1
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Cencetti G, Lucchini L, Santin G, Battiston F, Moro E, Pentland A, Lepri B. Temporal clustering of social interactions trades-off disease spreading and knowledge diffusion. J R Soc Interface 2024; 21:20230471. [PMID: 38166491 PMCID: PMC10761286 DOI: 10.1098/rsif.2023.0471] [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: 08/14/2023] [Accepted: 11/23/2023] [Indexed: 01/04/2024] Open
Abstract
Non-pharmaceutical measures such as preventive quarantines, remote working, school and workplace closures, lockdowns, etc. have shown effectiveness from an epidemic control perspective; however, they have also significant negative consequences on social life and relationships, work routines and community engagement. In particular, complex ideas, work and school collaborations, innovative discoveries and resilient norms formation and maintenance, which often require face-to-face interactions of two or more parties to be developed and synergically coordinated, are particularly affected. In this study, we propose an alternative hybrid solution that balances the slowdown of epidemic diffusion with the preservation of face-to-face interactions, that we test simulating a disease and a knowledge spreading simultaneously on a network of contacts. Our approach involves a two-step partitioning of the population. First, we tune the level of node clustering, creating 'social bubbles' with increased contacts within each bubble and fewer outside, while maintaining the average number of contacts in each network. Second, we tune the level of temporal clustering by pairing, for a certain time interval, nodes from specific social bubbles. Our results demonstrate that a hybrid approach can achieve better trade-offs between epidemic control and complex knowledge diffusion. The versatility of our model enables tuning and refining clustering levels to optimally achieve the desired trade-off, based on the potentially changing characteristics of a disease or knowledge diffusion process.
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Affiliation(s)
- Giulia Cencetti
- Digital Society Center, Fondazione Bruno Kessler, Trento, Italy
- Centre de Physique Théorique, CNRS, Aix-Marseille Univ, Université de Toulon, Marseille, France
| | - Lorenzo Lucchini
- DONDENA and BIDSA Research Centres—Bocconi University, Milan, Italy
| | - Gabriele Santin
- Digital Society Center, Fondazione Bruno Kessler, Trento, Italy
- Department of Environmental Sciences, Informatics and Statistics, University of Venice, Venezia, Italy
| | - Federico Battiston
- Department of Network and Data Science, Central European University, Vienna, Austria
| | - Esteban Moro
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Mathematics & GISC, Universidad Carlos III de Madrid, Leganes, Spain
| | - Alex Pentland
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bruno Lepri
- Digital Society Center, Fondazione Bruno Kessler, Trento, Italy
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2
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Arruda EF, Alexandre REA, Fragoso MD, do Val JBR, Thomas SS. A novel queue-based stochastic epidemic model with adaptive stabilising control. ISA TRANSACTIONS 2023; 140:121-133. [PMID: 37423884 DOI: 10.1016/j.isatra.2023.06.018] [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: 02/11/2022] [Revised: 06/19/2023] [Accepted: 06/19/2023] [Indexed: 07/11/2023]
Abstract
The main objective of this paper is to propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a setup under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate comprise the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly more straightforward and tractable than semi-Markov models with a similar level of generality. Based on stochastic stability, we derive a sufficient condition for a shrinking epidemic regarding the queuing system's occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of the COVID-19 epidemic in England and in the state of Amazonas, Brazil, and assess the effect of different stabilising strategies in the latter setting. Results suggest that the proposed approach can curb the epidemic with various occupation rate levels if the mitigation is timely.
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Affiliation(s)
- Edilson F Arruda
- Department of Decision Analytics and Risk, Southampton Business School, University of Southampton, 12 University Rd, Southampton SO17 1BJ, UK.
| | - Rodrigo E A Alexandre
- Alberto Luiz Coimbra Institute-Graduate School and Research in Engineering, Federal University of Rio de Janeiro, CP 68507, Rio de Janeiro 21941-972, Brazil.
| | - Marcelo D Fragoso
- National Laboratory for Scientific Computation, Av. Gettúlio Vargas 333, Quitandinha, Petrópolis RJ 25651-075, Brazil.
| | - João B R do Val
- School of Electrical Engineering, University of Campinas, Av. Albert Einstein 400, Cidade Universitária, Campinas, SP 13083-852, Brazil.
| | - Sinnu S Thomas
- School of Computer Science and Engineering, Digital University Kerala, Technocity, Mangalapuram Thonnakkal PO Thiruvananthapuram, Kerala 695317, India.
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3
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Lamata-Otín S, Reyna-Lara A, Soriano-Paños D, Latora V, Gómez-Gardeñes J. Collapse transition in epidemic spreading subject to detection with limited resources. Phys Rev E 2023; 108:024305. [PMID: 37723687 DOI: 10.1103/physreve.108.024305] [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: 05/03/2023] [Accepted: 07/21/2023] [Indexed: 09/20/2023]
Abstract
Compartmental models are the most widely used framework for modeling infectious diseases. These models have been continuously refined to incorporate all the realistic mechanisms that can shape the course of an epidemic outbreak. Building on a compartmental model that accounts for early detection and isolation of infectious individuals through testing, in this article we focus on the viability of detection processes under limited availability of testing resources, and we study how the latter impacts on the detection rate. Our results show that, in addition to the well-known epidemic transition at R_{0}=1, a second transition occurs at R_{0}^{★}>1 pinpointing the collapse of the detection system and, as a consequence, the switch from a regime of mitigation to a regime in which the pathogen spreads freely. We characterize the epidemic phase diagram of the model as a function of the relevant control parameters: the basic reproduction number, the maximum detection capacity of the system, and the fraction of individuals in shelter. Our analysis thus provides a valuable tool for estimating the detection resources and the level of confinement needed to face epidemic outbreaks.
