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Juneau CE, Briand AS, Collazzo P, Siebert U, Pueyo T. Effective contact tracing for COVID-19: A systematic review. GLOBAL EPIDEMIOLOGY 2023; 5:100103. [PMID: 36959868 PMCID: PMC9997056 DOI: 10.1016/j.gloepi.2023.100103] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/19/2023] [Accepted: 03/06/2023] [Indexed: 03/11/2023] Open
Abstract
Contact tracing is commonly recommended to control outbreaks of COVID-19, but its effectiveness is unclear. Following PRISMA guidelines, we searched four databases using a range of terms related to contact tracing effectiveness for COVID-19. We found 343 papers; 32 were included. All were observational or modelling studies. Observational studies (n = 14) provided consistent, very-low certainty evidence that contact tracing (alone or in combination with other interventions) was associated with better control of COVID-19 (e.g. in Hong Kong, only 1084 cases and four deaths were recorded in the first 4.5 months of the pandemic). Modelling studies (n = 18) provided consistent, high-certainty evidence that under assumptions of prompt and thorough tracing with effective quarantines, contact tracing could stop the spread of COVID-19 (e.g. by reducing the reproduction number from 2.2 to 0.57). A cautious interpretation indicates that to stop the spread of COVID-19, public health practitioners have 2-3 days from the time a new case develops symptoms to isolate the case and quarantine at least 80% of its contacts.
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Affiliation(s)
- Carl-Etienne Juneau
- Direction régionale de santé publique, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Québec, Canada
| | - Anne-Sara Briand
- École de santé publique, Université de Montréal, Montréal, Québec, Canada
| | - Pablo Collazzo
- Danube University Krems, Dr. Karl Dorrek-Strasse 30, 3500 Krems, Austria and IEEM Universidad de Montevideo, Lord Ponsonby 2542, 16000 Montevideo, Uruguay
| | - Uwe Siebert
- Institute for Technology Assessment, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Austria
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Shrestha N, Koju R, K C D, Mahato NK, Poudyal A, Subedi R, Gautam N, Vaidya A, Karki S. Perceived social support and compliance on stay-at-home order during COVID-19 emergency in Nepal: an evidence from web-based cross-sectional study. BMC Public Health 2023; 23:535. [PMID: 36944968 PMCID: PMC10028774 DOI: 10.1186/s12889-023-15396-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 03/07/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND After COVID-19 was declared a Public Health Emergency of International Concern by WHO, several non-pharmaceutical interventions were adopted for containing the virus. Success to which largely depend upon citizens' compliance to these measures. There is growing body of evidence linking social support with health promoting behaviour. Hence, this research aimed to study the effects on compliance with stay-at-home order in relation to their perceived social support. METHODS A web-based cross-sectional study was conducted among adult participants aged 18 years and above residing in Bagmati Province, Nepal. A convenient non-probability sampling method was adopted to select the required number of samples. The questionnaire was developed through an extensive review of literature, and consultations with the research advisor, subject experts, as well as peers and converted to online survey form using Google Forms. Perceived social support was measured using the Multidimensional Scale of Perceived Social Support (MSPSS) scale whereas compliance was assessed using a single screening question. Statistical analysis was performed using SPSS version 20 involving both the descriptive and inferential statistics. RESULTS Two fifth (40.2%) of the participants reported poor compliance with stay-at-home order which was found higher among participants who were not vaccinated against COVID-19 compared to those vaccinated (p value < 0.05). A significant difference was observed between sex and perceived support (p value < 0.05) with higher proportion (80.8%) of female participants reporting perceived support from family, friends, and significant others in comparison to male participants. CONCLUSION Overall, the results of this study suggest that the perceived support from family is higher compared to others. Further evidence might be helpful to understand contextual factors on compliance with public health measures. Tailoring behaviour change messages as per the community needs would help the response in such emergencies. The findings from this study might be useful as one of the evidence base for formulating plans and policy during emergencies of similar nature.
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Affiliation(s)
- Namuna Shrestha
- Public Health Promotion and Development Organization, Kathmandu, Nepal.
| | - Reena Koju
- Public Health and Environment Research Center, Kathmandu, Nepal
| | - Dirghayu K C
- Public Health Promotion and Development Organization, Kathmandu, Nepal
| | | | - Anil Poudyal
- Public Health Promotion and Development Organization, Kathmandu, Nepal
| | - Ranjeeta Subedi
- Public Health Promotion and Development Organization, Kathmandu, Nepal
| | - Nitisha Gautam
- Public Health Promotion and Development Organization, Kathmandu, Nepal
| | - Anju Vaidya
- Public Health Promotion and Development Organization, Kathmandu, Nepal
| | - Shristi Karki
- Public Health Promotion and Development Organization, Kathmandu, Nepal
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Cascini F, Failla G, Gobbi C, Pallini E, Hui J, Luxi W, Villani L, Quentin W, Boccia S, Ricciardi W. A cross-country comparison of Covid-19 containment measures and their effects on the epidemic curves. BMC Public Health 2022; 22:1765. [PMID: 36115936 PMCID: PMC9482299 DOI: 10.1186/s12889-022-14088-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 08/23/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND European countries are still searching to eliminate or contain the Covid-19 pandemic. A variety of approaches have achieved different levels of success in limiting the spread of the disease early and preventing avoidable deaths. Governmental policy responses may explain these differences and this study aims to describe evidence about the effectiveness of containment measures throughout the course of the pandemic in five European countries (France, Germany, Italy, Spain and the UK). METHODS The research approach adopted consisted of three steps: 1) Build a Containment Index (C.I.) that considers nine parameters to make an assessment on the strength of measures; 2) Develop dynamic epidemiological models for forecasting purposes; 3) Predict case numbers by assuming containment measures remain constant for a period of 30 days. RESULTS Our analysis revealed that in the five European countries we compared, the use of different approaches definitively affected the effectiveness of containment measures for the Covid-19 pandemic. CONCLUSION The evidence found in our research can be useful to inform policy makers' decisions when deciding to introduce or relax containment measures and their timing, both during the current pandemic or in addressing possible future health crises.
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Affiliation(s)
- Fidelia Cascini
- Section of Hygiene, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168, Roma, Italy
| | - Giovanna Failla
- Section of Hygiene, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168, Roma, Italy.
| | - Cecilia Gobbi
- Data Science & Advanced Analytics, IQVIA, 20124, Milan, Italy
| | | | - Jin Hui
- Data Science & Advanced Analytics, IQVIA, Bejing, 100006, China
| | - Wang Luxi
- Sales Effectiveness, Marketing Commercial Excellence, Novo Nordisk, Beijing, 100102, China
| | - Leonardo Villani
- Section of Hygiene, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168, Roma, Italy
| | - Wilm Quentin
- Department of Health Care Management, Technische Universität Berlin, 10623, Berlin, Germany
- European Observatory on Health Systems and Policies, 1060, Brussels, Belgium
| | - Stefania Boccia
- Section of Hygiene, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168, Roma, Italy
- Department of Woman and Child Health and Public Health - Public Health Area, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | - Walter Ricciardi
- Section of Hygiene, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168, Roma, Italy
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Fan C, Jiang X, Lee R, Mostafavi A. Data-driven contact network models of COVID-19 reveal trade-offs between costs and infections for optimal local containment policies. CITIES (LONDON, ENGLAND) 2022; 128:103805. [PMID: 35694433 PMCID: PMC9174357 DOI: 10.1016/j.cities.2022.103805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 11/29/2021] [Accepted: 06/02/2022] [Indexed: 06/15/2023]
Abstract
While several non-pharmacological measures have been implemented for a few months in an effort to slow the coronavirus disease (COVID-19) pandemic in the United States, the disease remains a danger in a number of counties as restrictions are lifted to revive the economy. Making a trade-off between economic recovery and infection control is a major challenge confronting many hard-hit counties. Understanding the transmission process and quantifying the costs of local policies are essential to the task of tackling this challenge. Here, we investigate the dynamic contact patterns of the populations from anonymized, geo-localized mobility data and census and demographic data to create data-driven, agent-based contact networks. We then simulate the epidemic spread with a time-varying contagion model in ten large metropolitan counties in the United States and evaluate a combination of mobility reduction, mask use, and reopening policies. We find that our model captures the spatial-temporal and heterogeneous case trajectory within various counties based on dynamic population behaviors. Our results show that a decision-making tool that considers both economic cost and infection outcomes of policies can be informative in making decisions of local containment strategies for optimal balancing of economic slowdown and virus spread.
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Affiliation(s)
- Chao Fan
- Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843-3136, United States of America
| | - Xiangqi Jiang
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843-3112, United States of America
| | - Ronald Lee
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843-3112, United States of America
| | - Ali Mostafavi
- Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843-3136, United States of America
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Zhang W, Liu S, Osgood N, Zhu H, Qian Y, Jia P. Using simulation modelling and systems science to help contain COVID-19: A systematic review. SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE 2022; 40:SRES2897. [PMID: 36245570 PMCID: PMC9538520 DOI: 10.1002/sres.2897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 05/23/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.
