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Journal articles on the topic 'Epidemiological dynamics'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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Pinto, Alberto, Maíra Aguiar, José Martins, and Nico Stollenwerk. "Dynamics of Epidemiological Models." Acta Biotheoretica 58, no. 4 (2010): 381–89. http://dx.doi.org/10.1007/s10441-010-9116-7.

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Popa, Alexandra, Jakob-Wendelin Genger, Michael D. Nicholson, et al. "Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2." Science Translational Medicine 12, no. 573 (2020): eabe2555. http://dx.doi.org/10.1126/scitranslmed.abe2555.

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Superspreading events shaped the coronavirus disease 2019 (COVID-19) pandemic, and their rapid identification and containment are essential for disease control. Here, we provide a national-scale analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) superspreading during the first wave of infections in Austria, a country that played a major role in initial virus transmissions in Europe. Capitalizing on Austria’s well-developed epidemiological surveillance system, we identified major SARS-CoV-2 clusters during the first wave of infections and performed deep whole-genome sequencing of more than 500 virus samples. Phylogenetic-epidemiological analysis enabled the reconstruction of superspreading events and charts a map of tourism-related viral spread originating from Austria in spring 2020. Moreover, we exploited epidemiologically well-defined clusters to quantify SARS-CoV-2 mutational dynamics, including the observation of low-frequency mutations that progressed to fixation within the infection chain. Time-resolved virus sequencing unveiled viral mutation dynamics within individuals with COVID-19, and epidemiologically validated infector-infectee pairs enabled us to determine an average transmission bottleneck size of 103 SARS-CoV-2 particles. In conclusion, this study illustrates the power of combining epidemiological analysis with deep viral genome sequencing to unravel the spread of SARS-CoV-2 and to gain fundamental insights into mutational dynamics and transmission properties.

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Otoo, Henry, Lewis Brew, and Benjamin Dadzie-Mensah. "Epidemiological Modelling of Yellow Fever Dynamics." Asian Research Journal of Mathematics 20, no. 8 (2024): 119–41. http://dx.doi.org/10.9734/arjom/2024/v20i8821.

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Aims: Yellow fever is a severe and often fatal viral illness caused by the yellow fever virus Despite being largely overlooked, yellow fever continues to silently claim lives in many parts of the world. The study focuses on the epidemiological modelling of yellow fever dynamics between a host (human) and vector (mosquito) populations The human population was divided into five main compartments: Susceptible, Exposed, Infected, Isolated, and Recovered. The vector population was also divided into two compartments: Susceptible and Infected. Nonlinear differential equations describing these compartments were formulated. Stability analysis and numerical simulations were then performed based on the formulated equations. From the stability analysis, it was observed that the disease-free equilibrium is both locally and globally asymptotically stable. Similarly, the endemic equilibrium was found to be locally and globally asymptotically stable. The simulation also revealed a direct correlation between the transmission rate and disease spread.

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Booton, Ross D., Yoh Iwasa, and Dylan Z. Childs. "How do toxicants affect epidemiological dynamics?" Oikos 128, no. 5 (2018): 729–40. http://dx.doi.org/10.1111/oik.05654.

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Rumyantseva, M. A., and N. V. Isaeva. "Modern epidemiological characteristic of gonococcal infection incidence manifestations." Perm Medical Journal 36, no. 4 (2019): 74–81. http://dx.doi.org/10.17816/pmj36474-81.

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Aim. To carry out the epidemiological analysis and assessment of manifestations of gonococcal infection incidence on the model of the city of Perm. Materials and methods. Retrospective analysis of gonococcal infection morbidity indices was implemented on the basis of the data of official statistics of Federal Budgetary Healthcare Institution Center of Hygiene and Epidemiology of Perm Krai for the years 19902016 (form 2, form 12), Territorial Board of Federal Service of State Statistics of Perm Krai (Permstat) and State Budgetary Institution of Healthcare of Perm Krai Regional Clinical Dermatovenerological Dispensary for the years 20122016. Epidemiological method includes evaluative-descriptive epidemiological methods: analysis and assessment of multiyear dynamics of gonorrhea incidence for the years 1990 to 2016, gender, age, urban structure of gonorrhea patients. Results. Analysis and assessment of gonococcal infection (GI) incidence manifestations in Perm and Perm Krai for the years 1990 to 2016 according to the data of official statistics permitted to detect the epidemiological characteristic features of this infection at the present stage. Among them are the following: in a multiyear dynamics a marked tendency to decrease, absence of regular cyclic fluctuations; in an annual dynamics a year-round observed epidemic level of morbidity irrespective of the periods of high and low levels, rises and falls, involvement into epidemic process as risk groups: adults aged 2130 and adolescents aged 1517, mainly males. Conclusions. The absence of regularly repeated fluctuations in a multiyear dynamics of morbidity shows no influence of periodically available causative agents and impossibility of epidemiologic control of GI process on gonorrhea incidence at the modern stage.

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Erten, E., Joseph Lizier, Mahendra Piraveenan, and Mikhail Prokopenko. "Criticality and Information Dynamics in Epidemiological Models." Entropy 19, no. 5 (2017): 194. http://dx.doi.org/10.3390/e19050194.

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Boles, Annette, Ramesh Kandimalla, and P. Hemachandra Reddy. "Dynamics of diabetes and obesity: Epidemiological perspective." Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease 1863, no. 5 (2017): 1026–36. http://dx.doi.org/10.1016/j.bbadis.2017.01.016.

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Bate, Andrew M., and Frank M. Hilker. "Complex Dynamics in an Eco-epidemiological Model." Bulletin of Mathematical Biology 75, no. 11 (2013): 2059–78. http://dx.doi.org/10.1007/s11538-013-9880-z.

