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Добірка наукової літератури з теми "COVID-19 Epidemic"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 11 вересня 2023

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Статті в журналах з теми "COVID-19 Epidemic"

1

Yoshikura, Hiroshi. "Measles Epidemic Influenced by COVID-19 Epidemic." Epidemiology International Journal 6, no. 3 (2022): 1–3. http://dx.doi.org/10.23880/eij-16000244.

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In Western Pacific, the number of measles cases dropped precipitously in early 2020. As the coverage of measles vaccine remained almost unchanged, the precipitous drop of the measles cases could not be attributed to measles vaccine. It was probably brought about by physical distancing and other measures to prevent spread of SARS-CoV-2. SARS-CoV-2 cases increased wave by wave, while the number of the deaths divided by that of the patients declined. The decline of the casefatality rate could not be attributable to SARS-CoV-2 vaccine, because the trend emerged from the start of the epidemic, far ahead of introduction of SARS-CoV-2 vaccine.
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2

Brierly, Joseph E. "Epidemic Cycle." Journal of Biotechnology & Bioinformatics Research 2, no. 1 (March 31, 2020): 1–4. http://dx.doi.org/10.47363/jbbr/2019(1)104.

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This article explains the natural progression of a typical viral epidemic. Epidemics historically go through a progressive cycle because once a person is victimized normally there is an immune and non-infectious period of one or more years. At this time both immunity and infectiousness has not been scientifically verified for the Covid-19 virus. However, likely the Covid-19 virus will progress the way of other past virus epidemics. At present there is much untested and possibly unreliable information regarding the Covid-19 epidemic. This article shows the most likely way the Covid-19 epidemic will progress over time.
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3

Bagal, Dilip Kumar, and Pravajyoti Patra. "COVID-19: A Never Seen Pandemic." YMER Digital 21, no. 08 (August 8, 2022): 321–25. http://dx.doi.org/10.37896/ymer21.08/27.

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People have long been affected by epidemics and pandemics of communicable illnesses. The outbreaks have been around for thousands of years. Even in our modern day, epidemics have ravaged civilization till it leads people to despair. In the meanwhile, viruses have always offered huge difficulties that have ignited horrific epidemics and pandemics. A pandemic is the widespread spread of a new sickness. Viral respiratory diseases, such as those caused by a novel influenza virus or the coronavirus COVID-19, are the most likely to evolve into a pandemic. A pandemic is not the same as an epidemic. In an epidemic, many more cases of a health condition occur than would typically develop in a community or area, however the ailment does not move outside. The World Health Organization (WHO) is responsible for declaring when a worldwide epidemic is underway. The WHO achieves this by monitoring outbreaks of a disease and receiving advice from worldwide health experts. This paper depicts the impact of COVID-19 in globally in various fields and also visualize the current status of this pandemic. Keywords: COVID-19; Confirmed Case; Pandemic; WHO
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4

Williamson, Graham. "COVID-19 Epidemic Editorial." Open Nursing Journal 14, no. 1 (March 16, 2020): 37–38. http://dx.doi.org/10.2174/1874434602014010037.

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5

Velavan, Thirumalaisamy P., and Christian G. Meyer. "The COVID‐19 epidemic." Tropical Medicine & International Health 25, no. 3 (February 16, 2020): 278–80. http://dx.doi.org/10.1111/tmi.13383.

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6

Mustafina, Bakhytkul, Aiman Mussina, Anel Serikbayeva, and Erdos Baranbayev. "Epidemiological forecast of COVID-19 in Kazakhstan." JOURNAL OF HEALTH DEVELOPMENT 4, no. 39 (2020): 49–53. http://dx.doi.org/10.32921/2225-9929-2020-4-39-49-53.

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Currently, the epidemic situation in Kazakhstan is stable.The main reason for this is a correctly calculated epidemic forecast. This article provides a forecast of the epidemic situation in Kazakhstan using a mathematical model. The model is used to analyze the epidemic situation between Kazakhstan and other countries. In addition, the article discusses the ways and consequences of eliminating the epidemic. The developed model is used to describe how the upcoming epidemic situation is developing. In other words, the article analyzes and evaluates the epidemic caused by coronavirus infection in Kazakhstan using a mathematical model
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7

Tamm, M. V. "COVID-19 in Moscow: prognoses and scenarios." FARMAKOEKONOMIKA. Modern Pharmacoeconomic and Pharmacoepidemiology 13, no. 1 (April 24, 2020): 43–51. http://dx.doi.org/10.17749/2070-4909.2020.13.1.43-51.

