Journal articles: 'Epidemiological modelling'

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GARNER, M. G., and S. A. HAMILTON. "Principles of epidemiological modelling." Revue Scientifique et Technique de l'OIE 30, no. 2 (August 1, 2011): 407–16. http://dx.doi.org/10.20506/rst.30.2.2045.

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Crépey, Pascal, Harold Noël, and Samuel Alizon. "Challenges for mathematical epidemiological modelling." Anaesthesia Critical Care & Pain Medicine 41, no. 2 (April 2022): 101053. http://dx.doi.org/10.1016/j.accpm.2022.101053.

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Habbema, J. D. F., E. S. Alley, A. P. Plaisier, G. J. van Oortmarssen, and J. H. F. Remme. "Epidemiological modelling for onchocerciasis control." Parasitology Today 8, no. 3 (March 1992): 99–103. http://dx.doi.org/10.1016/0169-4758(92)90248-z.

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Lambrou, George I., Kyriaki Hatziagapiou, Petros Toumpaniaris, Penelope Ioannidou, and Dimitrios Koutsouris. "Computational Modelling in Epidemiological Dispersion Using Diffusion and Epidemiological Equations." International Journal of Reliable and Quality E-Healthcare 8, no. 4 (October 2019): 1–37. http://dx.doi.org/10.4018/ijrqeh.2019100101.

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Although a considerable amount of knowledge is gathered concerning diseases and their transmission, still more is to learn on their mathematical modelling. The present work reviews the existent knowledge on models of epidemiological dispersion, the creation of a new form of an epidemiological diffusion equation, and the subsequent application of this equation to the investigation of epidemiological phenomena. Towards that scope, the authors have used mathematical models which have been previously reported, as well as algorithmic approaches of stochastic nature for the solution of complex functions. In particular, they have used dynamic programming algorithms, Robbins-Monro and Kiefer-Wolfowitz stochastic optimization algorithms, Markov chains and cellular automata. The modified diffusion equation could potentially provide a useful tool to the investigation of epidemiological phenomena. More research is required in order to explore the extent of its possibilities and uses.

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Furtat, І. E. "Modelling the Optimal Schemes of Population Vaccination Using Epidemiological Data." Mathematical and computer modelling. Series: Technical sciences 1, no. 20 (September 20, 2019): 104–13. http://dx.doi.org/10.32626/2308-5916.2019-20.104-113.

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Soloviov, S., and O. Bandurka. "Modelling the Optimal Schemes of Population Vaccination Using Epidemiological Data." Mathematical and computer modelling. Series: Technical sciences 1, no. 20 (September 20, 2019): 99–103. http://dx.doi.org/10.32626/2308-5916.2019-20.99-103.

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D, Raja. "The Power of Epidemiological Modelling inUnderstanding and Managing Infectious Diseases." Chettinad Health City Medical Journal 12, no. 1 (March 31, 2023): 1–2. http://dx.doi.org/10.24321/2278.2044.202301.

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Jere, Stanley, and Edwin Moyo. "Modelling Epidemiological Data Using Box-Jenkins Procedure." Open Journal of Statistics 06, no. 02 (2016): 295–302. http://dx.doi.org/10.4236/ojs.2016.62025.

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Friston, Karl, Anthony Costello, and Deenan Pillay. "‘Dark matter’, second waves and epidemiological modelling." BMJ Global Health 5, no. 12 (December 2020): e003978. http://dx.doi.org/10.1136/bmjgh-2020-003978.

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Recent reports using conventional Susceptible, Exposed, Infected and Removed models suggest that the next wave of the COVID-19 pandemic in the UK could overwhelm health services, with fatalities exceeding the first wave. We used Bayesian model comparison to revisit these conclusions, allowing for heterogeneity of exposure, susceptibility and transmission. We used dynamic causal modelling to estimate the evidence for alternative models of daily cases and deaths from the USA, the UK, Brazil, Italy, France, Spain, Mexico, Belgium, Germany and Canada over the period 25 January 2020 to 15 June 2020. These data were used to estimate the proportions of people (i) not exposed to the virus, (ii) not susceptible to infection when exposed and (iii) not infectious when susceptible to infection. Bayesian model comparison furnished overwhelming evidence for heterogeneity of exposure, susceptibility and transmission. Furthermore, both lockdown and the build-up of population immunity contributed to viral transmission in all but one country. Small variations in heterogeneity were sufficient to explain large differences in mortality rates. The best model of UK data predicts a second surge of fatalities will be much less than the first peak. The size of the second wave depends sensitively on the loss of immunity and the efficacy of Find-Test-Trace-Isolate-Support programmes. In summary, accounting for heterogeneity of exposure, susceptibility and transmission suggests that the next wave of the SARS-CoV-2 pandemic will be much smaller than conventional models predict, with less economic and health disruption. This heterogeneity means that seroprevalence underestimates effective herd immunity and, crucially, the potential of public health programmes.

