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Artículos de revistas sobre el tema "Legionnaires' Disease Mathematical models"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 1 de febrero de 2022

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1

Cassell, Kelsie, Paul Gacek, Therese Rabatsky-Ehr, Susan Petit, Matthew Cartter, and Daniel M. Weinberger. "Estimating the True Burden of Legionnaires’ Disease." American Journal of Epidemiology 188, no. 9 (June 21, 2019): 1686–94. http://dx.doi.org/10.1093/aje/kwz142.

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Abstract Over the past decade, the reported incidence of Legionnaires’ disease (LD) in the northeastern United States has increased, reaching 1–3 cases per 100,000 population. There is reason to suspect that this is an underestimate of the true burden, since LD cases may be underdiagnosed. In this analysis of pneumonia and influenza (P&I) hospitalizations, we estimated the percentages of cases due to Legionella, influenza, and respiratory syncytial virus (RSV) by age group. We fitted mixed-effects models to estimate attributable percents using weekly time series data on P&I hospitaliza

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2

Dobson, A. "Mathematical models for emerging disease." Science 346, no. 6215 (December 11, 2014): 1294–95. http://dx.doi.org/10.1126/science.aaa3441.

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3

Bakshi, Suruchi, Vijayalakshmi Chelliah, Chao Chen, and Piet H. van der Graaf. "Mathematical Biology Models of Parkinson's Disease." CPT: Pharmacometrics & Systems Pharmacology 8, no. 2 (November 2, 2018): 77–86. http://dx.doi.org/10.1002/psp4.12362.

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4

Grassly, Nicholas C., and Christophe Fraser. "Mathematical models of infectious disease transmission." Nature Reviews Microbiology 6, no. 6 (May 13, 2008): 477–87. http://dx.doi.org/10.1038/nrmicro1845.

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5

KLEIN, EILI, RAMANAN LAXMINARAYAN, DAVID L. SMITH, and CHRISTOPHER A. GILLIGAN. "Economic incentives and mathematical models of disease." Environment and Development Economics 12, no. 5 (October 2007): 707–32. http://dx.doi.org/10.1017/s1355770x0700383x.

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The fields of epidemiological disease modeling and economics have tended to work independently of each other despite their common reliance on the language of mathematics and exploration of similar questions related to human behavior and infectious disease. This paper explores the benefits of incorporating simple economic principles of individual behavior and resource optimization into epidemiological models, reviews related research, and indicates how future cross-discipline collaborations can generate more accurate models of disease and its control to guide policy makers.

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6

Meltzer, M. I., and R. A. I. Norval. "Mathematical models of tick-borne disease transmission." Parasitology Today 9, no. 8 (August 1993): 277–78. http://dx.doi.org/10.1016/0169-4758(93)90116-w.

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7

Donovan, Graham M. "Multiscale mathematical models of airway constriction and disease." Pulmonary Pharmacology & Therapeutics 24, no. 5 (October 2011): 533–39. http://dx.doi.org/10.1016/j.pupt.2011.01.003.

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8

Medley, Graham F. "Mathematical models of tick-borne disease transmission: Reply." Parasitology Today 9, no. 8 (August 1993): 292. http://dx.doi.org/10.1016/0169-4758(93)90123-w.

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9

DUNN, C. E., B. ROWLINGSON, R. S. BHOPAL, and P. DIGGLE. "Meteorological conditions and incidence of Legionnaires' disease in Glasgow, Scotland: application of statistical modelling." Epidemiology and Infection 141, no. 4 (June 12, 2012): 687–96. http://dx.doi.org/10.1017/s095026881200101x.

