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Velikova T, Mileva N, Naseva E. Method "Monte Carlo" in healthcare. World J Methodol 2024; 14:93930. [PMID: 39310240 PMCID: PMC11230067 DOI: 10.5662/wjm.v14.i3.93930] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/12/2024] [Accepted: 05/27/2024] [Indexed: 06/25/2024] Open
Abstract
In public health, simulation modeling stands as an invaluable asset, enabling the evaluation of new systems without their physical implementation, experimentation with existing systems without operational adjustments, and testing system limits without real-world repercussions. In simulation modeling, the Monte Carlo method emerges as a powerful yet underutilized tool. Although the Monte Carlo method has not yet gained widespread prominence in healthcare, its technological capabilities hold promise for substantial cost reduction and risk mitigation. In this review article, we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts. We underscore the significance of experiential insights derived from simulated experimentation, especially in resource-constrained scenarios where time, financial constraints, and limited resources necessitate innovative and efficient approaches. As public health faces increasing challenges, incorporating the Monte Carlo method presents an opportunity for enhanced system construction, analysis, and evaluation.
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Affiliation(s)
- Tsvetelina Velikova
- Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria
| | - Niya Mileva
- Medical Faculty, Medical University of Sofia, Sofia 1431, Bulgaria
| | - Emilia Naseva
- Faculty of Public Health “Prof. Tsekomir Vodenicharov, MD, Dsc,” Medical University of Sofia, Sofia 1431, Bulgaria
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2
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Haque S, Mengersen K, Barr I, Wang L, Yang W, Vardoulakis S, Bambrick H, Hu W. Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations. ENVIRONMENTAL RESEARCH 2024; 249:118568. [PMID: 38417659 DOI: 10.1016/j.envres.2024.118568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/22/2024] [Accepted: 02/25/2024] [Indexed: 03/01/2024]
Abstract
Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.
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Affiliation(s)
- Shovanur Haque
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Kerrie Mengersen
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia; Centre for Data Science (CDS), Queensland University of Technology (QUT), Brisbane, Australia
| | - Ian Barr
- World Health Organization Collaborating Centre for Reference and Research on Influenza, VIDRL, Doherty Institute, Melbourne, Australia; Department of Microbiology and Immunology, University of Melbourne, Victoria, Australia
| | - Liping Wang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Division of Infectious disease, Chinese Centre for Disease Control and Prevention, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Sotiris Vardoulakis
- HEAL Global Research Centre, Health Research Institute, University of Canberra, ACT Canberra, 2601, Australia
| | - Hilary Bambrick
- National Centre for Epidemiology and Population Health, The Australian National University, ACT 2601 Canberra, Australia
| | - Wenbiao Hu
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
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3
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Botz J, Wang D, Lambert N, Wagner N, Génin M, Thommes E, Madan S, Coudeville L, Fröhlich H. Modeling approaches for early warning and monitoring of pandemic situations as well as decision support. Front Public Health 2022; 10:994949. [PMID: 36452960 PMCID: PMC9702983 DOI: 10.3389/fpubh.2022.994949] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 10/21/2022] [Indexed: 11/15/2022] Open
Abstract
The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.