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Kang, G., L. Gunaseelan, and K. Abbas. "Epidemiological dynamics of bovine brucellosis in India." Annals of Global Health 81, no. 1 (2015): 127. http://dx.doi.org/10.1016/j.aogh.2015.02.793.

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Xia, Bjørnstad, and Grenfell. "Measles Metapopulation Dynamics: A Gravity Model for Epidemiological Coupling and Dynamics." American Naturalist 164, no. 2 (2004): 267. http://dx.doi.org/10.2307/3473444.

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Xia, Yingcun, Ottar N. Bjørnstad, and Bryan T. Grenfell. "Measles Metapopulation Dynamics: A Gravity Model for Epidemiological Coupling and Dynamics." American Naturalist 164, no. 2 (2004): 267–81. http://dx.doi.org/10.1086/422341.

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Castillo-Chavez, Carlos, Derdei Bichara, and Benjamin R. Morin. "Perspectives on the role of mobility, behavior, and time scales in the spread of diseases." Proceedings of the National Academy of Sciences 113, no. 51 (2016): 14582–88. http://dx.doi.org/10.1073/pnas.1604994113.

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The dynamics, control, and evolution of communicable and vector-borne diseases are intimately connected to the joint dynamics of epidemiological, behavioral, and mobility processes that operate across multiple spatial, temporal, and organizational scales. The identification of a theoretical explanatory framework that accounts for the pattern regularity exhibited by a large number of host–parasite systems, including those sustained by host–vector epidemiological dynamics, is but one of the challenges facing the coevolving fields of computational, evolutionary, and theoretical epidemiology. Host–parasite epidemiological patterns, including epidemic outbreaks and endemic recurrent dynamics, are characteristic to well-identified regions of the world; the result of processes and constraints such as strain competition, host and vector mobility, and population structure operating over multiple scales in response to recurrent disturbances (like El Niño) and climatological and environmental perturbations over thousands of years. It is therefore important to identify and quantify the processes responsible for observed epidemiological macroscopic patterns: the result of individual interactions in changing social and ecological landscapes. In this perspective, we touch on some of the issues calling for the identification of an encompassing theoretical explanatory framework by identifying some of the limitations of existing theory, in the context of particular epidemiological systems. Fostering the reenergizing of research that aims at disentangling the role of epidemiological and socioeconomic forces on disease dynamics, better understood as complex adaptive systems, is a key aim of this perspective.

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Grenfell, B. T. "Unifying the Epidemiological and Evolutionary Dynamics of Pathogens." Science 303, no. 5656 (2004): 327–32. http://dx.doi.org/10.1126/science.1090727.

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Kapoor, Suraj. "Monkey Pox 2022: evolving epidemiological dynamics of disease." D Y Patil Journal of Health Sciences 10, no. 3 (2022): 150. http://dx.doi.org/10.4103/dypj.dypj_53_22.

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Pomeroy, Laura W., Ottar N. Bjørnstad, and Edward C. Holmes. "The Evolutionary and Epidemiological Dynamics of the Paramyxoviridae." Journal of Molecular Evolution 66, no. 2 (2008): 98–106. http://dx.doi.org/10.1007/s00239-007-9040-x.

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Gandon, Sylvain, Troy Day, C. Jessica E. Metcalf, and Bryan T. Grenfell. "Forecasting Epidemiological and Evolutionary Dynamics of Infectious Diseases." Trends in Ecology & Evolution 31, no. 10 (2016): 776–88. http://dx.doi.org/10.1016/j.tree.2016.07.010.

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Magpantay, F. M. G., A. A. King, and P. Rohani. "Age-structure and transient dynamics in epidemiological systems." Journal of The Royal Society Interface 16, no. 156 (2019): 20190151. http://dx.doi.org/10.1098/rsif.2019.0151.

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Mathematical models of childhood diseases date back to the early twentieth century. In several cases, models that make the simplifying assumption of homogeneous time-dependent transmission rates give good agreement with data in the absence of secular trends in population demography or transmission. The prime example is afforded by the dynamics of measles in industrialized countries in the pre-vaccine era. Accurate description of the transient dynamics following the introduction of routine vaccination has proved more challenging, however. This is true even in the case of measles which has a well-understood natural history and an effective vaccine that confers long-lasting protection against infection. Here, to shed light on the causes of this problem, we demonstrate that, while the dynamics of homogeneous and age-structured models can be qualitatively similar in the absence of vaccination, they diverge subsequent to vaccine roll-out. In particular, we show that immunization induces changes in transmission rates, which in turn reshapes the age distribution of infection prevalence, which effectively modulates the amplitude of seasonality in such systems. To examine this phenomenon empirically, we fit transmission models to measles notification data from London that span the introduction of the vaccine. We find that a simple age-structured model provides a much better fit to the data than does a homogeneous model, especially in the transition period from the pre-vaccine to the vaccine era. Thus, we propose that age structure and heterogeneities in contact rates are critical features needed to accurately capture transient dynamics in the presence of secular trends.

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Nugraheni, Kartika, Trisilowati Trisilowati, and Agus Suryanto. "Dynamics of a Fractional Order Eco-Epidemiological Model." Journal of Tropical Life Science 7, no. 3 (2017): 243–50. http://dx.doi.org/10.11594/jtls.07.03.09.

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Pezzoni, Giulia, Arianna Bregoli, Chiara Chiapponi, Santina Grazioli, Antonello Di Nardo, and Emiliana Brocchi. "Retrospective Characterization of the 2006–2007 Swine Vesicular Disease Epidemic in Northern Italy by Whole Genome Sequence Analysis." Viruses 13, no. 7 (2021): 1186. http://dx.doi.org/10.3390/v13071186.