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SUMMARYThis study investigated the relationships between Legionnaires' disease (LD) incidence and weather in Glasgow, UK, by using advanced statistical methods. Using daily meteorological data and 78 LD cases with known exact date of onset, we fitted a series of Poisson log-linear regression models with explanatory variables for air temperature, relative humidity, wind speed and year, and sine-cosine terms for within-year seasonal variation. Our initial model showed an association between LD incidence and 2-day lagged humidity (positive, P = 0·0236) and wind speed (negative, P = 0·033). Howeve

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10

De Gaetano, Andrea, Thomas Hardy, Benoit Beck, Eyas Abu-Raddad, Pasquale Palumbo, Juliana Bue-Valleskey, and Niels Pørksen. "Mathematical models of diabetes progression." American Journal of Physiology-Endocrinology and Metabolism 295, no. 6 (December 2008): E1462—E1479. http://dx.doi.org/10.1152/ajpendo.90444.2008.

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Few attempts have been made to model mathematically the progression of type 2 diabetes. A realistic representation of the long-term physiological adaptation to developing insulin resistance is necessary for effectively designing clinical trials and evaluating diabetes prevention or disease modification therapies. Writing a good model for diabetes progression is difficult because the long time span of the disease makes experimental verification of modeling hypotheses extremely awkward. In this context, it is of primary importance that the assumptions underlying the model equations properly refl

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11

Cabanlit, Epimaco A., Elsie M. Cabanlit, Steiltjes M. Cabanlit, and Roxan Eve M. Cabanlit. "Mathematical Models for the Coronavirus Disease (Covid-19) Pandemic." International Journal of Scientific and Research Publications (IJSRP) 10, no. 4 (April 24, 2020): p10082. http://dx.doi.org/10.29322/ijsrp.10.04.2020.p10082.

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12

COEN, P. G., P. T. HEATH, M. L. BARBOUR, and G. P. GARNETT. "Mathematical models of Haemophilus influenzae type b." Epidemiology and Infection 120, no. 3 (June 1998): 281–95. http://dx.doi.org/10.1017/s0950268898008784.

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A review of empirical studies and the development of a simple theoretical framework are used to explore the relationship between Haemophilus influenzae type b (Hib) carriage and disease within populations. The models emphasize the distinction between asymptomatic and symptomatic infection. Maximum likelihood methods are used to estimate parameter values of the models and to evaluate whether models of infection and disease are satisfactory. The low incidence of carriage suggests that persistence of infection is only compatible with the absence of acquired immunity to asymptomatic infection. The

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13

Curcio, Luciano, Laura D'Orsi, and Andrea De Gaetano. "Seven Mathematical Models of Hemorrhagic Shock." Computational and Mathematical Methods in Medicine 2021 (June 3, 2021): 1–34. http://dx.doi.org/10.1155/2021/6640638.

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Although mathematical modelling of pressure-flow dynamics in the cardiocirculatory system has a lengthy history, readily finding the appropriate model for the experimental situation at hand is often a challenge in and of itself. An ideal model would be relatively easy to use and reliable, besides being ethically acceptable. Furthermore, it would address the pathogenic features of the cardiovascular disease that one seeks to investigate. No universally valid model has been identified, even though a host of models have been developed. The object of this review is to describe several of the most

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14

Dike, Chinyere Ogochukwu, Zaitul Marlizawati Zainuddin, and Ikeme John Dike. "Mathematical Models for Mitigating Ebola Virus Disease Transmission: A Review." Advanced Science Letters 24, no. 5 (May 1, 2018): 3536–43. http://dx.doi.org/10.1166/asl.2018.11432.

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15

Feinstein, A. R., C. K. Chan, J. M. Esdaile, R. I. Horwitz, M. J. McFarlane, and C. K. Wells. "Mathematical models and scientific reality in occurrence rates for disease." American Journal of Public Health 79, no. 9 (September 1989): 1303–4. http://dx.doi.org/10.2105/ajph.79.9.1303.

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16

Black, F. L., and B. Singer. "Elaboration Versus Simplification in Refining Mathematical Models of Infectious Disease." Annual Review of Microbiology 41, no. 1 (October 1987): 677–701. http://dx.doi.org/10.1146/annurev.mi.41.100187.003333.

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17

Garnett, G. P. "An introduction to mathematical models in sexually transmitted disease epidemiology." Sexually Transmitted Infections 78, no. 1 (February 1, 2002): 7–12. http://dx.doi.org/10.1136/sti.78.1.7.