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Aim: to present a mathematical model of the development of COVID-19 in Moscow along with the analysis of some scenarios of epidemic control and possible epidemic consequences.Materials and Methods. The modeling of the epidemics was based on the extended SEIR model proposed lately in the group of Prof. R. Neher and realized as a freely available software program. The authors based the choice of the parameters of modeling on published data on the epidemiological properties of the novel coronavirus SARS-CoV-2 and open access data on the registered cases of COVID-19 in Moscow for 8-27 March 2020.Results. Five potential scenarios of the development of COVID-19 epidemics are studied. The scenarios are differed by the levels of the control measures: Null Scenario corresponded to the lack of protective measures, Scenario A – mild measures of the epidemic control (closing of schools and universities, recommendations for senior citizens to stay inside), Scenario B – medium level of control (closing of all public places, recommendation for the citizens to stay inside), Scenarios C and D – complete lockdown (from the beginning of May 2020 within Scenario C and from the beginning of April 2020 within Scenario D). It was shown that within the Null Scenario, the lethality from the novel coronavirus in Moscow will exceed 100 thousand people, and the number of critically ill patients on the peak of the epidemics will exceed the capacities of the system of healthcare. Scenarios A and B did not provide for a radical decrease in the fatality rate, and the number of critically ill patients at the peak of epidemics will still exceed the capacities of the system of healthcare. Besides, within Scenario B, the epidemics will last for more than a year. Scenarios C and D will allow for the control of epidemics and a significant decrease in the rate of letha lity (by 30 and 400 times, respectively). At the same time, these two scenarios prevent the population from developing herd immunity, which would result in the population susceptibility to repeated epidemics outbreaks. Conclusion. The scenarios intended for the slow development of herd immunity in the conditions of epidemic control would not bring sufficient results: the lethality would remain unacceptably high, the capacities of the system of healthcare would be overloaded, and the time of limiting measures would be unacceptably long. Such measures as complete lockdown would stop the present epidemics. The earlier they are introduced, the more efficient will be the results. To prevent further repeated outbreaks of the epidemics, it is necessary to establish a system of available, quick, and efficient testing in combination with point isolation of the infected patients and their contacts.
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E, Rocchi, Peluso S, Sisti D, Rocchi M, and Carletti M. "Lessons From COVID-19 Epidemic?" Journal of Translational Science and Research 3, no. 1 (December 31, 2020): 1–3. http://dx.doi.org/10.24966/tsr-6899/100009.

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9

E, Rocchi, Peluso S, Sisti D, Rocchi M, and Carletti M. "Lessons From COVID-19 Epidemic?" Journal of Translational Science and Research 3, no. 1 (December 31, 2020): 1–3. http://dx.doi.org/10.24966/tsr-6899/100009.

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Gupta, Mayanka. "Epidemic Modelling of COVID-19." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1763–69. http://dx.doi.org/10.22214/ijraset.2021.37439.

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Abstract: COVID-19 has had a disastrous impact on millions of lives all over the world.199,466,211 confirmed cases of COVID19 and 4,244,541 deaths have been reported to WHO till 4th august. Analyzing the available data and predicting the pandemic trend is important since the situation can be controlled only when there is adequate preparation. Research using epidemiological models helps in analyzing different facets of COVID including infection, recovery and death rate. Predicting the daily increase of cases can help reduce the burden on health care workers and government by aiding them in planning the required resources in advance. Thus, in this project data driven epidemic modelling approach is used. COVID cases of 10 forthcoming days using three modelling techniques namely Polynomial Regression, Bayesian Ridge Regression and Support Vector Machine are predicted. The performance metric used to identify the best model are MSE and MAE. Polynomial Regression is found to have best performance followed by Bayesian ridge regression. Support Vector Machine has a poor performance. Keywords: Epidemic Modelling, COVID-19, Machine Learning, Polynomial Regression, Bayesian Ridge Regression, Support Vector Machine
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Більше джерел

Дисертації з теми "COVID-19 Epidemic"

1

Schmedding, Anna. "Epidemic Spread Modeling For Covid-19 Using Hard Data." W&M ScholarWorks, 2021. https://scholarworks.wm.edu/etd/1627047844.

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We present an individual-centric model for COVID-19 spread in an urban setting. We first analyze patient and route data of infected patients from January 20, 2020 ,to May 31, 2020, collected by the Korean Center for Disease Control & Prevention (KCDC) and illustrate how infection clusters develop as a function of time. This analysis offers a statistical characterization of mobility habits and patterns of individuals. We use this characterization to parameterize agent-based simulations that capture the spread of the disease, we evaluate simulation predictions with ground truth, and we evaluate different what-if counter-measure scenarios. Although the presented agent-based model is not a definitive model of how COVID-19 spreads in a population, its usefulness, limitations, and flexibility are illustrated and validated using hard data.
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2

Дробко, Ілля Вікторович. "Spanish flu and COVID-19. How it was and how it will be?" Thesis, Молодіжна наукова ліга, 2020. https://er.knutd.edu.ua/handle/123456789/15949.