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Brotto Rebuli, Karina, Mario Giacobini, and Luigi Bertolotti. "Caprine Arthritis Encephalitis Virus Disease Modelling Review." Animals 11, no. 5 (May 19, 2021): 1457. http://dx.doi.org/10.3390/ani11051457.

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Mathematical modelling is used in disease studies to assess the economical impacts of diseases, as well as to better understand the epidemiological dynamics of the biological and environmental factors that are associated with disease spreading. For an incurable disease such as Caprine Arthritis Encephalitis (CAE), this knowledge is extremely valuable. However, the application of modelling techniques to CAE disease studies has not been significantly explored in the literature. The purpose of the present work was to review the published studies, highlighting their scope, strengths and limitations, as well to provide ideas for future modelling approaches for studying CAE disease. The reviewed studies were divided into the following two major themes: Mathematical epidemiological modelling and statistical modelling. Regarding the epidemiological modelling studies, two groups of models have been addressed in the literature: With and without the sexual transmission component. Regarding the statistical modelling studies, the reviewed articles varied on modelling assumptions and goals. These studies modelled the dairy production, the CAE risk factors and the hypothesis of CAE being a risk factor for other diseases. Finally, the present work concludes with further suggestions for modelling studies on CAE.

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Adib, Keyrellous, Penelope A. Hancock, Aysel Rahimli, Bridget Mugisa, Fayez Abdulrazeq, Ricardo Aguas, Lisa J. White, Rana Hajjeh, Lubna Al Ariqi, and Pierre Nabeth. "A participatory modelling approach for investigating the spread of COVID-19 in countries of the Eastern Mediterranean Region to support public health decision-making." BMJ Global Health 6, no. 3 (March 2021): e005207. http://dx.doi.org/10.1136/bmjgh-2021-005207.

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Early on in the COVID-19 pandemic, the WHO Eastern Mediterranean Regional Office recognised the importance of epidemiological modelling to forecast the progression of the COVID-19 pandemic to support decisions guiding the implementation of response measures. We established a modelling support team to facilitate the application of epidemiological modelling analyses in the Eastern Mediterranean Region (EMR) countries. Here, we present an innovative, stepwise approach to participatory modelling of the COVID-19 pandemic that engaged decision-makers and public health professionals from countries throughout all stages of the modelling process. Our approach consisted of first identifying the relevant policy questions, collecting country-specific data and interpreting model findings from a decision-maker’s perspective, as well as communicating model uncertainty. We used a simple modelling methodology that was adaptable to the shortage of epidemiological data, and the limited modelling capacity, in our region. We discuss the benefits of using models to produce rapid decision-making guidance for COVID-19 control in the WHO EMR, as well as challenges that we have experienced regarding conveying uncertainty associated with model results, synthesising and comparing results across multiple modelling approaches, and modelling fragile and conflict-affected states.

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Liew, Danny, John J. McNeil, Anna Peeters, Stephen S. Lim, and Theo Vos. "Epidemiological modelling (including economic modelling) and its role in preventive drug therapy." Medical Journal of Australia 177, no. 7 (October 2002): 364–67. http://dx.doi.org/10.5694/j.1326-5377.2002.tb04839.x.

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Johnson, Kent R., and Marissa N. Lassere. "Epidemiological modelling (including economic modelling) and its role in preventive drug therapy." Medical Journal of Australia 178, no. 4 (February 17, 2002): 188–89. http://dx.doi.org/10.5694/j.1326-5377.2003.tb05143.x.

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YILMAZ ÇAĞIRGAN, Özge, and Abdurrahman CAGIRGAN. "Epidemiological modelling in infectious diseases: stages and classification." Mehmet Akif Ersoy Üniversitesi Veteriner Fakültesi Dergisi 5, no. 3 (December 30, 2020): 151–58. http://dx.doi.org/10.24880/maeuvfd.695267.

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Milne, Catherine E., George J. Gunn, Gary Entrican, and David Longbottom. "Epidemiological modelling of chlamydial abortion in sheep flocks." Veterinary Microbiology 135, no. 1-2 (March 2009): 128–33. http://dx.doi.org/10.1016/j.vetmic.2008.09.032.