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18

Sarbaz, Yashar, and Hakimeh Pourakbari. "A review of presented mathematical models in Parkinson’s disease: black- and gray-box models." Medical & Biological Engineering & Computing 54, no. 6 (November 7, 2015): 855–68. http://dx.doi.org/10.1007/s11517-015-1401-9.

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19

Weerasinghe, Hasitha N., Pamela M. Burrage, Kevin Burrage, and Dan V. Nicolau. "Mathematical Models of Cancer Cell Plasticity." Journal of Oncology 2019 (October 31, 2019): 1–14. http://dx.doi.org/10.1155/2019/2403483.

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Quantitative modelling is increasingly important in cancer research, helping to integrate myriad diverse experimental data into coherent pictures of the disease and able to discriminate between competing hypotheses or suggest specific experimental lines of enquiry and new approaches to therapy. Here, we review a diverse set of mathematical models of cancer cell plasticity (a process in which, through genetic and epigenetic changes, cancer cells survive in hostile environments and migrate to more favourable environments, respectively), tumour growth, and invasion. Quantitative models can help t

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20

Hughes, G. "Validating mathematical models of plant-disease progress in space and time." Mathematical Medicine and Biology 14, no. 2 (June 1, 1997): 85–112. http://dx.doi.org/10.1093/imammb/14.2.85.

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21

Fujiwara, Takeo. "Mathematical Analysis of Epidemic Disease Models and Application to COVID-19." Journal of the Physical Society of Japan 90, no. 2 (February 15, 2021): 023801. http://dx.doi.org/10.7566/jpsj.90.023801.

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22

Florea, Aurelia, and Cristian Lăzureanu. "A mathematical model of infectious disease transmission." ITM Web of Conferences 34 (2020): 02002. http://dx.doi.org/10.1051/itmconf/20203402002.

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In this paper we consider a three-dimensional nonlinear system which models the dynamics of a population during an epidemic disease. The considered model is a SIS-type system in which a recovered individual automatically becomes a susceptible one. We take into account the births and deaths, and we also consider that susceptible individuals are divided into two groups: non-vaccinated and vaccinated. In addition, we assume a medical scenario in which vaccinated people take a special measure to quarantine their newborns. We study the stability of the considered system. Numerical simulations point

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23

Weir, Mark H., Alexis L. Mraz, and Jade Mitchell. "An Advanced Risk Modeling Method to Estimate Legionellosis Risks Within a Diverse Population." Water 12, no. 1 (December 20, 2019): 43. http://dx.doi.org/10.3390/w12010043.

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Quantitative microbial risk assessment (QMRA) is a computational science leveraged to optimize infectious disease controls at both population and individual levels. Often, diverse populations will have different health risks based on a population’s susceptibility or outcome severity due to heterogeneity within the host. Unfortunately, due to a host homogeneity assumption in the microbial dose-response models’ derivation, the current QMRA method of modeling exposure volume heterogeneity is not an accurate method for pathogens such as Legionella pneumophila. Therefore, a new method to model with

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24

Bravo de la Parra, R., M. Marvá, E. Sánchez, and L. Sanz. "Discrete Models of Disease and Competition." Discrete Dynamics in Nature and Society 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/5310837.

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The aim of this work is to analyze the influence of the fast development of a disease on competition dynamics. To this end we present two discrete time ecoepidemic models. The first one corresponds to the case of one parasite affecting demography and intraspecific competition in a single host, whereas the second one contemplates the more complex case of competition between two different species, one of which is infected by the parasite. We carry out a complete mathematical analysis of the asymptotic behavior of the solutions of the corresponding systems of difference equations and derive inter

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25

El Khatib, N., O. Kafi, A. Sequeira, S. Simakov, Yu Vassilevski, and V. Volpert. "Mathematical modelling of atherosclerosis." Mathematical Modelling of Natural Phenomena 14, no. 6 (2019): 603. http://dx.doi.org/10.1051/mmnp/2019050.