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Advances in the epidemiological tracing of pathogen transmission have been largely driven by the increasing characterisation of whole-genome sequence data obtained at a finer resolution from infectious disease outbreaks. Dynamic models that integrate genomic and epidemiological data further enhance inference on the evolutionary history and transmission dynamics of epidemic outbreaks by reconstructing the network of ‘who-infected-whom’. Swine Vesicular Disease (SVD) was present in Italy from 1966 until 2015, and since the mid-1990s, it has mainly been circulating within Italy’s central-southern regions with sporadic incursions to the north of the country. However, a recrudescence of SVD in northern Italy was recorded between November 2006 and October 2007, leading to a large-scale epidemic that significantly affected the intensive pig industry of the Lombardy region. In this study, by using whole-genome sequence data in combination with epidemiological information on disease occurrences, we report a retrospective epidemiological investigation of the 2006–2007 SVD epidemic, providing new insights into the transmission dynamics and evolutionary mode of the two phases that characterised the epidemic event. Our analyses support evidence of undetected premises likely missed in the chain of observed infections, of which the role as the link between the two phases is reinforced by the tempo of SVD virus evolution. These silent transmissions, likely resulting from the gradual loss of a clear SVD clinical manifestation linked to sub-clinical infections, may pose a risk of failure in the early detection of new cases. This study emphasises the power of joint inference schemes based on genomic and epidemiological data integration to inform the transmission dynamics of disease epidemics, ultimately aimed at better disease control.

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Puzyryova, L. V., A. V. Mordyk, O. G. Ivanova, S. N. Rudneva, and M. P. Tatarintseva. "Dynamics of Main Indicators of the Epidemiological Situation on Tuberculosis in the Omsk Region." Epidemiology and Vaccine Prevention 16, no. 4 (2017): 87–92. http://dx.doi.org/10.31631/2073-3046-2017-16-4-87-92.

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The aim of the study is to analyze the dynamics of the main epidemiological indicators that characterize the epidemiological situation of tuberculosis in the Omsk Region from 2001 to 2015. Material and methods. For the analysis were used for reporting forms: № 8 («Information on active tuberculosis», № 33 («Information on tuberculosis patients»), № 61 («Information on contingents of patients with HIV infection»). The statistical processing was carried out with the help of the Microsoft Excel software package, applied the method of analyzing the dynamic series with the calculation of the rates of growth/decrease in epidemiological indicators and the determination of the average geometric index for aligned series, regression analysis. Results. It has been established that in 2009 in the Omsk Region, the main indicators that characterize the epidemiological situation of tuberculosis have decreased: morbidity (by 25.4%), prevalence (by 55.5%), mortality (by 53.1%). At the same time, there was an increase in the incidence of multidrug-resistant disease, an increase in the incidence of HIV infection and HIV-associated tuberculosis. Conclusions. The obtained results allow to assume the possibility of developing a new period of worsening of the epidemiological situation of tuberculosis, characterized by high mortality and a further growth of the reservoir of infection in the region.

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Mustafa, Arkan N. "Dynamics of an Eco-Epidemiological Model Consisting of Herding Prey and Harvested Predator." Journal of Zankoy Sulaimani - Part A 21, no. 2 (2019): 45–56. http://dx.doi.org/10.17656/jzs.10756.

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Yamaguchi, Takayuki, and Hiroshi Nishiura. "Predicting the Epidemiological Dynamics of Lung Cancer in Japan." Journal of Clinical Medicine 8, no. 3 (2019): 326. http://dx.doi.org/10.3390/jcm8030326.

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While the prevalence of smoking has steadily declined over time, the absolute numbers of lung cancer cases and deaths have continued to increase in Japan. We employed a simple mathematical model that describes the relationship between demographic dynamics and smoking prevalence to predict future epidemiological trends of lung cancer by age and sex. Never-smokers, smokers, and ex-smokers were assumed to experience different hazard of lung cancer, and the model was parameterized using data from 2014 and before, as learning data, and a future forecast was obtained from 2015 onwards. The maximum numbers of lung cancer cases and deaths in men will be 76,978 (95% confidence interval (CI): 76,630–77,253) and 63,284 (95% CI: 62,991–63507) in 2024, while those in women will be 42,838 (95% CI: 42,601–43,095) and 32,267 (95% CI: 32,063–32,460) in 2035 and 2036, respectively. Afterwards, the absolute numbers of cases and deaths are predicted to decrease monotonically. Our compartmental modeling approach is well suited to predicting lung cancer in Japan with dynamic ageing and drastic decline in smoking prevalence. The predicted burden is useful for anticipating demands for diagnosis, treatment, and care in the healthcare sector.

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Yang, Wan, Marc Lipsitch, and Jeffrey Shaman. "Inference of seasonal and pandemic influenza transmission dynamics." Proceedings of the National Academy of Sciences 112, no. 9 (2015): 2723–28. http://dx.doi.org/10.1073/pnas.1415012112.

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The inference of key infectious disease epidemiological parameters is critical for characterizing disease spread and devising prevention and containment measures. The recent emergence of surveillance records mined from big data such as health-related online queries and social media, as well as model inference methods, permits the development of new methodologies for more comprehensive estimation of these parameters. We use such data in conjunction with Bayesian inference methods to study the transmission dynamics of influenza. We simultaneously estimate key epidemiological parameters, including population susceptibility, the basic reproductive number, attack rate, and infectious period, for 115 cities during the 2003–2004 through 2012–2013 seasons, including the 2009 pandemic. These estimates discriminate key differences in the epidemiological characteristics of these outbreaks across 10 y, as well as spatial variations of influenza transmission dynamics among subpopulations in the United States. In addition, the inference methods appear to compensate for observational biases and underreporting inherent in the surveillance data.