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Gamage, Anuji, and Chrishantha Abeysena. "DAG: method for causal modelling in epidemiological research." Journal of the College of Community Physicians of Sri Lanka 23, no. 4 (April 19, 2018): 131. http://dx.doi.org/10.4038/jccpsl.v23i4.8129.

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Lamprinakou, Stamatina, Axel Gandy, and Emma McCoy. "Using a latent Hawkes process for epidemiological modelling." PLOS ONE 18, no. 3 (March 1, 2023): e0281370. http://dx.doi.org/10.1371/journal.pone.0281370.

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Understanding the spread of COVID-19 has been the subject of numerous studies, highlighting the significance of reliable epidemic models. Here, we introduce a novel epidemic model using a latent Hawkes process with temporal covariates for modelling the infections. Unlike other models, we model the reported cases via a probability distribution driven by the underlying Hawkes process. Modelling the infections via a Hawkes process allows us to estimate by whom an infected individual was infected. We propose a Kernel Density Particle Filter (KDPF) for inference of both latent cases and reproduction number and for predicting the new cases in the near future. The computational effort is proportional to the number of infections making it possible to use particle filter type algorithms, such as the KDPF. We demonstrate the performance of the proposed algorithm on synthetic data sets and COVID-19 reported cases in various local authorities in the UK, and benchmark our model to alternative approaches.

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Eržen, Ivan, Tina Kamenšek, Miha Fošnarič, and Janez Žibert. "Key Challenges in Modelling an Epidemic – What Have we Learned from the COVID-19 Epidemic so far." Slovenian Journal of Public Health 59, no. 3 (June 25, 2020): 117–19. http://dx.doi.org/10.2478/sjph-2020-0015.

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AbstractMathematical modelling can be useful for predicting how infectious diseases progress, enabling us to show the likely outcome of an epidemic and help inform public health interventions. Different modelling techniques have been used to predict and simulate the spread of COVID-19, but they have not always been useful for epidemiologists and decision-makers. To improve the reliability of the modelling results, it is very important to critically evaluate the data used and to check whether or not due regard has been paid to the different ways in which the disease spreads through the population. As building an epidemiological model that is reliable enough and suits the current epidemiological situation within a country or region, certain criteria must be met in the modelling process. It might be necessary to use a combination of two or more different types of models in order to cover all aspects of epidemic modelling. If we want epidemiological models to be a useful tool in combating the epidemic, we need to engage experts from epidemiology, data science and statistics.

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Winsberg, Eric, and Stephanie Harvard. "Purposes and duties in scientific modelling." Journal of Epidemiology and Community Health 76, no. 5 (January 13, 2022): 512–17. http://dx.doi.org/10.1136/jech-2021-217666.

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More people than ever are paying attention to philosophical questions about epidemiological models, including their susceptibility to the influence of social and ethical values, sufficiency to inform policy decisions under certain conditions, and even their fundamental nature. One important question pertains to the purposes of epidemiological models, for example, are COVID-19 models for ‘prediction’ or ‘projection’? Are they adequate for making causal inferences? Is one of their goals, or virtues, to change individual responses to the pandemic? In this essay, we offer our perspective on these questions and place them in the context of other recent philosophical arguments about epidemiological models. We argue that clarifying the intended purpose of a model, and assessing its adequacy for that purpose, are moral-epistemic duties, responsibilities which pertain to knowledge but have moral significance nonetheless. This moral significance, we argue, stems from the inherent value-ladenness of models, along with the potential for models to be used in political decision making in ways that conflict with liberal values and which could lead to downstream harms. Increasing conversation about the moral significance of modelling, we argue, could help us to resist further eroding our standards of democratic scrutiny in the COVID-19 era.

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Fentie Bezabih, Abayneh, Geremew Kenassa Edessa, and Koya Purnachandra Rao. "Eco-Epidemiological Modelling and Analysis of Prey-Predator Population." Science Journal of Applied Mathematics and Statistics 9, no. 1 (2021): 1. http://dx.doi.org/10.11648/j.sjams.20210901.11.

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Kitchovitch, Stephan, and Pietro Liò. "Community Structure in Social Networks: Applications for Epidemiological Modelling." PLoS ONE 6, no. 7 (July 18, 2011): e22220. http://dx.doi.org/10.1371/journal.pone.0022220.