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The work deals with COVID-19 and Spanish flu comparison analysis. It is possible to compare these two pandemics; they are looking really similar in some points, and for the most part proceed according to similar scenarios. Maybe we can predict and correct some mistakes of the past with knowledge of Spanish flu history.
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Svensson, Ida, and Desirée Bard. "Upplevelser av aktivitetsbalans och dess påverkan på stress för studenter under pandemin covid-19 : En kvalitativ intervjustudie." Thesis, Jönköping University, HHJ, Avd. för rehabilitering, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-52608.

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Titel: Upplevelser av aktivitetsbalans och dess påverkan på stress för studenter under pandemin covid-19. Syfte: Syftet med studien var att beskriva hur studenter på en svensk högskola upplever aktivitetsbalans och dess påverkan på stress under pandemin covid-19. Metod: En kvalitativ intervjustudie med 10 svenska informanter där materialet analyserades med en kvalitativ innehållsanalys. Resultat: I resultatet kom det fram 3 kategorier: Minskade möjligheter i att delta i aktiviteter påverkar aktivitetsbalansen, Strategier för att upprätthålla aktivitetsbalans och Relationen mellan aktivitetsbalans och stress. Resultatet visade upplevelserna av aktivitetsbalans och dess påverkan på stress hos informanterna. Slutsats: Fungerande rutiner och anpassade aktiviteter var av betydelse för upplevd aktivitetsbalans under pandemin covid-19. Upplevelsen av att pandemin påverkat aktivitetsbalans negativt var på grund av bristande rutiner och anpassningar i vardagen. Vid upplevd aktivitetsobalans sågs en verkan på stress men med tidigare erfarenhet av stresshantering kunde det undvikas.
Title: The experiences of occupational balance and its impact on stress for students during the covid-19 pandemic. Aim: The purpose of the study was to describe how students at a Swedish university experience occupational balance and its impact on stress during the pandemic covid-19. Method: A qualitative interview study was used with 10 Swedish participants and the material was analyzed with a qualitative content analysis. Result: The result revealed 3 categories: Reduced opportunities in participating in activities affect the occupational balance, Strategies for maintaining occupational balance and The relationship between occupational balance and stress. The results showed experiences of occupational balance and its impact on between informants Conclusion: Functioning routines and adapted activities were important for the perceived occupational balance during the covid-19 pandemic. The experience that the pandemic had a negative effect on the balance of activities was due to a lack of routines and adaptations in everyday life. In the case of perceived occupational imbalance, an effect on stress was seen, but with previous experience of stress management, it could be avoided.
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Васильєва, Тетяна Анатоліївна, Татьяна Анатольевна Васильева, Tetiana Anatoliivna Vasylieva, Сергій Вячеславович Лєонов, Сергей Вячеславович Леонов, Serhii Viacheslavovych Lieonov, Наталія Євгенівна Летуновська, Наталия Евгеньевна Летуновская, and Nataliia Yevhenivna Letunovska. "The economic impact of covid-19: forecasting for Ukrainian regions." Thesis, Sumy State University, 2020. https://essuir.sumdu.edu.ua/handle/123456789/80956.

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У тезах наведені економічні показники в Україні, що показують прямий вплив пандемії COVID-19 на соціально-економічне становище ряду вітчизняних галузей та регіонів. Обгрунтована доцільність використання економіко-математичних моделей для прогнозування розвитку подій під час такого роду епідемій.
В тезисах приведены экономические показатели в Украине, которые показывают прямое влияние пандемии COVID-19 на социально-экономическое положение ряда отечественных отраслей и регионов. Обоснована целесообразность использования экономико-математических моделей для прогнозирования развития событий во время такого рода эпидемий.
The abstracts present economic indicators in Ukraine that show the direct impact of the COVID-19 pandemic on the socio-economic situation of a number of domestic industries and regions. The expediency of using economic and mathematical models to predict the development of events during such epidemics is substantiated.
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Chen, Yulu. "Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection using Planet Remote Sensing Satellite Images." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1609843145639886.

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Іванов, Т. Л., та В. М. Лисевич. "Аналітична оцінка впливу наслідків пандемії COVID-19 на стан національної економіки України за видами діяльності". Thesis, Чернігів, 2020. http://ir.stu.cn.ua/123456789/20828.