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GULBUDAK, HAYRIYE. "AN IMMUNO-EPIDEMIOLOGICAL VECTOR–HOST MODEL WITH WITHIN-VECTOR VIRAL KINETICS." Journal of Biological Systems 28, no. 02 (2020): 233–75. http://dx.doi.org/10.1142/s0218339020400021.

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A current challenge for disease modeling and public health is understanding pathogen dynamics across scales since their ecology and evolution ultimately operate on several coupled scales. This is particularly true for vector-borne diseases, where within-vector, within-host, and between vector–host populations all play crucial roles in diversity and distribution of the pathogen. Despite recent modeling efforts to determine the effect of within-host virus-immune response dynamics on between-host transmission, the role of within-vector viral dynamics on disease spread is overlooked. Here, we formulate an age-since-infection-structured epidemic model coupled to nonlinear ordinary differential equations describing within-host immune-virus dynamics and within-vector viral kinetics, with feedbacks across these scales. We first define the within-host viral-immune response and within-vector viral kinetics-dependent basic reproduction number [Formula: see text] Then we prove that whenever [Formula: see text] the disease-free equilibrium is locally asymptotically stable, and under certain biologically interpretable conditions, globally asymptotically stable. Otherwise, if [Formula: see text] it is unstable and the system has a unique positive endemic equilibrium. In the special case of constant vector to host inoculum size, we show the positive equilibrium is locally asymptotically stable and the disease is weakly uniformly persistent. Furthermore, numerical results suggest that within-vector-viral kinetics and dynamic inoculum size may play a substantial role in epidemics. Finally, we address how the model can be utilized to better predict the success of control strategies such as vaccination and drug treatment.

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Bozhenova, Irina Viktorovna, Aleksandr Sergeevich Pankov, and Mikhail Ivanovich Samoylov. "Epidemiological characteristics of pertussis in the Orenburg Region." Disinfection affairs, no. 1 (March 2022): 49–54. http://dx.doi.org/10.35411/2076-457x-2022-1-49-54.

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The paper considers the relevance of the pertussis cough problem in the Orenburg region among different age groups. The manifestations of the epidemic process of pertussis in the long-term dynamics and among age groups for the period from 2005 to 2019 in the Orenburg region were studied and analyzed. A retrospective epidemiological analysis of the incidence of pertussis of the population was carried out, including an analysis of the long-term dynamics of the incidence of pertussis of the population, an analysis of the intra-annual dynamics of morbidity and an analysis of the long-term dynamics of morbidity among various population groups. It was found that during the study period, the average long-term incidence of pertussis in the Orenburg region was 1.0±0.21 per 100,000 population. The years with the registration of latent outbreak morbidity are highlighted – 2005, 2006 and 2019. The study established the years of the rise in morbidity – 2005, 2006, 2007, as well as 2017, 2018 and 2019. It is also established that all age groups of the child population are involved in the epidemic process of pertussis. The risk groups for the incidence of pertussis in Orenburg region are children under the age of 14 in the intra-annual dynamics of the incidence of whooping cough, spring-winter seasonality is noted. Keywords: pertussis, Orenburg region, retrospective epidemiological analysis, risk areas, risk groups, time of risk, incidence.

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Lazak, Fatima Zahae, and Redoune Moutj. "EPIDEMIOLOGICAL AND THERAPEUTIC PERSPECTIVES IN DISEASE MANAGEMENT." International Journal of Prevention Practice and Research 02, no. 03 (2022): 01–05. http://dx.doi.org/10.55640/medscience-abcd614.

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This paper explores the epidemiological landscape and therapeutic interventions related to a specific health concern. Epidemiological studies provide a comprehensive overview of the prevalence, incidence, risk factors, and patterns of this health issue across diverse populations. The discussion delves into the multifaceted nature of epidemiological research, elucidating the role of various factors—genetic, environmental, social, and behavioral—in influencing disease dynamics. Moreover, this paper analyzes therapeutic strategies, encompassing both traditional and innovative approaches, aiming to mitigate the burden of the health concern. Emphasis is placed on evidence-based interventions, advancements in treatment modalities, and their efficacy in improving patient outcomes. The synthesis of epidemiological data with therapeutic interventions forms a critical nexus in shaping public health policies, clinical practices, and future research directions, ultimately contributing to enhanced healthcare delivery and disease management. The interplay between epidemiology and therapeutics is critical in understanding and addressing the dynamics of diseases. Epidemiological studies elucidate the patterns, causes, and risk factors of diseases within populations, providing essential insights into disease transmission, prevalence, and impact. Concurrently, therapeutic interventions aim to alleviate, manage, or eradicate diseases through various modalities such as pharmaceuticals, lifestyle modifications, and behavioral interventions. This abstract explores the intricate relationship between epidemiology and therapeutics, emphasizing their roles in disease prevention, treatment strategies, and public health initiatives. Understanding the epidemiological landscape informs the development and implementation of effective therapeutic approaches, ultimately contributing to improved health outcomes and disease control within communities and globally

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Gea-Izquierdo, Enrique, Rossana Ruiz-Urbaez, Valentín Hernández-Barrera, and Ángel Gil-de-Miguel. "Seasonal Dynamics and Legionellosis-Associated Hospitalization in Spain: A Retrospective Study." Pathogens 14, no. 5 (2025): 411. https://doi.org/10.3390/pathogens14050411.