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Dunaievskyi, M., O. Lefterov, and V. Bolshakov. "Usage of Publicly Available Software for Epidemiological Trends Modelling." Cybernetics and Computer Technologies, no. 3 (October 27, 2020): 32–42. http://dx.doi.org/10.34229/2707-451x.20.3.4.

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Introduction. Outbreaks of infectious diseases and the COVID-19 pandemic in particular pose a serious public health challenge. The other side of the challenge is always opportunity, and today such opportunities are information technology, decision making systems, best practices of proactive management and control based on modern methods of data analysis (data driven decision making) and modeling. The article reviews the prospects for the use of publicly available software in modeling epidemiological trends. Strengths and weaknesses, main characteristics and possible aspects of application are considered. The purpose of the article is to review publicly available health software. Give situations in which one or another approach will be useful. Segment and determine the effectiveness of the underlying models. Note the prospects of high-performance computing to model the spread of epidemics. Results. Although deterministic models are ready for practical use without specific additional settings, they lose comparing to other groups in terms of their functionality. To obtain evaluation results from stochastic and agentoriented models, you first need to specify the epidemic model, which requires deeper knowledge in the field of epidemiology, a good understanding of the statistical basis and the basic assumptions on which the model is based. Among the considered software, EMOD (Epidemiological MODelling software) from the Institute of Disease Modeling is a leader in functionality. Conclusions. There is a free access to a relatively wide set of software, which was originally developed by antiepidemiological institutions for internal use in decision-making, however was later opened to the public. In general, these programs have been adapted to increase their practical application. Got narrowed focus on potential issues. The possibility of adaptive use was provided. We can note the sufficient informativeness and convenience of using the software of the group of deterministic methods. Also, such models have a rather narrow functional focus. Stochastic models provide more functionality, but lose some of their ease of use. We have the maximum functionality from agentoriented models, although for their most effective use you need to have the appropriate skills to write program code. Keywords: epidemiological software, deterministic modeling, stochastic modeling, agentoriented mode-ling, high performance computing, decision making systems.

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Castle, Matthew D., and Christopher A. Gilligan. "An Epidemiological Framework for Modelling Fungicide Dynamics and Control." PLoS ONE 7, no. 8 (August 10, 2012): e40941. http://dx.doi.org/10.1371/journal.pone.0040941.

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Liew, Danny, John J. McNeil, Anna Peeters, Stephen S. Lim, and Theo Vos. "In reply: Epidemiological modelling (including economic modelling) and its role in preventive drug therapy." Medical Journal of Australia 178, no. 4 (February 2003): 188–89. http://dx.doi.org/10.5694/j.1326-5377.2003.tb05144.x.

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Li, Shou-Li, Matthew J. Ferrari, Ottar N. Bjørnstad, Michael C. Runge, Christopher J. Fonnesbeck, Michael J. Tildesley, David Pannell, and Katriona Shea. "Concurrent assessment of epidemiological and operational uncertainties for optimal outbreak control: Ebola as a case study." Proceedings of the Royal Society B: Biological Sciences 286, no. 1905 (June 19, 2019): 20190774. http://dx.doi.org/10.1098/rspb.2019.0774.

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Determining how best to manage an infectious disease outbreak may be hindered by both epidemiological uncertainty (i.e. about epidemiological processes) and operational uncertainty (i.e. about the effectiveness of candidate interventions). However, these two uncertainties are rarely addressed concurrently in epidemic studies. We present an approach to simultaneously address both sources of uncertainty, to elucidate which source most impedes decision-making. In the case of the 2014 West African Ebola outbreak, epidemiological uncertainty is represented by a large ensemble of published models. Operational uncertainty about three classes of interventions is assessed for a wide range of potential intervention effectiveness. We ranked each intervention by caseload reduction in each model, initially assuming an unlimited budget as a counterfactual. We then assessed the influence of three candidate cost functions relating intervention effectiveness and cost for different budget levels. The improvement in management outcomes to be gained by resolving uncertainty is generally high in this study; appropriate information gain could reduce expected caseload by more than 50%. The ranking of interventions is jointly determined by the underlying epidemiological process, the effectiveness of the interventions and the size of the budget. An epidemiologically effective intervention might not be optimal if its costs outweigh its epidemiological benefit. Under higher-budget conditions, resolution of epidemiological uncertainty is most valuable. When budgets are tight, however, operational and epidemiological uncertainty are equally important. Overall, our study demonstrates that significant reductions in caseload could result from a careful examination of both epidemiological and operational uncertainties within the same modelling structure. This approach can be applied to decision-making for the management of other diseases for which multiple models and multiple interventions are available.