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The review presents the state of the art in the atherosclerosis modelling. It begins with the biological introduction describing the mechanisms of chronic inflammation of artery walls characterizing the development of atherosclerosis. In particular, we present in more detail models describing this chronic inflammation as a reaction-diffusion wave with regimes of propagation depending on the level of cholesterol (LDL) and models of rolling monocytes initializing the inflammation. Further development of this disease results in the formation of atherosclerotic plaque, vessel remodelling and possi

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26

Yanchevskaya, E. Ya, and O. A. Mesnyankina. "Mathematical Modelling and Prediction in Infectious Disease Epidemiology." RUDN Journal of Medicine 23, no. 3 (December 15, 2019): 328–34. http://dx.doi.org/10.22363/2313-0245-2019-23-3-328-334.

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Mathematical modeling of diseases is an urgent problem in the modern world. More and more researchers are turning to mathematical models to predict a particular disease, as they help the most correct and accurate study of changes in certain processes occurring in society. Mathematical modeling is indispensable in certain areas of medicine, where real experiments are impossible or difficult, for example, in epidemiology. The article is devoted to the historical aspects of studying the possibilities of mathematical modeling in medicine. The review demonstrates the main stages of development, ach

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27

Langemann, Dirk, Igor Nesteruk, and Jürgen Prestin. "Comparison of mathematical models for the dynamics of the Chernivtsi children disease." Mathematics and Computers in Simulation 123 (May 2016): 68–79. http://dx.doi.org/10.1016/j.matcom.2016.01.003.

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28

Roberts, Paul A., Eamonn A. Gaffney, Philip J. Luthert, Alexander J. E. Foss, and Helen M. Byrne. "Mathematical and computational models of the retina in health, development and disease." Progress in Retinal and Eye Research 53 (July 2016): 48–69. http://dx.doi.org/10.1016/j.preteyeres.2016.04.001.

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29

Durham, David P., and Elizabeth A. Casman. "Incorporating individual health-protective decisions into disease transmission models: a mathematical framework." Journal of The Royal Society Interface 9, no. 68 (July 20, 2011): 562–70. http://dx.doi.org/10.1098/rsif.2011.0325.

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It is anticipated that the next generation of computational epidemic models will simulate both infectious disease transmission and dynamic human behaviour change. Individual agents within a simulation will not only infect one another, but will also have situational awareness and a decision algorithm that enables them to modify their behaviour. This paper develops such a model of behavioural response, presenting a mathematical interpretation of a well-known psychological model of individual decision making, the health belief model, suitable for incorporation within an agent-based disease-transm

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30

Liu, Yifan. "Mathematical models of vaccine inventory design for a breakout of epidemic disease." PAMM 7, no. 1 (December 2007): 2150013–14. http://dx.doi.org/10.1002/pamm.200700367.

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31

Nkeki, C. I., and G. O. S. Ekhaguere. "Some actuarial mathematical models for insuring the susceptibles of a communicable disease." International Journal of Financial Engineering 07, no. 02 (May 18, 2020): 2050014. http://dx.doi.org/10.1142/s2424786320500140.

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Using epidemiological and actuarial analysis, this paper formulates some new actuarial mathematical models, called S-I-DR-S models, for insuring the susceptibles of a population exposed to a communicable disease. Epidemiologically, the population is structured into four demographic groups, namely: susceptibles [Formula: see text], infectives [Formula: see text], diseased [Formula: see text] and recovered [Formula: see text], with the latter automatically re-entering the group of susceptibles [Formula: see text]. The insurance policies are targeted at the members of the susceptible group who fa

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32

FENTON, ANDY. "Editorial: Mathematical modelling of infectious diseases." Parasitology 143, no. 7 (March 30, 2016): 801–4. http://dx.doi.org/10.1017/s0031182016000214.

