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Wang H, Wu D, Luo J, Zhang J. Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis. Sci Rep 2024; 14:15684. [PMID: 38977919 PMCID: PMC11231351 DOI: 10.1038/s41598-024-66968-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 07/05/2024] [Indexed: 07/10/2024] Open
Abstract
The global spread of COVID-19 has profoundly affected health and economies, highlighting the need for precise epidemic trend predictions for effective interventions. In this study, we used infectious disease models to simulate and predict the trajectory of COVID-19. An SEIR (susceptible, exposed, infected, removed) model was established using Wuhan data to reflect the pandemic. We then trained a genetic algorithm-based SEIR (GA-SEIR) model using data from a specific U.S. region and focused on individual susceptibility and infection dynamics. By integrating socio-psychological factors, we achieved a significant enhancement to the GA-SEIR model, leading to the development of an optimized version. This refined GA-SEIR model significantly improved our ability to simulate the spread and control of the epidemic and to effectively track trends. Remarkably, it successfully predicted the resurgence of COVID-19 in mainland China in April 2023, demonstrating its robustness and reliability. The refined GA-SEIR model provides crucial insights for public health authorities, enabling them to design and implement proactive strategies for outbreak containment and mitigation. Its substantial contributions to epidemic modelling and public health planning are invaluable, particularly in managing and controlling respiratory infectious diseases such as COVID-19.
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Affiliation(s)
- Haonan Wang
- School of Public Health, Southwest Medical University, No. 1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Danhong Wu
- Department of Applied Mathematics, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, People's Republic of China
| | - Jie Luo
- Department of Psychology, Durham University, Durham, DH1 3LE, UK
| | - Junhui Zhang
- School of Public Health, Southwest Medical University, No. 1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China.
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2
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George A, Lacey P, Badrinath P, Gray A, Turner P, Harwood C, Gregson M. Planning for healthcare services during the COVID-19 pandemic in the Southeast of England: a system dynamics modelling approach. BMJ Open 2023; 13:e072975. [PMID: 38081661 PMCID: PMC10729138 DOI: 10.1136/bmjopen-2023-072975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVES To develop, test, validate and implement a system dynamics model to simulate the pandemic progress and the impact of various interventions on viral spread, healthcare utilisation and demand in secondary care. DESIGN We adopted the system dynamics model incorporating susceptible, exposed, infection and recovery framework to simulate the progress of the pandemic and how the interventions for the COVID-19 response influence the outcomes with a focus on secondary care. SETTING This study was carried out covering all the local health systems in Southeast of England with a catchment population of six million with a specific focus on Kent and Medway system. PARTICIPANTS Six local health systems in Southeast of England using Kent and Medway as a case study. INTERVENTIONS Short to medium 'what if' scenarios incorporating human behaviour, non-pharmaceutical interventions and medical interventions were tested using the model with regular and continuous feedback of the model results to the local health system leaders for monitoring, planning and rapid response as needed. MAIN OUTCOME MEASURES Daily output from the model which included number infected in the population, hospital admissions needing COVID-19 care, occupied general beds, continuous positive airway pressure beds, intensive care beds, hospital discharge pathways and deaths. RESULTS We successfully implemented a regional series of models based on the local population needs which were used in healthcare planning as part of the pandemic response. CONCLUSIONS In this study, we have demonstrated the utility of system dynamics modelling incorporating local intelligence and collaborative working during the pandemic to respond rapidly and take decisions to protect the population. This led to strengthened cooperation among partners and ensured that the local population healthcare needs were met.