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Affiliation(s)
- Weiwei Zhang
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Shiyong Liu
- Institute of Advanced Studies in Humanities and Social SciencesBeijing Normal University at ZhuhaiZhuhaiChina
| | - Nathaniel Osgood
- Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada
- Department of Community Health and EpidemiologyUniversity of SaskatchewanSaskatoonCanada
| | - Hongli Zhu
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Ying Qian
- Business SchoolUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Peng Jia
- School of Resource and Environmental SciencesWuhan UniversityWuhanHubeiChina
- International Institute of Spatial Lifecourse HealthWuhan UniversityWuhanHubeiChina
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Abstract
Epidemics of infectious diseases, such as the one caused by the rapid spread of the coronavirus disease 2019 (COVID-19), have tested the world's more advanced health systems and have caused an enormous societal and economic damage. The mechanism of contagion is well understood. As people move around, over time, they regularly engage in social interactions. The spatiotemporal network representing these interactions constitutes the backbone on which an epidemic spreads, causing outbreaks. At the same time, advanced technological responses have claimed some success in controlling the epidemic based on digital contact tracing technologies. Motivated by these observations, we design, develop and evaluate a stochastic agent-basedSEIRmodel of epidemic spreading in spatiotemporal networks informed by mobility data of individuals (trajectories). The model focuses on individual variation in mobility patterns that affects the degree of exposure to the disease. Understanding the role that individual nodes play in the process of disease spreading through network effects is fundamental as it allows to (i) assess the risk of infection of individuals, (ii) assess the size of a disease outbreak due to specific individuals, and (iii) assess targeted intervention strategies that aim to control the epidemic spreading. We perform a comprehensive analysis of the model employing COVID-19 as a use case. The results indicate that simple individual-based intervention strategies that exhibit significant network effects can effectively control the spread of an epidemic. We have also demonstrated that targeted interventions can outperform generic intervention strategies. Overall, our work provides an evidence-based data-driven model to support decision making and inform public policy regarding intervention strategies for containing or mitigating the epidemic spread.
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Wilson AM, Aviles N, Petrie JI, Beamer PI, Szabo Z, Xie M, McIllece J, Chen Y, Son Y, Halai S, White T, Ernst KC, Masel J. Quantifying SARS-CoV-2 Infection Risk Within the Google/Apple Exposure Notification Framework to Inform Quarantine Recommendations. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2022; 42:162-176. [PMID: 34155669 PMCID: PMC8447042 DOI: 10.1111/risa.13768] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 01/31/2021] [Accepted: 05/24/2021] [Indexed: 05/13/2023]
Abstract
Most early Bluetooth-based exposure notification apps use three binary classifications to recommend quarantine following SARS-CoV-2 exposure: a window of infectiousness in the transmitter, ≥15 minutes duration, and Bluetooth attenuation below a threshold. However, Bluetooth attenuation is not a reliable measure of distance, and infection risk is not a binary function of distance, nor duration, nor timing. We model uncertainty in the shape and orientation of an exhaled virus-containing plume and in inhalation parameters, and measure uncertainty in distance as a function of Bluetooth attenuation. We calculate expected dose by combining this with estimated infectiousness based on timing relative to symptom onset. We calibrate an exponential dose-response curve based on infection probabilities of household contacts. The probability of current or future infectiousness, conditioned on how long postexposure an exposed individual has been symptom-free, decreases during quarantine, with shape determined by incubation periods, proportion of asymptomatic cases, and asymptomatic shedding durations. It can be adjusted for negative test results using Bayes' theorem. We capture a 10-fold range of risk using six infectiousness values, 11-fold range using three Bluetooth attenuation bins, ∼sixfold range from exposure duration given the 30 minute duration cap imposed by the Google/Apple v1.1, and ∼11-fold between the beginning and end of 14 day quarantine. Public health authorities can either set a threshold on initial infection risk to determine 14-day quarantine onset, or on the conditional probability of current and future infectiousness conditions to determine both quarantine and duration.
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Affiliation(s)
- Amanda M. Wilson
- Mel & Enid Zuckerman College of Public HealthUniversity of Arizona1295 N Martin AveTucsonAZ85724USA
- Rocky Mountain Center for Occupational and Environmental HealthUniversity of Utah391 Chipeta Way suite cSalt Lake CityUT84108USA
| | - Nathan Aviles
- Graduate Interdisciplinary Program in StatisticsUniversity of Arizona617 N. Santa Rita Ave.TucsonAZ85721USA
| | - James I. Petrie
- WeHealth Solutions PBC5325 Elkhorn Blvd # 7011SacramentoCA95842USA
- Applied MathematicsUniversity of Waterloo200 University Ave. WWaterlooOntarioN2L 3G1Canada
- Covid Watch (affiliation at time of writing, now dissolved)
| | - Paloma I. Beamer
- Mel & Enid Zuckerman College of Public HealthUniversity of Arizona1295 N Martin AveTucsonAZ85724USA
| | - Zsombor Szabo
- Covid Watch (affiliation at time of writing, now dissolved)
| | - Michelle Xie
- WeHealth Solutions PBC5325 Elkhorn Blvd # 7011SacramentoCA95842USA
- Covid Watch (affiliation at time of writing, now dissolved)
| | - Janet McIllece
- World Wide Technology1 World Wide WaySt. LouisMO63146USA
| | - Yijie Chen
- Systems and Industrial EngineeringUniversity of Arizona1127 E. James E. Rogers WayTucsonAZ85721USA
| | - Young‐Jun Son
- Systems and Industrial EngineeringUniversity of Arizona1127 E. James E. Rogers WayTucsonAZ85721USA
| | - Sameer Halai
- WeHealth Solutions PBC5325 Elkhorn Blvd # 7011SacramentoCA95842USA
- Covid Watch (affiliation at time of writing, now dissolved)
| | - Tina White
- Covid Watch (affiliation at time of writing, now dissolved)
| | - Kacey C. Ernst
- Mel & Enid Zuckerman College of Public HealthUniversity of Arizona1295 N Martin AveTucsonAZ85724USA
| | - Joanna Masel
- WeHealth Solutions PBC5325 Elkhorn Blvd # 7011SacramentoCA95842USA
- Ecology & Evolutionary BiologyUniversity of Arizona1041 E Lowell StTucsonAZ85721USA
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Najmi A, Nazari S, Safarighouzhdi F, Miller EJ, MacIntyre R, Rashidi TH. Easing or tightening control strategies: determination of COVID-19 parameters for an agent-based model. TRANSPORTATION 2022; 49:1265-1293. [PMID: 34276105 PMCID: PMC8275455 DOI: 10.1007/s11116-021-10210-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/01/2021] [Indexed: 05/09/2023]
Abstract
Some agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate strategies aimed to control the outbreak of the infectious disease. Nonetheless, COVID-19 parameter estimation methods are limited to observational epidemiologic studies which are essentially aggregated models. We propose a mathematical structure to determine parameters of agent-based models accounting for the mutual effects of parameters. We then use the agent-based model to assess the extent to which different control strategies can intervene the transmission of COVID-19. Easing social distancing restrictions, opening businesses, speed of enforcing control strategies, quarantining family members of isolated cases on the disease progression and encouraging the use of facemask are the strategies assessed in this study. We estimate the social distancing compliance level in Sydney greater metropolitan area and then elaborate the consequences of moderating the compliance level in the disease suppression. We also show that social distancing and facemask usage are complementary and discuss their interactive effects in detail.