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Bosse, Nikos I., Sam Abbott, Johannes Bracher, Habakuk Hain, Billy J. Quilty, Mark Jit, Edwin van Leeuwen, Anne Cori, and Sebastian Funk. "Comparing human and model-based forecasts of COVID-19 in Germany and Poland." PLOS Computational Biology 18, no. 9 (September 19, 2022): e1010405. http://dx.doi.org/10.1371/journal.pcbi.1010405.

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Forecasts based on epidemiological modelling have played an important role in shaping public policy throughout the COVID-19 pandemic. This modelling combines knowledge about infectious disease dynamics with the subjective opinion of the researcher who develops and refines the model and often also adjusts model outputs. Developing a forecast model is difficult, resource- and time-consuming. It is therefore worth asking what modelling is able to add beyond the subjective opinion of the researcher alone. To investigate this, we analysed different real-time forecasts of cases of and deaths from COVID-19 in Germany and Poland over a 1-4 week horizon submitted to the German and Polish Forecast Hub. We compared crowd forecasts elicited from researchers and volunteers, against a) forecasts from two semi-mechanistic models based on common epidemiological assumptions and b) the ensemble of all other models submitted to the Forecast Hub. We found crowd forecasts, despite being overconfident, to outperform all other methods across all forecast horizons when forecasting cases (weighted interval score relative to the Hub ensemble 2 weeks ahead: 0.89). Forecasts based on computational models performed comparably better when predicting deaths (rel. WIS 1.26), suggesting that epidemiological modelling and human judgement can complement each other in important ways.

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Baturina, N., and G. Anisimova. "The cholera spread Simulation." E3S Web of Conferences 224 (2020): 03024. http://dx.doi.org/10.1051/e3sconf/202022403024.

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One of the frequently used modern epidemiological methods is the Simulation of disease spread. We used AnyLogic simulation. During the model construction, we take into account the specific cholera features. They are: the pathways of infection transmission, the course duration and the duration of the incubation (latent) period, the possibility of vaccination, etc.Two approaches are presented here: system-dynamic model and agentbased one. The system-dynamic model is used for strategic modelling of the epidemiological situation, it reflects the global trends. The agent-based approach allows describing the individual behaviour of each agent person, who independently form events conditioning transitions between states.Used together they make it possible to reflect different aspects of the epidemiological process development. The combination of these two models gives more possibilities for their application in a real situation: give the opportunity to impose tactical nuances on strategic modelling.

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Mittal, Poonam, Monika Mangla, Nonita Sharma, Reena, Suneeta Satpathy, and Sachi Nandan Mohanty. "Fuzzy Modelling of Clinical and Epidemiological Factors for COVID-19." International Journal of System Dynamics Applications 11, no. 1 (January 1, 2022): 1–16. http://dx.doi.org/10.4018/ijsda.307566.

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During this pandemic outbreak of COVID-19, the whole world is getting severely affected in respect of population health and economy. This novel virus has brought the whole world including the most developed countries to a standstill in a very short span like never before. The prime reason for this unexpected outburst of COVID-19 is lack of effective medicine and lack of proper understanding of the influencing factors. Here, the authors aim to find the effect of epidemiological factors that influence its spread using a fuzzy approach. For the same, a total of nine factors have been considered which are classified into risk and preventive factors. This fuzzy model supports to understand and evaluate the impact of these factors on the spread of COVID-19. Also, the model establishes a basis for understanding the effect of risk factors on preventive factors and vice versa. It is worth mentioning that this is the first attempt to analyze the effect of clinical and epidemiological factors with respect to COVID-19 using a fuzzy approach.

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Lal, Rajnesh, Weidong Huang, and Zhenquan Li. "An application of the ensemble Kalman filter in epidemiological modelling." PLOS ONE 16, no. 8 (August 19, 2021): e0256227. http://dx.doi.org/10.1371/journal.pone.0256227.