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Affiliation(s)
- Abraham George
- Kent County Council Adult Social Care and Health, Maidstone, UK
| | - Peter Lacey
- The Whole Systems Partnership, Knaresborough, UK
| | | | - Alex Gray
- The Whole Systems Partnership, Knaresborough, UK
| | - Paul Turner
- The Whole Systems Partnership, Knaresborough, UK
| | | | - Mark Gregson
- The Whole Systems Partnership, Knaresborough, UK
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3
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Bhadola P, Deo N. Exploring complexity of class-A Beta-lactamase family using physiochemical-based multiplex networks. Sci Rep 2023; 13:20626. [PMID: 37996629 PMCID: PMC10667273 DOI: 10.1038/s41598-023-48128-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 11/22/2023] [Indexed: 11/25/2023] Open
Abstract
The Beta-lactamase protein family is vital in countering Beta-lactam antibiotics, a widely used antimicrobial. To enhance our understanding of this family, we adopted a novel approach employing a multiplex network representation of its multiple sequence alignment. Each network layer, derived from the physiochemical properties of amino acids, unveils distinct insights into the intricate interactions among nodes, thereby enabling the identification of key motifs. Nodes with identical property signs tend to aggregate, providing evidence of the presence of consequential functional and evolutionary constraints shaping the Beta-lactamase family. We further investigate the distribution of evolutionary links across various layers. We observe that polarity manifests the highest number of unique links at lower thresholds, followed by hydrophobicity and polarizability, wherein hydrophobicity exerts dominance at higher thresholds. Further, the combinations of polarizability and volume, exhibit multiple simultaneous connections at all thresholds. The combination of hydrophobicity, polarizability, and volume uncovers shared links exclusive to these layers, implying substantial evolutionary impacts that may have functional or structural implications. By assessing the multi-degree of nodes, we unveil the hierarchical influence of properties at each position, identifying crucial properties responsible for the protein's functionality and providing valuable insights into potential targets for modulating enzymatic activity.
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Affiliation(s)
- Pradeep Bhadola
- Centre for Theoretical Physics & Natural Philosophy, Mahidol University, Nakhonsawan Campus, Phayuha Khiri, NakhonSawan, 60130, Thailand.
| | - Nivedita Deo
- Department of Physics and Astrophysics, University of Delhi, Delhi, 110007, India.
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4
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Lima LL, Atman APF. Complexity in the dengue spreading: A network analysis approach. PLoS One 2023; 18:e0289690. [PMID: 37549129 PMCID: PMC10406222 DOI: 10.1371/journal.pone.0289690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/24/2023] [Indexed: 08/09/2023] Open
Abstract
In an increasingly interconnected society, preventing epidemics has become a major challenge. Numerous infectious diseases spread between individuals by a vector, creating bipartite networks of infection with the characteristics of complex networks. In the case of dengue, a mosquito-borne disease, these infection networks include a vector-the Aedes aegypti mosquito-which has expanded its endemic area due to climate change. In this scenario, innovative approaches are essential to help public agents in the fight against the disease. Using an agent-based model, we investigated the network morphology of a dengue endemic region considering four different serotypes and a small population. The degree, betweenness, and closeness distributions are evaluated for the bipartite networks, considering the interactions up to the second order for each serotype. We observed scale-free features and heavy tails in the degree distribution and betweenness and quantified the decay of the degree distribution with a q-Gaussian fit function. The simulation results indicate that the spread of dengue is primarily driven by human-to-human and human-to-mosquito interaction, reinforcing the importance of controlling the vector to prevent episodes of epidemic outbreaks.
