%), Sub-Saharan Africa (28.5%), North Africa (6.6%), South Central Asia (5.4%) and Central America/Caribbean (5.3%). The most common regions visited by travellers were Sub-Saharan Africa (33.5%), South America (24.5%), Central America/Caribbean (13.5%), Southeast Asia (12%) and South Central Asia (10%). The proportion of diagnoses of malaria, strongyloidiasis and unspecified self-limiting febrile syndrome < 3weeks remained unchanged during the study period. An increased frequency of diagnosis was reported for schistosomiasis, blastocystosis, giardiasis, dengue, diarrhoea, new cases of HIV, latent and pulmonary tuberculosis; a decrease was reported for syphilis, chikungunya fever, Chagas disease and eosinophilia. We detected interactions between time and sex or type of participant across the different diagnoses. Our study underscores the importance of epidemiological data in understanding infectious diseases dynamics among travellers and migrants, emphasising how demographic shifts, migration trends and healthcare policies affect disease profiles. Comprehensive data play an essential role in enhancing public health policies and travel advice.

Modeling and analysis of a fractional order spatio-temporal SEIR model: Stability and prediction

This study introduces a novel fractional-order spatio-temporal SEIR model for epidemic modeling, providing an advanced approach to understanding disease dynamics. Our model, categorizing the population into Susceptible (S), Exposed (E), Infected (I), and Recovered (R), incorporates fractional calculus to accurately reflect the complex, non-linear nature of infectious diseases. Key findings include the confirmation of the existence and uniqueness of the model’s solutions, ensuring reliability for epidemiological predictions. Through rigorous stability analysis at both disease-free and endemic equilibrium points, we identified critical parameters influencing epidemic outcomes. Numerical simulations reveal that the fractional order significantly impacts disease progression, offering valuable insights for intervention strategies.

Natural Disasters’ Impact on Water Quality and Public Health: A Case Study of the Cyclonic Season (2019–2023)

The impact of climate change has made weather events more extreme, unpredictable and frequent. In the last 4 years, Mozambique has been devastated by 8 major cyclones, resulting in material and human damage and affecting the functioning of basic local services, such as in the water and health sectors. In this study, we explored the environmental components of the climate–water quality–disease relationship that could drive the dynamics of waterborne diseases. Statistical models and geospatial information technologies (GITs) were used to analyse water quality and the relationship with waterborne diseases between 2016 and 2023. Results indicate that water quality is the main element, between precipitation natural disasters and waterborne diseases, on which a relevant public health intervention can act to ameliorate the future negative impacts of climate change and disease incidence. The results of this study also showed that the quality of water affected mainly by heavy rain events varies in different regions and in different seasons.

Data analytics for predicting disease outbreaks: A review of models and tools

The burgeoning field of data analytics has emerged as a pivotal force in the realm of public health, particularly in the context of predicting and mitigating disease outbreaks. This comprehensive review delves into the diverse landscape of models and tools employed in data analytics for disease outbreak prediction. With a focus on synthesizing existing knowledge, the paper aims to provide a nuanced understanding of the strengths, limitations, and future directions within this dynamic field. The review begins with an exploration of various models utilized for disease outbreak prediction, ranging from statistical approaches to machine learning models and epidemiological frameworks. Each model category is scrutinized for its efficacy in capturing the complexities inherent in infectious disease dynamics. Simultaneously, the paper investigates the array of tools and technologies leveraged in disease outbreak prediction, encompassing Geographic Information Systems (GIS), data visualization tools, and big data analytics platforms. A critical aspect of the review lies in the examination of diverse data sources contributing to predictive analytics. Epidemiological data, environmental factors, and the burgeoning influence of social media and web data are dissected for their roles in enhancing the accuracy and timeliness of outbreak predictions. Amidst the promises of data analytics, the paper navigates the challenges inherent in predicting disease outbreaks. Issues of data quality and availability, model complexity, interpretability, and ethical considerations are dissected, providing a holistic view of the hurdles that practitioners encounter. Drawing upon case studies and real-world applications, the review showcases instances where data analytics has proven successful in predicting disease outbreaks, shedding light on both triumphs and setbacks. The implications for public health, lessons learned from challenges, and the evolving role of data analytics in shaping global health preparedness are thoroughly discussed. As the paper unfolds, it illuminates future trends and innovations in the field, foreseeing the integration of advanced technologies, global collaboration for information sharing, and the adaptation of predictive analytics for emerging diseases. The review culminates in a comprehensive conclusion, summarizing key findings and emphasizing the potential transformative impact of data analytics on the landscape of disease outbreak prediction.

A novel study of the impact of vaccination on pneumonia via fractional approach

Substantial advancements have been made in applying fractional calculus to understanding the epidemiology of viral diseases. However, it remains troubling that over 2,000,000 children, particularly those under the age of 5 and elderly individuals over 65, succumb to pneumonia annually in developing nations. This paper uses a deterministic SVEIR (susceptible, vaccinated, exposed, infected, and recovered) model to investigate pneumonia disease dynamics from a mathematical perspective. The governing model has been generalized from Caputo fractional derivatives, and the generalized Euler’s method has been used to calculate the approximate solution of the governing model. The dynamical systems theory examines the model’s stability analysis, basic reproduction number, and equilibrium points. The linearization method for spatial equilibrium and the Lyapunov functional method demonstrate that the model is locally asymptotic stable for disease-free equilibrium. Graphical results are showcased through MATLAB21 to illustrate the resolution of the problem. The research findings suggest that the disease will eventually disappear from the population if vaccination coverage rises over the necessary vaccination threshold. According to sensitivity analysis, the most sensitive factors are the transmission rate and the pace at which exposed individuals become contagious. According to the study, we must strengthen treatment effectiveness to prevent disease spreading.

Supply chain network optimization for emergency materials considering demand satisfaction

AbstractIn order to effectively guarantee the smooth supply of emergency materials under public health emergencies, first, we predict the number of daily infected people based on the modified SEIRD infectious disease dynamics model and accordingly construct the dynamic demand prediction model of emergency materials so as to catch the change rule of the demand for emergency materials with greater precision. Subsequently, under the premise of demand satisfaction, we optimize the supply chain network of emergency materials and embed two insurance strategies in the supply system in advance: multi‐source purchase and buffer production capacity, to enhance the stability of supply chain by sacrificing part of the cost. Next, a bi‐objective linear programming model based on minimizing system cost and maximizing demand satisfaction is proposed with the intention of realizing the design of emergency materials supply chain network, which balances the risk and cost. Finally, through the analysis of the example, we find that compared with the traditional supply chain network of emergency materials, the total satisfaction rate of the supply chain network optimized by balancing cost and risk is increased by 11.70%, while the cost is increased by only 2.04%, which fully verifies the validity of the optimized design of supply chain network of emergency materials.