Relevant bibliographies by topics / Epidemics

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Epidemics'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2024

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Journal articles on the topic "Epidemics"

1

Moilanen, Ulla, and Sofia Paasikivi. "Esihistoriallisten tartuntatautien ja epidemioiden tutkimusmahdollisuudet Suomessa." Ennen ja nyt: Historian tietosanomat 23, no. 2 (June 1, 2023): 5–18. http://dx.doi.org/10.37449/ennenjanyt.125929.

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Epidemioiden historiallinen tutkimus painottaa usein kirjallisia lähteitä, mutta tartuntataudit ovat olleet ihmisten seuralaisina esihistoriallisista ajoista lähtien. Käsittelemme artikkelissa esihistoriallisten epidemioiden tutkimuskeinoja. Keskitymme muinaisten taudinaiheuttajien luonnontieteellisiin analyysimenetelmiin ja arkeologisessa aineistossa näkyviin epidemioiden epäsuoriin vaikutuksiin. Epidemioilla voi olla demografisia, poliittisia, sosiaalisia, uskonnollisia ja taloudellisia vaikutuksia, vaikka myös muut tekijät voivat laukaista kriisejä. Mahdollisista kriiseistä kertovat ilmiöt ovat usein monitulkintaisia, mutta minkä tahansa kriisiajanjakson tunnistaminen voi johtaa myös epidemian jäljille, sillä epidemiat liittyvät tyypillisesti muihin väestökriiseihin. Myös ilmastotekijät vaikuttavat tautien esiintymiseen. Esitämme, että arkeologisen aineiston monitieteisellä tutkimuksella voidaan tehdä päätelmiä kriisien kokonaisvaikutuksista ja että paras tapa esihistoriallisten epidemioiden tutkimukseen on kiinnittää huomiota sekä laajoihin että paikallisiin, pienimuotoisiin ilmiöihin ja arkeologisen aineiston piirteisiin monesta eri näkökulmasta. Historical research of epidemics often emphasises literary sources, but infectious diseases have accompanied humans since prehistoric times. This article discusses the ways prehistoric epidemics can be identified and studied. We focus on scientific analyses of ancient pathogens and the indirect impact of epidemics that may be visible in archaeological material. Epidemics can have demographic, political, social, religious and economic impacts, although other factors can also trigger similar crises. The phenomena are often complicated and challenging to identify and interpret. However, identifying any period of crisis in prehistory can also lead to the identification of an epidemic, since epidemics are typically linked to other population crises. Climate factors may also influence the emergence of diseases. The interdisciplinary study of archaeological data allows conclusions to be drawn about the overall impact of crises. Thus, the best way to study prehistoric epidemics is to focus on both large-scale and local, small-scale phenomena.
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Karpova, L. S., M. Yu Pelikh, K. M. Volik, N. M. Popovtseva, T. P. Stolyarova, and D. A. Lioznov. "Evaluating the Effectiveness of New Criteria for Early Detection of the Start and Intensity of Influenza Epidemics in Russian Federation." Epidemiology and Vaccinal Prevention 22, no. 6 (January 4, 2024): 4–18. http://dx.doi.org/10.31631/2073-3046-2023-22-6-4-18.

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Relevance. During the COVID-19 pandemic, an early determination of the start of the influenza epidemic by the incidence of influenza and SARS in total is impossible, due to the similarity of the clinical picture of SARS and lung cases of COVID-19.Aim. The goal is to calculate and test new criteria for early detection of the start of influenza epidemics and their intensity for each of the cities–reference bases (61) of the 2 WHO National Influenza Centers based on the incidence of clinically diagnosed influenza.Tasks. To evaluate the effectiveness of baseline influenza incidence and epidemic intensity thresholds for the general population and age groups of each city in the epidemic of 2022–2023. To give a retrospective assessment of the effectiveness of influenza baselines for cities, compared with the baselines of the corresponding Federal Districts, for the seasons from 2009 to 2022. To estimate the intensity of epidemics by influenza incidence over the previous epidemies of the pandemic cycle of influenza A/California/H1N1/ virus.Materials and methods. By the 2022–2023 season. baseline lines and thresholds of influenza incidence intensity were calculated using the method of moving epidemics according to clinical diagnostic data not only for federal districts, but also for each of the observed cities (61). The calculation of the baselines was carried out according to the data of the computer database of the Influenza Research Institute on the incidence of influenza by age groups in each city over the previous 5 years in the season from 2016–2017 to 2021–2022.Results. In the 2022-23 season application of new criteria for the start of epidemics (prev.- and post-epidemic baseline influenza incidence) and their intensity revealed: early onset of the influenza epidemic (07–13.11 2022); simultaneous onset in all children's age groups; geographical spread of the epidemic in federal districts; intensity of the epidemic in the general population and age groups. The thresholds for the intensity of influenza morbidity made it possible to clarify the intensity levels of influenza epidemics from 2009 to 2023 and to show that the pandemic cycle of the influenza A(H1N1) virus continues. A comparison of the effectiveness of urban baselines with federal ones in the epidemic of 2022–2023 showed that urban baseline flu incidence lines revealed the start of epidemics 1–3 weeks earlier: among the general population in 12 cities, persons over 15 years old – in 9, children 3–6 years old – in 6 and 7–14 years old – in 5. A retrospective assessment of the effectiveness of city and federal influenza baselines (from 2009 to 2022) showed their effectiveness both in the seasons from 2009 to 2016 (before the baseline calculation period) and after. The effectiveness of urban baselines for early detection of the start of epidemics depended on the etiology of the epidemic – more with influenza A(H3N2) than with influenza A(H1N1), the level of intensity of influenza diseases and the age group of the population (more in children with low intensity and in adults with an average level).Conclusion. The results obtained on the basis of population epidemiological data on the incidence of influenza, namely, new criteria for detecting the start of an epidemic in cities, can be used in health management bodies in cities and subjects of the Russian Federation for early detection of epidemics and management decisions, timely introduction of anti-epidemic measures, creating a stock of medicines. The expected effect of the method of early epidemiological diagnosis of epidemics is a decrease in morbidity, etc.
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Li, Wenjie, Yanyi Nie, Wenyao Li, Xiaolong Chen, Sheng Su, and Wei Wang. "Two competing simplicial irreversible epidemics on simplicial complex." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 9 (September 2022): 093135. http://dx.doi.org/10.1063/5.0100315.