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Affiliation(s)
- L. L. Lima
- Programa de Pos-Graduação em Modelagem Matemática e Computacional, Centro Federal de Educação Tecnológica de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - A. P. F. Atman
- Programa de Pos-Graduação em Modelagem Matemática e Computacional, Centro Federal de Educação Tecnológica de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Departamento de Física, Centro Federal de Educação Tecnológica de Minas Gerais- CEFET-MG, Belo Horizonte, Minas Gerais, Brazil
- National Institute of Science and Technology for Complex Systems-CEFET-MG, Belo Horizonte, Minas Gerais, Brazil
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5
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Rahaman H, Barik D. Investigation of airborne spread of COVID-19 using a hybrid agent-based model: a case study of the UK. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230377. [PMID: 37501658 PMCID: PMC10369033 DOI: 10.1098/rsos.230377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/04/2023] [Indexed: 07/29/2023]
Abstract
Agent-based models have been proven to be quite useful in understanding and predicting the SARS-CoV-2 virus-originated COVID-19 infection. Person-to-person contact was considered as the main mechanism of viral transmission in these models. However, recent understanding has confirmed that airborne transmission is the main route to infection spread of COVID-19. We have developed a computationally efficient agent-based hybrid model to study the aerial propagation of the virus and subsequent spread of infection. We considered virus, a continuous variable, spreads diffusively in air and members of populations as discrete agents possessing one of the eight different states at a particular time. The transition from one state to another is probabilistic and age linked. Recognizing that population movement is a key aspect of infection spread, the model allows unbiased movement of agents. We benchmarked the model to recapture the temporal stochastic infection count data of the UK. The model investigates various key factors such as movement, infection susceptibility, new variants, recovery rate and duration, incubation period and vaccination on the infection propagation over time. Furthermore, the model was applied to capture the infection spread in Italy and France.
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Affiliation(s)
- Hafijur Rahaman
- School of Chemistry, University of Hyderabad, Central University PO, Hyderabad 500046, Telangana, India
| | - Debashis Barik
- School of Chemistry, University of Hyderabad, Central University PO, Hyderabad 500046, Telangana, India
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6
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Heredia Cacha I, Sáinz-Pardo Díaz J, Castrillo M, López García Á. Forecasting COVID-19 spreading through an ensemble of classical and machine learning models: Spain's case study. Sci Rep 2023; 13:6750. [PMID: 37185927 PMCID: PMC10127188 DOI: 10.1038/s41598-023-33795-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 04/19/2023] [Indexed: 05/17/2023] Open
Abstract
In this work the applicability of an ensemble of population and machine learning models to predict the evolution of the COVID-19 pandemic in Spain is evaluated, relying solely on public datasets. Firstly, using only incidence data, we trained machine learning models and adjusted classical ODE-based population models, especially suited to capture long term trends. As a novel approach, we then made an ensemble of these two families of models in order to obtain a more robust and accurate prediction. We then proceed to improve machine learning models by adding more input features: vaccination, human mobility and weather conditions. However, these improvements did not translate to the overall ensemble, as the different model families had also different prediction patterns. Additionally, machine learning models degraded when new COVID variants appeared after training. We finally used Shapley Additive Explanation values to discern the relative importance of the different input features for the machine learning models' predictions. The conclusion of this work is that the ensemble of machine learning models and population models can be a promising alternative to SEIR-like compartmental models, especially given that the former do not need data from recovered patients, which are hard to collect and generally unavailable.
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Affiliation(s)
- Ignacio Heredia Cacha
- Instituto de Física de Cantabria (IFCA), CSIC-UC, Avda. los Castros s/n., 39005, Santander, Spain
| | - Judith Sáinz-Pardo Díaz
- Instituto de Física de Cantabria (IFCA), CSIC-UC, Avda. los Castros s/n., 39005, Santander, Spain
| | - María Castrillo
- Instituto de Física de Cantabria (IFCA), CSIC-UC, Avda. los Castros s/n., 39005, Santander, Spain
| | - Álvaro López García
- Instituto de Física de Cantabria (IFCA), CSIC-UC, Avda. los Castros s/n., 39005, Santander, Spain.