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Affiliation(s)
- Jonas Botz
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Danqi Wang
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
| | | | | | | | | | - Sumit Madan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Department of Computer Science, University of Bonn, Bonn, Germany
| | | | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
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4
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Bekker‐Nielsen Dunbar M, Hofmann F, Held L. Session 3 of the RSS Special Topic Meeting on Covid-19 Transmission: Replies to the discussion. JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A, (STATISTICS IN SOCIETY) 2022; 185:S158-S164. [PMID: 38607908 PMCID: PMC9878005 DOI: 10.1111/rssa.12985] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/14/2024]
Affiliation(s)
| | - Felix Hofmann
- Epidemiology, Biostatistics and Prevention Institute (EBPI)University of Zurich (UZH)ZurichSwitzerland
| | - Leonhard Held
- Epidemiology, Biostatistics and Prevention Institute (EBPI)University of Zurich (UZH)ZurichSwitzerland
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5
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Romero-Leiton JP, Prieto K, Reyes-Gonzalez D, Fuentes-Hernandez A. Optimal control and Bayes inference applied to complex microbial communities. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:6860-6882. [PMID: 35730286 DOI: 10.3934/mbe.2022323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Interactions between species are essential in ecosystems, but sometimes competition dominates over mutualism. The transition between mutualism-competition can have several implications and consequences, and it has hardly been studied in experimental settings. This work studies the mutualism between cross-feeding bacteria in strains that supply an essential amino acid for their mutualistic partner when both strains are exposed to antimicrobials. When the strains are free of antimicrobials, we found that, depending on the amount of amino acids freely available in the environment, the strains can exhibit extinction, mutualism, or competition. The availability of resources modulates the behavior of both species. When the strains are exposed to antimicrobials, the population dynamics depend on the proportion of bacteria resistant to the antimicrobial, finding that the extinction of both strains is eminent for low levels of the resource. In contrast, competition between both strains continues for high levels of the resource. An optimal control problem was then formulated to reduce the proportion of resistant bacteria, which showed that under cooperation, both strains (sensitive and resistant) are immediately controlled, while under competition, only the density of one of the strains is decreased. In contrast, its mutualist partner with control is increased. Finally, using our experimental data, we did parameters estimation in order to fit our mathematical model to the experimental data.
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Affiliation(s)
- Jhoana P Romero-Leiton
- Engineering Faculty, Cesmag University, Pasto, Colombia
- Design and Visual Arts Department, Georgian College, Barrie, Canada
| | - Kernel Prieto
- Design and Visual Arts Department, Georgian College, Barrie, Canada
| | - Daniela Reyes-Gonzalez
- Center for Genomic Sciences, National Autonomous University of Mexico, Cuernavaca, Mexico
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6
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Prieto K, Chávez–Hernández MV, Romero–Leiton JP. On mobility trends analysis of COVID-19 dissemination in Mexico City. PLoS One 2022; 17:e0263367. [PMID: 35143548 PMCID: PMC8830699 DOI: 10.1371/journal.pone.0263367] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 01/18/2022] [Indexed: 01/04/2023] Open
Abstract
This work presents a tool for forecasting the spread of the new coronavirus in Mexico City, which is based on a mathematical model with a metapopulation structure that uses Bayesian statistics and is inspired by a data-driven approach. The daily mobility of people in Mexico City is mathematically represented by an origin-destination matrix using the open mobility data from Google and the Transportation Mexican Survey. This matrix is incorporated in a compartmental model. We calibrate the model against borough-level incidence data collected between 27 February 2020 and 27 October 2020, while using Bayesian inference to estimate critical epidemiological characteristics associated with the coronavirus spread. Given that working with metapopulation models leads to rather high computational time consumption, and parameter estimation of these models may lead to high memory RAM consumption, we do a clustering analysis that is based on mobility trends to work on these clusters of borough separately instead of taken all of the boroughs together at once. This clustering analysis can be implemented in smaller or larger scales in different parts of the world. In addition, this clustering analysis is divided into the phases that the government of Mexico City has set up to restrict individual movement in the city. We also calculate the reproductive number in Mexico City using the next generation operator method and the inferred model parameters obtaining that this threshold is in the interval (1.2713, 1.3054). Our analysis of mobility trends can be helpful when making public health decisions.