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Affiliation(s)
- Ali Najmi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Sahar Nazari
- School of Engineering, Macquarie University, Sydney, Australia
- School of Chemical Engineering, University of New South Wales, Sydney, NSW Australia
| | - Farshid Safarighouzhdi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Eric J. Miller
- Department of Civil & Mineral Engineering, University of Toronto, 35 St. George Street, Room 305A, Toronto, ON M5S 1A4 Canada
| | - Raina MacIntyre
- Arizona State University College of Health Solutions, Phoenix, AZ USA
- Faculty of Medicine, Kirby Institute, The University of New South Wales, Sydney, NSW Australia
| | - Taha H. Rashidi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
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Tatapudi H, Das R, Das TK. Impact of vaccine prioritization strategies on mitigating COVID-19: an agent-based simulation study using an urban region in the United States. BMC Med Res Methodol 2021; 21:272. [PMID: 34865617 PMCID: PMC8645225 DOI: 10.1186/s12874-021-01458-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 11/03/2021] [Indexed: 01/03/2023] Open
Abstract
Background Approval of novel vaccines for COVID-19 had brought hope and expectations, but not without additional challenges. One central challenge was understanding how to appropriately prioritize the use of limited supply of vaccines. This study examined the efficacy of the various vaccine prioritization strategies using the vaccination campaign underway in the U.S. Methods The study developed a granular agent-based simulation model for mimicking community spread of COVID-19 under various social interventions including full and partial closures, isolation and quarantine, use of face mask and contact tracing, and vaccination. The model was populated with parameters of disease natural history, as well as demographic and societal data for an urban community in the U.S. with 2.8 million residents. The model tracks daily numbers of infected, hospitalized, and deaths for all census age-groups. The model was calibrated using parameters for viral transmission and level of community circulation of individuals. Published data from the Florida COVID-19 dashboard was used to validate the model. Vaccination strategies were compared using a hypothesis test for pairwise comparisons. Results Three prioritization strategies were examined: a minor variant of CDC’s recommendation, an age-stratified strategy, and a random strategy. The impact of vaccination was also contrasted with a no vaccination scenario. The study showed that the campaign against COVID-19 in the U.S. using vaccines developed by Pfizer/BioNTech and Moderna 1) reduced the cumulative number of infections by 10% and 2) helped the pandemic to subside below a small threshold of 100 daily new reported cases sooner by approximately a month when compared to no vaccination. A comparison of the prioritization strategies showed no significant difference in their impacts on pandemic mitigation. Conclusions The vaccines for COVID-19 were developed and approved much quicker than ever before. However, as per our model, the impact of vaccination on reducing cumulative infections was found to be limited (10%, as noted above). This limited impact is due to the explosive growth of infections that occurred prior to the start of vaccination, which significantly reduced the susceptible pool of the population for whom infection could be prevented. Hence, vaccination had a limited opportunity to reduce the cumulative number of infections. Another notable observation from our study is that instead of adhering strictly to a sequential prioritizing strategy, focus should perhaps be on distributing the vaccines among all eligible as quickly as possible, after providing for the most vulnerable. As much of the population worldwide is yet to be vaccinated, results from this study should aid public health decision makers in effectively allocating their limited vaccine supplies. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-021-01458-9.
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Affiliation(s)
- Hanisha Tatapudi
- Department of Industrial and Management System Engineering, University of South Florida, Tampa, Florida, USA.
| | - Rachita Das
- Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Tapas K Das
- Department of Industrial and Management System Engineering, University of South Florida, Tampa, Florida, USA
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Kumar N, Oke J, Nahmias-Biran BH. Activity-based epidemic propagation and contact network scaling in auto-dependent metropolitan areas. Sci Rep 2021; 11:22665. [PMID: 34811414 PMCID: PMC8608855 DOI: 10.1038/s41598-021-01522-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 10/26/2021] [Indexed: 01/03/2023] Open
Abstract
We build on recent work to develop a fully mechanistic, activity-based and highly spatio-temporally resolved epidemiological model which leverages person-trajectories obtained from an activity-based model calibrated for two full-scale prototype cities, consisting of representative synthetic populations and mobility networks for two contrasting auto-dependent city typologies. We simulate the propagation of the COVID-19 epidemic in both cities to analyze spreading patterns in urban networks across various activity types. Investigating the impact of the transit network, we find that its removal dampens disease propagation significantly, suggesting that transit restriction is more critical for mitigating post-peak disease spreading in transit dense cities. In the latter stages of disease spread, we find that the greatest share of infections occur at work locations. A statistical analysis of the resulting activity-based contact networks indicates that transit contacts are scale-free, work contacts are Weibull distributed, and shopping or leisure contacts are exponentially distributed. We validate our simulation results against existing case and mortality data across multiple cities in their respective typologies. Our framework demonstrates the potential for tracking epidemic propagation in urban networks, analyzing socio-demographic impacts and assessing activity- and mobility-specific implications of both non-pharmaceutical and pharmaceutical intervention strategies.
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Affiliation(s)
- Nishant Kumar
- ETH Zurich, Future Resilient Systems, Singapore-ETH Centre, Singapore, 138602, Singapore
| | - Jimi Oke
- Department of Civil and Environmental Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Bat-Hen Nahmias-Biran
- Department of Civil Engineering, Ariel University, Ariel, 40700, Israel.
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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Thomas Craig KJ, Rizvi R, Willis VC, Kassler WJ, Jackson GP. Effectiveness of Contact Tracing for Viral Disease Mitigation and Suppression: Evidence-Based Review. JMIR Public Health Surveill 2021; 7:e32468. [PMID: 34612841 PMCID: PMC8496751 DOI: 10.2196/32468] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/02/2021] [Accepted: 09/07/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Contact tracing in association with quarantine and isolation is an important public health tool to control outbreaks of infectious diseases. This strategy has been widely implemented during the current COVID-19 pandemic. The effectiveness of this nonpharmaceutical intervention is largely dependent on social interactions within the population and its combination with other interventions. Given the high transmissibility of SARS-CoV-2, short serial intervals, and asymptomatic transmission patterns, the effectiveness of contact tracing for this novel viral agent is largely unknown. OBJECTIVE This study aims to identify and synthesize evidence regarding the effectiveness of contact tracing on infectious viral disease outcomes based on prior scientific literature. METHODS An evidence-based review was conducted to identify studies from the PubMed database, including preprint medRxiv server content, related to the effectiveness of contact tracing in viral outbreaks. The search dates were from database inception to July 24, 2020. Outcomes of interest included measures of incidence, transmission, hospitalization, and mortality. RESULTS Out of 159 unique records retrieved, 45 (28.3%) records were reviewed at the full-text level, and 24 (15.1%) records met all inclusion criteria. The studies included utilized mathematical modeling (n=14), observational (n=8), and systematic review (n=2) approaches. Only 2 studies considered digital contact tracing. Contact tracing was mostly evaluated in combination with other nonpharmaceutical interventions and/or pharmaceutical interventions. Although some degree of effectiveness in decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality was observed, these results were highly dependent on epidemic severity (R0 value), number of contacts traced (including presymptomatic and asymptomatic cases), timeliness, duration, and compliance with combined interventions (eg, isolation, quarantine, and treatment). Contact tracing effectiveness was particularly limited by logistical challenges associated with increased outbreak size and speed of infection spread. CONCLUSIONS Timely deployment of contact tracing strategically layered with other nonpharmaceutical interventions could be an effective public health tool for mitigating and suppressing infectious outbreaks by decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality.
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Affiliation(s)
- Kelly Jean Thomas Craig
- Center for AI, Research, and Evaluation, IBM Watson Health, IBM Corporation, Cambridge, MA, United States
| | - Rubina Rizvi
- Center for AI, Research, and Evaluation, IBM Watson Health, IBM Corporation, Cambridge, MA, United States
| | - Van C Willis
- Center for AI, Research, and Evaluation, IBM Watson Health, IBM Corporation, Cambridge, MA, United States
| | - William J Kassler
- Center for AI, Research, and Evaluation, IBM Watson Health, IBM Corporation, Cambridge, MA, United States
- Palantir Technologies, Denver, CO, United States
| | - Gretchen Purcell Jackson
- Center for AI, Research, and Evaluation, IBM Watson Health, IBM Corporation, Cambridge, MA, United States
- Vanderbilt University Medical Center, Nashville, TN, United States
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Rella SA, Kulikova YA, Dermitzakis ET, Kondrashov FA. Rates of SARS-CoV-2 transmission and vaccination impact the fate of vaccine-resistant strains. Sci Rep 2021; 11:15729. [PMID: 34330988 PMCID: PMC8324827 DOI: 10.1038/s41598-021-95025-3] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 07/20/2021] [Indexed: 12/21/2022] Open
Abstract
Vaccines are thought to be the best available solution for controlling the ongoing SARS-CoV-2 pandemic. However, the emergence of vaccine-resistant strains may come too rapidly for current vaccine developments to alleviate the health, economic and social consequences of the pandemic. To quantify and characterize the risk of such a scenario, we created a SIR-derived model with initial stochastic dynamics of the vaccine-resistant strain to study the probability of its emergence and establishment. Using parameters realistically resembling SARS-CoV-2 transmission, we model a wave-like pattern of the pandemic and consider the impact of the rate of vaccination and the strength of non-pharmaceutical intervention measures on the probability of emergence of a resistant strain. As expected, we found that a fast rate of vaccination decreases the probability of emergence of a resistant strain. Counterintuitively, when a relaxation of non-pharmaceutical interventions happened at a time when most individuals of the population have already been vaccinated the probability of emergence of a resistant strain was greatly increased. Consequently, we show that a period of transmission reduction close to the end of the vaccination campaign can substantially reduce the probability of resistant strain establishment. Our results suggest that policymakers and individuals should consider maintaining non-pharmaceutical interventions and transmission-reducing behaviours throughout the entire vaccination period.
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Affiliation(s)
- Simon A Rella
- Institute of Science and Technology Austria, 1 Am Campus, 3400, Klosterneuburg, Austria
| | | | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.
| | - Fyodor A Kondrashov
- Institute of Science and Technology Austria, 1 Am Campus, 3400, Klosterneuburg, Austria.