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Іванов, Т. Л. Аналітична оцінка впливу наслідків пандемії COVID-19 на стан національної економіки України за видами діяльності : магістерська робота: 051 Економіка / Т. Л. Іванов, В. М. Лисевич ; керівник роботи Шадура-Никипорець Н. Т. ; Національний університет «Чернігівська політехніка», кафедра теоретичної та прикладної економіки. – Чернігів, 2020. – 79 с.
Предметом дослідження ВКР виступають економічні, історичні, соціальні аспекти динаміки розвитку пандемії на території України. Об’єктом дослідження є стан національної економіки України під час пандемії CoVid-19 та перспективи розвитку країни в постпандемічний період. Метою дипломної роботи є оцінка впливів пандемії CoVid-19 на стан економіки України як в цілому, так і по галузям, визначення соціально-економічних втрат, яких зазнає країна та висування пропозицій щодо вирішення проблемних питань. Основні завдання роботи: розкриття поняття пандемії та особливості її прояву, хронології виникнення та поширення пандемії коронавірусної інфекції COVID-19 в Україні та світі, аналіз основних макроекономічних показників України напередодні пандемії та під час трьох періодів (перша хвиля, «міжсезоння» та друга хвиля), а також економічної активності країни, висування власних пропозицій щодо вирішення проблемних питань у дослідженій сфері. В ході виконання даної роботи авторами були сформульовані висновки щодо заходів подолання наслідків пандемії.
The subject of the thesis is economic, historical and social aspects of the dynamics of the pandemic in Ukraine. The object of the study is the state of the national economy of Ukraine during the CoVid-19 pandemic and the country's development prospects in the post-pandemic period. The purpose of the thesis is to assess the impact of the COVID-19 pandemic on the state of Ukrainian economy as a whole and by industry, to identify socio-economic losses of the country and to make proposals in order address issues. The main objectives of the thesis: to reveal the concept of pandemic and its peculiarity, the time line and the spread of COVID-19 pandemic in Ukraine and the World, to analyze the major macroeconomic indicators of Ukraine right before the pandemic and over three periods (the first wave, the off-season and the second wave), as well as country’s economic activity, to suggest our own hypothesis on how to fight with economic consequences of the pandemic in the future. In the course of this work, we formulated conclusions on the measures to overcome the effect of the pandemic on the Ukrainian economy.
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Heidrich, Peter [Verfasser], Thomas [Akademischer Betreuer] Götz, and Thomas [Gutachter] Götz. "Analysis and Numerical Simulations of Epidemic Models on the Example of COVID-19 and Dengue / Peter Heidrich ; Gutachter: Thomas Götz ; Betreuer: Thomas Götz." Koblenz, 2021. http://d-nb.info/1231794356/34.

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Trimarchi, Biagio. "Distributed Identification of a Network Model for Pandemic Spreading." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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The recent outburst of the Covid-19 epidemic has made clear the necessity of developing dynamical models for the prediction and the control of large scale epidemic systems. Due to the large impact they have on the life of people and on the economy of the countries, it is extremely important to design models that are able to predict the evolution of such complex phenomena. In the thesis a compartmental model for epidemics is developed and implemented in a Python toolbox suited for distributed computation. Based on the proposed model and on available data, an identification algorithm, based on a gradient free descent method, is proposed to find model parameters that best fit the data. The distributed nature of the system allows for the implementation of a scheme in which computation is distributed among different spatial regions.
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Andersson, Daniel. "Döden väntar inte : Anpassningar av vanliga begravningar under Covid-19 pandemin." Thesis, Karlstads universitet, Institutionen för samhälls- och kulturvetenskap (from 2013), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-80152.

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Syftet med denna rapport är att undersöka om kristna församlingar tvingats göra kompromisser eller anpassa sina begravningsritualer under corona-pandemin. Tidigare forskning om hur begravningar har hanterats vid andra epidemier, så som aids-epidemin på 80- och 90-talet och kolera-epidemin i Guinea-Bissau 1994, samt hur begravningstraditionen har utvecklats i slutet av 1900-talet har fungerat som inspiration för vad som ska undersökas i denna rapport. För att få ett svar på rapportens syfte har en kvalitativ insamlingsmetod bestående av strukturerade intervjuer av religiösa samfund och begravningsbyråer använts för att sammanställa ett empiriskt material i form av en databas. Med hjälp av en kvalitativ innehållsanalys har dessa intervjuer analyserats för att ge ett svar på rapportens frågeställning. Genom denna analys av intervjuer med fem kristna församlingar och två begravningsbyråer så går det att konstatera att anpassningar och kompromisser har varit nödvändiga när det kommer till hantering av avlidna och planering samt genomförande av begravningar. Rapporten visar att under pågående pandemi har man lyckats hitta lösningar för att kunna genomföra begravningar, utan att ritualerna påverkas nämnvärt.
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Moretto, Irene. "Aspetti numerici nell'applicazione del modello SIR." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/22165/.