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Higher-order interactions have significant implications for the dynamics of competing epidemic spreads. In this paper, a competing spread model for two simplicial irreversible epidemics (i.e., susceptible–infected–removed epidemics) on higher-order networks is proposed. The simplicial complexes are based on synthetic (including homogeneous and heterogeneous) and real-world networks. The spread process of two epidemics is theoretically analyzed by extending the microscopic Markov chain approach. When the two epidemics have the same 2-simplex infection rate and the 1-simplex infection rate of epidemic [Formula: see text] ([Formula: see text]) is fixed at zero, an increase in the 1-simplex infection rate of epidemic [Formula: see text] ([Formula: see text]) causes a transition from continuous growth to sharp growth in the spread of epidemic [Formula: see text] with [Formula: see text]. When [Formula: see text], the growth of epidemic [Formula: see text] is always continuous. With the increase of [Formula: see text], the outbreak threshold of epidemic [Formula: see text] is delayed. When the difference in 1-simplex infection rates between the two epidemics reaches approximately three times, the stronger side obviously dominates. Otherwise, the coexistence of the two epidemics is always observed. When the 1-simplex infection rates are symmetrical, the increase in competition will accelerate the spread process and expand the spread area of both epidemics; when the 1-simplex infection rates are asymmetrical, the spread area of one epidemic increases with an increase in the 1-simplex infection rate from this epidemic while the other decreases. Finally, the influence of 2-simplex infection rates on the competing spread is discussed. An increase in 2-simplex infection rates leads to sharp growth in one of the epidemics.
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Karpova, L. S., T. P. Stolyarova, and N. M. Popovtseva. "Parameters of the Influenza Epidemic in Russia in the 2019-2020 Season." Epidemiology and Vaccinal Prevention 19, no. 6 (January 14, 2021): 8–17. http://dx.doi.org/10.31631/2073-3046-2020-19-6-8-17.

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Relevance. The National influenza center uses additional criteria: baselines and thresholds for epidemic intensity for early recognition of the onset and assessment of epidemic intensity. Aim. To characterize the parameters of the flu epidemic in the Russian Federation in the 2019-2020 season and assess the intensity of the last 2 epidemics and the effectiveness of baselines and intensity thresholds. Materials and methods. The database of the research Institute of influenza on weekly morbidity, hospitalization, deaths from influenza and ARVI in the cities-reference bases of the National center for influenza was used. The analysis of the flu situation is based on comparing the current incidence of influenza and ARVI with weekly epic thresholds and baselines. A comparative assessment of the intensity of the last 2 influenza epidemics was carried out using intensity thresholds calculated by the moving epidemic method (MEM). Results. The main parameters of the 2019-20 influenza epidemic compared to the previous one are described. There was an increase in the incidence of clinically diagnosed «fluenza» in the pre-epidemic period and the early onset of both epidemics and reaching a peak in the 6th calendar week. This season, the epidemic started among school children, then among adults, and in the past-among the adult population, the incidence of children 0-2 years old was below the thresholds in both epidemics. This season, the average duration of the epidemic and the incidence in cities were higher among school children and adults, and over the entire period of the epidemic, the incidence was higher in all age groups across the country. This season, the epidemic started in the North Caucasus Federal district, its intensity was medium and low, in the past - it started simultaneously in the Siberian, Ural and North Caucasus districts, and its intensity was very high (in the North Caucasus) and high (in the Siberian Federal district) and the average level of 6 districts. The intensity and duration of epidemics were higher in districts with an early onset (in the North Caucasus and Siberian Federal districts). Both epidemics in the country were of moderate intensity, but there were fewer deaths from influenza in the last epidemic. Conclusion. Comparative characteristics of epidemics using baselines and intensity thresholds allowed us to identify the features of the epidemic process in the seasons 2018-19 and 2019-20: the earlier onset of both epidemics; a noticeable increase in the incidence of fluenza before the beginning of epidemics; a decrease in the intensity of epidemics and mortality from influenza; features of the epidemic process in Federal districts. The effectiveness of using additional criteria in the analysis of the influenza epidemic in Russia and this season has been confirmed. In both epidemics, the weekly epidemic thresholds for influenza and ARVI were more sensitive when determining the start of the epidemic, and the end of it, on the contrary, were baselines, and the baselines of morbidity and hospitalization with a diagnosis «influenza» determined the beginning and end of the epidemic even more precisely.
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Shi, Zizhong, Junru Li, and Xiangdong Hu. "Risk Assessment and Response Strategy for Pig Epidemics in China." Veterinary Sciences 10, no. 8 (July 26, 2023): 485. http://dx.doi.org/10.3390/vetsci10080485.