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13
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Najmi A, Nazari S, Safarighouzhdi F, Miller EJ, MacIntyre R, Rashidi TH. Easing or tightening control strategies: determination of COVID-19 parameters for an agent-based model. TRANSPORTATION 2021; 49:1265-1293. [PMID: 34276105 DOI: 10.1101/2020.06.20.20135186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/01/2021] [Indexed: 05/28/2023]
Abstract
Some agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate strategies aimed to control the outbreak of the infectious disease. Nonetheless, COVID-19 parameter estimation methods are limited to observational epidemiologic studies which are essentially aggregated models. We propose a mathematical structure to determine parameters of agent-based models accounting for the mutual effects of parameters. We then use the agent-based model to assess the extent to which different control strategies can intervene the transmission of COVID-19. Easing social distancing restrictions, opening businesses, speed of enforcing control strategies, quarantining family members of isolated cases on the disease progression and encouraging the use of facemask are the strategies assessed in this study. We estimate the social distancing compliance level in Sydney greater metropolitan area and then elaborate the consequences of moderating the compliance level in the disease suppression. We also show that social distancing and facemask usage are complementary and discuss their interactive effects in detail.
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Affiliation(s)
- Ali Najmi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Sahar Nazari
- School of Engineering, Macquarie University, Sydney, Australia
- School of Chemical Engineering, University of New South Wales, Sydney, NSW Australia
| | - Farshid Safarighouzhdi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Eric J Miller
- Department of Civil & Mineral Engineering, University of Toronto, 35 St. George Street, Room 305A, Toronto, ON M5S 1A4 Canada
| | - Raina MacIntyre
- Arizona State University College of Health Solutions, Phoenix, AZ USA
- Faculty of Medicine, Kirby Institute, The University of New South Wales, Sydney, NSW Australia
| | - Taha H Rashidi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
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14
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Shah H, Shah S, Tanwar S, Gupta R, Kumar N. Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends. MULTIMEDIA SYSTEMS 2021; 28:1189-1222. [PMID: 34276140 PMCID: PMC8275905 DOI: 10.1007/s00530-021-00818-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 05/29/2021] [Indexed: 05/05/2023]
Abstract
The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI's flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.
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Affiliation(s)
- Het Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Saiyam Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Sudeep Tanwar
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Rajesh Gupta
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Neeraj Kumar
- Department of Computer Science Engineering, Thapar Institute of Engineering and Technology, Deemed to be University, Patiala, India
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand India
- King Abdul Aziz University, Jeddah, Saudi Arabia
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15
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Hssayeni MD, Chala A, Dev R, Xu L, Shaw J, Furht B, Ghoraani B. The forecast of COVID-19 spread risk at the county level. JOURNAL OF BIG DATA 2021; 8:99. [PMID: 34249603 PMCID: PMC8261401 DOI: 10.1186/s40537-021-00491-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 06/30/2021] [Indexed: 05/07/2023]
Abstract
The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people's lives and restart the economy quickly and safely. People's social behavior, reflected in their mobility data, plays a major role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreaks in the United States. The daily data are fed to a deep learning model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (r=0.83 (p = 0.005)) between the model predicted and actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. A lower correlation was reported for the counties with total cases of <1000 during the test interval. The average mean absolute error (MAE) was 605.4 and decreased with a decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread. It showed that the average daily cases decreased with a decrease in the retiree percentage and increased with an increase in the young percentage. Lessons learned from this study not only can help with managing the COVID-19 pandemic but also can help with early and effective management of possible future pandemics. The code used for this study was made publicly available on https://github.com/Murtadha44/covid-19-spread-risk. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1186/s40537-021-00491-1.
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Affiliation(s)
- Murtadha D. Hssayeni
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431 USA
| | | | - Roger Dev
- LexisNexis Risk Solution, Alpharetta, GA USA
| | - Lili Xu
- LexisNexis Risk Solution, Alpharetta, GA USA
| | - Jesse Shaw
- LexisNexis Risk Solution, Alpharetta, GA USA
| | - Borko Furht
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431 USA
| | - Behnaz Ghoraani
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431 USA
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16
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Coletti P, Libin P, Petrof O, Willem L, Abrams S, Herzog SA, Faes C, Kuylen E, Wambua J, Beutels P, Hens N. A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies. BMC Infect Dis 2021; 21:503. [PMID: 34053446 PMCID: PMC8164894 DOI: 10.1186/s12879-021-06092-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 04/20/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. METHODS We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown. RESULTS Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period. CONCLUSIONS Contacts during leisure activities are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment of social contacts in the population is therefore crucial to adjust to evolving behavioral changes that can affect epidemic diffusion.
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Affiliation(s)
- Pietro Coletti
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium.
| | - Pieter Libin
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
- Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium
- Rega Institute for Medical Research, Katholieke Universiteit Leuven, Herestraat 49, Leuven, 3000, Belgium
| | - Oana Petrof
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
| | - Lander Willem
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
| | - Steven Abrams
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
- Global Health Institute, Family Medicine and Population Health, University of Antwerp, Wilrijk, Belgium
| | - Sereina A Herzog
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
- Institute for Medical Informatics, Statistics and Documentation, Auenbruggerplatz 2, Graz, 8036, Austria
| | - Christel Faes
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
| | - Elise Kuylen
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
| | - James Wambua
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia
| | - Niel Hens
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
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17
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Kerr CC, Mistry D, Stuart RM, Rosenfeld K, Hart GR, Núñez RC, Cohen JA, Selvaraj P, Abeysuriya RG, Jastrzębski M, George L, Hagedorn B, Panovska-Griffiths J, Fagalde M, Duchin J, Famulare M, Klein DJ. Controlling COVID-19 via test-trace-quarantine. Nat Commun 2021. [PMID: 34017008 DOI: 10.1101/2020.07.15.20154765] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023] Open
Abstract
Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures. However, these interventions have had enormous societal and economic costs. Here, we demonstrate the feasibility of an alternative control strategy, test-trace-quarantine: routine testing of primarily symptomatic individuals, tracing and testing their known contacts, and placing their contacts in quarantine. We perform this analysis using Covasim, an open-source agent-based model, which has been calibrated to detailed demographic, mobility, and epidemiological data for the Seattle region from January through June 2020. With current levels of mask use and schools remaining closed, we find that high but achievable levels of testing and tracing are sufficient to maintain epidemic control even under a return to full workplace and community mobility and with low vaccine coverage. The easing of mobility restrictions in June 2020 and subsequent scale-up of testing and tracing programs through September provided real-world validation of our predictions. Although we show that test-trace-quarantine can control the epidemic in both theory and practice, its success is contingent on high testing and tracing rates, high quarantine compliance, relatively short testing and tracing delays, and moderate to high mask use. Thus, in order for test-trace-quarantine to control transmission with a return to high mobility, strong performance in all aspects of the program is required.
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Affiliation(s)
- Cliff C Kerr
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA.
| | - Dina Mistry
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Robyn M Stuart
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
- Burnet Institute, Melbourne, VIC, Australia
| | - Katherine Rosenfeld
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Gregory R Hart
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Rafael C Núñez
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Jamie A Cohen
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Prashanth Selvaraj
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | | | | | - Lauren George
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Brittany Hagedorn
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Jasmina Panovska-Griffiths
- Department of Applied Health Research, University College London, London, UK
- Wolfson Centre for Mathematical Biology and The Queen's College, Oxford University, Oxford, UK
| | | | | | - Michael Famulare
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Daniel J Klein
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
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18
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Post L, Culler K, Moss CB, Murphy RL, Achenbach CJ, Ison MG, Resnick D, Singh LN, White J, Boctor MJ, Welch SB, Oehmke JF. Surveillance of the Second Wave of COVID-19 in Europe: Longitudinal Trend Analyses. JMIR Public Health Surveill 2021; 7:e25695. [PMID: 33818391 PMCID: PMC8080962 DOI: 10.2196/25695] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 02/11/2021] [Accepted: 04/04/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic has severely impacted Europe, resulting in a high caseload and deaths that varied by country. The second wave of the COVID-19 pandemic has breached the borders of Europe. Public health surveillance is necessary to inform policy and guide leaders. OBJECTIVE This study aimed to provide advanced surveillance metrics for COVID-19 transmission that account for weekly shifts in the pandemic, speed, acceleration, jerk, and persistence, to better understand countries at risk for explosive growth and those that are managing the pandemic effectively. METHODS We performed a longitudinal trend analysis and extracted 62 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in Europe as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS New COVID-19 cases slightly decreased from 158,741 (week 1, January 4-10, 2021) to 152,064 (week 2, January 11-17, 2021), and cumulative cases increased from 22,507,271 (week 1) to 23,890,761 (week 2), with a weekly increase of 1,383,490 between January 10 and January 17. France, Germany, Italy, Spain, and the United Kingdom had the largest 7-day moving averages for new cases during week 1. During week 2, the 7-day moving average for France and Spain increased. From week 1 to week 2, the speed decreased (37.72 to 33.02 per 100,000), acceleration decreased (0.39 to -0.16 per 100,000), and jerk increased (-1.30 to 1.37 per 100,000). CONCLUSIONS The United Kingdom, Spain, and Portugal, in particular, are at risk for a rapid expansion in COVID-19 transmission. An examination of the European region suggests that there was a decrease in the COVID-19 caseload between January 4 and January 17, 2021. Unfortunately, the rates of jerk, which were negative for Europe at the beginning of the month, reversed course and became positive, despite decreases in speed and acceleration. Finally, the 7-day persistence rate was higher during week 2 than during week 1. These measures indicate that the second wave of the pandemic may be subsiding, but some countries remain at risk for new outbreaks and increased transmission in the absence of rapid policy responses.