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Questo lavoro si concentra sull'applicazione del classico modello a compartimenti di Kermack–McKendrick (SIR), per controllare l'andamento dell'epidemia COVID-19 in Italia, utilizzando i dati messi a disposizione dal Dipartimento della Protezione Civile italiano, relativi alla regione Veneto. L'implementazione di tale modello pone interessanti problemi legati sia alla soluzione numerica del problema inverso, attraverso cui avviene la calibrazione dei parametri del modello, che alla soluzione numerica dei problemi differenziali richiesti all'interno della calibrazione e successivamente in fase di previsione. Un primo obiettivo di questa tesi è quello di fare un'analisi dei metodi numerici per risolvere il sistema di equazioni differenziali che descrive il modello SIR e determinare, attraverso opportuni confronti, l'algoritmo più adatto a risolvere il problema di calibrazione. Inoltre, si è studiata una modifica del SIR che prevede l'utilizzo di parametri come funzioni dipendenti dal tempo, nota come modello forzato, con l'obiettivo di ottenere un migliore adattamento della curva epidemiologica in base alle misure di contenimento introdotte.
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Книги з теми "COVID-19 Epidemic"

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Fang, Xiaoyi, and Xiuyun Lin, eds. A Guide to Mental Health in Family Under the COVID-19 Epidemic. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6545-6.

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2

Goźliński, Paweł. Jutro jest teraz. Warszawa: Wydawnictwo Agora, 2020.

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3

South Africa Beyond Covid-19: Trends, change and recovery. La Vergne: Jonathan Ball Publishers, 2020.

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4

Mungomba, Nakubyana. Rapid assessment of effects of COVID-19 on education, health, food & nutrition and social welfare in Zambia: Education excerpt. Lusaka: Zambia Institute for Policy Analysis & Research (ZIPAR), 2020.

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5

author, Mungomba Nakubyana, Zambia Institute for Policy Analysis & Research, and UNICEF, eds. Rapid assessment of effects of COVID-19 on education, health, food & nutrition and social welfare in Zambia: Food & nutrition excerpt. Lusaka: Zambia Institute for Policy Analysis & Research (ZIPAR), 2020.

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6

Hŏ, Yun-jŏng. K'orona rip'ot'ŭ: Taehan Min'guk ch'ogi pangyŏk 88-il ŭi kirok = COVID-19 report. Sŏul-si: Tong Asia, 2020.

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7

Politicheskai︠a︡ immunologii︠a︡: Monografii︠a︡. Moskva: Prospekt, 2021.

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Farrán, Roque. Leer, meditar, escribir: La práctica de la filosofía en pandemia. Adrogué?, Argentina]: La Cebra, 2020.

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Albin, Ricardo Cravo. Pandemia e pandemônio: Relatos indignados de um Brasil desgovernado. Rio de Janeiro: Batel, 2021.

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Qurayn, Aḥmad ʻAlī Muḥammad. Kūrūnā indhār min Rabb al-Barīyah wa-tarbiyat lil-basharīyah ʻalá manhaj Khayr al-Insānīyah: Al-Qurʼān munashshiṭ lil-manāʻah al-basharīyah, aḥkām fiqhīyah fī waqt al-balāʼ : al-wiqāyah min al-wabāʼ : al-ḥajr al-manzilī fī al-Islām. al-Qāhirah: Majmaʻ al-ʻĀlamīyah, 2021.

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Частини книг з теми "COVID-19 Epidemic"

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Racław, Mariola, and Dobroniega Głębocka. "Unmasking features of the state of the epidemic." In Ageing and COVID-19, 71–87. London: Routledge, 2021. http://dx.doi.org/10.4324/b22774-7.

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Smallman-Raynor, Matthew, and Andrew D. Cliff. "Eight Centuries of Epidemic and Pandemic Control." In COVID-19 and Similar Futures, 61–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70179-6_7.

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Adiga, Aniruddha, Bryan Lewis, Simon Levin, Madhav V. Marathe, H. Vincent Poor, S. S. Ravi, Daniel J. Rosenkrantz, et al. "AI Techniques for Forecasting Epidemic Dynamics: Theory and Practice." In Artificial Intelligence in Covid-19, 193–228. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08506-2_9.