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The field of disease ecology – the study of the spread and impact of parasites and pathogens within their host populations and communities – has a long history of using mathematical models. Dating back over 100 years, researchers have used mathematics to describe the spread of disease-causing agents, understand the relationship between host density and transmission and plan control strategies. The use of mathematical modelling in disease ecology exploded in the late 1970s and early 1980s through the work of Anderson and May (Anderson and May, 1978, 1981, 1992; May and Anderson, 1978), who deve

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33

El Khatib, N., S. Génieys, B. Kazmierczak, and V. Volpert. "Mathematical modelling of atherosclerosis as an inflammatory disease." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1908 (December 13, 2009): 4877–86. http://dx.doi.org/10.1098/rsta.2009.0142.

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Atherosclerosis is an inflammatory disease. The atherosclerosis process starts when low-density lipoproteins (LDLs) enter the intima of the blood vessel, where they are oxidized (ox-LDLs). The anti-inflammatory response triggers the recruitment of monocytes. Once in the intima, the monocytes are transformed into macrophages and foam cells, leading to the production of inflammatory cytokines and further recruitment of monocytes. This auto-amplified process leads to the formation of an atherosclerotic plaque and, possibly, to its rupture. In this paper we develop two mathematical models based on

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34

Michor, Franziska. "Mathematical Models of Cancer Evolution and Cure." Blood 126, no. 23 (December 3, 2015): SCI—54—SCI—54. http://dx.doi.org/10.1182/blood.v126.23.sci-54.sci-54.

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Abstract Since the pioneering work of Salmon and Durie, the availability of a quantitative measure of malignant cell burden in multiple myeloma has been used to make clinical predictions and to model tumor cell growth. Here, we analyzed a large set of tumor response data from three randomized controlled clinical trials (total sample size n=1,469 evaluable patients) to establish and validate a novel mathematical model of MM cell dynamics based on responses to bortezomib-based chemotherapy regimens. Dynamics of treatment response in newly diagnosed patients were most consistent with a mathematic

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35

Goncharova, Anastaciya B., Eugeny P. Kolpak, Madina M. Rasulova, and Alina V. Abramova. "Mathematical modeling of cancer treatment." Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes 16, no. 4 (2020): 437–46. http://dx.doi.org/10.21638/11701/spbu10.2020.408.

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The paper proposes mathematical models of ovarian neoplasms. The models are based on a mathematical model of interference competition. Two types of cells are involved in the competition for functional space: normal and tumor cells. The mathematical interpretation of the models is the Cauchy problem for a system of ordinary differential equations. The dynamics of tumor growth is determined on the basis of the model. A model for the distribution of conditional patients according to four stages of the disease, a model for assessing survival times for groups of conditional patients, and a chemothe

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36

VAN HEST, N. A. H., C. J. P. A. HOEBE, J. W. DEN BOER, J. K. VERMUNT, E. P. F. IJZERMAN, W. G. BOERSMA, and J. H. RICHARDUS. "Incidence and completeness of notification of Legionnaires' disease in The Netherlands: covariate capture–recapture analysis acknowledging regional differences." Epidemiology and Infection 136, no. 4 (June 22, 2007): 540–50. http://dx.doi.org/10.1017/s0950268807008977.

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SUMMARYTo estimate incidence and completeness of notification of Legionnaires' disease (LD) in The Netherlands in 2000 and 2001, we performed a capture–recapture analysis using three registers: Notifications, Laboratory results and Hospital admissions. After record-linkage, 373 of the 780 LD patients identified were notified. Ascertained under-notification was 52·2%. Because of expected and observed regional differences in the incidence rate of LD, alternatively to conventional log-linear capture–recapture models, a covariate (region) capture–recapture model, not previously used for estimating

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37

Chung, Chun Yen, Hung Yuan Chung, and Wen Tsai Sung. "Mathematical Models for the Dynamics Simulation of Tuberculosis." Applied Mechanics and Materials 418 (September 2013): 265–68. http://dx.doi.org/10.4028/www.scientific.net/amm.418.265.