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Affiliation(s)
- Santiago Lamata-Otín
- Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain
- GOTHAM lab, Institute of Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain
| | - Adriana Reyna-Lara
- Instituto Tecnológico y de Estudios Superiores de Monterrey, 64849 Monterrey, Nuevo León, México
| | - David Soriano-Paños
- GOTHAM lab, Institute of Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain
- Institute Gulbenkian of Science (IGC), 2780-156 Oeiras, Portugal
| | - Vito Latora
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
- Dipartimento di Fisica ed Astronomia, Università di Catania and INFN, I-95123 Catania, Italy
- Complexity Science Hub Vienna, A-1080 Vienna, Austria
| | - Jesús Gómez-Gardeñes
- Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain
- GOTHAM lab, Institute of Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain
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4
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Krishna C, Kumar D, Kushwaha DS. A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges. WIRELESS PERSONAL COMMUNICATIONS 2023; 131:1-48. [PMID: 37360140 PMCID: PMC10235850 DOI: 10.1007/s11277-023-10535-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/21/2023] [Indexed: 06/28/2023]
Abstract
Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies. Numerous attempts have been made to fight Covid-19-like pandemics using technologies such as the Internet of Things, wireless body area network, blockchain, and machine learning. Since the disease is highly contagious, novel research in patients' health monitoring system is essential for the constant monitoring of pandemic patients with minimal or no human intervention. With the ongoing pandemic of SARS-CoV-2, popularly known as Covid-19, innovations for monitoring of patients' vitals and storing them securely have risen more than ever. Analyzing the stored patients' data can further assist healthcare workers in their decision-making process. In this paper, we surveyed the research works on remote monitoring of pandemic patients admitted in hospitals or quarantined at home. First, an overview of pandemic patient monitoring is given followed by a brief introduction of enabling technologies i.e. Internet of Things, blockchain, and machine learning to implement the system. The reviewed works have been classified into three categories; remote monitoring of pandemic patients using IoT, blockchain-based storage or sharing platforms for patients' data, and processing/analyzing the stored patients' data using machine learning for prognosis and diagnosis. We also identified several open research issues to set directions for future research.
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Affiliation(s)
- Charu Krishna
- Department of Computer Science & Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, UP 211004 India
| | - Dinesh Kumar
- Department of Computer Science & Engineering, National Institute of Technology Jamshedpur, Jamshedpur, Jharkhand 831014 India
| | - Dharmender Singh Kushwaha
- Department of Computer Science & Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, UP 211004 India
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5
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Khan MMUR, Arefin MR, Tanimoto J. Investigating the trade-off between self-quarantine and forced quarantine provisions to control an epidemic: An evolutionary approach. APPLIED MATHEMATICS AND COMPUTATION 2022; 432:127365. [PMID: 35812766 PMCID: PMC9257552 DOI: 10.1016/j.amc.2022.127365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/19/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
During a pandemic event like the present COVID-19, self-quarantine, mask-wearing, hygiene maintenance, isolation, forced quarantine, and social distancing are the most effective nonpharmaceutical measures to control the epidemic when the vaccination and proper treatments are absent. In this study, we proposed an epidemiological model based on the SEIR dynamics along with the two interventions defined as self-quarantine and forced quarantine by human behavior dynamics. We consider a disease spreading through a population where some people can choose the self-quarantine option of paying some costs and be safer than the remaining ones. The remaining ones act normally and send to forced quarantine by the government if they get infected and symptomatic. The government pays the forced quarantine costs for individuals, and the government has a budget limit to treat the infected ones. Each intervention derived from the so-called behavior model has a dynamical equation that accounts for a proper balance between the costs for each case, the total budget, and the risk of infection. We show that the infection peak cannot be reduced if the authority does not enforce a proactive (quantified by a higher sensitivity parameter) intervention. While comparing the impact of both self- and forced quarantine provisions, our results demonstrate that the latter is more influential to reduce the disease prevalence and the social efficiency deficit (a gap between social optimum payoff and equilibrium payoff).
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Affiliation(s)
- Md Mamun-Ur-Rashid Khan
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga-shi, Fukuoka 816-8580, Japan
- Department of Mathematics, University of Dhaka, Dhaka 1000, Bangladesh
| | - Md Rajib Arefin
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga-shi, Fukuoka 816-8580, Japan
- Department of Mathematics, University of Dhaka, Dhaka 1000, Bangladesh
| | - Jun Tanimoto
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga-shi, Fukuoka 816-8580, Japan
- Faculty of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga-shi, Fukuoka 816-8580, Japan
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6
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Zhang X, Ruan Z, Zheng M, Zhou J, Boccaletti S, Barzel B. Epidemic spreading under mutually independent intra- and inter-host pathogen evolution. Nat Commun 2022; 13:6218. [PMID: 36266285 PMCID: PMC9584276 DOI: 10.1038/s41467-022-34027-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 10/10/2022] [Indexed: 12/24/2022] Open
Abstract
The dynamics of epidemic spreading is often reduced to the single control parameter R0 (reproduction-rate), whose value, above or below unity, determines the state of the contagion. If, however, the pathogen evolves as it spreads, R0 may change over time, potentially leading to a mutation-driven spread, in which an initially sub-pandemic pathogen undergoes a breakthrough mutation. To predict the boundaries of this pandemic phase, we introduce here a modeling framework to couple the inter-host network spreading patterns with the intra-host evolutionary dynamics. We find that even in the extreme case when these two process are driven by mutually independent selection forces, mutations can still fundamentally alter the pandemic phase-diagram. The pandemic transitions, we show, are now shaped, not just by R0, but also by the balance between the epidemic and the evolutionary timescales. If mutations are too slow, the pathogen prevalence decays prior to the appearance of a critical mutation. On the other hand, if mutations are too rapid, the pathogen evolution becomes volatile and, once again, it fails to spread. Between these two extremes, however, we identify a broad range of conditions in which an initially sub-pandemic pathogen can breakthrough to gain widespread prevalence.
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Affiliation(s)
- Xiyun Zhang
- Department of Physics, Jinan University, Guangzhou, Guangdong, 510632, China.
| | - Zhongyuan Ruan
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, Zhejiang, 310023, China
| | - Muhua Zheng
- School of Physics and Electronic Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
| | - Jie Zhou
- School of Physics and Electronic Science, East China Normal University, Shanghai, 200241, China
| | - Stefano Boccaletti
- CNR - Institute of Complex Systems, Via Madonna del Piano 10, I-50019, Sesto Fiorentino, Italy
- Moscow Institute of Physics and Technology (National Research University), 9 Institutskiy per., Dolgoprudny, Moscow Region, 141701, Russian Federation
- Universidad Rey Juan Carlos, Calle Tulipán s/n, 28933 Móstoles, Madrid, Spain
| | - Baruch Barzel
- Department of Mathematics, Bar-Ilan University, Ramat-Gan, 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, 5290002, Israel
- Network Science Institute, Northeastern University, Boston, MA, 02115, USA
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7
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Burbano Lombana DA, Zino L, Butail S, Caroppo E, Jiang ZP, Rizzo A, Porfiri M. Activity-driven network modeling and control of the spread of two concurrent epidemic strains. APPLIED NETWORK SCIENCE 2022; 7:66. [PMID: 36186912 PMCID: PMC9514203 DOI: 10.1007/s41109-022-00507-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain-phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible-exposed-infectious-removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time.