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7
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Malaspina G, Racković S, Valdeira F. A hybrid compartmental model with a case study of COVID-19 in Great Britain and Israel. JOURNAL OF MATHEMATICS IN INDUSTRY 2023; 13:1. [PMID: 36777087 PMCID: PMC9897620 DOI: 10.1186/s13362-022-00130-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 12/20/2022] [Indexed: 06/18/2023]
Abstract
Given the severe impact of COVID-19 on several societal levels, it is of crucial importance to model the impact of restriction measures on the pandemic evolution, so that governments are able to make informed decisions. Even though there have been countless attempts to propose diverse models since the rise of the outbreak, the increase in data availability and start of vaccination campaigns calls for updated models and studies. Furthermore, most of the works are focused on a very particular place or application and we strive to attain a more general model, resorting to data from different countries. In particular, we compare Great Britain and Israel, two highly different scenarios in terms of vaccination plans and social structure. We build a network-based model, complex enough to model different scenarios of government-mandated restrictions, but generic enough to be applied to any population. To ease the computational load we propose a decomposition strategy for our model.
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Affiliation(s)
- Greta Malaspina
- Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia
| | - Stevo Racković
- Institute for Systems and Robotics, Instituto Superior Técnico, Lisbon, Portugal
| | - Filipa Valdeira
- Department of Environmental Science and Policy, Università degli Studi di Milano, Milan, Italy
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8
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Rehman AU, Mian SH, Usmani YS, Abidi MH, Mohammed MK. Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach. Healthcare (Basel) 2023; 11:healthcare11020260. [PMID: 36673628 PMCID: PMC9858678 DOI: 10.3390/healthcare11020260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/08/2023] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
In 2020, coronavirus (COVID-19) was declared a global pandemic and it remains prevalent today. A necessity to model the transmission of the virus has emerged as a result of COVID-19's exceedingly contagious characteristics and its rapid propagation throughout the world. Assessing the incidence of infection could enable policymakers to identify measures to halt the pandemic and gauge the required capacity of healthcare centers. Therefore, modeling the susceptibility, exposure, infection, and recovery in relation to the COVID-19 pandemic is crucial for the adoption of interventions by regulatory authorities. Fundamental factors, such as the infection rate, mortality rate, and recovery rate, must be considered in order to accurately represent the behavior of the pandemic using mathematical models. The difficulty in creating a mathematical model is in identifying the real model variables. Parameters might vary significantly across models, which can result in variations in the simulation results because projections primarily rely on a particular dataset. The purpose of this work was to establish a susceptible-exposed-infected-recovered (SEIR) model describing the propagation of the COVID-19 outbreak throughout the Kingdom of Saudi Arabia (KSA). The goal of this study was to derive the essential COVID-19 epidemiological factors from actual data. System dynamics modeling and design of experiment approaches were used to determine the most appropriate combination of epidemiological parameters and the influence of COVID-19. This study investigates how epidemiological variables such as seasonal amplitude, social awareness impact, and waning time can be adapted to correctly estimate COVID-19 scenarios such as the number of infected persons on a daily basis in KSA. This model can also be utilized to ascertain how stress (or hospital capacity) affects the percentage of hospitalizations and the number of deaths. Additionally, the results of this study can be used to establish policies or strategies for monitoring or restricting COVID-19 in Saudi Arabia.
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Affiliation(s)
- Ateekh Ur Rehman
- Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
- Correspondence:
| | - Syed Hammad Mian
- Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia
| | - Yusuf Siraj Usmani
- Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
| | - Mustufa Haider Abidi
- Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia
| | - Muneer Khan Mohammed
- Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia
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9
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Ali Z, Rabiei F, Rashidi MM, Khodadadi T. A fractional-order mathematical model for COVID-19 outbreak with the effect of symptomatic and asymptomatic transmissions. EUROPEAN PHYSICAL JOURNAL PLUS 2022; 137:395. [PMID: 35368740 PMCID: PMC8958840 DOI: 10.1140/epjp/s13360-022-02603-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 03/14/2022] [Indexed: 05/05/2023]
Abstract
The purpose of this paper is to investigate the transmission dynamics of a fractional-order mathematical model of COVID-19 including susceptible ( S ), exposed ( E ), asymptomatic infected ( I 1 ), symptomatic infected ( I 2 ), and recovered ( R ) classes named SEI 1 I 2 R model, using the Caputo fractional derivative. Here, SEI 1 I 2 R model describes the effect of asymptomatic and symptomatic transmissions on coronavirus disease outbreak. The existence and uniqueness of the solution are studied with the help of Schaefer- and Banach-type fixed point theorems. Sensitivity analysis of the model in terms of the variance of each parameter is examined, and the basic reproduction number ( R 0 ) to discuss the local stability at two equilibrium points is proposed. Using the Routh-Hurwitz criterion of stability, it is found that the disease-free equilibrium will be stable for R 0 < 1 whereas the endemic equilibrium becomes stable for R 0 > 1 and unstable otherwise. Moreover, the numerical simulations for various values of fractional-order are carried out with the help of the fractional Euler method. The numerical results show that asymptomatic transmission has a lower impact on the disease outbreak rather than symptomatic transmission. Finally, the simulated graph of total infected population by proposed model here is compared with the real data of second-wave infected population of COVID-19 outbreak in India.
