1
|
Kim S, Abdulali A, Lee S. Heterogeneity is a key factor describing the initial outbreak of COVID-19. APPLIED MATHEMATICAL MODELLING 2023; 117:714-725. [PMID: 36643779 PMCID: PMC9827748 DOI: 10.1016/j.apm.2023.01.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 11/11/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
Assessing the transmission potential of emerging infectious diseases, such as COVID-19, is crucial for implementing prompt and effective intervention policies. The basic reproduction number is widely used to measure the severity of the early stages of disease outbreaks. The basic reproduction number of standard ordinary differential equation models is computed for homogeneous contact patterns; however, realistic contact patterns are far from homogeneous, specifically during the early stages of disease transmission. Heterogeneity of contact patterns can lead to superspreading events that show a significantly high level of heterogeneity in generating secondary infections. This is primarily due to the large variance in the contact patterns of complex human behaviours. Hence, in this work, we investigate the impacts of heterogeneity in contact patterns on the basic reproduction number by developing two distinct model frameworks: 1) an SEIR-Erlang ordinary differential equation model and 2) an SEIR stochastic agent-based model. Furthermore, we estimated the transmission probability of both models in the context of COVID-19 in South Korea. Our results highlighted the importance of heterogeneity in contact patterns and indicated that there should be more information than one quantity (the basic reproduction number as the mean quantity), such as a degree-specific basic reproduction number in the distributional sense when the contact pattern is highly heterogeneous.
Collapse
Affiliation(s)
- Sungchan Kim
- Department of Applied Mathematics, Kyung Hee University, Republic of Korea
| | - Arsen Abdulali
- Department of Engineering, University of Cambridge, United Kingdom
| | - Sunmi Lee
- Department of Applied Mathematics, Kyung Hee University, Republic of Korea
| |
Collapse
|
2
|
Chang M, Ku Y. LSTM model for predicting the daily number of asthma patients in Seoul, South Korea, using meteorological and air pollution data. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:37440-37448. [PMID: 36574119 DOI: 10.1007/s11356-022-24956-9] [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: 08/29/2022] [Accepted: 12/20/2022] [Indexed: 06/18/2023]
Abstract
Asthma is a common respiratory disease that is affected by air pollutants and meteorological factors. In this study, we developed models that predict the daily number of patients receiving treatment for asthma using air pollution and meteorological data. A neural network with long short-term memory (LSTM) and fully connected (FC) layers was used. The daily number of asthma patients in the city of Seoul, the capital of South Korea, was collected from the National Health Insurance Service. The data from 2015 to 2018 were used as the training and validation datasets for model development. Unseen data from 2019 were used for testing. The daily number of asthma patients per 100,000 inhabitants was predicted. The LSTM-FC neural network model achieved a Pearson correlation coefficient of 0.984 (P < 0.001) and root mean square error of 3.472 between the predicted and original values on the unseen testing dataset. The factors that impacted the prediction were the number of asthma patients in the previous time step before the predicted date, type of day (regular day and day after a holiday), minimum temperature, SO2, daily changes in the amount of cloud, and daily changes in diurnal temperature range. We successfully developed a neural network that predicts the onset and exacerbation of asthma, and we identified the crucial influencing air pollutants and meteorological factors. This study will help us to establish appropriate measures according to the daily predicted number of asthma patients and reduce the daily onset and exacerbation of asthma in the susceptible population.
Collapse
Affiliation(s)
- Munyoung Chang
- Department of Otorhinolaryngology-Head and Neck Surgery, Chung-Ang University College of Medicine, 84 Heukseok-Ro, Dongjak-Gu, 06974, Seoul, South Korea.
- Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, 08826, Seoul, South Korea.
| | - Yunseo Ku
- Department of Biomedical Engineering, Chungnam National University College of Medicine, 99 Daehak-Ro, Yuseong-Gu, 34134, Daejeon, South Korea
| |
Collapse
|
3
|
Krivorotko OI, Kabanikhin SI, Bektemesov MA, Sosnovskaya MI, Neverov AV. Simulation of COVID-19 Spread Scenarios in the Republic of Kazakhstan Based on Regularization of the Agent-Based Model. JOURNAL OF APPLIED AND INDUSTRIAL MATHEMATICS 2023; 17:94-109. [PMCID: PMC10184641 DOI: 10.1134/s1990478923010118] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 09/28/2023]
Abstract
We propose an algorithm for modeling scenarios for newly diagnosed cases of COVID-19 in the Republic of Kazakhstan. The algorithm is based on treating incomplete epidemiological data and solving the inverse problem of reconstructing the parameters of the agent-based model (ABM) using the set of available epidemiological data. The main tool for constructing the ABM is the Covasim open library. In the event of a drastic change in the situation (appearance of a new strain, removal or introduction of restrictive measures, etc.), the model parameters are updated taking into account additional information for the previous month (online data assimilation). The inverse problem is solved by stochastic global optimization (of tree-structured Parzen estimators). As an example, we give two scenarios of COVID-19 propagation calculated on December 12, 2021 for the period up to January 20, 2022. The scenario that took into account the New Year holidays (published on December 12, 2021 on http://covid19-modeling.ru ) almost coincided with what happened in reality (the error was 0.2%).
Collapse
Affiliation(s)
- O. I. Krivorotko
- Institute of Computational Mathematics and Mathematical Geophysics, Siberian
Branch,
Russian Academy of Sciences, Novosibirsk, 630090 Russia
- Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of
Sciences,
Novosibirsk, 630090 Russia
- Novosibirsk State University, Novosibirsk, 630090 Russia
| | - S. I. Kabanikhin
- Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of
Sciences,
Novosibirsk, 630090 Russia
- Novosibirsk State University, Novosibirsk, 630090 Russia
| | - M. A. Bektemesov
- Abai Kazakh National Pedagogical University, Almaty, 050010 Kazakhstan
| | | | - A. V. Neverov
- Institute of Computational Mathematics and Mathematical Geophysics, Siberian
Branch,
Russian Academy of Sciences, Novosibirsk, 630090 Russia
- Novosibirsk State University, Novosibirsk, 630090 Russia
| |
Collapse
|
4
|
Harweg T, Wagner M, Weichert F. Agent-Based Simulation for Infectious Disease Modelling over a Period of Multiple Days, with Application to an Airport Scenario. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:545. [PMID: 36612868 PMCID: PMC9819456 DOI: 10.3390/ijerph20010545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/16/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
With the COVID-19 pandemic, the role of infectious disease spreading in public places has been brought into focus more than ever. Places that are of particular interest regarding the spread of infectious diseases are international airport terminals, not only for the protection of staff and ground crew members but also to help minimize the risk of the spread of infectious entities such as COVID-19 around the globe. Computational modelling and simulation can help in understanding and predicting the spreading of infectious diseases in any such scenario. In this paper, we propose a model, which combines a simulation of high geometric detail regarding virus spreading with an account of the temporal progress of infection dynamics. We, thus, introduce an agent-based social force model for tracking the spread of infectious diseases by modelling aerosol traces and concentration of virus load in the air. We complement this agent-based model to have consistency over a period of several days. We then apply this model to investigate simulations in a realistic airport setting with multiple virus variants of varying contagiousness. According to our experiments, a virus variant has to be at least twelve times more contagious than the respective control to result in a level of infection of more than 30%. Combinations of agent-based models with temporal components can be valuable tools in an attempt to assess the risk of infection attributable to a particular virus and its variants.