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Affiliation(s)
- Daniel Alberto Burbano Lombana
- Center for Urban Science and Progress, Tandon School of Engineering, New York University, 370 Jay Street, Brooklyn, NY 11201 USA
- Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Six MetroTech Center, Brooklyn, NY 11201 USA
- Department of Electrical and Computer Engineering, Rutgers University, 94 Brett Rd, Piscataway, NJ 08854 USA
| | - Lorenzo Zino
- Engineering and Technology Institute Groningen, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Sachit Butail
- Department of Mechanical Engineering, Northern Illinois University, DeKalb, IL 60115 USA
| | - Emanuele Caroppo
- Department of Mental Health, Local Health Unit Roma 2, 00159 Rome, Italy
- University Research Center He.R.A., Universitá Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Zhong-Ping Jiang
- Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, 370 Jay Street, Brooklyn, NY 11201 USA
| | - Alessandro Rizzo
- Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129 Turin, Italy
- Institute for Invention, Innovation and Entrepreneurship, Tandon School of Engineering, New York University, Six MetroTech Center, Brooklyn, NY 11201 USA
| | - Maurizio Porfiri
- Center for Urban Science and Progress, Tandon School of Engineering, New York University, 370 Jay Street, Brooklyn, NY 11201 USA
- Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Six MetroTech Center, Brooklyn, NY 11201 USA
- Department of Biomedical Engineering, Tandon School of Engineering, New York University, Six MetroTech Center, Brooklyn, NY 11201 USA
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8
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Scheduling mechanisms to control the spread of COVID-19. PLoS One 2022; 17:e0272739. [PMID: 36107933 PMCID: PMC9477360 DOI: 10.1371/journal.pone.0272739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 07/15/2022] [Indexed: 11/19/2022] Open
Abstract
We study scheduling mechanisms that explore the trade-off between containing the spread of COVID-19 and performing in-person activity in organizations. Our mechanisms, referred to as group scheduling, are based on partitioning the population randomly into groups and scheduling each group on appropriate days with possible gaps (when no one is working and all are quarantined). Each group interacts with no other group and, importantly, any person who is symptomatic in a group is quarantined. We show that our mechanisms effectively trade-off in-person activity for more effective control of the COVID-19 virus spread. In particular, we show that a mechanism which partitions the population into two groups that alternatively work in-person for five days each, flatlines the number of COVID-19 cases quite effectively, while still maintaining in-person activity at 70% of pre-COVID-19 level. Other mechanisms that partitions into two groups with less continuous work days or more spacing or three groups achieve even more aggressive control of the virus at the cost of a somewhat lower in-person activity (about 50%). We demonstrate the efficacy of our mechanisms by theoretical analysis and extensive experimental simulations on various epidemiological models based on real-world data.
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9
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Hacohen A, Cohen R, Efroni S, Bachelet I, Barzel B. Distribution equality as an optimal epidemic mitigation strategy. Sci Rep 2022; 12:10430. [PMID: 35729241 PMCID: PMC9210068 DOI: 10.1038/s41598-022-12261-x] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 04/28/2022] [Indexed: 12/17/2022] Open
Abstract
Upon the development of a therapeutic, a successful response to a global pandemic relies on efficient worldwide distribution, a process constrained by our global shipping network. Most existing strategies seek to maximize the outflow of the therapeutics, hence optimizing for rapid dissemination. Here we find that this intuitive approach is, in fact, counterproductive. The reason is that by focusing strictly on the quantity of disseminated therapeutics, these strategies disregard the way in which this quantity distributes across destinations. Most crucially-they overlook the interplay of the therapeutic spreading patterns with those of the pathogens. This results in a discrepancy between supply and demand, that prohibits efficient mitigation even under optimal conditions of superfluous flow. To solve this, we design a dissemination strategy that naturally follows the predicted spreading patterns of the pathogens, optimizing not just for supply volume, but also for its congruency with the anticipated demand. Specifically, we show that epidemics spread relatively uniformly across all destinations, prompting us to introduce an equality constraint into our dissemination that prioritizes supply homogeneity. This strategy may, at times, slow down the supply rate in certain locations, however, thanks to its egalitarian nature, which mimics the flow of the pathogens, it provides a dramatic leap in overall mitigation efficiency, potentially saving more lives with orders of magnitude less resources.
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Affiliation(s)
- Adar Hacohen
- Augmanity, Rehovot, Israel
- Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Reuven Cohen
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Sol Efroni
- Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | | | - Baruch Barzel
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel.
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel.
- Network Science Institute, Northeastern University, Boston, MA, USA.
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10
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Schwarzendahl FJ, Grauer J, Liebchen B, Löwen H. Mutation induced infection waves in diseases like COVID-19. Sci Rep 2022; 12:9641. [PMID: 35688998 PMCID: PMC9186490 DOI: 10.1038/s41598-022-13137-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 05/20/2022] [Indexed: 12/16/2022] Open
Abstract
After more than 6 million deaths worldwide, the ongoing vaccination to conquer the COVID-19 disease is now competing with the emergence of increasingly contagious mutations, repeatedly supplanting earlier strains. Following the near-absence of historical examples of the long-time evolution of infectious diseases under similar circumstances, models are crucial to exemplify possible scenarios. Accordingly, in the present work we systematically generalize the popular susceptible-infected-recovered model to account for mutations leading to repeatedly occurring new strains, which we coarse grain based on tools from statistical mechanics to derive a model predicting the most likely outcomes. The model predicts that mutations can induce a super-exponential growth of infection numbers at early times, which self-amplify to giant infection waves which are caused by a positive feedback loop between infection numbers and mutations and lead to a simultaneous infection of the majority of the population. At later stages-if vaccination progresses too slowly-mutations can interrupt an ongoing decrease of infection numbers and can cause infection revivals which occur as single waves or even as whole wave trains featuring alternative periods of decreasing and increasing infection numbers. This panorama of possible mutation-induced scenarios should be tested in more detailed models to explore their concrete significance for specific infectious diseases. Further, our results might be useful for discussions regarding the importance of a release of vaccine-patents to reduce the risk of mutation-induced infection revivals but also to coordinate the release of measures following a downwards trend of infection numbers.