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Legionellosis is a serious respiratory disease with a high mortality rate, particularly if it is untreated or occurs in the immunocompromised. Legionellosis must be reported in the Spanish Epidemiological Surveillance System. To optimize the epidemiologic knowledge of legionellosis and improve prevention, we have investigated whether the disease is associated with seasonality. This study has described legionellosis cases, the temporal trend by seasonality, hospitalization rate, case fatality rate, and costs by autonomous community and season. We retrospectively reviewed cases of legionellosis, documented patient and clinical characteristics, diagnostics, and seasonality of infection. This study combined national legionellosis notification and hospital discharge data that were linked via the Spanish National Health Service to provide a dataset of hospitalized cases occurring between 2002 and 2021 in Spain. There was a significant increase in the number of legionellosis cases due to the season of the year in Spain. An association between legionellosis and factors related to seasonality is suggested. An increasing trend in case fatality rate, seasonality, and regionality and a decrease in legionellosis hospitalization in Spain were identified. The characterization of changes in legionellosis trend and seasonality and timely synchronization and harmonization of hospitalization records are essential to strengthen disease monitoring and inform potential interventions in an epidemiological way.

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Lion, S., and S. Gandon. "Spatial evolutionary epidemiology of spreading epidemics." Proceedings of the Royal Society B: Biological Sciences 283, no. 1841 (2016): 20161170. http://dx.doi.org/10.1098/rspb.2016.1170.

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Most spatial models of host–parasite interactions either neglect the possibility of pathogen evolution or consider that this process is slow enough for epidemiological dynamics to reach an equilibrium on a fast timescale. Here, we propose a novel approach to jointly model the epidemiological and evolutionary dynamics of spatially structured host and pathogen populations. Starting from a multi-strain epidemiological model, we use a combination of spatial moment equations and quantitative genetics to analyse the dynamics of mean transmission and virulence in the population. A key insight of our approach is that, even in the absence of long-term evolutionary consequences, spatial structure can affect the short-term evolution of pathogens because of the build-up of spatial differentiation in mean virulence. We show that spatial differentiation is driven by a balance between epidemiological and genetic effects, and this quantity is related to the effect of kin competition discussed in previous studies of parasite evolution in spatially structured host populations. Our analysis can be used to understand and predict the transient evolutionary dynamics of pathogens and the emergence of spatial patterns of phenotypic variation.

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Filatova, T. G., A. I. Kovalenko, and M. M. Leri. "DYNAMICS OF MENINGOCOCCAL INFECTION RATE IN THE REPUBLIC OF KARELIA." Epidemiology and Infectious Diseases 18, no. 1 (2013): 23–28. http://dx.doi.org/10.17816/eid40709.

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The paper presents the results of an epidemiological analysis of the incidence of meningococcal infection in the Republic of Karelia over epidemic and interepidemic periods. Year over year meningococcal infection rate in the Republic has been remaining to be higher than similar data in Russian Federation. In the years of epidemiological outbreak the number of regions being affected and the disease incidence in children under 14 increases. The largest lethality has been observed in the beginning of epidemiological outbreak. The results of the study indicate the exchange of the leader of meningococcus of serogroup В by meningococcus of serogroup C and A. In the absence of planned vaccinal prevention there remains the threat for the rise of meningococcal infection rate.

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Bica, Ion, Zhichun Zhai, and Rui Hu. "A modified Susceptible-Infected-Recovered epidemiological model." Annals of the University of Craiova - Mathematics and Computer Science Series 49, no. 2 (2022): 291–308. http://dx.doi.org/10.52846/ami.v49i2.1560.

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"Objectives This paper proposes an infectious disease model incorporating two new model compartments, hospitalization, and intensive care unit. Methods The model dynamics are analyzed using the local and global stability theory of nonlinear systems of ordinary differential equations. For the numerical simulations, we used the Rosenbrock method for stiff initial value problems. We obtained numerical simulations using MAPLE software. The returned MAPLE procedure was called only for points inside the range on which the method evaluated the numerical solution of the system with specified initial conditions. Results We proposed a new model to describe the dynamics of microparasitic infections. Numerical simulations revealed that the proposed model fitted with the expected behaviour of mi- croparasitic infections with ”acute epidemicity.” The numerical simulations showed consistency in the behaviour of the system. Conclusions The model proposed has ”robust” dynamics, supported by the global stability of its endemic state and the consistency of the numerical simulations regarding the model’s timeevolution behaviour. The introduction of the hospitalization and intensive care unit compartments in the proposed model revealed that it is essential to consider such policies in the case of ”acuteepidemicity” of microparasitic infections."

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Saifuddin, Md, Sudip Samanta, Santanu Biswas, and Joydev Chattopadhyay. "An Eco-Epidemiological Model with Different Competition Coefficients and Strong-Allee in the Prey." International Journal of Bifurcation and Chaos 27, no. 08 (2017): 1730027. http://dx.doi.org/10.1142/s0218127417300270.

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Strong-Allee effect and competition coefficients provide useful insight to the dynamics of eco-epidemiological systems. Basically, different competition coefficients in the prey population lead to the emergent carrying capacity. The study of population dynamics under the influence of strong-Allee parameter has attracted a lot attention from researchers. But the dynamics of such systems under the combined effect of disease, Allee and emergent carrying capacity is yet to receive attention from researchers. A simple eco-epidemiological model with strong-Allee effect in prey population is proposed and analyzed with the aim to observe the dynamics of such system under the combined influence of strong-Allee parameter and competition coefficients. The basic mathematical features of the model are analyzed with the help of equilibrium analysis, stability analysis, bistability and bifurcation theory. Our numerical simulations reveal that in the absence of strong-Allee effect, the three species eco-epidemiological system produces chaos by increasing the force of infection. However, we observe that chaotic dynamics thus obtained can be controlled by the Allee parameter as well as the competition coefficients.

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Fefferman, Nina H., Charles A. Price, and Oliver C. Stringham. "Considering humans as habitat reveals evidence of successional disease ecology among human pathogens." PLOS Biology 20, no. 9 (2022): e3001770. http://dx.doi.org/10.1371/journal.pbio.3001770.