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In recent years, following malaria, tuberculosis, AIDS, Novel Influenza, and other infectious diseases, have an enormous impact on the entire globe, and directly and profoundly awaken the public, making them cognitive and alert regarding emerging and re-emerging infectious diseases. For some countries or developing regions, tuberculosis is still very serious, however, the public is still unclear TB development and change a variety of factors, therefore, need a model theory of tuberculosis. In view of this, the global epidemic, scientists and statisticians hope to further develop a complete ins

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38

Shain, Kenneth H. "Mathematical Models of Cancer Evolution and Cure." Blood 126, no. 23 (December 3, 2015): SCI—55—SCI—55. http://dx.doi.org/10.1182/blood.v126.23.sci-55.sci-55.

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You cannot cure what you do not understand. So how can mathematical modeling address this pressing issue? The advances in therapeutic success in multiple myeloma over the last decades have hinged on an an army of researchers identifying a critical genetic, epigenetic and biochemical signaling factors within of MM cells as well as the tumor microenvironment (TME). Unfortunately, despite these large scale efforts we do not yet offer our patients curative intent therapy. The inability to provide curative therapy, especially in the setting of HRMM, is characterized by evolving resistance to lines

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39

Brownell, A. L., B. G. Jenkins, and O. Isacson. "Dopamine imaging markers and predictive mathematical models for progressive degeneration in Parkinson's disease." Biomedicine & Pharmacotherapy 53, no. 3 (April 1999): 131–40. http://dx.doi.org/10.1016/s0753-3322(99)80078-x.

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40

Chowell, G. "Mathematical models to elucidate the transmission dynamics and control of vector-borne disease." International Journal of Infectious Diseases 53 (December 2016): 6–7. http://dx.doi.org/10.1016/j.ijid.2016.11.020.

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41

Jäger, Jens, Sebastian Marwitz, Jana Tiefenau, Janine Rasch, Olga Shevchuk, Christian Kugler, Torsten Goldmann, and Michael Steinert. "Human Lung Tissue Explants Reveal Novel Interactions during Legionella pneumophila Infections." Infection and Immunity 82, no. 1 (October 28, 2013): 275–85. http://dx.doi.org/10.1128/iai.00703-13.

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ABSTRACTHistological and clinical investigations describe late stages of Legionnaires' disease but cannot characterize early events of human infection. Cellular or rodent infection models lack the complexity of tissue or have nonhuman backgrounds. Therefore, we developed and applied a novel model forLegionella pneumophilainfection comprising living human lung tissue. We stimulated lung explants withL. pneumophilastrains and outer membrane vesicles (OMVs) to analyze tissue damage, bacterial replication, and localization as well as the transcriptional response of infected tissue. Interestingly,

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42

Tchuenche, Jean M. "Patient-dependent effects in disease control: a mathematical model." ANZIAM Journal 48, no. 4 (April 2007): 583–96. http://dx.doi.org/10.1017/s1446181100003230.

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AbstractThe state of a patient is an important concept in biomedical sciences. While analytical methods for predicting and exploring treatment strategies of disease dynamics have proven to have useful applications in public health policy and planning, the state of a patient has attracted less attention, at least mathematically. As a result, models constructed in relation to treatment strategies may not be very informative. We derive a patient-dependent parameter from an age-physiology dependent population model, and show that a single treatment strategy is not always optimal. Also, we derive a

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43

Rodriguez-Brenes, Ignacio A., and Dominik Wodarz. "Preventing clonal evolutionary processes in cancer: Insights from mathematical models." Proceedings of the National Academy of Sciences 112, no. 29 (July 21, 2015): 8843–50. http://dx.doi.org/10.1073/pnas.1501730112.

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Clonal evolutionary processes can drive pathogenesis in human diseases, with cancer being a prominent example. To prevent or treat cancer, mechanisms that can potentially interfere with clonal evolutionary processes need to be understood better. Mathematical modeling is an important research tool that plays an ever-increasing role in cancer research. This paper discusses how mathematical models can be useful to gain insights into mechanisms that can prevent disease initiation, help analyze treatment responses, and aid in the design of treatment strategies to combat the emergence of drug-resist

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44

Ishtiaq, Amna. "Dynamics of COVID-19 Transmission: Compartmental-based Mathematical Modeling." Life and Science 1, supplement (December 23, 2020): 5. http://dx.doi.org/10.37185/lns.1.1.134.