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Thapa, Pawan. "Predicating COVID19 Epidemic in Nepal Using the SIR Model." In Artificial Intelligence for COVID-19, 229–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69744-0_14.

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Ranade, Jayadeva. "The COVID-19 Epidemic and China's Economy." In Xi Jinping: China's Third New Era, 143–52. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003349549-21.

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Battineni, Gopi, Nalini Chintalapudi, and Francesco Amenta. "Epidemic Models in Prediction of COVID-19." In Algorithms for Intelligent Systems, 19–34. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4236-1_2.

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Nesteruk, Igor. "Applications of the General SIR Model for Calculations of the COVID-19 Epidemic Waves in Ukraine." In COVID-19 Pandemic Dynamics, 141–46. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6416-5_11.

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Shanmuga Eswari, M., N. Indumathi, and R. Ramalakshmi. "COVID-19 Epidemic Analysis and Prediction in Virudhunagar District Using Machine Learning." In Artificial Intelligence for COVID-19, 311–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69744-0_18.

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Awotunde, Joseph Bamidele, Rasheed Gbenga Jimoh, Idowu Dauda Oladipo, Muyideen Abdulraheem, Taibat Bolanle Jimoh, and Gbemisola Janet Ajamu. "Big Data and Data Analytics for an Enhanced COVID-19 Epidemic Management." In Artificial Intelligence for COVID-19, 11–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69744-0_2.

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Singh, Yumnam Somananda, Yumnam Kirani, and Yumnam Jayanta Singh. "Local Analytical System for Early Epidemic Detection." In COVID-19: Prediction, Decision-Making, and its Impacts, 29–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9682-7_4.

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Тези доповідей конференцій з теми "COVID-19 Epidemic"

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Hausberger, Andreas, Barbara Tappeiner, René Baranyi, and Thomas Grechenig. "Long COVID Diary: A User Centered Approach for the Design of a Mobile Application Supporting Long COVID Patients." In Special Session on "COVID-19 epidemic data mining and EXploration". SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010972300003123.

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Bansal, Adhishree, Aarushi Bhardwaj, and Aman Sharma. "Forecasting the Trend of Covid-19 Epidemic." In 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2020. http://dx.doi.org/10.1109/pdgc50313.2020.9315795.

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Wang, Ranran, Gang Hu, Chi Jiang, Huimin Lu, and Yin Zhang. "Data Analytics for the COVID-19 Epidemic." In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2020. http://dx.doi.org/10.1109/compsac48688.2020.00-83.

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Martianova, A. E., V. Yu Kuznetsova, and I. M. Azhmukhamedov. "Mathematical Model of the COVID-19 Epidemic." In Research Technologies of Pandemic Coronavirus Impact (RTCOV 2020). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.201105.012.

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Aziz, Maysoon M., and Arqam Salim Mahmood. "Mathematical model of epidemic disease COVID-19." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING ICCMSE 2021. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0136379.

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Elkhovskaya, Liubov, Sergey Kovalchuk, Ilya Potapov, and Dmitrii Ovchinnikov. "COVID-19 Treatment Process Identification: A Case Study in Russian Hospital for Cardiology." In Special Session on "COVID-19 epidemic data mining and EXploration". SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010970400003123.

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Papadakis, Nikolaos, and Dimitrios Bakirtzis. "A Data Driven Investigation on the Relationship between Tests, Confirmed Cases and Positive Rate: The Case of Greece." In Special Session on "COVID-19 epidemic data mining and EXploration". SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010975300003123.

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Chu, Shenglan, Mengyao Liu, Bowen Wang, Huan Zhao, Chenglin Liu, Jiayi Guo, and Bin Zhao. "Dynamics analysis and countermeasures of covid-19 epidemic." In ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3469213.3470330.

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Tang, Kaiping, Yiqi Ma, Hao Yan, and Yunbo Rao. "A New Prediction Method of COVID-19 Epidemic." In 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2021. http://dx.doi.org/10.1109/prai53619.2021.9551052.

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Tang, Kaiping, Yiqi Ma, Hao Yan, and Yunbo Rao. "A New Prediction Method of COVID-19 Epidemic." In 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2021. http://dx.doi.org/10.1109/prai53619.2021.9551052.

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Звіти організацій з теми "COVID-19 Epidemic"

1

Hassan, Tarek Alexander, Stephan Hollander, Laurence van Lent, Markus Schwedeler, and Ahmed Tahoun. Firm-level Exposure to Epidemic Diseases: Covid-19, SARS, and H1N1. Cambridge, MA: National Bureau of Economic Research, April 2020. http://dx.doi.org/10.3386/w26971.