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Affiliation(s)
- Fabian Jan Schwarzendahl
- Institut für Theoretische Physik II: Weiche Materie, Heinrich-Heine-Universität Düsseldorf, 40225, Düsseldorf, Germany.
| | - Jens Grauer
- Institut für Theoretische Physik II: Weiche Materie, Heinrich-Heine-Universität Düsseldorf, 40225, Düsseldorf, Germany
| | - Benno Liebchen
- Institute of Condensed Matter Physics, Technische Universität Darmstadt, Darmstadt, Germany
| | - Hartmut Löwen
- Institut für Theoretische Physik II: Weiche Materie, Heinrich-Heine-Universität Düsseldorf, 40225, Düsseldorf, Germany
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11
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Reyna-Lara A, Soriano-Paños D, Arenas A, Gómez-Gardeñes J. The interconnection between independent reactive control policies drives the stringency of local containment. CHAOS, SOLITONS, AND FRACTALS 2022; 158:112012. [PMID: 35370369 PMCID: PMC8956273 DOI: 10.1016/j.chaos.2022.112012] [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: 02/24/2022] [Accepted: 03/14/2022] [Indexed: 06/14/2023]
Abstract
The lack of medical treatments and vaccines upon the arrival of the SARS-CoV-2 virus has made non-pharmaceutical interventions the best allies in safeguarding human lives in the face of the COVID-19 pandemic. Here we propose a self-organized epidemic model with multi-scale control policies that are relaxed or strengthened depending on the extent of the epidemic outbreak. We show that optimizing the balance between the effects of epidemic control and the associated socio-economic cost is strongly linked to the stringency of control measures. We also show that non-pharmaceutical interventions acting at different spatial scales, from creating social bubbles at the household level to constraining mobility between different cities, are strongly interrelated. We find that policy functionality changes for better or worse depending on network connectivity, meaning that some populations may allow for less restrictive measures than others if both have the same resources to respond to the evolving epidemic.
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Affiliation(s)
- Adriana Reyna-Lara
- Department of Condensed Matter Physics, University of Zaragoza, E-50009 Zaragoza, Spain
- GOTHAM Lab-Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, E-50018 Zaragoza, Spain
| | - David Soriano-Paños
- GOTHAM Lab-Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, E-50018 Zaragoza, Spain
- Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal
| | - Alex Arenas
- Departament d'Enginyeria Informática i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Jesús Gómez-Gardeñes
- Department of Condensed Matter Physics, University of Zaragoza, E-50009 Zaragoza, Spain
- GOTHAM Lab-Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, E-50018 Zaragoza, Spain
- Center for Computational Social Science (CCSS), Kobe University, 657-8501 Kobe, Japan
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12
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Kasis A, Timotheou S, Monshizadeh N, Polycarpou M. Optimal intervention strategies to mitigate the COVID-19 pandemic effects. Sci Rep 2022; 12:6124. [PMID: 35414076 PMCID: PMC9004223 DOI: 10.1038/s41598-022-09857-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 03/22/2022] [Indexed: 12/14/2022] Open
Abstract
Governments across the world are currently facing the task of selecting suitable intervention strategies to cope with the effects of the COVID-19 pandemic. This is a highly challenging task, since harsh measures may result in economic collapse while a relaxed strategy might lead to a high death toll. Motivated by this, we consider the problem of forming intervention strategies to mitigate the impact of the COVID-19 pandemic that optimize the trade-off between the number of deceases and the socio-economic costs. We demonstrate that the healthcare capacity and the testing rate highly affect the optimal intervention strategies. Moreover, we propose an approach that enables practical strategies, with a small number of policies and policy changes, that are close to optimal. In particular, we provide tools to decide which policies should be implemented and when should a government change to a different policy. Finally, we consider how the presented results are affected by uncertainty in the initial reproduction number and infection fatality rate and demonstrate that parametric uncertainty has a more substantial effect when stricter strategies are adopted.
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Affiliation(s)
- Andreas Kasis
- Department of Electrical and Computer Engineering, KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus.
| | - Stelios Timotheou
- Department of Electrical and Computer Engineering, KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus
| | - Nima Monshizadeh
- Engineering and Technology Institute, University of Groningen, Nijenborgh 4, 9747AG, Groningen, The Netherlands
| | - Marios Polycarpou
- Department of Electrical and Computer Engineering, KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus
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13
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Vassallo L, Perez IA, Alvarez-Zuzek LG, Amaya J, Torres MF, Valdez LD, La Rocca CE, Braunstein LA. An epidemic model for COVID-19 transmission in Argentina: Exploration of the alternating quarantine and massive testing strategies. Math Biosci 2022; 346:108664. [PMID: 34271015 PMCID: PMC8276572 DOI: 10.1016/j.mbs.2021.108664] [Citation(s) in RCA: 2] [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: 02/22/2021] [Revised: 07/01/2021] [Accepted: 07/01/2021] [Indexed: 01/10/2023]
Abstract
The COVID-19 pandemic has challenged authorities at different levels of government administration around the globe. When faced with diseases of this severity, it is useful for the authorities to have prediction tools to estimate in advance the impact on the health system as well as the human, material, and economic resources that will be necessary. In this paper, we construct an extended Susceptible-Exposed-Infected-Recovered model that incorporates the social structure of Mar del Plata, the 4°most inhabited city in Argentina and head of the Municipality of General Pueyrredón. Moreover, we consider detailed partitions of infected individuals according to the illness severity, as well as data of local health resources, to bring predictions closer to the local reality. Tuning the corresponding epidemic parameters for COVID-19, we study an alternating quarantine strategy: a part of the population can circulate without restrictions at any time, while the rest is equally divided into two groups and goes on successive periods of normal activity and lockdown, each one with a duration of τ days. We also implement a random testing strategy with a threshold over the population. We found that τ=7 is a good choice for the quarantine strategy since it reduces the infected population and, conveniently, it suits a weekly schedule. Focusing on the health system, projecting from the situation as of September 30, we foresee a difficulty to avoid saturation of the available ICU, given the extremely low levels of mobility that would be required. In the worst case, our model estimates that four thousand deaths would occur, of which 30% could be avoided with proper medical attention. Nonetheless, we found that aggressive testing would allow an increase in the percentage of people that can circulate without restrictions, and the medical facilities to deal with the additional critical patients would be relatively low.
