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Alhomaid A, Alzeer AH, Alsaawi F, Aljandal A, Al-Jafar R, Albalawi M, Alotaibi D, Alabdullatif R, AlGhassab R, Mominkhan DM, Alharbi M, Alghamdi AA, Almoklif M, Alabdulaali MK. The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach. Saudi Pharm J 2024; 32:101886. [PMID: 38162709 PMCID: PMC10755097 DOI: 10.1016/j.jsps.2023.101886] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 11/25/2023] [Indexed: 01/03/2024] Open
Abstract
Objectives This paper aims to measure the impact of the implemented nonpharmaceutical interventions (NPIs) in the Kingdom of Saudi Arabia (KSA) during the pandemic using simulation modeling. Methods To measure the impact of NPI, a hybrid agent-based and system dynamics simulation model was built and validated. Data were collected prospectively on a weekly basis. The core epidemiological model is based on a complex Susceptible-Exposed-Infectious-Recovered and Dead model of epidemic dynamics. Reverse engineering was performed on a weekly basis throughout the study period as a mean for model validation which reported on four outcomes: total cases, active cases, ICU cases, and deaths cases. To measure the impact of each NPI, the observed values of active and total cases were captured and compared to the projected values of active and total cases from the simulation. To measure the impact of each NPI, the study period was divided into rounds of incubation periods (cycles of 14 days each). The behavioral change of the spread of the disease was interpreted as the impact of NPIs that occurred at the beginning of the cycle. The behavioral change was measured by the change in the initial reproduction rate (R0). Results After 18 weeks of the reverse engineering process, the model achieved a 0.4 % difference in total cases for prediction at the end of the study period. The results estimated that NPIs led to 64 % change in The R0. Our breakdown analysis of the impact of each NPI indicates that banning going to schools had the greatest impact on the infection reproduction rate (24 %). Conclusion We used hybrid simulation modeling to measure the impact of NPIs taken by the KSA government. The finding further supports the notion that early NPIs adoption can effectively limit the spread of COVID-19. It also supports using simulation for building mathematical modeling for epidemiological scenarios.
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Affiliation(s)
- Ahmad Alhomaid
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
| | | | - Fahad Alsaawi
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
| | | | - Rami Al-Jafar
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
- School of Public Health, Imperial College London, London, UK
| | - Marwan Albalawi
- Department of Digital Health, Lean Business Services, Riyadh, Saudi Arabia
| | - Dana Alotaibi
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
| | | | - Razan AlGhassab
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
| | - Dalia M. Mominkhan
- National Health Command Center, Ministry of Health, Riyadh, Saudi Arabia
| | - Muaddi Alharbi
- National Health Command Center, Ministry of Health, Riyadh, Saudi Arabia
| | - Ahmad A. Alghamdi
- National Health Command Center, Ministry of Health, Riyadh, Saudi Arabia
| | - Maryam Almoklif
- National Health Command Center, Ministry of Health, Riyadh, Saudi Arabia
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Reshetnikov A, Berdutin V, Zaporozhtsev A, Romanov S, Abaeva O, Prisyazhnaya N, Vyatkina N. Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation. BMC Med Inform Decis Mak 2023; 23:48. [PMID: 36918871 PMCID: PMC10012312 DOI: 10.1186/s12911-023-02135-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 02/08/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND Outbreaks of infectious diseases are a complex phenomenon with many interacting factors. Regional health authorities need prognostic modeling of the epidemic process. METHODS For these purposes, various mathematical algorithms can be used, which are a useful tool for studying the infections spread dynamics. Epidemiological models act as evaluation and prognosis models. The authors outlined the experience of developing a short-term predictive algorithm for the spread of the COVID-19 in the region of the Russian Federation based on the SIR model: Susceptible (vulnerable), Infected (infected), Recovered (recovered). The article describes in detail the methodology of a short-term predictive algorithm, including an assessment of the possibility of building a predictive model and the mathematical aspects of creating such forecast algorithms. RESULTS Findings show that the predicted results (the mean square of the relative error of the number of infected and those who had recovered) were in agreement with the real-life situation: σ(I) = 0.0129 and σ(R) = 0.0058, respectively. CONCLUSIONS The present study shows that despite a large number of sophisticated modifications, each of which finds its scope, it is advisable to use a simple SIR model to quickly predict the spread of coronavirus infection. Its lower accuracy is fully compensated by the adaptive calibration of parameters based on monitoring the current situation with updating indicators in real-time.