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Gupta, Sumedha, Kosali Simon, and Coady Wing. Mandated and Voluntary Social Distancing During The COVID-19 Epidemic: A Review. Cambridge, MA: National Bureau of Economic Research, November 2020. http://dx.doi.org/10.3386/w28139.

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Schmidt-Sane, Megan, Eva Niederberger, and Tabitha Hrynick. Key Considerations: Operational Considerations for Building Community Resilience for COVID-19 Response and Recovery. Institute of Development Studies (IDS), January 2021. http://dx.doi.org/10.19088/sshap.2021.002.

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Анотація:
As the unequal impact of the COVID-19 pandemic continues, there is a need to robustly support vulnerable communities and bolster ‘community resilience.’ A community resilience approach means to work in partnership with communities and strengthen their capacities to mitigate the impact of the pandemic, including its social and economic fallout. However, this is not resilience which returns the status quo. This moment demands transformative change in which inequalities are tackled and socioeconomic conditions are improved. While a community resilience approach is relatively new to epidemic preparedness and response, it frames epidemic shocks more holistically and from the perspective of a whole system. While epidemic response often focuses on mitigating vulnerabilities, there is an opportunity to use a resilience framework to build existing capacities to manage health, social, psychosocial, and economic impacts of an epidemic. This makes a resilience approach more localised, adaptable, and sustainable in the long-term, which are key tenets of an epidemic response informed by social science. This brief presents considerations for how health and humanitarian practitioners can support communities to respond to and recover from COVID-19 using a community resilience approach. This brief was developed for SSHAP by IDS (led by Megan Schmidt-Sane with Tabitha Hrynick) with Anthrologica (Eva Niederberger).
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Schmidt-Sane, Megan, Eva Niederberger, and Tabitha Hrynick. Key Considerations: Operational Considerations for Building Community Resilience for COVID-19 Response and Recovery. Institute of Development Studies (IDS), January 2021. http://dx.doi.org/10.19088/sshap.2021.004.

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Анотація:
As the unequal impact of the COVID-19 pandemic continues, there is a need to robustly support vulnerable communities and bolster ‘community resilience.’ A community resilience approach means to work in partnership with communities and strengthen their capacities to mitigate the impact of the pandemic, including its social and economic fallout. However, this is not resilience which returns the status quo. This moment demands transformative change in which inequalities are tackled and socioeconomic conditions are improved. While a community resilience approach is relatively new to epidemic preparedness and response, it frames epidemic shocks more holistically and from the perspective of a whole system. While epidemic response often focuses on mitigating vulnerabilities, there is an opportunity to use a resilience framework to build existing capacities to manage health, social, psychosocial, and economic impacts of an epidemic. This makes a resilience approach more localised, adaptable, and sustainable in the long-term, which are key tenets of an epidemic response informed by social science. This brief presents considerations for how health and humanitarian practitioners can support communities to respond to and recover from COVID-19 using a community resilience approach. This brief was developed for SSHAP by IDS (led by Megan Schmidt-Sane with Tabitha Hrynick) with Anthrologica (Eva Niederberger).
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5

Schmidt-Sane, Megan, Eva Niederberger, and Tabitha Hrynick. Key Considerations: Operational Considerations for Building Community Resilience for COVID-19 Response and Recovery. Institute of Development Studies (IDS), January 2021. http://dx.doi.org/10.19088/sshap.2021.025.

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Анотація:
As the unequal impact of the COVID-19 pandemic continues, there is a need to robustly support vulnerable communities and bolster ‘community resilience.’ A community resilience approach means to work in partnership with communities and strengthen their capacities to mitigate the impact of the pandemic, including its social and economic fallout. However, this is not resilience which returns the status quo. This moment demands transformative change in which inequalities are tackled and socioeconomic conditions are improved. While a community resilience approach is relatively new to epidemic preparedness and response, it frames epidemic shocks more holistically and from the perspective of a whole system. While epidemic response often focuses on mitigating vulnerabilities, there is an opportunity to use a resilience framework to build existing capacities to manage health, social, psychosocial, and economic impacts of an epidemic. This makes a resilience approach more localised, adaptable, and sustainable in the long-term, which are key tenets of an epidemic response informed by social science. This brief presents considerations for how health and humanitarian practitioners can support communities to respond to and recover from COVID-19 using a community resilience approach. This brief was developed for SSHAP by IDS (led by Megan Schmidt-Sane with Tabitha Hrynick) with Anthrologica (Eva Niederberger).
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6

Schmidt-Sane, Megan, Eva Niederberger, and Tabitha Hrynick. Key Considerations: Operational Considerations for Building Community Resilience for COVID-19 Response and Recovery. Institute of Development Studies (IDS), January 2021. http://dx.doi.org/10.19088/sshap.2021.029.