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Affiliation(s)
- Kernel Prieto
- Instituto de Matemáticas, Universidad Nacional Autónoma de México, Mexico, México
| | - M. Victoria Chávez–Hernández
- Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Mexico, México
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7
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Prieto K. Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches. PLoS One 2022; 17:e0259958. [PMID: 35061688 PMCID: PMC8782335 DOI: 10.1371/journal.pone.0259958] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 10/29/2021] [Indexed: 12/24/2022] Open
Abstract
The COVID-19 pandemic has been widely spread and affected millions of people and caused hundreds of deaths worldwide, especially in patients with comorbilities and COVID-19. This manuscript aims to present models to predict, firstly, the number of coronavirus cases and secondly, the hospital care demand and mortality based on COVID-19 patients who have been diagnosed with other diseases. For the first part, I present a projection of the spread of coronavirus in Mexico, which is based on a contact tracing model using Bayesian inference. I investigate the health profile of individuals diagnosed with coronavirus to predict their type of patient care (inpatient or outpatient) and survival. Specifically, I analyze the comorbidity associated with coronavirus using Machine Learning. I have implemented two classifiers: I use the first classifier to predict the type of care procedure that a person diagnosed with coronavirus presenting chronic diseases will obtain (i.e. outpatient or hospitalised), in this way I estimate the hospital care demand; I use the second classifier to predict the survival or mortality of the patient (i.e. survived or deceased). I present two techniques to deal with these kinds of unbalanced datasets related to outpatient/hospitalised and survived/deceased cases (which occur in general for these types of coronavirus datasets) to obtain a better performance for the classification.
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Affiliation(s)
- Kernel Prieto
- Instituto de Matemáticas, Universidad Nacional Autónoma de México, Mexico City, México
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Kühn MJ, Abele D, Mitra T, Koslow W, Abedi M, Rack K, Siggel M, Khailaie S, Klitz M, Binder S, Spataro L, Gilg J, Kleinert J, Häberle M, Plötzke L, Spinner CD, Stecher M, Zhu XX, Basermann A, Meyer-Hermann M. Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution. Math Biosci 2021; 339:108648. [PMID: 34216635 PMCID: PMC8243656 DOI: 10.1016/j.mbs.2021.108648] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 05/31/2021] [Accepted: 06/06/2021] [Indexed: 12/16/2022]
Abstract
Non-pharmaceutical interventions (NPIs) are important to mitigate the spread of infectious diseases as long as no vaccination or outstanding medical treatments are available. We assess the effectiveness of the sets of non-pharmaceutical interventions that were in place during the course of the Coronavirus disease 2019 (Covid-19) pandemic in Germany. Our results are based on hybrid models, combining SIR-type models on local scales with spatial resolution. In order to account for the age-dependence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we include realistic prepandemic and recently recorded contact patterns between age groups. The implementation of non-pharmaceutical interventions will occur on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. In order to account for spatial heterogeneity, we use a graph approach and we include high-quality information on commuting activities combined with traveling information from social networks. The remaining uncertainty will be accounted for by a large number of randomized simulation runs. Based on the derived factors for the effectiveness of different non-pharmaceutical interventions over the past months, we provide different forecast scenarios for the upcoming time.