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The realization that ecological principles play an important role in infectious disease dynamics has led to a renaissance in epidemiological theory. Ideas from ecological succession theory have begun to inform an understanding of the relationship between the individual microbiome and health but have not yet been applied to investigate broader, population-level epidemiological dynamics. We consider human hosts as habitat and apply ideas from succession to immune memory and multi-pathogen dynamics in populations. We demonstrate that ecologically meaningful life history characteristics of pathogens and parasites, rather than epidemiological features alone, are likely to play a meaningful role in determining the age at which people have the greatest probability of being infected. Our results indicate the potential importance of microbiome succession in determining disease incidence and highlight the need to explore how pathogen life history traits and host ecology influence successional dynamics. We conclude by exploring some of the implications that inclusion of successional theory might have for understanding the ecology of diseases and their hosts.

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Mamedov, Kanan Adil ogly, and Azalia Aysarovna Sokolova. "EPIDEMIOLOGICAL FEATURES OF MULTIPLE SCLEROSIS IN KHANTY-MANSIYSK AUTONOMOUS OKRUG – YUGRA." Scientific medical Bulletin of Ugra 40, no. 2 (2024): 64–66. https://doi.org/10.25017/2306-1367-2024-40-8-64-66.

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The continuing interest in epidemiological studies of multiple sclerosis (MS) is due to the constant dynamics of epidemiological indicators and attempts to identify modifiable risk factors for the development of the disease. Epidemiological features have been studied and factors affecting the risk of multiple sclerosis (MS) in the Khanty-Mansiysk Autonomous Okrug – Yugra have been identified.

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Viboud, Cécile, Martha I. Nelson, Yi Tan, and Edward C. Holmes. "Contrasting the epidemiological and evolutionary dynamics of influenza spatial transmission." Philosophical Transactions of the Royal Society B: Biological Sciences 368, no. 1614 (2013): 20120199. http://dx.doi.org/10.1098/rstb.2012.0199.

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In the past decade, rapid increases in the availability of high-resolution molecular and epidemiological data, combined with developments in statistical and computational methods to simulate and infer migration patterns, have provided key insights into the spatial dynamics of influenza A viruses in humans. In this review, we contrast findings from epidemiological and molecular studies of influenza virus transmission at different spatial scales. We show that findings are broadly consistent in large-scale studies of inter-regional or inter-hemispheric spread in temperate regions, revealing intense epidemics associated with multiple viral introductions, followed by deep troughs driven by seasonal bottlenecks. However, aspects of the global transmission dynamics of influenza viruses are still debated, especially with respect to the existence of tropical source populations experiencing high levels of genetic diversity and the extent of prolonged viral persistence between epidemics. At the scale of a country or community, epidemiological studies have revealed spatially structured diffusion patterns in seasonal and pandemic outbreaks, which were not identified in molecular studies. We discuss the role of sampling issues in generating these conflicting results, and suggest strategies for future research that may help to fully integrate the epidemiological and evolutionary dynamics of influenza virus over space and time.

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Sofonea, Mircea T., Bastien Reyné, Baptiste Elie, et al. "Memory is key in capturing COVID-19 epidemiological dynamics." Epidemics 35 (June 2021): 100459. http://dx.doi.org/10.1016/j.epidem.2021.100459.

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Adegbite, Gbenga, Sunday Edeki, Itunuoluwa Isewon, et al. "Investigating the epidemiological factors responsible for malaria transmission dynamics." IOP Conference Series: Earth and Environmental Science 993, no. 1 (2022): 012008. http://dx.doi.org/10.1088/1755-1315/993/1/012008.

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Abstract Despite concerted efforts by the World Health Organization (WHO) to control malaria, it is still being diagnosed in patients visiting hospitals in Tropical Countries of the World. Hence, this study investigated the risk factors responsible for malaria transmission dynamics through a hospital case study. Data of patients that presented with malaria from June 2019 to December 2020 were acquired from Covenant University Medical Centre in Ota, South West Nigeria. Descriptive statistical analyses were carried out so as to examine the factors associated with malaria incidence rate such as age, gender and travel history using the R programming platform. 14% of the total outpatient visits from June 2019 to December 2020 presented with malaria. Furthermore, the mean of the ages of those that presented with malaria, was 23.10 whereas the median of their ages was 22.0. Out of the total malaria cases, 57.7% were males whereas 42.3% were females. Results also showed that there was a significant positive correlation between malaria and travel. In conclusion, it is recommended that malaria control policy formulators should focus on the most vulnerable group of individuals as identified in this study. Further, more efforts should be geared towards curbing malaria importation as a result of human travel, by the different health authorities across the globe.

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Davison, Matt, C. Essex, and J. S. Shiner. "Global Predictability of Chaotic Epidemiological Dynamics in Coupled Populations." Open Systems & Information Dynamics 10, no. 04 (2003): 311–20. http://dx.doi.org/10.1023/b:opsy.0000009553.55368.7e.

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When the dynamics of an epidemic are chaotic, detailed prediction is effectively impossible, except perhaps in the short term. However, a probability distribution underlying the motion does allow for the long term prediction of statistical measures such as the mean or the standard deviation. Even this weaker long term predictability might be lost if distinct populations with chaotic dynamics are coupled. We show that such coupling can result in a phenomenon we call “sensitive dependence on neglected dynamics”. In light of this phenomenon, it is somewhat surprising that when two logistic maps are coupled, the long term predictability of the mean and standard deviation is maintained. This is true even though the probability distribution describing the time series depends on the coupling strength. The coupling-strength dependence does reveal itself in the loss of predictability of higher order moments such as skewness and kurtosis.

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Arquam, Md, Anurag Singh, and Hocine Cherifi. "Impact of Seasonal Conditions on Vector-Borne Epidemiological Dynamics." IEEE Access 8 (2020): 94510–25. http://dx.doi.org/10.1109/access.2020.2995650.