Collapse
Affiliation(s)
- Thomas Harweg
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 16, 44227 Dortmund, North Rhine-Westphalia, Germany
| | - Mathias Wagner
- Department of Pathology, University of Saarland Medical School, Homburg Saar Campus, Kirrberger Strasse 100, 66424 Homburg Saar, Saarland, Germany
| | - Frank Weichert
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 16, 44227 Dortmund, North Rhine-Westphalia, Germany
| |
Collapse
|
5
|
Beerman JT, Beaumont GG, Giabbanelli PJ. A Scoping Review of Three Dimensions for Long-Term COVID-19 Vaccination Models: Hybrid Immunity, Individual Drivers of Vaccinal Choice, and Human Errors. Vaccines (Basel) 2022; 10:1716. [PMID: 36298581 PMCID: PMC9607873 DOI: 10.3390/vaccines10101716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 09/27/2022] [Accepted: 10/11/2022] [Indexed: 11/16/2022] Open
Abstract
The virus that causes COVID-19 changes over time, occasionally leading to Variants of Interest (VOIs) and Variants of Concern (VOCs) that can behave differently with respect to detection kits, treatments, or vaccines. For instance, two vaccination doses were 61% effective against the BA.1 predominant variant, but only 24% effective when BA.2 became predominant. While doses still confer protection against severe disease outcomes, the BA.5 variant demonstrates the possibility that individuals who have received a few doses built for previous variants can still be infected with newer variants. As previous vaccines become less effective, new ones will be released to target specific variants and the whole process of vaccinating the population will restart. While previous models have detailed logistical aspects and disease progression, there are three additional key elements to model COVID-19 vaccination coverage in the long term. First, the willingness of the population to participate in regular vaccination campaigns is essential for long-term effective COVID-19 vaccination coverage. Previous research has shown that several categories of variables drive vaccination status: sociodemographic, health-related, psychological, and information-related constructs. However, the inclusion of these categories in future models raises questions about the identification of specific factors (e.g., which sociodemographic aspects?) and their operationalization (e.g., how to initialize agents with a plausible combination of factors?). While previous models separately accounted for natural- and vaccine-induced immunity, the reality is that a significant fraction of individuals will be both vaccinated and infected over the coming years. Modeling the decay in immunity with respect to new VOCs will thus need to account for hybrid immunity. Finally, models rarely assume that individuals make mistakes, even though this over-reliance on perfectly rational individuals can miss essential dynamics. Using the U.S. as a guiding example, our scoping review summarizes these aspects (vaccinal choice, immunity, and errors) through ten recommendations to support the modeling community in developing long-term COVID-19 vaccination models.
Collapse
Affiliation(s)
- Jack T. Beerman
- Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
| | - Gwendal G. Beaumont
- Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
- IMT Mines Ales, 6 Av. de Clavieres, 30100 Ales, France
| | - Philippe J. Giabbanelli
- Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
| |
Collapse
|
6
|
Petrakova V, Krivorotko O. Mean field game for modeling of COVID-19 spread. JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS 2022; 514:126271. [PMID: 35462634 PMCID: PMC9017063 DOI: 10.1016/j.jmaa.2022.126271] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Indexed: 06/14/2023]
Abstract
The paper presents one of the possible approaches to pandemic spread modeling. The proposed model is based on the mean-field control inside separate groups of population, namely, suspectable (S), infected (I), removed (R) and cross-immune (C) ones. The numerical algorithm to solve this problem ensures conservation of the total population mass during timeline. The numerical experiments demonstrate modeling results for COVID-19 spread in Novosibirsk (Russia) for two 100-day periods.
Collapse
Affiliation(s)
- Viktoriya Petrakova
- Institute of Computational Modeling of SB RAS, 50/44 Akademgorodok str., 660036, Krasnoyarsk, Russia
| | - Olga Krivorotko
- Institute of Computational Mathematics and Mathematical Geophysics of SB RAS, 6 Ac. Lavrentieva ave., 630090, Novosibirsk, Russia
- Novosibirsk State University, 2 Pirogova str., 630090, Novosibirsk, Russia
| |
Collapse
|
7
|
Zhang W, Liu S, Osgood N, Zhu H, Qian Y, Jia P. Using simulation modelling and systems science to help contain COVID-19: A systematic review. SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE 2022; 40:SRES2897. [PMID: 36245570 PMCID: PMC9538520 DOI: 10.1002/sres.2897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 05/23/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.