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Since the novel coronavirus (COVID-19) outbreak in China, and due to the open accessibility of COVID-19 data, several researchers and modellers revisited the classical epidemiological models to evaluate their practical applicability. While mathematical compartmental models can predict various contagious viruses’ dynamics, their efficiency depends on the model parameters. Recently, several parameter estimation methods have been proposed for different models. In this study, we evaluated the Ensemble Kalman filter’s performance (EnKF) in the estimation of time-varying model parameters with synthetic data and the real COVID-19 data of Hubei province, China. Contrary to the previous works, in the current study, the effect of damping factors on an augmented EnKF is studied. An augmented EnKF algorithm is provided, and we present how the filter performs in estimating models using uncertain observational (reported) data. Results obtained confirm that the augumented-EnKF approach can provide reliable model parameter estimates. Additionally, there was a good fit of profiles between model simulation and the reported COVID-19 data confirming the possibility of using the augmented-EnKF approach for reliable model parameter estimation.

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Fentie Bezabih, Abayneh, Geremew Kenassa Edessa, and Koya Purnachandra Rao. "Epidemiological Modelling and Analysis of COVID-19 Pandemic with Treatment." Mathematical Modelling and Applications 6, no. 1 (2021): 1. http://dx.doi.org/10.11648/j.mma.20210601.11.

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Voelkl, Bernhard. "Quantitative characterization of animal social organization: Applications for epidemiological modelling." Mathematical Biosciences and Engineering 17, no. 5 (2020): 5005–26. http://dx.doi.org/10.3934/mbe.2020271.

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E, Samoli, Rodopoulou S, Klompmaker J, Wolf K, Hvidtfeldt U, Cesaroni G, de Hoogh K, Strak M, Katsouyanni K, and Hoek G. "Modelling multi-level survival data in multi-center epidemiological studies." Environmental Epidemiology 3 (October 2019): 347. http://dx.doi.org/10.1097/01.ee9.0000609828.16927.5f.

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Hughes, M., A. Seesaghur, and D. De Silva. "Modelling Evolving Cancer Risk During Epidemiological Transition Using Economic Data." Value in Health 17, no. 7 (November 2014): A563—A564. http://dx.doi.org/10.1016/j.jval.2014.08.1868.

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Nadler, Philip, Shuo Wang, Rossella Arcucci, Xian Yang, and Yike Guo. "An epidemiological modelling approach for COVID-19 via data assimilation." European Journal of Epidemiology 35, no. 8 (August 2020): 749–61. http://dx.doi.org/10.1007/s10654-020-00676-7.

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Abstract The global pandemic of the 2019-nCov requires the evaluation of policy interventions to mitigate future social and economic costs of quarantine measures worldwide. We propose an epidemiological model for forecasting and policy evaluation which incorporates new data in real-time through variational data assimilation. We analyze and discuss infection rates in the UK, US and Italy. We furthermore develop a custom compartmental SIR model fit to variables related to the available data of the pandemic, named SITR model, which allows for more granular inference on infection numbers. We compare and discuss model results which conducts updates as new observations become available. A hybrid data assimilation approach is applied to make results robust to initial conditions and measurement errors in the data. We use the model to conduct inference on infection numbers as well as parameters such as the disease transmissibility rate or the rate of recovery. The parameterisation of the model is parsimonious and extendable, allowing for the incorporation of additional data and parameters of interest. This allows for scalability and the extension of the model to other locations or the adaption of novel data sources.

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Skwara, Urszula, Luís Mateus, Raquel Filipe, Filipe Rocha, Maíra Aguiar, and Nico Stollenwerk. "Superdiffusion and epidemiological spreading." Ecological Complexity 36 (December 2018): 168–83. http://dx.doi.org/10.1016/j.ecocom.2018.07.006.

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Nichita, Eleodor, Mary-Anne Pietrusiak, Fangli Xie, Peter Schwanke, and Anjali Pandya. "Modelling COVID-19 transmission using IDSIM, an epidemiological-modelling desktop app with multi-level immunization capabilities." Canada Communicable Disease Report 48, no. 10 (October 26, 2022): 449–64. http://dx.doi.org/10.14745/ccdr.v48i10a05.

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Switkay, Hal M. "A probabilistic epidemiological model." Model Assisted Statistics and Applications 16, no. 1 (March 25, 2021): 15–23. http://dx.doi.org/10.3233/mas-210511.

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We construct a model for the progress of the 2020 coronavirus epidemic in the United States of America, using probabilistic methods rather than the traditional compartmental model. We employ the generalized beta family of distributions, including those supported on bounded intervals and those supported on semi-infinite intervals. We compare the best-fit distributions for daily new cases and daily new deaths in America to the corresponding distributions for United Kingdom, Spain, and Italy. We explore how such a model might be justified theoretically in comparison to the apparently more natural compartmental model. We compare forecasts based on these models to observations, and find the forecasts useful in predicting total pandemic deaths.