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   The current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-Cov2) demands scientists all over the world to make their possible contributions in whatever way possible to control this disease. In such health emergency, mathematical epidemiologists are playing a pivotal role by constructing different mathematical and statistical models for predicting different future scenario and their impact on different intervention strategies to policy makers and health legislators. Compartmental-based models (CBM), are a type

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45

FORYS, URSULA. "INTERLEUKIN MATHEMATICAL MODEL OF AN IMMUNE SYSTEM." Journal of Biological Systems 03, no. 03 (September 1995): 889–902. http://dx.doi.org/10.1142/s0218339095000794.

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Some generalizations of Marchuk's model of an infectious disease with respect to the role of interleukins are presented in this paper. Basic properties of the models are studied. Results of numerical simulations with different coefficients corresponding to the different forms of the disease are shown.

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46

Christen, Paula, and Lesong Conteh. "How are mathematical models and results from mathematical models of vaccine-preventable diseases used, or not, by global health organisations?" BMJ Global Health 6, no. 9 (September 2021): e006827. http://dx.doi.org/10.1136/bmjgh-2021-006827.

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While epidemiological and economic evidence has the potential to provide answers to questions, guide complex programmes and inform resource allocation decisions, how this evidence is used by global health organisations who commission it and what organisational actions are generated from the evidence remains unclear. This study applies analytical tools from organisational science to understand how evidence produced by infectious disease epidemiologists and health economists is used by global health organisations. A conceptual framework that embraces evidence use typologies and relates findings

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47

Bowong, S., A. Temgoua, Y. Malong, and J. Mbang. "Mathematical Study of a Class of Epidemiological Models with Multiple Infectious Stages." International Journal of Nonlinear Sciences and Numerical Simulation 21, no. 3-4 (May 26, 2020): 259–74. http://dx.doi.org/10.1515/ijnsns-2017-0244.

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AbstractThis paper deals with the mathematical analysis of a general class of epidemiological models with multiple infectious stages for the transmission dynamics of a communicable disease. We provide a theoretical study of the model. We derive the basic reproduction number $\mathcal R_0$ that determines the extinction and the persistence of the infection. We show that the disease-free equilibrium is globally asymptotically stable whenever $\mathcal R_0 \leq 1$, while when $\mathcal R_0 \gt 1$, the disease-free equilibrium is unstable and there exists a unique endemic equilibrium point which i

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48

Дерпак, V. Derpak, Полухин, V. Polukhin, Еськов, Valeriy Eskov, Пашнин, and A. Pashnin. "Mathematical modeling of involuntary movements in health and disease." Complexity. Mind. Postnonclassic 4, no. 2 (September 25, 2015): 75–86. http://dx.doi.org/10.12737/12002.

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The problem of voluntary or involuntary movements are discussed more than 150 years. Traditionally, the tremor was considered involuntary movements and tapping - arbitrary. Real stochastic and chaotic analysis of these two types of motion shows them as chaotic motion (involuntary as a result of the test, rather than by the presence of the target). Introduced new criteria for the separation of these two types of motion in the form of paired comparisons matrix samples tremorogramm and teppingramm. Models of the evolution of the tremor in the mode of the three transitions: normal postural tremor,

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49

Chowdhury, Debashish, and Dietrich Stauffer. "Systematics of the models of immune response and autoimmune disease." Journal of Statistical Physics 59, no. 3-4 (May 1990): 1019–42. http://dx.doi.org/10.1007/bf01025860.

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50

Miller, Joel C. "Mathematical models of SIR disease spread with combined non-sexual and sexual transmission routes." Infectious Disease Modelling 2, no. 1 (February 2017): 35–55. http://dx.doi.org/10.1016/j.idm.2016.12.003.

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