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Affiliation(s)
- Lautaro Vassallo
- Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR), CONICET - Universidad Nacional de Mar del Plata, 7600 Mar del Plata, Buenos Aires, Argentina; Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, 7600 Mar del Plata, Buenos Aires, Argentina.
| | - Ignacio A Perez
- Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR), CONICET - Universidad Nacional de Mar del Plata, 7600 Mar del Plata, Buenos Aires, Argentina; Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, 7600 Mar del Plata, Buenos Aires, Argentina
| | | | - Julián Amaya
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, 7600 Mar del Plata, Buenos Aires, Argentina
| | - Marcos F Torres
- Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR), CONICET - Universidad Nacional de Mar del Plata, 7600 Mar del Plata, Buenos Aires, Argentina; Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, 7600 Mar del Plata, Buenos Aires, Argentina
| | - Lucas D Valdez
- Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR), CONICET - Universidad Nacional de Mar del Plata, 7600 Mar del Plata, Buenos Aires, Argentina; Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, 7600 Mar del Plata, Buenos Aires, Argentina
| | - Cristian E La Rocca
- Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR), CONICET - Universidad Nacional de Mar del Plata, 7600 Mar del Plata, Buenos Aires, Argentina; Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, 7600 Mar del Plata, Buenos Aires, Argentina
| | - Lidia A Braunstein
- Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR), CONICET - Universidad Nacional de Mar del Plata, 7600 Mar del Plata, Buenos Aires, Argentina; Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, 7600 Mar del Plata, Buenos Aires, Argentina; Physics Department, Boston University, Boston, MA 02215, United States
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14
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Estimating the generation interval from the incidence rate, the optimal quarantine duration and the efficiency of fast switching periodic protocols for COVID-19. Sci Rep 2022; 12:4623. [PMID: 35301351 PMCID: PMC8929281 DOI: 10.1038/s41598-022-08197-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 03/03/2022] [Indexed: 11/08/2022] Open
Abstract
The transmissibility of an infectious disease is usually quantified in terms of the reproduction number [Formula: see text] representing, at a given time, the average number of secondary cases caused by an infected individual. Recent studies have enlightened the central role played by w(z), the distribution of generation times z, namely the time between successive infections in a transmission chain. In standard approaches this quantity is usually substituted by the distribution of serial intervals, which is obtained by contact tracing after measuring the time between onset of symptoms in successive cases. Unfortunately, this substitution can cause important biases in the estimate of [Formula: see text]. Here we present a novel method which allows us to simultaneously obtain the optimal functional form of w(z) together with the daily evolution of [Formula: see text], over the course of an epidemic. The method uses, as unique information, the daily series of incidence rate and thus overcomes biases present in standard approaches. We apply our method to one year of data from COVID-19 officially reported cases in the 21 Italian regions, since the first confirmed case on February 2020. We find that w(z) has mean value [Formula: see text] days with a standard deviation [Formula: see text] day, for all Italian regions, and these values are stable even if one considers only the first 10 days of data recording. This indicates that an estimate of the most relevant transmission parameters can be already available in the early stage of a pandemic. We use this information to obtain the optimal quarantine duration and to demonstrate that, in the case of COVID-19, post-lockdown mitigation policies, such as fast periodic switching and/or alternating quarantine, can be very efficient.
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15
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Cornes FE, Frank GA, Dorso CO. COVID-19 spreading under containment actions. PHYSICA A 2022; 588:126566. [PMID: 34744295 PMCID: PMC8565045 DOI: 10.1016/j.physa.2021.126566] [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: 02/16/2021] [Revised: 07/15/2021] [Indexed: 06/13/2023]
Abstract
We propose an epidemiological model that explores the effect of human mobility on the spatio-temporal dynamics of the COVID-19 outbreak, in the spirit to those considered in Refs. Barmak et al. (2011, 2016) and Medus and Dorso (2011) [1]. We assume that people move around in a city of 120 × 120 blocks with 300 inhabitants in each block. The mobility pattern is associated to a complex network in which nodes represent blocks while the links represent the traveling path of the individuals (see below). We implemented three confinement strategies in order to mitigate the disease spreading: (1) global confinement, (2) partial restriction to mobility, and (3) localized confinement. In the first case, it was observed that a global isolation policy prevents the massive outbreak of the disease. In the second case, a partial restriction to mobility could lead to a massive contagion if this was not complemented with sanitary measures such as the use of masks and social distancing. Finally, a local isolation policy was proposed, conditioned to the health status of each block. It was observed that this mitigation strategy was able to contain and even reduce the outbreak of the disease by intervening in specific regions of the city according to their level of contagion. It was also observed that this strategy is capable of controlling the epidemic in the case that a certain proportion of those infected are asymptomatic.
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Affiliation(s)
- F E Cornes
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellón I, Ciudad Universitaria, 1428 Buenos Aires, Argentina
| | - G A Frank
- Unidad de Investigación y Desarrollo de las Ingenierías, Universidad Tecnológica Nacional, Facultad Regional Buenos Aires, Av. Medrano 951, 1179 Buenos Aires, Argentina
| | - C O Dorso
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellón I, Ciudad Universitaria, 1428 Buenos Aires, Argentina
- Instituto de Física de Buenos Aires, Pabellón I, Ciudad Universitaria, 1428 Buenos Aires, Argentina
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16
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d’Andrea V, Gallotti R, Castaldo N, De Domenico M. Individual risk perception and empirical social structures shape the dynamics of infectious disease outbreaks. PLoS Comput Biol 2022; 18:e1009760. [PMID: 35171901 PMCID: PMC8849607 DOI: 10.1371/journal.pcbi.1009760] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 12/15/2021] [Indexed: 12/20/2022] Open
Abstract
The dynamics of a spreading disease and individual behavioral changes are entangled processes that have to be addressed together in order to effectively manage an outbreak. Here, we relate individual risk perception to the adoption of a specific set of control measures, as obtained from an extensive large-scale survey performed via Facebook-involving more than 500,000 respondents from 64 countries-showing that there is a "one-to-one" relationship between perceived epidemic risk and compliance with a set of mitigation rules. We then develop a mathematical model for the spreading of a disease-sharing epidemiological features with COVID-19-that explicitly takes into account non-compliant individual behaviors and evaluates the impact of a population fraction of infectious risk-deniers on the epidemic dynamics. Our modeling study grounds on a wide set of structures, including both synthetic and more than 180 real-world contact patterns, to evaluate, in realistic scenarios, how network features typical of human interaction patterns impact the spread of a disease. In both synthetic and real contact patterns we find that epidemic spreading is hindered for decreasing population fractions of risk-denier individuals. From empirical contact patterns we demonstrate that connectivity heterogeneity and group structure significantly affect the peak of hospitalized population: higher modularity and heterogeneity of social contacts are linked to lower peaks at a fixed fraction of risk-denier individuals while, at the same time, such features increase the relative impact on hospitalizations with respect to the case where everyone correctly perceive the risks.