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Strengthening the analysis and risk assessment of the pig epidemic will help to better prevent and mitigate epidemic risks and promote the high-quality development of the pig industry. Based on a systematic understanding of live pig epidemics, a risk assessment index system was constructed, and the spatial and temporal variation characteristics of pig epidemics in China were explored by the entropy method. In recent years, the overall trend in pig epidemics over time first increased and then decreased; in space, the acceleration of the spread of epidemics across the country weakened. China still faces challenges, including many types and a wide range of diseases, large total livestock breeding and weak epidemic prevention and control capacity, and a large risk of introduced foreign animal epidemics. The spatial and temporal variations in the pig epidemic risk were obvious; one high-risk area, two medium–high-risk areas and 10 medium-risk areas have been found in recent years, during which time, the epidemic risk was highest in Beijing, Hainan, Liaoning, Tibet and Zhejiang. However, there were significant differences in the regional distribution of the risk level of pig epidemics in different years. To further build a secure “defense system” for the high-quality development of the pig industry, it is recommended to improve the monitoring and early warning system of pig epidemic risk, perfect the pig epidemic prevention and control system, and strengthen the regional collaboration mechanism of epidemic prevention and control.
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Garcia-Soto, M., R. E. Fullilove, M. T. Fullilove, and K. Haynes-Sanstad. "The Peculiar Epidemic, Part I: Social Response to AIDS in Alameda County." Environment and Planning A: Economy and Space 30, no. 4 (April 1998): 731–46. http://dx.doi.org/10.1068/a300731.

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The morbidity and mortality caused by epidemics threaten social functioning of complex societies. Societies mount a social response to epidemics in order to contain the potential damage from uncontrolled disease. Despite the threat posed by epidemics, social and contextual ‘vulnerabilities’ often impede efforts to contain epidemics. The AIDS epidemic provides an example of a ‘peculiar’ epidemic, in which threat to social welfare failed to provoke adequate social efforts at containment. In order to examine the miscarriage of epidemic response, we interviewed 31 AIDS providers in Alameda County, California, about the development of their AIDS-related services. According to the people interviewed, epidemic response in the County was marred by stigma associated with AIDS, lack of adequate funding, difficulties in building collaborative effort in a fragmented care system, and other political and social problems. In spite of these obstacles, social mobilization enabled directors of a wide variety of health care agencies to incorporate care and prevention into their services. The findings support the concept that ‘vulnerabilities’ can derail epidemic response, making widespread social mobilization an essential tool for epidemic control. The discussion centers on the implication of these findings for the theoretical understanding of social response to epidemics.
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Li, Xin, Xingyuan He, Lu Zhou, and Shushu Xie. "Impact of Epidemics on Enterprise Innovation: An Analysis of COVID-19 and SARS." Sustainability 14, no. 9 (April 26, 2022): 5223. http://dx.doi.org/10.3390/su14095223.

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This study analyzes the impact of SARS and COVID-19, the two most severe epidemics to occur in China since the 21st century, on corporate innovation, in order to find a path for sustained innovation growth under the epidemic. For COVID-19, the analysis used data from China’s A-share-listed companies from 2019 to 2020; a longer period (1999–2006) and a wider sample of Chinese industrial enterprises were used for the SARS epidemic. The empirical model was constructed using the difference-in-differences method. Both COVID-19 and SARS were found to have significantly reduced enterprise innovation. However, the effect of SARS disappeared after two years. For COVID-19, information asymmetry, financing constraints, and economic policy uncertainty moderated the epidemic’s effect on innovation. The results show that financing constraints and economic policy uncertainty reduce the epidemic’s negative impact. However, while most previous studies have found that an epidemic reduces the information asymmetry between investors and enterprises in the short term, thus raising enterprise innovation, we found that information asymmetry aggravated the epidemic’s negative impact. These findings can be applied to alleviate the current epidemic’s negative impact as well as improve enterprise innovation thereafter.
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Karpova, L. S., T. P. Stolyarova, N. M. Popovtseva, K. A. Stolyarov, and D. M. Danilenko. "Differences Depending on the Etiology of Influenza Epidemics in 2014-2017." Epidemiology and Vaccine Prevention 17, no. 1 (February 20, 2018): 13–19. http://dx.doi.org/10.31631/2073-3046-2018-17-1-13-19.

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The goal is to identify features of epidemic process of influenza depending on the etiology of epidemics to clarification of the forecast for future epidemics. Analysis of epidemics of influenza in Russia conducted according to the Federal center for influenza on morbidity, hospitalization and deaths from influenza in 59 Russian cities. The epidemic of influenza A(H1N1)pdm09 2015–16 different from the mixed epidemics of influenza (A(H3N2) and B) 2014–15 and 2016–17 high development rate, high incidence of influenza and ARI at its peak, the incidence of hospitalization with a diagnosis of «influenza» (14%) and high mortality among the infected (6,0 on 100000). The epidemic of influenza A(H3N2) and B started earlier (December). They had a longer duration and the incidence in the cities and in the country, but less than the incidence at the peak of the epidemic and the incidence of hospitalization with a diagnosis of «influenza» (7.5 and 7.3%) and smaller (8.6 and 20 times) the mortality from the influenza. In these epidemics among the dead was higher than the percentage of children under 14 years and persons over 65 years of age than in the influenza epidemic 2015–16. And among the deaths increased the proportion of persons with chronic lung disease and immunodeficiency, but decreased the proportion of individuals with obesity and diseases of the liver and kidneys. For the period from 2009 to 2017 the tendency to increase the intensity of influenza A(H3N2) epidemics was 2.4 times greater than the decrease in the intensity of epidemic of influenza A(H1N1)pdm09.
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Fitzpatrick, Mike. "Epidemics of epidemics." British Journal of General Practice 59, no. 566 (September 1, 2009): 705. http://dx.doi.org/10.3399/bjgp09x471747.

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Kleczkowski, A., and C. A. Gilligan. "Parameter estimation and prediction for the course of a single epidemic outbreak of a plant disease." Journal of The Royal Society Interface 4, no. 16 (July 17, 2007): 865–77. http://dx.doi.org/10.1098/rsif.2007.1036.