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Affiliation(s)
- Lori Post
- Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Kasen Culler
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Charles B Moss
- Institute of Food and Agricultural Sciences, University of Florida, Gainsville, FL, United States
| | - Robert L Murphy
- Institute of Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Chad J Achenbach
- Divison of Infectious Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Michael G Ison
- Divison of Infectious Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Danielle Resnick
- International Food Policy Research Institute, Washington DC, DC, United States
| | - Lauren Nadya Singh
- Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Janine White
- Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Michael J Boctor
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Sarah B Welch
- Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - James Francis Oehmke
- Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
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19
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Linka K, Goriely A, Kuhl E. Global and local mobility as a barometer for COVID-19 dynamics. Biomech Model Mechanobiol 2021; 20:651-669. [PMID: 33449276 PMCID: PMC7809648 DOI: 10.1007/s10237-020-01408-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 11/28/2020] [Indexed: 12/31/2022]
Abstract
The spreading of infectious diseases including COVID-19 depends on human interactions. In an environment where behavioral patterns and physical contacts are constantly evolving according to new governmental regulations, measuring these interactions is a major challenge. Mobility has emerged as an indicator for human activity and, implicitly, for human interactions. Here, we study the coupling between mobility and COVID-19 dynamics and show that variations in global air traffic and local driving mobility can be used to stratify different disease phases. For ten European countries, our study shows a maximal correlation between driving mobility and disease dynamics with a time lag of [Formula: see text] days. Our findings suggest that trends in local mobility allow us to forecast the outbreak dynamics of COVID-19 for a window of two weeks and adjust local control strategies in real time.
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Affiliation(s)
- Kevin Linka
- Department of Mechanical Engineering, Stanford University, Stanford, California USA
| | - Alain Goriely
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, California USA
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20
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Singh DE, Marinescu MC, Guzmán-Merino M, Durán C, Delgado-Sanz C, Gomez-Barroso D, Carretero J. Simulation of COVID-19 Propagation Scenarios in the Madrid Metropolitan Area. Front Public Health 2021; 9:636023. [PMID: 33796497 PMCID: PMC8007867 DOI: 10.3389/fpubh.2021.636023] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/11/2021] [Indexed: 12/23/2022] Open
Abstract
This work presents simulation results for different mitigation and confinement scenarios for the propagation of COVID-19 in the metropolitan area of Madrid. These scenarios were implemented and tested using EpiGraph, an epidemic simulator which has been extended to simulate COVID-19 propagation. EpiGraph implements a social interaction model, which realistically captures a large number of characteristics of individuals and groups, as well as their individual interconnections, which are extracted from connection patterns in social networks. Besides the epidemiological and social interaction components, it also models people's short and long-distance movements as part of a transportation model. These features, together with the capacity to simulate scenarios with millions of individuals and apply different contention and mitigation measures, gives EpiGraph the potential to reproduce the COVID-19 evolution and study medium-term effects of the virus when applying mitigation methods. EpiGraph, obtains closely aligned infected and death curves related to the first wave in the Madrid metropolitan area, achieving similar seroprevalence values. We also show that selective lockdown for people over 60 would reduce the number of deaths. In addition, evaluate the effect of the use of face masks after the first wave, which shows that the percentage of people that comply with mask use is a crucial factor for mitigating the infection's spread.
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Affiliation(s)
- David E. Singh
- Department Computer Science, Universidad Carlos III de Madrid, Leganés, Spain
| | | | | | - Christian Durán
- Department Computer Science, Universidad Carlos III de Madrid, Leganés, Spain
| | - Concepción Delgado-Sanz
- CIBER en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- National Centre for Epidemiology, Carlos III Institute of Health, Madrid, Spain
| | - Diana Gomez-Barroso
- CIBER en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- National Centre for Epidemiology, Carlos III Institute of Health, Madrid, Spain
| | - Jesus Carretero
- Department Computer Science, Universidad Carlos III de Madrid, Leganés, Spain
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21
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Sturniolo S, Waites W, Colbourn T, Manheim D, Panovska-Griffiths J. Testing, tracing and isolation in compartmental models. PLoS Comput Biol 2021; 17:e1008633. [PMID: 33661888 PMCID: PMC7932151 DOI: 10.1371/journal.pcbi.1008633] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 12/14/2020] [Indexed: 01/12/2023] Open
Abstract
Existing compartmental mathematical modelling methods for epidemics, such as SEIR models, cannot accurately represent effects of contact tracing. This makes them inappropriate for evaluating testing and contact tracing strategies to contain an outbreak. An alternative used in practice is the application of agent- or individual-based models (ABM). However ABMs are complex, less well-understood and much more computationally expensive. This paper presents a new method for accurately including the effects of Testing, contact-Tracing and Isolation (TTI) strategies in standard compartmental models. We derive our method using a careful probabilistic argument to show how contact tracing at the individual level is reflected in aggregate on the population level. We show that the resultant SEIR-TTI model accurately approximates the behaviour of a mechanistic agent-based model at far less computational cost. The computational efficiency is such that it can be easily and cheaply used for exploratory modelling to quantify the required levels of testing and tracing, alone and with other interventions, to assist adaptive planning for managing disease outbreaks.
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Affiliation(s)
- Simone Sturniolo
- Scientific Computing Department, UKRI, Rutherford Appleton Laboratory, Harwell, United Kingdom
| | - William Waites
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Tim Colbourn
- UCL Institute for Global Health, London, United Kingdom
| | - David Manheim
- University of Haifa Health and Risk Communication Research Center, Haifa, Israel
| | - Jasmina Panovska-Griffiths
- UCL Institute for Global Health, London, United Kingdom
- Department of Applied Health Research, UCL, London, United Kingdom
- Wolfson Centre for Mathematical Biology and The Queen’s College, Oxford University, Oxford, United Kingdom
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22
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Fan C, Lee S, Yang Y, Oztekin B, Li Q, Mostafavi A. Effects of population co-location reduction on cross-county transmission risk of COVID-19 in the United States. APPLIED NETWORK SCIENCE 2021; 6:14. [PMID: 33623817 PMCID: PMC7891476 DOI: 10.1007/s41109-021-00361-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 02/03/2021] [Indexed: 05/05/2023]
Abstract
The objective of this study is to examine the transmission risk of COVID-19 based on cross-county population co-location data from Facebook. The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the preventive actions of social distancing, travel reduction and stay-at-home orders are recognized as essential non-pharmacologic approaches to control the infection and spatial spread of COVID-19. Prior studies demonstrated that human movement and mobility drove the spatiotemporal distribution of COVID-19 in China. Little is known, however, about the patterns and effects of co-location reduction on cross-county transmission risk of COVID-19. This study utilizes Facebook co-location data for all counties in the United States from March to early May 2020 for conducting spatial network analysis where nodes represent counties and edge weights are associated with the co-location probability of populations of the counties. The analysis examines the synchronicity and time lag between travel reduction and pandemic growth trajectory to evaluate the efficacy of social distancing in ceasing the population co-location probabilities, and subsequently the growth in weekly new cases across counties. The results show that the mitigation effects of co-location reduction appear in the growth of weekly new confirmed cases with one week of delay. The analysis categorizes counties based on the number of confirmed COVID-19 cases and examines co-location patterns within and across groups. Significant segregation is found among different county groups. The results suggest that within-group co-location probabilities (e.g., co-location probabilities among counties with high numbers of cases) remain stable, and social distancing policies primarily resulted in reduced cross-group co-location probabilities (due to travel reduction from counties with large number of cases to counties with low numbers of cases). These findings could have important practical implications for local governments to inform their intervention measures for monitoring and reducing the spread of COVID-19, as well as for adoption in future pandemics. Public policy, economic forecasting, and epidemic modeling need to account for population co-location patterns in evaluating transmission risk of COVID-19 across counties.
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Affiliation(s)
- Chao Fan
- Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843 USA
| | - Sanghyeon Lee
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843 USA
| | - Yang Yang
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843 USA
| | - Bora Oztekin
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843 USA
| | - Qingchun Li
- Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843 USA
| | - Ali Mostafavi
- Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843 USA
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Friston KJ, Parr T, Zeidman P, Razi A, Flandin G, Daunizeau J, Hulme OJ, Billig AJ, Litvak V, Price CJ, Moran RJ, Lambert C. Testing and tracking in the UK: A dynamic causal modelling study. Wellcome Open Res 2021. [DOI: 10.12688/wellcomeopenres.16004.2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
By equipping a previously reported dynamic causal modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a dynamic causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.