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Velasco-Hernández, Jorge X. "An epidemiological model for the dynamics of Chagas' disease." Biosystems 26, no. 2 (1991): 127–34. http://dx.doi.org/10.1016/0303-2647(91)90043-k.

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Lietman, Thomas M., Teshome Gebre, Berhan Ayele, et al. "The epidemiological dynamics of infectious trachoma may facilitate elimination." Epidemics 3, no. 2 (2011): 119–24. http://dx.doi.org/10.1016/j.epidem.2011.03.004.

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Castle, Matthew D., and Christopher A. Gilligan. "An Epidemiological Framework for Modelling Fungicide Dynamics and Control." PLoS ONE 7, no. 8 (2012): e40941. http://dx.doi.org/10.1371/journal.pone.0040941.

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El Chaal, Rachid, Said Bouchefra, and Moulay Othman Aboutafail. "Stochastic Dynamics and Extinction Time in SIR Epidemiological Models." Acadlore Transactions on Applied Mathematics and Statistics 1, no. 3 (2023): 181–202. http://dx.doi.org/10.56578/atams010305.

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Fedorovitch, G. V. "Prediction of the Dynamics of Polymorbid Pathology." Medicina 11, no. 1 (2023): 56–76. http://dx.doi.org/10.29234/2308-9113-2023-11-1-56-76.

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A unified methodological approach is proposed to describe the nosological structure of polymorbidity, to systematize its clinical and epidemiological properties and patterns, as well as for the phenomena associated with it. A probabilistic model is used to describe at the phenomenological level the picture of the formation of the resulting statistics of polymorbidity in a population. The ergodic hypothesis underlies the model of morbidity in the population. The results have the meaning of ensemble averages. The microscopic (internal) states of the system can take on all possible values compatible with the given values of the macroscopic (external) parameters. Of all the possible microscopic states, those that have the highest statistical weight are realized with the maximum probability. The real system defines the block diagram of the model. Those aspects of the system that correspond to the objectives of the study are displayed. The direct task is to draw up an epidemiological picture according to the clinical parameters of the disease. The inverse task is the formation of an individual (clinical) description of nosology based on epidemiological (population average) data. A special state probability linear regression method is proposed to compare theoretical results with observations. The test allows estimating the parameters of the real distribution. The adequacy of the model is checked when it is "fitted" to the observational data. The fitting parameters rationally characterize polymorbid pathology – the structure and incidence rate.

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Zakharov, Victor, and Yulia Balykina. "Balance Model of COVID-19 Epidemic Based on Percentage Growth Rate." Informatics and Automation 20, no. 5 (2021): 1034–64. http://dx.doi.org/10.15622/20.5.2.

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The paper examines the possibility of using an alternative approach to predicting statistical indicators of a new COVID-19 virus type epidemic. A systematic review of models for predicting epidemics of new infections in foreign and Russian literature is presented. The accuracy of the SIR model for the spring 2020 wave of COVID-19 epidemic forecast in Russia is analyzed. As an alternative to modeling the epidemic spread using the SIR model, a new CIR discrete stochastic model is proposed based on the balance of the epidemic indicators at the current and past time points. The new model describes the dynamics of the total number of cases (C), the total number of recoveries and deaths (R), and the number of active cases (I). The system parameters are the percentage increase in the C(t) value and the characteristic of the dynamic balance of the epidemiological process, first introduced in this paper. The principle of the dynamic balance of epidemiological process assumes that any process has the property of similarity between the value of the total number of cases in the past and the value of the total number of recoveries and deaths at present. To calculate the values of the dynamic balance characteristic, an integer linear programming problem is used. In general, the dynamic characteristic of the epidemiological process is not constant. An epidemiological process the dynamic characteristic of which is not constant is called non-stationary. To construct mid-term forecasts of indicators of the epidemiological process at intervals of stationarity of the epidemiological process, a special algorithm has been developed. The question of using this algorithm on the intervals of stationarity and non-stationarity is being examined. Examples of the CIR model application for making forecasts of the considered indicators for the epidemic in Russia in May-June 2020 are given.

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Agliullin, D. R., G. R. Khasanova, E. A. Abdulaeva, et al. "Epidemiological aspects of central serous chorioretinopathy." Kazan medical journal 102, no. 2 (2021): 228–33. http://dx.doi.org/10.17816/kmj2021-228.

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Aim. To analyze the incidence of central serous chorioretinopathy among the Kazans population between 2009 and 2018. Methods. A descriptive epidemiological study of the incidence of Central serous chorioretinopathy of the population of Kazan between 2009 and 2018 was conducted. It included an analysis of long-term changes in the incidence of male and female population and an assessment of the structure of morbidity by sex for the entire period and in dynamics. Testing for differences was performed using the nonparametric MannWhitney U test and Chi-square test with Yates correction. Results. 831 new cases of central serous chorioretinopathy were registered in Kazan during 20092018, the ratio of men and women was approximately 1:1. In the dynamics of morbidity, the proportion of men increased from 24.2% in 2009 to 60.7% in 2018 (р=0.000002), while the proportion of women decreased from 75.8% in 2009 to 39.3% in 2018 (р=0.000002). The long-term dynamics for 20092018 is characterized by a statistically significant increase in the incidence rate of central serous chorioretinopathy in men (p=0.004) from 3.2 per 100 000 in 2009 to 14.8 per 100 000 in 2018. During the study period, the incidence rate in women remains at the same level, varying from 5.4 per 100 000 to 8 per 100 000 (p=0.663). Men are more likely to have central serous chorioretinopathy at a younger age (р=0.0001). The median age at the time of diagnosis in women was 55 years (Q25Q75 4565 years), in men 45 years (Q25Q75 3756 years). Conclusion. From 2009 to 2018, the incidence rate of central serous chorioretinopathy among men in Kazan significantly increased in both intensive (p=0.004) and extensive indicators (p=0.000002); сentral serous chorioretinopathy in men develops at an earlier age compared with women (median age of women at the time of diagnosis was 55 years, median age of men 45 years, p=0.0001).