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Many epidemics of plant diseases are characterized by large variability among individual outbreaks. However, individual epidemics often follow a well-defined trajectory which is much more predictable in the short term than the ensemble (collection) of potential epidemics. In this paper, we introduce a modelling framework that allows us to deal with individual replicated outbreaks, based upon a Bayesian hierarchical analysis. Information about ‘similar’ replicate epidemics can be incorporated into a hierarchical model, allowing both ensemble and individual parameters to be estimated. The model is used to analyse the data from a replicated experiment involving spread of Rhizoctonia solani on radish in the presence or absence of a biocontrol agent, Trichoderma viride . The rate of primary (soil-to-plant) infection is found to be the most variable factor determining the final size of epidemics. Breakdown of biological control in some replicates results in high levels of primary infection and increased variability. The model can be used to predict new outbreaks of disease based upon knowledge from a ‘library’ of previous epidemics and partial information about the current outbreak. We show that forecasting improves significantly with knowledge about the history of a particular epidemic, whereas the precision of hindcasting to identify the past course of the epidemic is largely independent of detailed knowledge of the epidemic trajectory. The results have important consequences for parameter estimation, inference and prediction for emerging epidemic outbreaks.
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Dissertations / Theses on the topic "Epidemics"

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Chen, Jiunn-charn. "Prevention of epidemics /." The Ohio State University, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487266691095848.

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Paterson, Ryan. "Modeling man-made epidemics." Thesis, Monterey, California. Naval Postgraduate School, 2002. http://hdl.handle.net/10945/6037.

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Approved for public release; distribution is unlimited.
This thesis develops a mathematical model to explore epidemic spread through the Ground Combat Element (GCE) of the Marine Expeditionary Unit (MEU). The model will simulate an epidemic caused by a biological attack using an agent that has the ability to spread through person-to-person contact (small pox, hemorrhagic fever, etc.) A stochastic modeling process will be used along with widely accepted mathematical formulas for an SEIR (Susceptible-Exposed-Infectious-Removed) epidemic model. A heterogeneous population composed of numerous homogenous subgroups with varying interaction rates simulates the unique structure of military combat units. The model will be evaluated to determine which units facilitate the most rapid spread of the epidemic. The model will then test a number of different scenarios to determine the effects of varying quarantine techniques, vaccination strategies and protective postures on the spread of the disease.
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Sanatkar, Mohammad Reza. "Epidemics on complex networks." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/14097.

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Master of Science
Department of Electrical and Computer Engineering
Karen Garrett
Bala Natarajan
Caterina Scoglio
In this thesis, we propose a statistical model to predict disease dispersal in dynamic networks. We model the process of disease spreading using discrete time Markov chain. In this case, the vector of probability of infection is the state vector and every element of the state vector is a continuous variable between zero and one. In discrete time Markov chains, state probability vectors in each time step depends on state probability vector in the previous time step and one step transition probability matrix. The transition probability matrix can be time variant or time invariant. If this matrix’s elements are functions of elements of vector state probability in previous step, the corresponding Markov chain is non linear dynamical system. However, if those elements are independent of vector state probability, the corresponding Markov chain is a linear dynamical system. We especially focus on the dispersal of soybean rust. In our problem, we have a network of US counties and we aim at predicting that which counties are more likely to get infected by soybean rust during a year based on observations of soybean rust up to that time as well as corresponding observations to previous years. Other data such as soybean and kudzu densities in each county, daily wind data, and distance between counties helps us to build the model. The rapid growth in the number of Internet users in recent years has led malware generators to exploit this potential to attack computer users around the word. Internet users are frequent targets of malicious software every day. The ability of malware to exploit the infrastructures of networks for propagation determines how detrimental they can be to the network’s security. Malicious software can make large outbreaks if they are able to exploit the structure of the Internet and interactions between users to propagate. Epidemics typically start with some initial infected nodes. Infected nodes can cause their healthy neighbors to become infected with some probability. With time and in some cases with external intervention, infected nodes can be cured and go back to a healthy state. The study of epidemic dispersals on networks aims at explaining how epidemics evolve and spread in networks. One of the most interesting questions regarding an epidemic spread in a network is whether the epidemic dies out or results in a massive outbreak. Epidemic threshold is a parameter that addresses this question by considering both the network topology and epidemic strength.
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Strazzulla, Anthony Mark. "Diagnosis in Hippocrates' Epidemics." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0014441.

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Munday, Paul. "Importance Sampling in Spatial Epidemics." Thesis, University of Oxford, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.504438.

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Sarzynska, Marta. "Spatial community structure and epidemics." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:fd841775-0fdb-4c95-a1a8-01065ada1838.