Collapse
Affiliation(s)
- Weiwei Zhang
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Shiyong Liu
- Institute of Advanced Studies in Humanities and Social SciencesBeijing Normal University at ZhuhaiZhuhaiChina
| | - Nathaniel Osgood
- Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada
- Department of Community Health and EpidemiologyUniversity of SaskatchewanSaskatoonCanada
| | - Hongli Zhu
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Ying Qian
- Business SchoolUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Peng Jia
- School of Resource and Environmental SciencesWuhan UniversityWuhanHubeiChina
- International Institute of Spatial Lifecourse HealthWuhan UniversityWuhanHubeiChina
| |
Collapse
|
8
|
Yari Eili M, Rezaeenour J. An approach based on process mining to assess the quarantine strategies' effect in reducing the COVID-19 spread. LIBRARY HI TECH 2022. [DOI: 10.1108/lht-01-2022-0062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe feasibility of process mining combined with simulation techniques in estimating the effectiveness of COVID-19 prevention strategies on infection and mortality trends to determine best practices is assessed in this study. The quarantine event log is built from the CUSP (the COVID-19 US State Policy) database, where the dates of implemented social policies in the USA to respond to the COVID-19 pandemic are documented.Design/methodology/approachCOVID-19 is a highly infectious disease leading to a very high death toll worldwide. In most countries, the governments have resorted to a series of drastic strategies to prevent the outbreak by restricting the activities and movement among their population for a predefined time. Heretofore, different approaches have been published to estimate quarantine strategies and the majority signify the positive effect on managing this pandemic. Notably, the process perspective of COVID-19 datasets is of less concern among researchers. The purpose of this paper is to exploit the process mining techniques to model and analyze the quarantine implementation processes.FindingsThe discovered process model has 51 process variants for 51 cases (states), which indicate the quarantine activities were executed in different orders and periods during the pandemic. The time interval analysis between activities reveals the states with the most extended quarantine periods. These primary process mining insights are applied to define scenarios and variables of an agent-based model. The simulation findings indicate a meaningful relation between enforcing quarantine strategies and a declining trend of infection by 90% in the case of following strict quarantine and mask mandates. It is observed that in the post-quarantine period, the disease repeats its ascending trend unless implementation of different intervention strategies likes vaccination.Originality/valueThis study is the first in introducing process mining techniques in analyzing the COVID-19 quarantine strategies impact. The findings provide valuable insights for policymakers to proper control strategies and the process mining research community in expanding more process-related analysis on this pandemic. Also, the results have broad implications for research in other fields like information science to estimate the impact of quarantine strategies on process patterns in library systems.
Collapse
|
9
|
Developing Reusable COVID-19 Disaster Management Plans Using Agent-Based Analysis. SUSTAINABILITY 2022. [DOI: 10.3390/su14126981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Since late 2019, the COVID-19 biological disaster has informed us once again that, essentially, learning from best practices from past experiences is envisaged as the top strategy to develop disaster management (DM) resilience. Particularly in Indonesia, however, DM activities are challenging, since we have not experienced such a disaster, implying that the related knowledge is not available. The existing DM knowledge written down during activities is generally structured as in a typical government document, which is not easy to comprehend by stakeholders. This paper therefore sets out to develop an Indonesia COVID-19 Disaster Management Plan (DISPLAN) template, employing an Agent-Based Knowledge Analysis Framework. The framework allows the complexities to be parsed before depositing them into a unified repository, facilitating sharing, reusing, and a better decision-making system. It also can instantiate any DISPLAN for lower administration levels, provincial and regency, to harmonise holistic DM activities. With Design Science Research (DSR) guiding these processes, once the plan is developed, we successfully evaluate it with a real case study of the Manokwari Regency. To ensure its effectivity and usability, we also conduct a post-evaluation with two authorities who are highly involved in the Indonesia task force at the regency level. The results from this post-evaluation are highly promising.
Collapse
|
10
|
Saleem F, AL-Ghamdi ASALM, Alassafi MO, AlGhamdi SA. Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:5099. [PMID: 35564493 PMCID: PMC9099605 DOI: 10.3390/ijerph19095099] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/11/2022] [Accepted: 04/20/2022] [Indexed: 01/27/2023]
Abstract
COVID-19 is a disease caused by SARS-CoV-2 and has been declared a worldwide pandemic by the World Health Organization due to its rapid spread. Since the first case was identified in Wuhan, China, the battle against this deadly disease started and has disrupted almost every field of life. Medical staff and laboratories are leading from the front, but researchers from various fields and governmental agencies have also proposed healthy ideas to protect each other. In this article, a Systematic Literature Review (SLR) is presented to highlight the latest developments in analyzing the COVID-19 data using machine learning and deep learning algorithms. The number of studies related to Machine Learning (ML), Deep Learning (DL), and mathematical models discussed in this research has shown a significant impact on forecasting and the spread of COVID-19. The results and discussion presented in this study are based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Out of 218 articles selected at the first stage, 57 met the criteria and were included in the review process. The findings are therefore associated with those 57 studies, which recorded that CNN (DL) and SVM (ML) are the most used algorithms for forecasting, classification, and automatic detection. The importance of the compartmental models discussed is that the models are useful for measuring the epidemiological features of COVID-19. Current findings suggest that it will take around 1.7 to 140 days for the epidemic to double in size based on the selected studies. The 12 estimates for the basic reproduction range from 0 to 7.1. The main purpose of this research is to illustrate the use of ML, DL, and mathematical models that can be helpful for the researchers to generate valuable solutions for higher authorities and the healthcare industry to reduce the impact of this epidemic.