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24
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Scott N, Palmer A, Delport D, Abeysuriya R, Stuart RM, Kerr CC, Mistry D, Klein DJ, Sacks‐Davis R, Heath K, Hainsworth SW, Pedrana A, Stoove M, Wilson D, Hellard ME. Modelling the impact of relaxing COVID-19 control measures during a period of low viral transmission. Med J Aust 2021; 214:79-83. [PMID: 33207390 PMCID: PMC7753668 DOI: 10.5694/mja2.50845] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 09/01/2020] [Indexed: 11/17/2022]
Abstract
OBJECTIVES To assess the risks associated with relaxing coronavirus disease 2019 (COVID-19)-related physical distancing restrictions and lockdown policies during a period of low viral transmission. DESIGN Network-based viral transmission risks in households, schools, workplaces, and a variety of community spaces and activities were simulated in an agent-based model, Covasim. SETTING The model was calibrated for a baseline scenario reflecting the epidemiological and policy environment in Victoria during March-May 2020, a period of low community viral transmission. INTERVENTION Policy changes for easing COVID-19-related restrictions from May 2020 were simulated in the context of interventions that included testing, contact tracing (including with a smartphone app), and quarantine. MAIN OUTCOME MEASURE Increase in detected COVID-19 cases following relaxation of restrictions. RESULTS Policy changes that facilitate contact of individuals with large numbers of unknown people (eg, opening bars, increased public transport use) were associated with the greatest risk of COVID-19 case numbers increasing; changes leading to smaller, structured gatherings with known contacts (eg, small social gatherings, opening schools) were associated with lower risks. In our model, the rise in case numbers following some policy changes was notable only two months after their implementation. CONCLUSIONS Removing several COVID-19-related restrictions within a short period of time should be undertaken with care, as the consequences may not be apparent for more than two months. Our findings support continuation of work from home policies (to reduce public transport use) and strategies that mitigate the risk associated with re-opening of social venues.
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Affiliation(s)
| | | | | | | | | | - Cliff C Kerr
- Institute for Disease ModelingBellevueWAUnited States of America
| | - Dina Mistry
- Institute for Disease ModelingBellevueWAUnited States of America
| | - Daniel J Klein
- Institute for Disease ModelingBellevueWAUnited States of America
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25
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Pasetto D, Lemaitre JC, Bertuzzo E, Gatto M, Rinaldo A. Range of reproduction number estimates for COVID-19 spread. Biochem Biophys Res Commun 2021; 538:253-258. [PMID: 33342517 PMCID: PMC7723757 DOI: 10.1016/j.bbrc.2020.12.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 12/01/2020] [Indexed: 12/15/2022]
Abstract
To monitor local and global COVID-19 outbreaks, and to plan containment measures, accessible and comprehensible decision-making tools need to be based on the growth rates of new confirmed infections, hospitalization or case fatality rates. Growth rates of new cases form the empirical basis for estimates of a variety of reproduction numbers, dimensionless numbers whose value, when larger than unity, describes surging infections and generally worsening epidemiological conditions. Typically, these determinations rely on noisy or incomplete data gained over limited periods of time, and on many parameters to estimate. This paper examines how estimates from data and models of time-evolving reproduction numbers of national COVID-19 infection spread change by using different techniques and assumptions. Given the importance acquired by reproduction numbers as diagnostic tools, assessing their range of possible variations obtainable from the same epidemiological data is relevant. We compute control reproduction numbers from Swiss and Italian COVID-19 time series adopting both data convolution (renewal equation) and a SEIR-type model. Within these two paradigms we run a comparative analysis of the possible inferences obtained through approximations of the distributions typically used to describe serial intervals, generation, latency and incubation times, and the delays between onset of symptoms and notification. Our results suggest that estimates of reproduction numbers under these different assumptions may show significant temporal differences, while the actual variability range of computed values is rather small.
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Affiliation(s)
- Damiano Pasetto
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, 30172, Venezia-Mestre, (IT), Italy,Corresponding author
| | - Joseph C. Lemaitre
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, (CH), Switzerland
| | - Enrico Bertuzzo
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, 30172, Venezia-Mestre, (IT), Italy
| | - Marino Gatto
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133, Milan, (IT), Italy
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, (CH), Switzerland,Dipartimento di Ingegneria Civile Edile ed Ambientale, Università di Padova, I-35131, Padua, (IT), Italy
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26
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Contreras S, Dehning J, Loidolt M, Zierenberg J, Spitzner FP, Urrea-Quintero JH, Mohr SB, Wilczek M, Wibral M, Priesemann V. The challenges of containing SARS-CoV-2 via test-trace-and-isolate. Nat Commun 2021; 12:378. [PMID: 33452267 PMCID: PMC7810722 DOI: 10.1038/s41467-020-20699-8] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 12/10/2020] [Indexed: 01/19/2023] Open
Abstract
Without a cure, vaccine, or proven long-term immunity against SARS-CoV-2, test-trace-and-isolate (TTI) strategies present a promising tool to contain its spread. For any TTI strategy, however, mitigation is challenged by pre- and asymptomatic transmission, TTI-avoiders, and undetected spreaders, which strongly contribute to "hidden" infection chains. Here, we study a semi-analytical model and identify two tipping points between controlled and uncontrolled spread: (1) the behavior-driven reproduction number [Formula: see text] of the hidden chains becomes too large to be compensated by the TTI capabilities, and (2) the number of new infections exceeds the tracing capacity. Both trigger a self-accelerating spread. We investigate how these tipping points depend on challenges like limited cooperation, missing contacts, and imperfect isolation. Our results suggest that TTI alone is insufficient to contain an otherwise unhindered spread of SARS-CoV-2, implying that complementary measures like social distancing and improved hygiene remain necessary.
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Affiliation(s)
- Sebastian Contreras
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077, Göttingen, Germany
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Beauchef 851, 8370456, Santiago, Chile
| | - Jonas Dehning
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077, Göttingen, Germany
| | - Matthias Loidolt
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077, Göttingen, Germany
| | - Johannes Zierenberg
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077, Göttingen, Germany
| | - F Paul Spitzner
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077, Göttingen, Germany
| | - Jorge H Urrea-Quintero
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077, Göttingen, Germany
| | - Sebastian B Mohr
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077, Göttingen, Germany
| | - Michael Wilczek
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Friedrich-Hund-Platz 1, 37077, Göttingen, Germany
| | - Michael Wibral
- Campus Institute for Dynamics of Biological Networks, University of Göttingen, Hermann-Rein-Straße 3, 37075, Göttingen, Germany
| | - Viola Priesemann
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077, Göttingen, Germany.
- Institute for the Dynamics of Complex Systems, University of Göttingen, Friedrich-Hund-Platz 1, 37077, Göttingen, Germany.
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27
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Barat S, Parchure R, Darak S, Kulkarni V, Paranjape A, Gajrani M, Yadav A, Kulkarni V. An Agent-Based Digital Twin for Exploring Localized Non-pharmaceutical Interventions to Control COVID-19 Pandemic. TRANSACTIONS OF THE INDIAN NATIONAL ACADEMY OF ENGINEERING : AN INTERNATIONAL JOURNAL OF ENGINEERING AND TECHNOLOGY 2021; 6:323-353. [PMID: 35837574 PMCID: PMC7845792 DOI: 10.1007/s41403-020-00197-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 12/18/2020] [Indexed: 01/12/2023]
Abstract
The COVID-19 epidemic created, at the time of writing the paper, highly unusual and uncertain socio-economic conditions. The world economy was severely impacted and business-as-usual activities severely disrupted. The situation presented the necessity to make a trade-off between individual health and safety on one hand and socio-economic progress on the other. Based on the current understanding of the epidemiological characteristics of COVID-19, a broad set of control measures has emerged along dimensions such as restricting people's movements, high-volume testing, contract tracing, use of face masks, and enforcement of social-distancing. However, these interventions have their own limitations and varying level of efficacy depending on factors such as the population density and the socio-economic characteristics of the area. To help tailor the intervention, we develop a configurable, fine-grained agent-based simulation model that serves as a virtual representation, i.e., a digital twin of a diverse and heterogeneous area such as a city. In this paper, to illustrate our techniques, we focus our attention on the Indian city of Pune in the western state of Maharashtra. We use the digital twin to simulate various what-if scenarios of interest to (1) predict the spread of the virus; (2) understand the effectiveness of candidate interventions; and (3) predict the consequences of introduction of interventions possibly leading to trade-offs between public health, citizen comfort, and economy. Our model is configured for the specific city of interest and used as an in-silico experimentation aid to predict the trajectory of active infections, mortality rate, load on hospital, and quarantine facility centers for the candidate interventions. The key contributions of this paper are: (1) a novel agent-based model that seamlessly captures people, place, and movement characteristics of the city, COVID-19 virus characteristics, and primitive set of candidate interventions, and (2) a simulation-driven approach to determine the exact intervention that needs to be applied under a given set of circumstances. Although the analysis presented in the paper is highly specific to COVID-19, our tools are generic enough to serve as a template for modeling the impact of future pandemics and formulating bespoke intervention strategies.