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Affiliation(s)
- Zeeshan Ali
- School of Engineering, Monash University Malaysia, 47500 Subang Jaya, Selangor Malaysia
| | - Faranak Rabiei
- School of Engineering, Monash University Malaysia, 47500 Subang Jaya, Selangor Malaysia
| | - Mohammad M. Rashidi
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054 Sichuan People’s Republic of China
| | - Touraj Khodadadi
- Department of Information Technology, Malaysia University of Science and Technology, 47810 Petaling Jaya, Selangor Malaysia
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10
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Prabakaran R, Jemimah S, Rawat P, Sharma D, Gromiha MM. A novel hybrid SEIQR model incorporating the effect of quarantine and lockdown regulations for COVID-19. Sci Rep 2021; 11:24073. [PMID: 34912038 PMCID: PMC8674241 DOI: 10.1038/s41598-021-03436-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022] Open
Abstract
Mitigating the devastating effect of COVID-19 is necessary to control the infectivity and mortality rates. Hence, several strategies such as quarantine of exposed and infected individuals and restricting movement through lockdown of geographical regions have been implemented in most countries. On the other hand, standard SEIR based mathematical models have been developed to understand the disease dynamics of COVID-19, and the proper inclusion of these restrictions is the rate-limiting step for the success of these models. In this work, we have developed a hybrid Susceptible-Exposed-Infected-Quarantined-Removed (SEIQR) model to explore the influence of quarantine and lockdown on disease propagation dynamics. The model is multi-compartmental, and it considers everyday variations in lockdown regulations, testing rate and quarantine individuals. Our model predicts a considerable difference in reported and actual recovered and deceased cases in qualitative agreement with recent reports.
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Affiliation(s)
- R Prabakaran
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Sherlyn Jemimah
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Puneet Rawat
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Divya Sharma
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - M Michael Gromiha
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan.
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Tay BK, Roby CA, Wu JW, Tan DY. Dynamical Analysis of Universal Masking on the Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18179027. [PMID: 34501616 PMCID: PMC8430774 DOI: 10.3390/ijerph18179027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 08/23/2021] [Accepted: 08/24/2021] [Indexed: 01/07/2023]
Abstract
We investigate the impact of the delay in compulsory mask wearing on the spread of COVID-19 in the community, set in the Singapore context. By using modified SEIR-based compartmental models, we focus on macroscopic population-level analysis of the relationships between the delay in compulsory mask wearing and the maximum infection, through a series of scenario-based analysis. Our analysis suggests that collective masking can meaningfully reduce the transmission of COVID-19 in the community, but only if implemented within a critical time window of approximately before 80–100 days delay after the first infection is detected, coupled with strict enforcement to ensure compliance throughout the duration. We also identify a delay threshold of about 100 days that results in masking enforcement having little significant impact on the Maximum Infected Values. The results therefore highlight the necessity for rapid implementation of compulsory mask wearing to curb the spread of the pandemic.
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