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Affiliation(s)
| | | | | | - Manlio De Domenico
- CoMuNe Lab, Fondazione Bruno Kessler, Trento, Italy
- Department of Physics and Astronomy “G. Galilei”, University of Padova, Padova, Italy
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17
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Steinegger B, Arola-Fernández L, Granell C, Gómez-Gardeñes J, Arenas A. Behavioural response to heterogeneous severity of COVID-19 explains temporal variation of cases among different age groups. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210119. [PMID: 34802272 PMCID: PMC8607153 DOI: 10.1098/rsta.2021.0119] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Together with seasonal effects inducing outdoor or indoor activities, the gradual easing of prophylaxis caused second and third waves of SARS-CoV-2 to emerge in various countries. Interestingly, data indicate that the proportion of infections belonging to the elderly is particularly small during periods of low prevalence and continuously increases as case numbers increase. This effect leads to additional stress on the health care system during periods of high prevalence. Furthermore, infections peak with a slight delay of about a week among the elderly compared to the younger age groups. Here, we provide a mechanistic explanation for this phenomenology attributable to a heterogeneous prophylaxis induced by the age-specific severity of the disease. We model the dynamical adoption of prophylaxis through a two-strategy game and couple it with an SIR spreading model. Our results also indicate that the mixing of contacts among the age groups strongly determines the delay between their peaks in prevalence and the temporal variation in the distribution of cases. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.
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Affiliation(s)
- Benjamin Steinegger
- Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona 43007, Spain
| | - Lluís Arola-Fernández
- Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona 43007, Spain
| | - Clara Granell
- Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona 43007, Spain
| | - Jesús Gómez-Gardeñes
- Department of Condensed Matter Physics, University of Zaragoza, Zaragoza E-50009, Spain
- GOTHAM Lab—BIFI, University of Zaragoza, Zaragoza E-50018, Spain
| | - Alex Arenas
- Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona 43007, Spain
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18
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Ghiffari A, Hasyim H, Iskandar I, Kamaluddin MT, Anwar C. SARS-CoV-2 Variants of Concern Increased Transmission and Decrease Vaccine Efficacy in the COVID-19 Pandemic in Palembang Indonesia. ACTA BIO-MEDICA : ATENEI PARMENSIS 2022; 93:e2022018. [PMID: 35315393 PMCID: PMC8972875 DOI: 10.23750/abm.v93i1.12224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 09/30/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND AND AIM The number of COVID-19 cases surging despite the large scale of health promotion campaigns. This study aimed to find disease transmissibility and affected vaccine efficacy associated with the mutation of the SARS-CoV-2 variant of concern. METHODS The study was a descriptive temporal survey design with secondary ecological data: the whole-genome sequence (WGS) from the Global Initiative on Sharing Avian Influenza (GISAID) and COVID-19 data from the Palembang City Health Office website. Bioinformatics software was used to detect mutations. RESULTS Palembang submitted 43 whole genome sequences, 13 of which were Pangoline sequences classifications. CONCLUSIONS The two concern variations, Alpha and Delta, were associated with increased transmissions and decreased vaccination efficacy using temporal analysis. Regulations governing the relaxation of mobility restrictions should be based on high rates of testing and tracing, and universal vaccination programs should require that all received two doses of any vaccines as fast as possible.
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Affiliation(s)
- Ahmad Ghiffari
- Department of Parasitology, Faculty of Medicine, Universitas Muhammadiyah Palembang, Palembang, Indonesia, Department of Environmental Science, Graduate School, Universitas Sriwijaya, Palembang, Indonesia
| | - Hamzah Hasyim
- Faculty of Public Health Universitas Sriwijaya, Indralaya, Indonesia
| | - Iskhaq Iskandar
- Department of Physics, Faculty of Mathematics and Natural Science, Universitas Sriwijaya, Indralaya, Indonesia
| | | | - Chairil Anwar
- Department of Parasitology, Faculty of Medicine, Universitas Sriwijaya, Palembang, Indonesia
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19
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Murata T. COVID-19 and Networks. NEW GENERATION COMPUTING 2021; 39:469-481. [PMID: 34522061 PMCID: PMC8429891 DOI: 10.1007/s00354-021-00134-2] [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: 12/11/2020] [Accepted: 08/26/2021] [Indexed: 06/13/2023]
Abstract
Ongoing COVID-19 pandemic poses many challenges to the research of artificial intelligence. Epidemics are important in network science for modeling disease spread over networks of contacts between individuals. To prevent disease spread, it is desirable to introduce prioritized isolation of the individuals contacting many and unspecified others, or connecting different groups. Finding such influential individuals in social networks, and simulating the speed and extent of the disease spread are what we need for combating COVID-19. This article focuses on the following topics, and discusses some of the traditional and emerging research attempts: (1) topics related to epidemics in network science, such as epidemic modeling, influence maximization and temporal networks, (2) recent research of network science for COVID-19 and (3) datasets and resources for COVID-19 research.
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Affiliation(s)
- Tsuyoshi Murata
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, W8-59 2-12-1 Ookayama, Meguro, Tokyo 152-8552 Japan
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20
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Mégarbane B, Bourasset F, Scherrmann JM. Is Curfew Effective in Limiting SARS-CoV-2 Progression? An Evaluation in France Based on Epidemiokinetic Analyses. J Gen Intern Med 2021; 36:2731-2738. [PMID: 34131877 PMCID: PMC8205314 DOI: 10.1007/s11606-021-06953-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 05/24/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Since late summer 2020, the French authorities implemented a curfew/lightened lockdown-alternating strategy instead of strict lockdown, to improve acceptability and limit socioeconomic consequences. However, data on curfew-related efficacy to control the epidemic are scarce. OBJECTIVE To investigate the effects on COVID-19 spread in France of curfew combined to local and/or nationwide restrictions from late summer 2020 to mid-February 2021. DESIGN We conducted a comparative evaluation using a susceptible-infected-recovered (SIR)-based model completed with epidemiokinetic tools. MAIN MEASURES We analyzed the time-course of epidemic progression rate under curfew in French Guyana and five metropolitan regions where additional restrictions were implemented at different times. Using linear regressions of the decay/increase rates in daily contaminations, we calculated the epidemic regression half-lives (t1/2β) for each identified period. KEY RESULTS In French Guyana, two decay periods with rapid regression (t1/2β of ~10 days) were observed under curfew, with slowing (t1/2β of ~43 days) when curfew was lightened. During the 2-week pre-lockdown curfew (2020/10/17-2020/11/02) in Provence-Alpes-Côte-d'Azur, Auvergne-Rhône-Alpes, and Ile-de-France, the epidemic progression was unchanged. During the post-lockdown curfew (2020/12/15-2020/02/14), the epidemic slowly regressed in Grand-Est (t1/2β of ~37 days), whereas its progression rate plateaued in Auvergne-Rhône-Alpes and increased immediately in Provence-Alpes-Côte-d'Azur, Ile-de-France, and Nouvelle-Aquitaine, whatever the curfew starting time was (06:00 or 08:00 pm). Interestingly, a delayed slow decay (17 days < t1/2β < 64 days) occurred under curfew in all regions except Ile-de-France. CONCLUSIONS Curfew allowed the temporary control of SARS-CoV-2 epidemic, however variably in the French regions, without preventing lockdown necessity. To accelerate the epidemic regression such as observed in French Guyana, curfew should be implemented timely with additional restrictions.