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28
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Mojtabavi H, Javidi N, Naviaux AF, Janne P, Gourdin M, Mohammadpour M, Saghazadeh A, Rezaei N. Exploration of the Epidemiological and Emotional Impact of Quarantine and Isolation During the COVID-19 Pandemic. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1318:687-703. [PMID: 33973206 DOI: 10.1007/978-3-030-63761-3_38] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Starting in December 2019 in Wuhan Municipal Health Commission, the coronavirus disease 2019 (COVID-19) has crossed the borders forming a pandemic in 2020. The absence of pharmacological interventions has pushed governments to apply different sets of old, non-pharmacological interventions, which are, though temporary, helpful to prevent further pandemic propagation. In the context of COVID-19, research confirms that quarantine is useful, mainly if applied early and if combined with other public health measures. However, the efficacy of quarantine and isolation is limited in many ways, ranging from legal issues and suspension of economic activities to mental health considerations. This chapter is an exploration of (i) epidemiological impact of isolation and quarantine; (ii) emotional impact of isolation and quarantine; and (iii) the possible effect of culture on quarantine experience.
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Affiliation(s)
- Helia Mojtabavi
- Research Center for Immunodeficiencies, Tehran University of Medical Sciences, Tehran, Iran.,MetaCognition Interest Group (MCIG), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Nasirudin Javidi
- Behavioral Sciences Research Center, Life style institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.,Clinical Psychology and Psychotherapy Studies (CPPS), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Anne-Frédérique Naviaux
- College of Psychiatrists of Ireland and Health Service Executive (HSE), Summerhill Community Mental Health Service, Summerhill, Wexford, Ireland.,Faculty of Medicine, Université catholique de Louvain, Woluwé-Saint-Lambert, Belgium
| | - Pascal Janne
- Université catholique de Louvain, CHU UCL Namur, Avenue Dr. G. Thérasse, Yvoir, Belgium.,Faculty of Psychology, Université catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
| | - Maximilien Gourdin
- Faculty of Medicine, Université catholique de Louvain, Woluwé-Saint-Lambert, Belgium.,Université catholique de Louvain, CHU UCL Namur, Avenue Dr. G. Thérasse, Yvoir, Belgium
| | - Mahsa Mohammadpour
- MetaCognition Interest Group (MCIG), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Amene Saghazadeh
- Research Center for Immunodeficiencies, Tehran University of Medical Sciences, Tehran, Iran.,Systematic Review and Meta-analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Nima Rezaei
- Research Center for Immunodeficiencies, Tehran University of Medical Sciences, Tehran, Iran. .,Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran. .,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
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29
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Maheshwari P, Albert R. Network model and analysis of the spread of Covid-19 with social distancing. APPLIED NETWORK SCIENCE 2020; 5:100. [PMID: 33392389 PMCID: PMC7770744 DOI: 10.1007/s41109-020-00344-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 12/08/2020] [Indexed: 05/16/2023]
Abstract
The first mitigation response to the Covid-19 pandemic was to limit person-to-person interaction as much as possible. This was implemented by the temporary closing of many workplaces and people were required to follow social distancing. Networks are a great way to represent interactions among people and the temporary severing of these interactions. Here, we present a network model of human-human interactions that could be mediators of disease spread. The nodes of this network are individuals and different types of edges denote family cliques, workplace interactions, interactions arising from essential needs, and social interactions. Each individual can be in one of four states: susceptible, infected, immune, and dead. The network and the disease parameters are informed by the existing literature on Covid-19. Using this model, we simulate the spread of an infectious disease in the presence of various mitigation scenarios. For example, lockdown is implemented by deleting edges that denote non-essential interactions. We validate the simulation results with the real data by matching the basic and effective reproduction numbers during different phases of the spread. We also simulate different possibilities of the slow lifting of the lockdown by varying the transmission rate as facilities are slowly opened but people follow prevention measures like wearing masks etc. We make predictions on the probability and intensity of a second wave of infection in each of these scenarios.
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Affiliation(s)
- Parul Maheshwari
- Department of Physics, The Pennsylvania State University, University Park, PA 16802 USA
| | - Réka Albert
- Department of Physics, The Pennsylvania State University, University Park, PA 16802 USA
- Biology Department, The Pennsylvania State University, University Park, PA 16802 USA
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30
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Sun K, Wang W, Gao L, Wang Y, Luo K, Ren L, Zhan Z, Chen X, Zhao S, Huang Y, Sun Q, Liu Z, Litvinova M, Vespignani A, Ajelli M, Viboud C, Yu H. Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020. [PMID: 32817975 DOI: 10.1101/2020.08.09.20171132] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
A long-standing question in infectious disease dynamics concerns the role of transmission heterogeneities, driven by demography, behavior and interventions. Based on detailed patient and contact tracing data in Hunan, China we find 80% of secondary infections traced back to 15% of SARS-CoV-2 primary infections, indicating substantial transmission heterogeneities. Transmission risk scales positively with the duration of exposure and the closeness of social interactions and is modulated by demographic and clinical factors. The lockdown period increases transmission risk in the family and households, while isolation and quarantine reduce risks across all types of contacts. The reconstructed infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom presentation. Modeling indicates SARS-CoV-2 control requires the synergistic efforts of case isolation, contact quarantine, and population-level interventions, owing to the specific transmission kinetics of this virus. One Sentence Summary Public health measures to control SARS-CoV-2 could be designed to block the specific transmission characteristics of the virus.
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31
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Romano S, Fierro A, Liccardo A. Beyond the peak: A deterministic compartment model for exploring the Covid-19 evolution in Italy. PLoS One 2020; 15:e0241951. [PMID: 33156859 PMCID: PMC7647079 DOI: 10.1371/journal.pone.0241951] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 10/25/2020] [Indexed: 12/24/2022] Open
Abstract
Novel Covid-19 has had a huge impact on the world's population since December 2019. The very rapid spreading of the virus worldwide, with its heavy toll of death and overload of the healthcare systems, induced the scientific community to focus on understanding, monitoring and foreseeing the epidemic evolution, weighing up the impact of different containment measures. An immense literature was produced in few months. Many papers were focused on predicting the peak features through a variety of different models. In the present paper, combining the surveillance data-set with data on mobility and testing, we develop a deterministic compartment model aimed at performing a retrospective analysis to understand the main modifications occurred to the characteristic parameters that regulate the epidemic spreading. We find that, besides self-protective behaviors, a reduction of susceptibility should have occurred in order to explain the fast descent of the epidemic after the peak. A sensitivity analysis of the basic reproduction number, in response to variations of the epidemiological parameters that can be influenced by policy-makers, shows the primary importance of a rigid isolation procedure for the diagnosed cases, combined with an intensive effort in performing extended testing campaigns. Future scenarios depend on the ability to protect the population from the injection of new cases from abroad, and to pursue in applying rigid self-protective measures.
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Affiliation(s)
- Silvio Romano
- Physics Department, Università degli Studi di Napoli “Federico II”, Napoli, Italy
| | | | - Antonella Liccardo
- Physics Department, Università degli Studi di Napoli “Federico II”, Napoli, Italy
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32
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Pinky L, Dobrovolny HM. SARS-CoV-2 coinfections: Could influenza and the common cold be beneficial? J Med Virol 2020; 92:2623-2630. [PMID: 32557776 PMCID: PMC7300957 DOI: 10.1002/jmv.26098] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 05/26/2020] [Accepted: 05/27/2020] [Indexed: 12/15/2022]
Abstract
The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread around the world, causing serious illness and death and creating a heavy burden on the healthcare systems of many countries. Since the virus first emerged in late November 2019, its spread has coincided with peak circulation of several seasonal respiratory viruses, yet some studies have noted limited coinfections between SARS-CoV-2 and other viruses. We use a mathematical model of viral coinfection to study SARS-CoV-2 coinfections, finding that SARS-CoV-2 replication is easily suppressed by many common respiratory viruses. According to our model, this suppression is because SARS-CoV-2 has a lower growth rate (1.8/d) than the other viruses examined in this study. The suppression of SARS-CoV-2 by other pathogens could have implications for the timing and severity of a second wave.