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Affiliation(s)
- Andrey Reshetnikov
- Institute of Social Sciences, Sechenov First Moscow State Medical University, Moscow, Russian Federation.
| | - Vitalii Berdutin
- Contract Department, Federal Budgetary Institution of Healthcare "Volga District Medical Center of the Federal Medical and Biological Agency", Nizhny Novgorod, Russian Federation
| | - Alexander Zaporozhtsev
- Department of Theoretical and Applied Mechanics, Federal State Budgetary Educational Institution of Higher Education "Nizhny Novgorod State Technical University Named After R.E. Alekseev", Nizhny Novgorod, Russian Federation
| | - Sergey Romanov
- Department of Sociology of Medicine, Health Economics, and Health Insurance, Sechenov First Moscow State Medical University, Moscow, Russian Federation
| | - Olga Abaeva
- Department of Sociology of Medicine, Health Economics, and Health Insurance, Sechenov First Moscow State Medical University, Moscow, Russian Federation
| | - Nadezhda Prisyazhnaya
- Institute of Social Sciences, Sechenov First Moscow State Medical University, Moscow, Russian Federation
| | - Nadezhda Vyatkina
- Institute of Social Sciences, Sechenov First Moscow State Medical University, Moscow, Russian Federation
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Dynamical modelling of street protests using the Yellow Vest Movement and Khabarovsk as case studies. Sci Rep 2022; 12:20447. [PMID: 36443352 PMCID: PMC9705368 DOI: 10.1038/s41598-022-23917-z] [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: 05/18/2022] [Accepted: 11/07/2022] [Indexed: 11/29/2022] Open
Abstract
Social protests, in particular in the form of street protests, are a frequent phenomenon of modern world often making a significant disruptive effect on the society. Understanding the factors that can affect their duration and intensity is therefore an important problem. In this paper, we consider a mathematical model of protests dynamics describing how the number of protesters change with time. We apply the model to two events such as the Yellow Vest Movement 2018-2019 in France and Khabarovsk protests 2019-2020 in Russia. We show that in both cases our model provides a good description of the protests dynamics. We consider how the model parameters can be estimated by solving the inverse problem based on the available data on protesters number at different time. The analysis of parameter sensitivity then allows for determining which factor(s) may have the strongest effect on the protests dynamics.
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Kokurin MM, Kokurin MY, Semenova AV. Iteratively regularized Gauss-Newton type methods for approximating quasi-solutions of irregular nonlinear operator equations in Hilbert space with an application to COVID-19 epidemic dynamics. APPLIED MATHEMATICS AND COMPUTATION 2022; 431:127312. [PMID: 35726337 PMCID: PMC9198416 DOI: 10.1016/j.amc.2022.127312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 12/16/2021] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
We investigate a class of iteratively regularized methods for finding a quasi-solution of a noisy nonlinear irregular operator equation in Hilbert space. The iteration uses an a priori stopping rule involving the error level in input data. In assumptions that the Frechet derivative of the problem operator at the desired quasi-solution has a closed range, and that the quasi-solution fulfills the standard source condition, we establish for the obtained approximation an accuracy estimate linear with respect to the error level. The proposed iterative process is applied to the parameter identification problem for a SEIR-like model of the COVID-19 pandemic.
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Affiliation(s)
- M M Kokurin
- Mari State University, 424020 Lenin sqr. 1, Yoshkar-Ola, Russia
| | - M Yu Kokurin
- Mari State University, 424020 Lenin sqr. 1, Yoshkar-Ola, Russia
| | - A V Semenova
- Mari State University, 424020 Lenin sqr. 1, Yoshkar-Ola, Russia
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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.