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Levkova, E. A., R. I. Sepiashvili, and S. Z. Savin. "Problems of creating predictive models of the COVID19 coronavirus pandemic." RUDN Journal of Medicine 25, no. 1 (2021): 31–38. http://dx.doi.org/10.22363/2313-0245-2021-25-1-31-38.

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Relevance. The article is devoted to creating prognostic models based on epidemiological and immunological data. Objective: to study the comparative dynamic epidemiological and immunological characteristics of patients with COVID-19. Materials and methods. Methodological approaches to the use of system analysis of epidemiological and immunological characteristics of patients with COVID-19 using multivariate analysis are described. The used technologies of computer-aided analysis systems, algorithms for recognizing, measuring and identifying the condition of patients, and methods of statistical data processing made it possible to create a universal information predictive model for calculating the dynamics of infectious diseases prone to generalization (pandemics), as well as to understand in which groups these new infectious diseases are most dangerous. Results and discussion. Using the methods of system analysis, the epidemiological and immunological aspects of predictive models of the coronavirus pandemic were evaluated using the most objective international data, which increased the information content of the analysis. Conclusions . Creating predictive epidemiological and immunological models of the pandemic is an urgent and promising task to combat the medical and social consequences of the spread of coronavirus infection in Russia.

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Lavrova, Anastasia I., Eugene B. Postnikov, Olga A. Manicheva, and Boris I. Vishnevsky. "Bi-logistic model for disease dynamics caused by Mycobacterium tuberculosis in Russia." Royal Society Open Science 4, no. 9 (2017): 171033. http://dx.doi.org/10.1098/rsos.171033.

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In this work, we explore epidemiological dynamics by the example of tuberculosis in Russian Federation. It has been shown that the epidemiological dynamics correlates linearly with the virulence of Mycobacterium tuberculosis during the period 1987–2012. To construct an appropriate model, we have analysed (using LogLet decomposition method) epidemiological World Health Organization (WHO) data (period 1980–2014) and obtained, as result of their integration, a curve approximated by a bi-logistic function. This fact allows a subdivision of the whole population into parts, each of them satisfies the Verhulst-like models with different constant virulences introduced into each subsystem separately. Such a subdivision could be interconnected with the heterogeneous structure of mycobacterial population that has a high ability of adaptation to the host and strong mutability.

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Ferreira, Adeval Lino, and Kalel Bispo Gimenez Araujo Araujo. "Stability of the Epidemiological Model SIR with Loss of Immunity." Semina: Ciências Exatas e Tecnológicas 44 (September 11, 2023): e47860. http://dx.doi.org/10.5433/1679-0375.2023.v44.47860.

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This study approaches the analysis of the stability of the epidemiological model SIR with loss of immunity. This is a model given by a system of ordinary differential equations. Initially, we present the model and its interpretation. Then we define the constants and elements that compose the model, so we present the results obtained using the qualitative theory of ordinary differential equations, especially the theory of planar systems related to the dynamics of fixed points. Finally, we show that the system representing the SIR model is globally stable and they have two types of dynamic that {depend on model constants}, and their meaning for epidemiology.

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Tookes, Hansel, Tyler S. Bartholomew, Shana Geary, et al. "Rapid Identification and Investigation of an HIV Risk Network Among People Who Inject Drugs –Miami, FL, 2018." AIDS and Behavior 24, no. 1 (2019): 246–56. http://dx.doi.org/10.1007/s10461-019-02680-9.

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Abstract Prevention of HIV outbreaks among people who inject drugs remains a challenge to ending the HIV epidemic in the United States. The first legal syringe services program (SSP) in Florida implemented routine screening in 2018 leading to the identification of ten anonymous HIV seroconversions. The SSP collaborated with the Department of Health to conduct an epidemiologic investigation. All seven acute HIV seroconversions were linked to care (86% within 30 days) and achieved viral suppression (mean 70 days). Six of the seven individuals are epidemiologically and/or socially linked to at least two other seroconversions. Analysis of the HIV genotypes revealed that two individuals are connected molecularly at 0.5% genetic distance. We identified a risk network with complex transmission dynamics that could not be explained by epidemiological methods or molecular analyses alone. Providing wrap-around services through the SSP, including routine screening, intensive linkage and patient navigation, could be an effective model for achieving viral suppression for people who inject drugs.

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LIAO, C. M., S. C. YANG, C. P. CHIO, and S. C. CHEN. "Understanding influenza virus-specific epidemiological properties by analysis of experimental human infections." Epidemiology and Infection 138, no. 6 (2009): 825–35. http://dx.doi.org/10.1017/s0950268809991178.

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SUMMARYThis study aimed to estimate the natural history and transmission parameters based on experimental viral shedding and symptom dynamics in order to understand the key epidemiological factors that characterize influenza (sub)type epidemics. A simple statistical algorithm was developed by combining a well-defined mathematical scheme of epidemiological determinants and experimental human influenza infection. Here we showed that (i) the observed viral shedding dynamics mapped successfully the estimated time-profile of infectiousness and (ii) the profile of asymptomatic probability was obtained based on observed temporal variation of symptom scores. Our derived estimates permitted evaluation of relationships between various model-derived and data-based estimations, allowing evaluation of trends proposed previously but not tested fully. As well as providing insights into the dynamics of viral shedding and symptom scores, a more profound understanding of influenza epidemiological parameters and determinants could enhance the viral kinetic studies of influenza during infection in the respiratory tracts of experimentally infected individuals.

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