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Networks are a useful quantitative representation for complex systems of interacting entities arising in fields such as biological, physical and social sciences. A network representation provides a degree of simplification while capturing key connectivity patterns. This thesis focuses on two main themes: the study of community structure, an important mesoscopic feature of many networks, and its application to study spatiotemporal spread of infectious diseases. Community detection seeks to partition a network into dense sets of nodes that are connected sparsely to other dense sets. The notion of denseness is often relative to some "null model" that describes baseline connectivity that can be construed to occur randomly. In the first part of the thesis, we discuss the incorporation of spatial information into null models for community detection. We develop a spatial null model based on the radiation model of mobility. We test different spatial null models using static and temporal (multilayer) spatial benchmarks with planted partitions that represent interactions between human populations. Our results indicate that it is important to incorporate spatial information into null models for community detection, but it is best to incorporate only relevant information into null models, as extraneous information can lower performance. In the second part of the thesis, we present the results of community detection with different null models on disease-correlation networks generated form real and synthetic time series of disease occurrence. We use data sets for endemic diseases (established in a region, with occasional epidemic outbreaks) and emerging diseases (newly-discovered or introduced into a region for the first time). We study the spatial and temporal organization of partitions. Finally, we apply community detection with different null models to synthetic time series generated from an agent-based model (ABM) simulating the spread of endemic and emerging diseases between spatially-embedded cities with a planted, transport-based community structure. We compare the findings on real and synthetic data sets, and we searched for model parameter regimes in which we are able to detect planted partitions or other interesting communities. For emerging diseases, we find spatial communities that are associated with the first times the infection reached a node in both ABM and disease data. For endemic diseases, we are unable to find planted or spatial communities in the ABM data, but we detect spatial communities for two of the three disease data sets. For these diseases, we also detect temporal communities corresponding to some of the important time points in disease history. We hope that these results show that community structure of disease correlation networks appears to be more complicated than simple spatial patterns and is a fascinating topic to study.
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Blount, Steven Michael 1958. "Computational methods for stochastic epidemics." Diss., The University of Arizona, 1997. http://hdl.handle.net/10150/288714.

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Compartmental models constructed for stochastic epidemics are usually difficult to analyze mathematically or computationally. Researchers have mostly resorted to deterministic approximations or simulation to investigate these models. This dissertation describes three original computational methods for analyzing compartmental models of stochastic epidemics. The first method is the Markov Process Method which computes the probability law for the epidemic by solving the Chapman-Kolmogorov ordinary differential equations as an initial value problem using standard numerical analysis techniques. It is limited to models with small populations and few compartments and requires sophisticated numerical analysis tools and relatively extensive computer resources. The second method is the Probability Vector Method which can estimate the first few moments of a discrete time epidemic model over a limited time period (i.e. if Y(t) is the number of individuals in a given compartment then this method can estimate E[ Yr for small positive integers r. Size restrictions limit the maximum order of the moment that can be computed. For compartmental models with a constant, homogeneous population, this method requires modest computational resources to estimate the first two moments of Y(t). The third method is the Linear Extrapolation Method, which computes the moments of a compartmental model with a large population by extrapolating from the given moments of the same model with smaller populations. This method is limited to models that have some alternate way of calculating the moments for small populations. These moments should be computed exactly from probabilistic principles. When this is not practical, any method that can produce accurate estimates of these moments for small populations can be used. Two compartmental epidemic models are analyzed using these three methods. First, the simple susceptible/infective epidemic is used to illustrate each method and serves as a benchmark for accuracy and performance. These computations show that each algorithm is capable of producing acceptably accurate solutions (at least for the specific parameters that were used). Next, an HIV/AIDS model is analyzed and the numerical results are presented and compared with the deterministic and simulation solutions. Only the probability vector method could compete with simulation on the larger (i.e. more compartments) HIV/AIDS model.
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Neal, Peter. "Epidemics with two levels of mixing." Thesis, University of Nottingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.394751.

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Livingston, Samantha 1980. "Stochastic models for epidemics on networks." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28437.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 37).
In this thesis, I looked at an extension of the Reed-Frost epidemic model which had two-sub-populations. By setting up a Markov chain to model the epidemic and finding the transition probabilities of that chain, MATLAB could be used to solve for the expected number of susceptibles and the expected duration. I simulated the model with more tan two sub-populations to find the average number of susceptibles and reviewed previously solved stochastic spatial models to understand how to solve the multiple-population Reed-Frost model on a network.
by Samantha Livingston.
M.Eng.
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BARREIROS, Emanoel Francisco Spósito. "The epidemics of programming language adoption." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/18000.