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Wang, Ning, Yuting Fu, Hu Zhang, and Huipeng Shi. "An evaluation of mathematical models for the outbreak of COVID-19." Precision Clinical Medicine 3, no. 2 (May 22, 2020): 85–93. http://dx.doi.org/10.1093/pcmedi/pbaa016.

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Abstract Mathematical modelling performs a vital part in estimating and controlling the recent outbreak of coronavirus disease 2019 (COVID-19). In this epidemic, most countries impose severe intervention measures to contain the spread of COVID-19. The policymakers are forced to make difficult decisions to leverage between health and economic development. How and when to make clinical and public health decisions in an epidemic situation is a challenging question. The most appropriate solution is based on scientific evidence, which is mainly dependent on data and models. So one of the most critical problems during this crisis is whether we can develop reliable epidemiological models to forecast the evolution of the virus and estimate the effectiveness of various intervention measures and their impacts on the economy. There are numerous types of mathematical model for epidemiological diseases. In this paper, we present some critical reviews on mathematical models for the outbreak of COVID-19. Some elementary models are presented as an initial formulation for an epidemic. We give some basic concepts, notations, and foundation for epidemiological modelling. More related works are also introduced and evaluated by considering epidemiological features such as disease tendency, latent effects, susceptibility, basic reproduction numbers, asymptomatic infections, herd immunity, and impact of the interventions.

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Aylett-Bullock, Joseph, Robert Tucker Gilman, Ian Hall, David Kennedy, Egmond Samir Evers, Anjali Katta, Hussien Ahmed, et al. "Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward." BMJ Global Health 7, no. 3 (March 2022): e007822. http://dx.doi.org/10.1136/bmjgh-2021-007822.

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The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations at risk. Epidemiological modelling is vital to guiding evidence-informed or data-driven decision making. In forced displacement contexts, and in particular refugee and internally displaced people (IDP) settlements, it meets several challenges including data availability and quality, the applicability of existing models to those contexts, the accurate modelling of cultural differences or specificities of those operational settings, the communication of results and uncertainties, as well as the alignment of strategic goals between diverse partners in complex situations. In this paper, we systematically review the limited epidemiological modelling work applied to refugee and IDP settlements so far, and discuss challenges and identify lessons learnt from the process. With the likelihood of disease outbreaks expected to increase in the future as more people are displaced due to conflict and climate change, we call for the development of more approaches and models specifically designed to include the unique features and populations of refugee and IDP settlements. To strengthen collaboration between the modelling and the humanitarian public health communities, we propose a roadmap to encourage the development of systems and frameworks to share needs, build tools and coordinate responses in an efficient and scalable manner, both for this pandemic and for future outbreaks.

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Banerjee, Malay, Alexey Tokarev, and Vitaly Volpert. "Immuno-epidemiological model of two-stage epidemic growth." Mathematical Modelling of Natural Phenomena 15 (2020): 27. http://dx.doi.org/10.1051/mmnp/2020012.

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Epidemiological data on seasonal influenza show that the growth rate of the number of infected individuals can increase passing from one exponential growth rate to another one with a larger exponent. Such behavior is not described by conventional epidemiological models. In this work an immuno-epidemiological model is proposed in order to describe this two-stage growth. It takes into account that the growth in the number of infected individuals increases the initial viral load and provides a passage from the first stage of epidemic where only people with weak immune response are infected to the second stage where people with strong immune response are also infected. This scenario may be viewed as an increase of the effective number of susceptible increasing the effective growth rate of infected.

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Fernández-Navarro, Pablo, Javier González-Palacios, Mario González-Sánchez, Rebeca Ramis, Olivier Nuñez, Francisco Palmí-Perales, and Virgilio Gómez-Rubio. "Ranking spatial areas by risk of cancer: modelling in epidemiological surveillance." Annals of Cancer Epidemiology 4 (November 2020): 10. http://dx.doi.org/10.21037/ace-20-15.

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Islam, Sabique, Scott Parr, Richard Prazenica, Dahai Liu, and Sirish Namilae. "Predictive modelling of fuel shortages during hurricane evacuation: An epidemiological approach." IET Intelligent Transport Systems 15, no. 8 (June 8, 2021): 1064–75. http://dx.doi.org/10.1049/itr2.12083.

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Morand, Serge. "Life history evolution of nematodes: linking epidemiological modelling and comparative tests." Nematology 4, no. 5 (2002): 593–99. http://dx.doi.org/10.1163/15685410260438854.