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Affiliation(s)
- Martin J Kühn
- Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
| | - Daniel Abele
- Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany
| | - Tanmay Mitra
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Wadim Koslow
- Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany
| | - Majid Abedi
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Kathrin Rack
- Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany
| | - Martin Siggel
- Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany
| | - Sahamoddin Khailaie
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Margrit Klitz
- Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany
| | - Sebastian Binder
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Luca Spataro
- Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany
| | - Jonas Gilg
- Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany
| | - Jan Kleinert
- Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany
| | - Matthias Häberle
- Earth Observation Center, Department EO Data Science, German Aerospace Center, Weßling, Germany
| | - Lena Plötzke
- Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany
| | - Christoph D Spinner
- Technical University of Munich, School of Medicine, University Hospital rechts der Isar, Department of Internal Medicine II, Munich, Germany
| | - Melanie Stecher
- University Hospital of Cologne, Department I for Internal Medicine, University of Cologne; German Center for Infection Research (DZIF), Cologne, Germany
| | - Xiao Xiang Zhu
- Earth Observation Center, Department EO Data Science, German Aerospace Center, Weßling, Germany
| | - Achim Basermann
- Institute for Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
| | - Michael Meyer-Hermann
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
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IBARGÜEN-MONDRAGÓN EDUARDO, PRIETO KERNEL, HIDALGO-BONILLA SANDRAPATRICIA. A MODEL ON BACTERIAL RESISTANCE CONSIDERING A GENERALIZED LAW OF MASS ACTION FOR PLASMID REPLICATION. J BIOL SYST 2021. [DOI: 10.1142/s0218339021400118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Bacterial plasmids play a fundamental role in antibiotic resistance. However, a lack of knowledge about their biology is an obstacle in fully understanding the mechanisms and properties of plasmid-mediated resistance. This has motivated investigations of real systems in vitro to analyze the transfer and replication of plasmids. In this work, we address this issue with mathematical modeling. We formulate and perform a qualitative analysis of a nonlinear system of ordinary differential equations describing the competition dynamics between plasmids and sensitive and resistant bacteria. In addition, we estimated parameter values from empirical data. Our model predicts scenarios consistent with biological phenomena. The elimination or spread of infection depends on factors associated with bacterial reproduction and the transfer and replication of plasmids. From the estimated parameters, three bacterial growth experiments were analyzed in vitro. We determined the experiment with the highest bacterial growth rate and the highest rate of plasmid transfer. Moreover, numerical simulations were performed to predict bacterial growth.
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Affiliation(s)
| | - KERNEL PRIETO
- Instituto de Matemáticas, Universidad Nacional Autónoma de México, Cuernavaca, México
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Hao Y, Xu T, Hu H, Wang P, Bai Y. Prediction and analysis of Corona Virus Disease 2019. PLoS One 2020; 15:e0239960. [PMID: 33017421 PMCID: PMC7535054 DOI: 10.1371/journal.pone.0239960] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 09/16/2020] [Indexed: 12/24/2022] Open
Abstract
The outbreak of Corona Virus Disease 2019 (COVID-19) in Wuhan has significantly impacted the economy and society globally. Countries are in a strict state of prevention and control of this pandemic. In this study, the development trend analysis of the cumulative confirmed cases, cumulative deaths, and cumulative cured cases was conducted based on data from Wuhan, Hubei Province, China from January 23, 2020 to April 6, 2020 using an Elman neural network, long short-term memory (LSTM), and support vector machine (SVM). A SVM with fuzzy granulation was used to predict the growth range of confirmed new cases, new deaths, and new cured cases. The experimental results showed that the Elman neural network and SVM used in this study can predict the development trend of cumulative confirmed cases, deaths, and cured cases, whereas LSTM is more suitable for the prediction of the cumulative confirmed cases. The SVM with fuzzy granulation can successfully predict the growth range of confirmed new cases and new cured cases, although the average predicted values are slightly large. Currently, the United States is the epicenter of the COVID-19 pandemic. We also used data modeling from the United States to further verify the validity of the proposed models.
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Affiliation(s)
- Yan Hao
- School of Information and Communication Engineering, North University of China, Taiyuan, China
| | - Ting Xu
- Department of Mathematics, School of Science, North University of China, Taiyuan, China
| | - Hongping Hu
- Department of Mathematics, School of Science, North University of China, Taiyuan, China
| | - Peng Wang
- Department of Mathematics, School of Science, North University of China, Taiyuan, China
| | - Yanping Bai
- Department of Mathematics, School of Science, North University of China, Taiyuan, China
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Manenti F, Galeazzi A, Bisotti F, Prifti K, Dell'Angelo A, Di Pretoro A, Ariatti C. Analogies between SARS-CoV-2 infection dynamics and batch chemical reactor behavior. Chem Eng Sci 2020; 227:115918. [PMID: 32834062 PMCID: PMC7305736 DOI: 10.1016/j.ces.2020.115918] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 06/15/2020] [Accepted: 06/17/2020] [Indexed: 11/30/2022]
Abstract
The batch reactor dynamics is used to predict SARS-CoV-2 spreading. The reactor model can phenomenologically explain the virus spreading. The model predicts the peak (day and entity) and the infection extinction. Initial Value Problem for ODE has been solved with an unknown initial condition. Algorithm robustness and convergence have been tested.