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Context: In Software Engineering, technology transfer has been treated as a problem that concernsonly two agents (innovation and adoption agents) working together to fill the knowledge gap between them. In this scenario, the transfer is carried out in a “peer-to-peer” fashion, not changing the reality of individuals and organizations around them. This approach works well when one is just seeking the adoption of a technology by a“specific client”. However, it can not solve a common problem that is the adoption of new technologies by a large mass of potential new users. In a wider context like this, it no longer makes sense to focus on “peer-to-peer” transfer. A new way of looking at the problem is necessary. It makes more sense to approach it as diffusion of innovations, where there is an information spreading in a community, similar to that observed in epidemics. Objective: This thesis proposes a paradigm shift to show the adoption of programming languages can be formally addressed as an epidemic. This focus shift allows the dynamics of programming language adoption to be mathematically modelled as such, and besides finding models that explain the community’s behaviour when adopting programming languages, it allows some predictions to be made, helping both individuals who wish to adopt a new language that might seem to be a new industry standard, and language designers to understand in real time the adoption of a particular language by a community. Method: After a proof of concept with data from Sourceforge (2000 to 2009), data from GitHub (2009 to January 2016), a well-known open source software repository, and Stack Overflow (2008 to March 2016), a popular Q&A system for software developers, were obtained and preprocessed. Using cumulative biological growth functions, often used in epidemiological contexts, we obtained adjusted models to the data. Once with the adjusted models, we evaluated their predictive capabilities through repeated applications of hypothesis testing and statistical calculations in different versions of the models obtained after adjusting the functions to samples of different time frames from the repositories. Results: We show that programming language adoption can be formally considered an epidemiological phenomenon by adjusting a well-known mathematical function used to describe such phenomena. We also show that, using the models found, it is possible to forecast programming languages adoption. We also show that it is possible to have similar insights by observing user data, as well as data from the community itself, not using software developers as susceptible individuals. Limitations: The forecast of the adoption outcome (asymptote) needs to be taken with care because it varies depending on the sample size, which also influences the quality of forecasts in general. Unfortunately, we not always have control over the sample size, because it depends on the population under analysis. The forecast of programming language adoption is only valid for the analysed population; generalizations should be made with caution. Conclusion: Addressing programming languages adoption as an epidemiological phenomenon allows us to perform analyses not possible otherwise. We can have an overview of a population in real time regarding the use of a programming language, which allows us, as innovation agents, to adjust our technology if it is not achieving the desired “penetration”; as adoption agents, we may decide, ahead of our competitors, to adopt a seemingly promising technology that may ultimately become a standard.
Contexto: Em Engenharia de Software, transferência de tecnologia tem sido tratada como um problema pontual, um processo que diz respeito a dois agentes (os agentes de inovação e adoção) trabalhando juntos para preencher uma lacuna no conhecimento entre estes dois. Neste cenário, a transferência é realizada “ponto a ponto”, envolvendo e tendo efeito apenas nos indivíduos que participam do processo. Esta abordagem funciona bem quando se está buscando apenas a adoção da tecnologia por um “cliente” específico. No entanto, ela não consegue resolver um problema bastante comum que é a adoção de novas tecnologias por uma grande massa de potenciais novos usuários. Neste contexto mais amplo, não faz mais sentido focar em transferência ponto a ponto, faz-se necessária uma nova maneira de olhar para o problema. É mais interessante abordá-lo como difusão de inovações, onde existe um espalhamento da informação em uma comunidade, de maneira semelhante ao que se observa em epidemias. Objetivo: Esta tese de doutorado mostra que a adoção de linguagens de programação pode ser tratada formalmente como uma epidemia. Esta mudança conceitual na maneira de olhar para o fenômeno permite que a dinâmica da adoção de linguagens de programação seja modelada matematicamente como tal, e além de encontrar modelos que expliquem o comportamento da comunidade quando da adoção de uma linguagem de programação, permite que algumas previsões sejam realizadas, ajudando tanto indivíduos que desejem adotar uma nova linguagem que parece se apresentar como um novo padrão industrial, quanto ajudando projetistas de linguagens a entender em tempo real a adoção de uma determinada linguagem pela comunidade. Método: Após uma prova de conceito com dados do Sourceforge (2000 a 2009), dados do GitHub (2009 a janeiro de 2016) um repositório de projetos software de código aberto, e Stack Overflow (2008 a março de 2016) um popular sistema de perguntas e respostas para desenvolvedores de software, from obtidos e pré processados. Utilizando uma função de crescimento biológico cumulativo, frequentemente usada em contextos epidemiológicos, obtivemos modelos ajustados aos dados. Uma vez com os modelos ajustados, realizamos avaliações de sua precisão. Avaliamos suas capacidades de previsão através de repetidas aplicações de testes de hipóteses e cálculos de estatísticas em diferentes versões dos modelos, obtidas após ajustes das funções a amostras de diferentes tamanhos dos dados obtidos. Resultados: Mostramos que a adoção de linguagens de programação pode ser considerada formalmente um fenômeno epidemiológico através do ajuste de uma função matemática reconhecidamente útil para descrever tais fenômenos. Mostramos também que é possível, utilizando os modelos encontrados, realizar previsões da adoção de linguagens de programação em uma determinada comunidade. Ainda, mostramos que é possível obter conclusões semelhantes observando dados de usuários e dados da comunidade apenas, não usando desenvolvedores de software como indivíduos suscetíveis. Limitações: A previsão do limite superior da adoção (assíntota) não é confiável, variando muito dependendo do tamanho da amostra, que também influencia na qualidade das previsões em geral. Infelizmente, nem sempre teremos controle sob o tamanho da amostra, pois ela depende da população em análise. A adoção da linguagem de programação só é válida para a população em análise; generalizações devem ser realizadas com cautela. Conclusão: Abordar o fenômeno de adoção de linguagens de programação como um fenômeno epidemiológico nos permite realizar análises que não são possíveis de outro modo. Podemos ter uma visão geral de uma população em tempo real no que diz respeito ao uso de uma linguagem de programação, o que nos permite, com agentes de inovação, ajustar a tecnologia caso ela não esteja alcançando o alcance desejado; como agentes de adoção, podemos decidir por adotar uma tecnologia aparentemente promissora que pode vir a se tornar um padrão.
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Books on the topic "Epidemics"

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Eberhard-Metzger, Claudia. Las epidemias. Madrid: Acento Editorial, 1998.

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Bjørnstad, Ottar N. Epidemics. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97487-3.

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Bjørnstad, Ottar N. Epidemics. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-12056-5.

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Haugen, David M. Epidemics. Detroit [Mich.]: Gale cengage Learning/Greenhaven Press, 2011.

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Bisen, Prakash S., and Ruchika Raghuvanshi. Emerging Epidemics. Hoboken, NJ: John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118393277.

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Christopher, Mari, ed. Global epidemics. Bronx, NY: H.W. Wilson Company, 2007.

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1964-, Dudley William, ed. Epidemics: Opposing viewpoints. San Diego, Calif: Greenhaven Press, 1999.

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John, Balint, ed. Ethics and epidemics. Amsterdam: Elsevier, 2006.

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1960-, Williams Mary E., ed. Epidemics: Opposing viewpoints. Farmington Hills, MI: Greenhaven Press, 2005.

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Chong, Alberto. Technology and epidemics. [Washington, D.C.]: International Monetary Fund, African Department, 1999.

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Book chapters on the topic "Epidemics"

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Chakraborty, Rhyddhi. "Epidemics." In Encyclopedia of Global Bioethics, 1–13. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-05544-2_174-1.

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Chakraborty, Rhyddhi. "Epidemics." In Encyclopedia of Global Bioethics, 1–13. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-05544-2_174-2.

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Chakraborty, Rhyddhi. "Epidemics." In Encyclopedia of Global Bioethics, 1–13. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-05544-2_174-3.