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Affiliation(s)
- Bruno Mégarbane
- Department of Medical and Toxicological Critical Care, Lariboisière Hospital, Federation of Toxicology, APHP, Paris, France. .,University of Paris, INSERM UMRS-1144, Paris, France.
| | - Fanchon Bourasset
- Laboratory of Integrative Research in Neurosciences and Cognitive Psychology, Bourgogne Franche-Comté University, Besancon, France
| | - Jean-Michel Scherrmann
- University of Paris, INSERM UMRS-1144, Paris, France.,Laboratory of Pharmacokinetics, Faculty of Pharmacy, University of Paris, Paris, France
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21
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Milligan WR, Fuller ZL, Agarwal I, Eisen MB, Przeworski M, Sella G. Impact of essential workers in the context of social distancing for epidemic control. PLoS One 2021; 16:e0255680. [PMID: 34347855 PMCID: PMC8336873 DOI: 10.1371/journal.pone.0255680] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 07/21/2021] [Indexed: 02/07/2023] Open
Abstract
New emerging infectious diseases are identified every year, a subset of which become global pandemics like COVID-19. In the case of COVID-19, many governments have responded to the ongoing pandemic by imposing social policies that restrict contacts outside of the home, resulting in a large fraction of the workforce either working from home or not working. To ensure essential services, however, a substantial number of workers are not subject to these limitations, and maintain many of their pre-intervention contacts. To explore how contacts among such "essential" workers, and between essential workers and the rest of the population, impact disease risk and the effectiveness of pandemic control, we evaluated several mathematical models of essential worker contacts within a standard epidemiology framework. The models were designed to correspond to key characteristics of cashiers, factory employees, and healthcare workers. We find in all three models that essential workers are at substantially elevated risk of infection compared to the rest of the population, as has been documented, and that increasing the numbers of essential workers necessitates the imposition of more stringent controls on contacts among the rest of the population to manage the pandemic. Importantly, however, different archetypes of essential workers differ in both their individual probability of infection and impact on the broader pandemic dynamics, highlighting the need to understand and target intervention for the specific risks faced by different groups of essential workers. These findings, especially in light of the massive human costs of the current COVID-19 pandemic, indicate that contingency plans for future epidemics should account for the impacts of essential workers on disease spread.
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Affiliation(s)
- William R. Milligan
- Department of Biological Sciences, Columbia University, New York City, New York, United States of America
| | - Zachary L. Fuller
- Department of Biological Sciences, Columbia University, New York City, New York, United States of America
| | - Ipsita Agarwal
- Department of Biological Sciences, Columbia University, New York City, New York, United States of America
| | - Michael B. Eisen
- Howard Hughes Medical Institute, University of California, Berkeley, California, United States of America
- Department of Molecular and Cell Biology, University of California, Berkeley, California, United States of America
| | - Molly Przeworski
- Department of Biological Sciences, Columbia University, New York City, New York, United States of America
- Department of Systems Biology, Columbia University, New York City, New York, United States of America
- Program for Mathematical Genomics, Columbia University, New York City, New York, United States of America
| | - Guy Sella
- Department of Biological Sciences, Columbia University, New York City, New York, United States of America
- Program for Mathematical Genomics, Columbia University, New York City, New York, United States of America
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22
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Ghosh S, Senapati A, Chattopadhyay J, Hens C, Ghosh D. Optimal test-kit-based intervention strategy of epidemic spreading in heterogeneous complex networks. CHAOS (WOODBURY, N.Y.) 2021; 31:071101. [PMID: 34340350 DOI: 10.1063/5.0053262] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
We propose a deterministic compartmental model of infectious disease that considers the test kits as an important ingredient for the suppression and mitigation of epidemics. A rigorous simulation (with an analytical argument) is provided to reveal the effective reduction of the final outbreak size and the peak of infection as a function of basic reproduction number in a single patch. Furthermore, to study the impact of long and short-distance human migration among the patches, we consider heterogeneous networks where the linear diffusive connectivity is determined by the network link structure. We numerically confirm that implementation of test kits in a fraction of nodes (patches) having larger degrees or betweenness centralities can reduce the peak of infection (as well as the final outbreak size) significantly. A next-generation matrix-based analytical treatment is provided to find out the critical transmission probability in the entire network for the onset of epidemics. Finally, the optimal intervention strategy is validated in two real networks: the global airport network and the transportation network of Kolkata, India.
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Affiliation(s)
- Subrata Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, India
| | - Abhishek Senapati
- Agricultural and Ecological Research Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, India
| | - Joydev Chattopadhyay
- Agricultural and Ecological Research Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, India
| | - Chittaranjan Hens
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, India
| | - Dibakar Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, India
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23
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Cohen K, Leshem A. Suppressing the impact of the COVID-19 pandemic using controlled testing and isolation. Sci Rep 2021; 11:6279. [PMID: 33737580 PMCID: PMC7973790 DOI: 10.1038/s41598-021-85458-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 03/01/2021] [Indexed: 01/08/2023] Open
Abstract
The Corona virus disease has significantly affected lives of people around the world. Existing quarantine policies led to large-scale lock-downs because of the slow tracking of the infection paths, and indeed we see new waves of the disease. This can be solved by contact tracing combined with efficient testing policies. Since the number of daily tests is limited, it is crucial to exploit them efficiently to improve the outcome of contact tracing (technological or human-based epidemiological investigations). We develop a controlled testing framework to achieve this goal. The key is to test individuals with high probability of being infected to identify them before symptoms appear. These probabilities are updated based on contact tracing and test results. We demonstrate that the proposed method could reduce the quarantine and morbidity rates compared to existing methods by up to a 50%. The results clearly demonstrate the necessity of accelerating the epidemiological investigations by using technological contact tracing. Furthermore, proper use of the testing capacity using the proposed controlled testing methodology leads to significantly improved results under both small and large testing capacities. We also show that for small new outbreaks controlled testing can prevent the large spread of new waves. Author contributions statement: The authors contributed equally to this work, including conceptualization, analysis, methodology, software, and drafting the work.