The pandemic infection of SARS-CoV-2 presents analogies with the behavior of chemical reactors. Susceptible population (A), active infected population (B), recovered cases (C) and deaths (D) can be assumed to be molecules of chemical compounds and their dynamics seem well aligned with those of composition and conversions in chemical syntheses. Thanks to these analogies, it is possible to generate pandemic predictive models based on chemical and physical considerations and regress their kinetic parameters, either globally or locally, to predict the peak time, entity and end of the infection with certain reliability. These predictions can strongly support the emergency plans decision making process. The model predictions have been validated with data from Chinese provinces that already underwent complete infection dynamics. For all the other countries, the evolution is re-regressed and re-predicted every day, updating a pandemic prediction database on Politecnico di Milano’s webpage based on the real-time available data.
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Affiliation(s)
- F Manenti
- Politecnico di Milano, Dipartimento di Chimica, Materiali e Ingegneria Chimica "Giulio Natta", Center for Sustainable Process Engineering Research (SuPER), Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - A Galeazzi
- Politecnico di Milano, Dipartimento di Chimica, Materiali e Ingegneria Chimica "Giulio Natta", Center for Sustainable Process Engineering Research (SuPER), Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - F Bisotti
- Politecnico di Milano, Dipartimento di Chimica, Materiali e Ingegneria Chimica "Giulio Natta", Center for Sustainable Process Engineering Research (SuPER), Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - K Prifti
- Politecnico di Milano, Dipartimento di Chimica, Materiali e Ingegneria Chimica "Giulio Natta", Center for Sustainable Process Engineering Research (SuPER), Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - A Dell'Angelo
- Politecnico di Milano, Dipartimento di Chimica, Materiali e Ingegneria Chimica "Giulio Natta", Center for Sustainable Process Engineering Research (SuPER), Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - A Di Pretoro
- Politecnico di Milano, Dipartimento di Chimica, Materiali e Ingegneria Chimica "Giulio Natta", Center for Sustainable Process Engineering Research (SuPER), Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Laboratoire de Génie Chimique, Université de Toulouse, CNRS/INP/UPS, Toulouse, France
| | - C Ariatti
- Politecnico di Milano, Dipartimento di Chimica, Materiali e Ingegneria Chimica "Giulio Natta", Center for Sustainable Process Engineering Research (SuPER), Piazza Leonardo da Vinci 32, 20133 Milano, Italy
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12
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Chirumbolo S, Bjørklund G. Wheat and chaffs in the interpretation of the current COVID19 outbreak in Italy. Virusdisease 2020; 31:85-93. [PMID: 32656304 PMCID: PMC7274266 DOI: 10.1007/s13337-020-00602-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 05/24/2020] [Indexed: 11/30/2022] Open
Abstract
The COVID19 outbreak in Italy is still a big concern. The Italian Government has recommended citizens to respect faithfully any compulsory legal disposition in order to stay home and so contributing in escaping viral contacts and slowing down epidemic. Emergency has raised a widely animated debate about how to read and comprehend the daily case numbers, the medical and caregivers availability, the needs to swab asymptomatic subjects. In this review the authors discuss about the many wheat and chaffs of how this virus disease is addressed .
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Affiliation(s)
- Salvatore Chirumbolo
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- Council for Nutritional and Environmental Medicine (CONEM), Mo i Rana, Norway
- CONEM Scientific Secretary, strada Le Grazie 9, 37134 Verona, Italy
| | - Geir Bjørklund
- Council for Nutritional and Environmental Medicine (CONEM), Mo i Rana, Norway
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