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Shekhar, Shashi, and Hui Xiong. "Epidemics." In Encyclopedia of GIS, 287. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_360.

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Kamieński, Łukasz. "Epidemics." In The Palgrave Encyclopedia of Global Security Studies, 1–12. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-74336-3_532-1.

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Raczynski, Stanislaw. "Epidemics." In Catastrophes and Unexpected Behavior Patterns in Complex Artificial Populations, 103–22. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2574-9_6.

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Chakraborty, Rhyddhi. "Epidemics." In Encyclopedia of Global Bioethics, 1130–41. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-09483-0_174.

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Kamieński, Łukasz. "Epidemics." In The Palgrave Encyclopedia of Global Security Studies, 470–81. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-319-74319-6_532.

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Epstein, Jonathan H., and Hume E. Field. "Anthropogenic Epidemics." In Bats and Viruses, 249–79. Hoboken, NJ: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781118818824.ch10.

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Ferhani, Adam, and Gregory Stiles. "Mapping epidemics." In Mapping and Politics in the Digital Age, 87–101. Abingdon, Oxon : New York, NY ; Routledge, 2019. |: Routledge, 2018. http://dx.doi.org/10.4324/9781351124485-6.

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Conference papers on the topic "Epidemics"

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Pinto, Conrado C., and Daniel R. Figueiredo. "Identifying Asymptomatic Nodes in Network Epidemics using Betweenness Centrality." In Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/wperformance.2024.2414.

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Epidemics of certain viruses in a population can have major impact effects, as is the case in the recent global pandemic caused by the COVID-19 virus. Identifying infected individuals during the course of an epidemic is extremely important for measuring spread and designing more effective control measures. However, in some epidemics infected individuals do not exhibit clear symptoms despite being infected and contributing to the contagion of others (called asymptomatic). This work addresses the problem of identifying asymptomatic individuals in network epidemics based on the observation of infected (symptomatic) individuals. The main contribution of this work is the evaluation of different centrality measures to identify asymptomatic individuals when a fraction of the infected nodes in a network epidemic is observed at a given moment in time. In particular, a variation of the betweenness centrality measure is proposed in this work. An evaluation using different network models and different asymptomatic rates shows that the proposed centrality measure outperforms other centrality measures in many scenarios. Furthermore, the performance of centrality measures increases as the fraction of asymptomatic decreases, showing an interesting trade-off.
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Schwartz, Ira B., and Lora Billings. "Stochastic epidemic outbreaks: why epidemics are like lasers." In Second International Symposium on Fluctuations and Noise, edited by Zoltan Gingl. SPIE, 2004. http://dx.doi.org/10.1117/12.547642.

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Thaler, Jonathan, Thorsten Altenkirch, and Peer-Olaf Siebers. "Pure Functional Epidemics." In IFL 2018: 30th Symposium on Implementation and Application of Functional Languages. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3310232.3310372.

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Li, Yunna. "Impact of inter-city population mobility and public transportation policies on infectious epidemics." In Post-Oil City Planning for Urban Green Deals Virtual Congress. ISOCARP, 2020. http://dx.doi.org/10.47472/aoto6191.

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This study takes the COVID-19 outbreak in early 2020 as the research object, and obtains the population outflow data of Wuhan from January 1 to February 1, 2020 based on the Baidu Migration Big Data system, and compares the data on the number of epidemics in each city published on the official website, studies the relevance of population movement between cities and epidemic infection, and analyzes the role of policy-oriented epidemic control by controlling public transportation during special periods of infectious diseases. The results show that the spread of the infectious epidemic between cities in the early outbreak stage is strongly correlated with the population outflow from the source city of the infection. After the traffic connection is cut off in the later stage, it is more related to crowd gathering and prevention and control measures; During the special epidemic period, we should not blindly advocate the “public transportation priority” policy. We should distinguish between passenger and freight transportation, formulate rough rules at the inter-regional and intra-city traffic levels, and specify corresponding safeguards under the framework of the “Healthy City”.
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Souza, Ronald, and Daniel Figueiredo. "Characterizing Protection Effects on Network Epidemics driven by Random Walks." In Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wperformance.2020.11109.

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Protection effects (PFx) denote protective measures taken by individuals (such as to wear masks and wash hands) upon their risk-perception towards an ongoing epidemic outbreak. The holistic force produced may fundamentally change the course of a spreading, with respect to both its reach and duration. This work proposes a model for PFx on network epidemics where nodes are sites mobile-agents may visit. Risk aversion is encoded as random-walks biased to safe sites. Assuming the network is a complete graph, the model is analyzed and framed as a classical SIS. We find a regime under which PFx preclude endemic steady-states upon arbitrarily large rates for both walk and transmissibility. Simulation results support our theoretical findings.
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Bulygin, Yuriy. "Epidemics of Mobile Worms." In 2007 IEEE International Performance, Computing, and Communications Conference. IEEE, 2007. http://dx.doi.org/10.1109/pccc.2007.358929.

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Roychoudhury, Sohini, Sanjoy Das, Caterina Scoglio, Swagatam Das, Bijaya K. Panigrahi, and Shyyam S. Pattnaik. "Mitigation strategies in epidemics." In the 12th annual conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1830483.1830725.

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Lotidis, Kyriakos, Aris L. Moustakas, and Nicholas Bambos. "Controlling Epidemics via Testing." In 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021. http://dx.doi.org/10.1109/cdc45484.2021.9683289.

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Baranov, E. "Demographic Aspects of Epidemics in the USSR in Modern Historiography." In XIII Ural Demographic Forum. Global challenges to demographic development. Institute of Economics of the Ural Branch of RAS, 2022. http://dx.doi.org/10.17059/udf-2022-1-2.