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Affiliation(s)
- Kobi Cohen
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Amir Leshem
- Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel.
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24
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Das A, Dhar A, Goyal S, Kundu A, Pandey S. COVID-19: Analytic results for a modified SEIR model and comparison of different intervention strategies. CHAOS, SOLITONS, AND FRACTALS 2021; 144:110595. [PMID: 33424141 PMCID: PMC7785284 DOI: 10.1016/j.chaos.2020.110595] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 12/10/2020] [Accepted: 12/14/2020] [Indexed: 05/03/2023]
Abstract
The Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological model is one of the standard models of disease spreading. Here we analyse an extended SEIR model that accounts for asymptomatic carriers, believed to play an important role in COVID-19 transmission. For this model we derive a number of analytic results for important quantities such as the peak number of infections, the time taken to reach the peak and the size of the final affected population. We also propose an accurate way of specifying initial conditions for the numerics (from insufficient data) using the fact that the early time exponential growth is well-described by the dominant eigenvector of the linearized equations. Secondly we explore the effect of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ). The two intervention strategies (SD and TQ) try to reduce the disease reproductive number, R 0 , to a target value R 0 target < 1 , but in distinct ways, which we implement in our model equations. We find that for the same R 0 target < 1 , TQ is more efficient in controlling the pandemic than SD. However, for TQ to be effective, it has to be based on contact tracing and our study quantifies the required ratio of tests-per-day to the number of new cases-per-day. Our analysis shows that the largest eigenvalue of the linearised dynamics provides a simple understanding of the disease progression, both pre- and post- intervention, and explains observed data for many countries. We apply our results to the COVID data for India to obtain heuristic projections for the course of the pandemic, and note that the predictions strongly depend on the assumed fraction of asymptomatic carriers.
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Affiliation(s)
- Arghya Das
- International Center for Theoretical Sciences, Tata Institute of Fundamental Research, Bangalore 560089, India
| | - Abhishek Dhar
- International Center for Theoretical Sciences, Tata Institute of Fundamental Research, Bangalore 560089, India
| | - Srashti Goyal
- International Center for Theoretical Sciences, Tata Institute of Fundamental Research, Bangalore 560089, India
| | - Anupam Kundu
- International Center for Theoretical Sciences, Tata Institute of Fundamental Research, Bangalore 560089, India
| | - Saurav Pandey
- International Center for Theoretical Sciences, Tata Institute of Fundamental Research, Bangalore 560089, India
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Plazas A, Malvestio I, Starnini M, Díaz-Guilera A. Modeling partial lockdowns in multiplex networks using partition strategies. APPLIED NETWORK SCIENCE 2021; 6:27. [PMID: 33821212 PMCID: PMC8012750 DOI: 10.1007/s41109-021-00366-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/25/2021] [Indexed: 05/13/2023]
Abstract
UNLABELLED National stay-at-home orders, or lockdowns, were imposed in several countries to drastically reduce the social interactions mainly responsible for the transmission of the SARS-CoV-2 virus. Despite being essential to slow down the COVID-19 pandemic, these containment measures are associated with an economic burden. In this work, we propose a network approach to model the implementation of a partial lockdown, breaking the society into disconnected components, or partitions. Our model is composed by two main ingredients: a multiplex network representing human contacts within different contexts, formed by a Household layer, a Work layer, and a Social layer including generic social interactions, and a Susceptible-Infected-Recovered process that mimics the epidemic spreading. We compare different partition strategies, with a twofold aim: reducing the epidemic outbreak and minimizing the economic cost associated to the partial lockdown. We also show that the inclusion of unconstrained social interactions dramatically increases the epidemic spreading, while different kinds of restrictions on social interactions help in keeping the benefices of the network partition. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s41109-021-00366-7.
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Affiliation(s)
- Adrià Plazas
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Barcelona, Catalonia Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, 08028 Barcelona, Catalonia Spain
| | - Irene Malvestio
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Barcelona, Catalonia Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, 08028 Barcelona, Catalonia Spain
| | | | - Albert Díaz-Guilera
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Barcelona, Catalonia Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, 08028 Barcelona, Catalonia Spain
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Silva PCL, Batista PVC, Lima HS, Alves MA, Guimarães FG, Silva RCP. COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions. CHAOS, SOLITONS, AND FRACTALS 2020; 139:110088. [PMID: 32834624 PMCID: PMC7340090 DOI: 10.1016/j.chaos.2020.110088] [Citation(s) in RCA: 151] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 06/26/2020] [Accepted: 07/02/2020] [Indexed: 05/09/2023]
Abstract
The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.
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Affiliation(s)
- Petrônio C L Silva
- Grupo de Pesquisa em Ciência de Dados e Inteligência Computacional - {ci∂ic}, Brazil
- Instituto Federal do Norte de Minas Gerais (IFNMG), Brazil
- Machine Intelligence and Data Science (MINDS) Laboratory, Federal University of Minas Gerais, Brazil
| | - Paulo V C Batista
- Grupo de Pesquisa em Ciência de Dados e Inteligência Computacional - {ci∂ic}, Brazil
- Instituto Federal do Norte de Minas Gerais (IFNMG), Brazil
| | - Hélder S Lima
- Instituto Federal do Norte de Minas Gerais (IFNMG), Brazil
| | - Marcos A Alves
- Machine Intelligence and Data Science (MINDS) Laboratory, Federal University of Minas Gerais, Brazil
- Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, 31270-901, MG, Brazil
| | - Frederico G Guimarães
- Machine Intelligence and Data Science (MINDS) Laboratory, Federal University of Minas Gerais, Brazil
- Department of Electrical Engineering, Universidade Federal de Minas Gerais (UFMG), Brazil
| | - Rodrigo C P Silva
- Machine Intelligence and Data Science (MINDS) Laboratory, Federal University of Minas Gerais, Brazil
- Department of Computer Science, Universidade Federal de Ouro Preto (UFOP), Brazil
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