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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
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Mathematical Modeling and Short-Term Forecasting of the COVID-19 Epidemic in Bulgaria: SEIRS Model with Vaccination. MATHEMATICS 2022. [DOI: 10.3390/math10152570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Data from the World Health Organization indicate that Bulgaria has the second-highest COVID-19 mortality rate in the world and the lowest vaccination rate in the European Union. In this context, to find the crucial epidemiological parameters that characterize the ongoing pandemic in Bulgaria, we introduce an extended SEIRS model with time-dependent coefficients. In addition to this, vaccination and vital dynamics are included in the model. We construct an appropriate Cauchy problem for a system of nonlinear ordinary differential equations and prove that its unique solution possesses some biologically reasonable features. Furthermore, we propose a numerical scheme and give an algorithm for the parameters identification in the obtained discrete problem. We show that the found values are close to the parameters values in the original differential problem. Based on the presented analysis, we develop a strategy for short-term prediction of the spread of the pandemic among the host population. The proposed model, as well as the methods and algorithms for parameters identification and forecasting, could be applied to COVID-19 data in every single country in the world.
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Krivorotko OI, Kabanikhin SI, Sosnovskaya MI, Andornaya DV. Sensitivity and identifiability analysis of COVID-19 pandemic models. Vavilovskii Zhurnal Genet Selektsii 2022; 25:82-91. [PMID: 35083396 PMCID: PMC8696171 DOI: 10.18699/vj21.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 12/17/2020] [Accepted: 12/18/2020] [Indexed: 12/02/2022] Open
Abstract
The paper presents the results of sensitivity-based identifiability analysis of the COVID-19 pandemic
spread models in the Novosibirsk region using the systems of differential equations and mass balance law. The
algorithm is built on the sensitivity matrix analysis using the methods of differential and linear algebra. It allows
one to determine the parameters that are the least and most sensitive to data changes to build a regularization for solving an identification problem of the most accurate pandemic spread scenarios in the region. The
performed analysis has demonstrated that the virus contagiousness is identifiable from the number of daily
confirmed, critical and recovery cases. On the other hand, the predicted proportion of the admitted patients
who require a ventilator and the mortality rate are determined much less consistently. It has been shown that
building a more realistic forecast requires adding additional information about the process such as the number
of daily hospital admissions. In our study, the problems of parameter identification using additional information about the number of daily confirmed, critical and mortality cases in the region were reduced to minimizing
the corresponding misfit functions. The minimization problem was solved through the differential evolution
method that is widely applied for stochastic global optimization. It has been demonstrated that a more general
COVID-19 spread compartmental model consisting of seven ordinary differential equations describes the main
trend of the spread and is sensitive to the peaks of confirmed cases but does not qualitatively describe small
statistical datasets such as the number of daily critical cases or mortality that can lead to errors in forecasting.
A more detailed agent-oriented model has been able to capture statistical data with additional noise to build
scenarios of COVID-19 spread in the region.
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Affiliation(s)
- O I Krivorotko
- Institute of Computational Mathematics and Mathematical Geophysics of Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
| | - S I Kabanikhin
- Institute of Computational Mathematics and Mathematical Geophysics of Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
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Piotrowski AP, Piotrowska AE. Differential evolution and particle swarm optimization against COVID-19. Artif Intell Rev 2021; 55:2149-2219. [PMID: 34426713 PMCID: PMC8374127 DOI: 10.1007/s10462-021-10052-w] [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] [Accepted: 07/17/2021] [Indexed: 11/29/2022]
Abstract
COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used.
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Affiliation(s)
- Adam P. Piotrowski
- Institute of Geophysics, Polish Academy of Sciences, Ks. Janusza 64, 01-452 Warsaw, Poland
| | - Agnieszka E. Piotrowska
- Faculty of Polish Studies, University of Warsaw, Krakowskie Przedmiescie 26/28, 00-927 Warsaw, Poland
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