Collapse
Affiliation(s)
- Farrukh Saleem
- Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Abdullah Saad AL-Malaise AL-Ghamdi
- Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Madini O. Alassafi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | | |
Collapse
|
11
|
Vakil V, Trappe W. Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:4541. [PMID: 35457409 PMCID: PMC9032231 DOI: 10.3390/ijerph19084541] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/01/2022] [Accepted: 04/06/2022] [Indexed: 12/13/2022]
Abstract
The course of the COVID-19 pandemic has given rise to many disease trends at various population scales, ranging from local to global. Understanding these trends and the epidemiological phenomena that lead to the changing dynamics associated with disease progression is critical for public health officials and the global community to rein in further spread of this and other virulent diseases. Classic epidemiological modeling based on dynamical systems are powerful tools used for modeling and understanding diseases, but often necessitate modifications to the classic compartmental models to reflect empirical observations. In this paper, we present a collection of extensions to the classic SIRS model to support public health decisions associated with viral pandemics. Specifically, we present models that reflect different levels of disease severity among infected individuals, capture the effect of vaccination on different population groups, capture the effect of different vaccines with different levels of effectiveness, and model the impact of a vaccine with varying number of doses. Further, our mathematical models support the investigation of a pandemic's trend under the emergence of new variants and the associated reduction in vaccine effectiveness. Our models are supported through numerical simulations, which we use to illustrate phenomena that have been observed in the COVID-19 pandemic. Our findings also confirm observations that the mild infectious group accounts for the majority of infected individuals, and that prompt immunization results in weaker pandemic waves across all levels of infection as well as a lower number of disease-caused deaths. Finally, using our models, we demonstrate that, when dealing with a single variant and having access to a highly effective vaccine, a three-dose vaccine has a strong ability to reduce the infectious population. However, when a new variant with higher transmissibility and lower vaccine efficiency emerges, it becomes the dominant circulating variant, as was observed in the recent emergence of the Omicron variant.
Collapse
|
12
|
Cincotti S, Raberto M, Teglio A. Why do we need agent-based macroeconomics? REVIEW OF EVOLUTIONARY POLITICAL ECONOMY 2022; 3:5-29. [PMID: 38624911 PMCID: PMC8962940 DOI: 10.1007/s43253-022-00071-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 01/31/2022] [Indexed: 11/29/2022]
Abstract
We are entering the third decade of the twenty-first century with profound uncertainties and crucial challenges for the world economy. Phenomena like climate change, digital transformation, migration, demographic changes, and the ongoing COVID pandemic need to be understood and promptly addressed. We argue that the agent-based approach in economics is well suited to tackle these topics, because of its capacity to integrate the "micro" and "macro" dimensions by modelling the network of interactions among heterogeneous economic agents and their aggregate outcomes. This paper explains why the agent-based methodology is needed to overcome the limitations of the neoclassical approach in economics, which has not been able to properly address those challenges. To do so, the paper retraces the main stages of the scientific evolution in a general historical and epistemological perspective, showing how the paradigm of reductionism, which led to extraordinary advances after the scientific revolution of the seventeenth century, is less effective when addressing the main challenges ahead. On the other hand, the sciences of chaos theory and complex systems can provide the economic discipline with more suitable instruments to face those challenges. Finally, the paper briefly presents the contributions of the special issue, which use applications of agent-based models to study the main problems of our times.
Collapse
Affiliation(s)
- Silvano Cincotti
- DIME, University of Genova, Via Opera Pia 15, Genova, 16145 Italy
| | - Marco Raberto
- DIME, University of Genova, Via Opera Pia 15, Genova, 16145 Italy
| | - Andrea Teglio
- Department of Economics, Ca’ Foscari University of Venice, Cannaregio 873, Venezia, 30121 State Italy
| |
Collapse
|