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Анотація:
As the unequal impact of the COVID-19 pandemic continues, there is a need to robustly support vulnerable communities and bolster ‘community resilience.’ A community resilience approach means to work in partnership with communities and strengthen their capacities to mitigate the impact of the pandemic, including its social and economic fallout. However, this is not resilience which returns the status quo. This moment demands transformative change in which inequalities are tackled and socioeconomic conditions are improved. While a community resilience approach is relatively new to epidemic preparedness and response, it frames epidemic shocks more holistically and from the perspective of a whole system. While epidemic response often focuses on mitigating vulnerabilities, there is an opportunity to use a resilience framework to build existing capacities to manage health, social, psychosocial, and economic impacts of an epidemic. This makes a resilience approach more localised, adaptable, and sustainable in the long-term, which are key tenets of an epidemic response informed by social science. This brief presents considerations for how health and humanitarian practitioners can support communities to respond to and recover from COVID-19 using a community resilience approach. This brief was developed for SSHAP by IDS (led by Megan Schmidt-Sane with Tabitha Hrynick) with Anthrologica (Eva Niederberger).
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7

P., DALLA VILLA. Overcoming the impact of COVID-19 on animal welfare: COVID-19 Thematic Platform on Animal Welfare. O.I.E (World Organisation for Animal Health), October 2020. http://dx.doi.org/10.20506/bull.2020.nf.3137.

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Анотація:
The World Organisation for Animal Health (OIE) represents 182 countries with a focus on animal health, animal welfare and veterinary public health. The OIE has several Collaborating Centres that support the work of the organisation. The Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise ‘Giuseppe Caporale’ (IZSAM) is the Secretariat for the OIE Collaborating Centre Network on Veterinary Emergencies (EmVetNet). In April 2020, the IZSAM initiated a COVID-19 Thematic Platform on Animal Welfare. The working group represented the EmVetNet Collaborating Centres, international institutions, veterinary associations, authorities and animal welfare organisations. Lincoln Memorial University College of Veterinary Medicine recruited summer research students whom catalogued over 1,200 animal welfare related reports and provided 64 report narratives for the working group. IZSAM launched the EmVetNet website (https://emvetnet.izs.it) for public and private exchange of information, materials, and guidelines related to veterinary emergencies. The EmVetNet COVID-19 Thematic Platform on Animal Welfare continues to meet to address emerging issues, strengthen the network for future emergencies, and share information with stakeholders including national Veterinary Services responding to the epidemic.
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P., DALLA VILLA. Overcoming the impact of COVID-19 on animal welfare: COVID-19 Thematic Platform on Animal Welfare. O.I.E (World Organisation for Animal Health), October 2020. http://dx.doi.org/10.20506/bull.2020.nf.3137.

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Анотація:
The World Organisation for Animal Health (OIE) represents 182 countries with a focus on animal health, animal welfare and veterinary public health. The OIE has several Collaborating Centres that support the work of the organisation. The Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise ‘Giuseppe Caporale’ (IZSAM) is the Secretariat for the OIE Collaborating Centre Network on Veterinary Emergencies (EmVetNet). In April 2020, the IZSAM initiated a COVID-19 Thematic Platform on Animal Welfare. The working group represented the EmVetNet Collaborating Centres, international institutions, veterinary associations, authorities and animal welfare organisations. Lincoln Memorial University College of Veterinary Medicine recruited summer research students whom catalogued over 1,200 animal welfare related reports and provided 64 report narratives for the working group. IZSAM launched the EmVetNet website (https://emvetnet.izs.it) for public and private exchange of information, materials, and guidelines related to veterinary emergencies. The EmVetNet COVID-19 Thematic Platform on Animal Welfare continues to meet to address emerging issues, strengthen the network for future emergencies, and share information with stakeholders including national Veterinary Services responding to the epidemic.
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Baker, Scott, R. A. Farrokhnia, Steffen Meyer, Michaela Pagel, and Constantine Yannelis. How Does Household Spending Respond to an Epidemic? Consumption During the 2020 COVID-19 Pandemic. Cambridge, MA: National Bureau of Economic Research, April 2020. http://dx.doi.org/10.3386/w26949.

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Dave, Dhaval, Andrew Friedson, Kyutaro Matsuzawa, Drew McNichols, and Joseph Sabia. Did the Wisconsin Supreme Court Restart a COVID-19 Epidemic? Evidence from a Natural Experiment. Cambridge, MA: National Bureau of Economic Research, June 2020. http://dx.doi.org/10.3386/w27322.

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