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The paper attempts to identify the main approaches to the study of epidemics in the USSR, which have been developed in modern historical and demographic research, as well as to determine the results of a study of the demographic aspects of epidemics during the period of forced modernisation and the active stage of social transformation in Russia in the 1930s. The study allows a deeper understanding of historical trends in the evolution of public health, the development of morbidity and epidemics. Formulation of the issue of analysing approaches to the historical and demographic studies on the topic of epidemics in the USSR determines the paper novelty. The main historiographical sources were historical and demographic studies pub lished at the present stage of development of Russian historiography. It is shown that the topic of epidemics in the USSR was developed in the framework of studies on demographic catastrophes and crises and in the course of the implementation of attempts at historical generalisations and identification of demographic patterns. Epidemics were characterised as a demographic factor. Works on the «epidemiological transition» are deemed to be of historiographical significance. The article concluded that the examined studies characterised the role of epidemics in the occurrence of demographic upheavals, revealed the leading role of infectious diseases in the causes of death in the 1930s, and established the negative impact of epidemics on modernisation processes in the demographic sphere
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Shalak, Alexander. "The Extrapolation of Experience Neutralization Epidemic Diseases in the 1940s. On the Coronavirus in Modern Russia (on the Example of the Irkutsk Region)." In Irkutsk Historical and Economic Yearbook 2021. Baikal State University, 2021. http://dx.doi.org/10.17150/978-5-7253-3040-3.11.

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The purpose of this article is to understand the historical experience of the Soviet state in neutralizing epidemic diseases during the Great Patriotic War and in the post-war period. Extrapolation of this experience to the current situation associated with the coronavirus epidemic makes it possible to understand the complex of required measures for the successful neutralization of such diseases. It was in the 1940s that, despite the critical situation in the social sphere, it was possible not only to prevent mass epidemics in the Irkutsk region, but also to reduce mortality in comparison with the pre-war period. Effective public management was the main factor in the successful solution of problems in this area.
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Reports on the topic "Epidemics"

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Vaishnav, Y. Coronaviruses: Epidemics and Pandemics. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1618194.

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Eichenbaum, Martin, Sergio Rebelo, and Mathias Trabandt. The Macroeconomics of Epidemics. Cambridge, MA: National Bureau of Economic Research, March 2020. http://dx.doi.org/10.3386/w26882.

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Aksoy, Cevat Giray, Barry Eichengreen, and Orkun Saka. The Political Scar of Epidemics. Cambridge, MA: National Bureau of Economic Research, June 2020. http://dx.doi.org/10.3386/w27401.

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Atkeson, Andrew. Behavior and the Dynamics of Epidemics. Cambridge, MA: National Bureau of Economic Research, May 2021. http://dx.doi.org/10.3386/w28760.

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Pu, lei, Peng Sun, and hongchao zheng. Effects of Exercise on Cardiovascular Epidemics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0024.

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Sow, Khoudia, and Mariam Boyon. Roundtable report: Epidemic preparedness and response in Senegal. Institute of Development Studies, August 2024. http://dx.doi.org/10.19088/sshap.2024.034.

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On 8 December 2023, SSHAP convened a roundtable discussion on social science research in epidemic preparedness and response in Senegal. The event was organised in Dakar by the Centre Régional de Recherche et de Formation à la Prise en Charge Clinique de Fann (CRCF) based at the Centre Hospitalier National Universitaire (CHNU), and the Centre des Opérations d'Urgence Sanitaire (COUS). It took place in the Conseil National de Lutte contre le SIDA. The objectives were to: Share strategic information derived primarily from social science research with the various actors involved in epidemic preparedness and response; Help the actors put in place targeted and appropriate interventions for future epidemics; and Highlight the roles social science researchers can play in preparing for and responding to epidemics. This report shares a summary of considerations from the roundtable discussion. The report also includes background information about Senegal's epidemic preparedness and response system as well as the challenges the system faces. This additional information is based on a review of scientific articles and various public health and social science documents from research and other institutions.
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Moore, Timothy, and Rosalie Liccardo Pacula. Causes and Consequences of Illicit Drug Epidemics. Cambridge, MA: National Bureau of Economic Research, December 2021. http://dx.doi.org/10.3386/w29528.

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Caparini, Marina. Multilateral Peace Operations and the Challenges of Epidemics and Pandemics. Stockholm International Peace Research Institute, October 2022. http://dx.doi.org/10.55163/awyk9746.

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This paper provides a broad overview of how multilateral peace operations have responded to cholera and Ebola epidemics and the HIV/ AIDS and Covid-19 pandemics over the past 20 years. Such public health crises can be especially lethal in fragile and conflict-affected areas. Peace operations possess resources and capacities that enable them to contribute in varying ways to state and humanitarian responses. Multilateral peace operations have acted to protect the health of peacekeepers and to prevent peacekeepers from spreading infectious diseases. They have also directly provided security to health and humanitarian personnel, health services and supplies to some non-mission personnel and local communities, and communications capacities to dispel dis/misinformation and inform local populations about health measures. Another area where peace operations have given indirect support to epidemic/ pandemic response measures is by offering political engagement, coordination, training and material support to host state actors as well as supporting the rule of law and capacity building of local security and police personnel. The paper concludes by considering arguments against and in favour of more strategic involvement of peace operations in future epidemics and pandemics.
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Eichenbaum, Martin, Sergio Rebelo, and Mathias Trabandt. Epidemics in the Neoclassical and New Keynesian Models. Cambridge, MA: National Bureau of Economic Research, June 2020. http://dx.doi.org/10.3386/w27430.

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Carlberg, Matthew A. Epidemics Don't Cause Wars, But They Can End 'Em. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada403988.

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