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AbstractNematodes form a highly diversified, monophyletic group present in all environments as free-living individuals. As parasites they may infect plants, invertebrates and vertebrates. This group is then a unique model for testing evolutionary and ecological consequences of being a parasite. I summarise some recently published studies on the evolution of life traits of nematodes. Some predictions were obtained using the framework of epidemiological modelling and were tested using the comparative approach (and then some comparative methods).

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MURRAY, N. "Using modelling techniques to estimate epidemiological characteristics when information is scarce." Australian Veterinary Journal 80, no. 12 (December 2002): 756–57. http://dx.doi.org/10.1111/j.1751-0813.2002.tb11345.x.

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Briggs, D., J. Gulliver, C. Dambra, and M. Petrakis. "MODELLING POPULATION DISTRIBUTION FOR EPIDEMIOLOGICAL STUDIES USING NIGHT-TIME SATELLITE DATA." Epidemiology 14, Supplement (September 2003): S36. http://dx.doi.org/10.1097/00001648-200309001-00067.

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Willinger, R., G. A. Ryan, A. J. Mclean, and C. M. Kopp. "Mechanisms of brain injury related to mathematical modelling and epidemiological data." Accident Analysis & Prevention 26, no. 6 (December 1994): 767–79. http://dx.doi.org/10.1016/0001-4575(94)90053-1.

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Watier, Laurence, and Sylvia Richardson. "Modelling of an Epidemiological Time Series by a Threshold Autoregressive Model." Statistician 44, no. 3 (1995): 353. http://dx.doi.org/10.2307/2348706.

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Al-Shomrani, Mohammed M., Salihu S. Musa, and Abdullahi Yusuf. "Unfolding the Transmission Dynamics of Monkeypox Virus: An Epidemiological Modelling Analysis." Mathematics 11, no. 5 (February 23, 2023): 1121. http://dx.doi.org/10.3390/math11051121.

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Monkeypox (mpox) is a zoonotic viral disease that has caused recurring outbreaks in West Africa. The current global mpox virus (mpoxv) epidemic in endemic and non-endemic areas has seriously threatened public health. In this study, we design an SEIR-based deterministic model that considers prodromal stage, differential infectivity, and hospitalisation to investigate the transmission behaviour of mpoxv, which could help enhance control interventions. The model is theoretically analyzed by computing essential epidemiological quantities/dynamics, such as the basic reproduction number, which estimates the number of secondary infections caused by a typical primary case in an entirely susceptible community. Stability of the model’s equilibrium states is examined to evaluate the transmission potential of the mpoxv. Furthermore, partial rank correlation coefficient was adopted for sensitivity analysis to determine the top-rank model’s parameters for controlling the spread of mpoxv. Moreover, numerical simulations and model predictions are performed and are used to evaluate the influence of some crucial model parameters that help in strengthening the prevention and control of mpoxv infection.

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FABRICIUS, G., P. E. BERGERO, M. E. ORMAZABAL, A. L. MALTZ, and D. F. HOZBOR. "Modelling pertussis transmission to evaluate the effectiveness of an adolescent booster in Argentina." Epidemiology and Infection 141, no. 4 (July 6, 2012): 718–34. http://dx.doi.org/10.1017/s0950268812001380.

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SUMMARYDue to the current epidemiological situation of pertussis, several countries have implemented vaccination strategies that include a booster dose for adolescents. Since there is still no evidence showing that the adolescent booster has a positive effect on the most vulnerable group represented by infants, it is difficult to universalize the recommendation to include such reinforcement. In this work we present an age-structured compartmental deterministic model that considers the outstanding epidemiological features of the disease in order to assess the impact of the booster dose at age 11 years (Tdap booster) to infants. To this end, we performed different parameterizations of the model that represent distinct possible epidemiological scenarios. The results obtained show that the inclusion of a single Tdap dose at age 11 years significantly reduces the incidence of the disease within this age group, but has a very low impact on the risk group (0–1 year). An effort to improve the coverage of the first dose would have a much greater impact on infants. These results hold in the 18 scenarios considered, which demonstrates the robustness of these conclusions.

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Gilioli, G., M. Groppi, M. P. Vesperoni, J. Baumgärtner, and A. P. Gutierrez. "An epidemiological model of East Coast Fever in African livestock." Ecological Modelling 220, no. 13-14 (July 2009): 1652–62. http://dx.doi.org/10.1016/j.ecolmodel.2009.03.017.

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Journal articles on the topic 'Epidemiological modelling'

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