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Affiliation(s)
- Lubna Pinky
- Department of PediatricsUniversity of Tennessee Health Science CenterMemphisTennessee
| | - Hana M. Dobrovolny
- Department of Physics & AstronomyTexas Christian UniversityFort WorthTexas
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33
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Spelta A, Flori A, Pierri F, Bonaccorsi G, Pammolli F. After the lockdown: simulating mobility, public health and economic recovery scenarios. Sci Rep 2020; 10:16950. [PMID: 33046737 PMCID: PMC7550600 DOI: 10.1038/s41598-020-73949-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 09/23/2020] [Indexed: 12/24/2022] Open
Abstract
The spread of SARS-COV-2 has affected many economic and social systems. This paper aims at estimating the impact on regional productive systems in Italy of the interplay between the epidemic and the mobility restriction measures put in place to contain the contagion. We focus then on the economic consequences of alternative lockdown lifting schemes. We leverage a massive dataset of human mobility which describes daily movements of over four million individuals in Italy and we model the epidemic spreading through a metapopulation SIR model, which provides the fraction of infected individuals in each Italian district. To quantify economic backslashes this information is combined with socio-economic data. We then carry out a scenario analysis to model the transition to a post-lockdown phase and analyze the economic outcomes derived from the interplay between (a) the timing and intensity of the release of mobility restrictions and (b) the corresponding scenarios on the severity of virus transmission rates. Using a simple model for the spreading disease and parsimonious assumptions on the relationship between the infection and the associated economic backlashes, we show how different policy schemes tend to induce heterogeneous distributions of losses at the regional level depending on mobility restrictions. Our work shed lights on how recovery policies need to balance the interplay between mobility flows of disposable workers and the diffusion of contagion.
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Affiliation(s)
- Alessandro Spelta
- Department of Economics and Management, University of Pavia, Via San Felice 7, 27100, Pavia, Italy.
- Impact, Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini, 4/B, 20156, Milan, Italy.
| | - Andrea Flori
- Impact, Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini, 4/B, 20156, Milan, Italy
| | - Francesco Pierri
- Impact, Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini, 4/B, 20156, Milan, Italy.
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Giuseppe Ponzio 34/5, 20133, Milan, Italy.
| | - Giovanni Bonaccorsi
- Impact, Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini, 4/B, 20156, Milan, Italy
| | - Fabio Pammolli
- Impact, Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini, 4/B, 20156, Milan, Italy
- CADS, Joint Center for Analysis, Decisions and Society, Human Technopole, Via Cristina Belgioioso, 171, 20157, Milan, Italy
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Firth JA, Hellewell J, Klepac P, Kissler S, Kucharski AJ, Spurgin LG. Using a real-world network to model localized COVID-19 control strategies. Nat Med 2020; 26:1616-1622. [PMID: 32770169 DOI: 10.1038/s41591-020-1036-8] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 07/27/2020] [Indexed: 11/09/2022]
Abstract
Case isolation and contact tracing can contribute to the control of COVID-19 outbreaks1,2. However, it remains unclear how real-world social networks could influence the effectiveness and efficiency of such approaches. To address this issue, we simulated control strategies for SARS-CoV-2 transmission in a real-world social network generated from high-resolution GPS data that were gathered in the course of a citizen-science experiment3,4. We found that tracing the contacts of contacts reduced the size of simulated outbreaks more than tracing of only contacts, but this strategy also resulted in almost half of the local population being quarantined at a single point in time. Testing and releasing non-infectious individuals from quarantine led to increases in outbreak size, suggesting that contact tracing and quarantine might be most effective as a 'local lockdown' strategy when contact rates are high. Finally, we estimated that combining physical distancing with contact tracing could enable epidemic control while reducing the number of quarantined individuals. Our findings suggest that targeted tracing and quarantine strategies would be most efficient when combined with other control measures such as physical distancing.
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Affiliation(s)
- Josh A Firth
- Department of Zoology, University of Oxford, Oxford, UK.,Merton College, University of Oxford, Oxford, UK
| | - Joel Hellewell
- Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Petra Klepac
- Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.,Department for Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Stephen Kissler
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Adam J Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Lewis G Spurgin
- School of Biological Sciences, University of East Anglia, Norwich, UK.
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Friston KJ, Parr T, Zeidman P, Razi A, Flandin G, Daunizeau J, Hulme OJ, Billig AJ, Litvak V, Price CJ, Moran RJ, Costello A, Pillay D, Lambert C. Effective immunity and second waves: a dynamic causal modelling study. Wellcome Open Res 2020; 5:204. [PMID: 33088924 PMCID: PMC7549178 DOI: 10.12688/wellcomeopenres.16253.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/22/2020] [Indexed: 12/18/2022] Open
Abstract
This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.
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Affiliation(s)
- Karl J. Friston
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Thomas Parr
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Peter Zeidman
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Adeel Razi
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, 3800, Australia
| | - Guillaume Flandin
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Jean Daunizeau
- Institut du Cerveau et de la Moelle épinière, INSERM UMRS 1127, Paris, France
| | - Oliver J. Hulme
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Hvidovre, Denmark
- London Mathematical Laboratory, Hammersmith, London, UK
| | | | - Vladimir Litvak
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Cathy J. Price
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Rosalyn J. Moran
- Centre for Neuroimaging Science, Department of Neuroimaging, IoPPN, King's College London, London, UK
| | - Anthony Costello
- UCL Institute for Global Health, Institute of Child Health, University College London, London, UK
| | - Deenan Pillay
- UCL Division of Infection and Immunity, University College London, London, UK
| | - Christian Lambert
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
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36
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Karatayev VA, Anand M, Bauch CT. Local lockdowns outperform global lockdown on the far side of the COVID-19 epidemic curve. Proc Natl Acad Sci U S A 2020; 117:24575-24580. [PMID: 32887803 PMCID: PMC7533690 DOI: 10.1073/pnas.2014385117] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
In the late stages of an epidemic, infections are often sporadic and geographically distributed. Spatially structured stochastic models can capture these important features of disease dynamics, thereby allowing a broader exploration of interventions. Here we develop a stochastic model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission among an interconnected group of population centers representing counties, municipalities, and districts (collectively, "counties"). The model is parameterized with demographic, epidemiological, testing, and travel data from Ontario, Canada. We explore the effects of different control strategies after the epidemic curve has been flattened. We compare a local strategy of reopening (and reclosing, as needed) schools and workplaces county by county, according to triggers for county-specific infection prevalence, to a global strategy of province-wide reopening and reclosing, according to triggers for province-wide infection prevalence. For trigger levels that result in the same number of COVID-19 cases between the two strategies, the local strategy causes significantly fewer person-days of closure, even under high intercounty travel scenarios. However, both cases and person-days lost to closure rise when county triggers are not coordinated and when testing rates vary among counties. Finally, we show that local strategies can also do better in the early epidemic stage, but only if testing rates are high and the trigger prevalence is low. Our results suggest that pandemic planning for the far side of the COVID-19 epidemic curve should consider local strategies for reopening and reclosing.
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Affiliation(s)
- Vadim A Karatayev
- School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada;
| | - Madhur Anand
- School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Chris T Bauch
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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37
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Donell ST, Thaler M, Budhiparama NC, Buttaro MA, Chen AF, Diaz-Ledezma C, Gomberg B, Hirschmann MT, Karachalios T, Karpukhin A, Sandiford NA, Shao H, Tandogan R, Violante B, Zagra L, Kort NP. Preparation for the next COVID-19 wave: The European Hip Society and European Knee Associates recommendations. Knee Surg Sports Traumatol Arthrosc 2020; 28:2747-2755. [PMID: 32803277 PMCID: PMC7429418 DOI: 10.1007/s00167-020-06213-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 08/06/2020] [Indexed: 12/18/2022]
Abstract
PURPOSE To plan for the continuance of elective hip and knee arthroplasty during a resurgence or new wave of COVID-19 infections. METHOD A systematic review was conducted using the terms "COVID-19" or "SARS-Cov-2" and "second wave". No relevant citations were found to inform on recommendations the plan. Therefore, an expert panel of the European Hip Society and the European Knee Associates was formed to provide the recommendations. RESULTS Overall, the recommendations consider three phases; review of the first wave, preparation for the next wave, and during the next wave. International and national policies will drive most of the management. The recommendations focus on the preparation phase and, in particular, the actions that the individual surgeon needs to undertake to continue with, and practice, elective arthroplasty during the next wave, as well as planning their personal and their family's lives. The recommendations expect rigorous data collection during the next wave, so that a cycle of continuous improvement is created to take account of any future waves. CONCLUSIONS The recommendations for planning to continue elective hip and knee arthroplasty during a new phase of the SARS-Cov-2 pandemic provide a framework to reduce the risk of a complete shutdown of elective surgery. This involves engaging with hospital managers and other specialities in the planning process. Individuals have responsibilities to themselves, their colleagues, and their families, beyond the actual delivery of elective arthroplasty.
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Affiliation(s)
- Simon T. Donell
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Martin Thaler
- Department of Orthopaedic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Nicolaas C. Budhiparama
- Nicolaas Institute of Constructive Orthopaedic Research and Education Foundation for Arthroplasty and Sports Medicine, Jakarta, Indonesia
| | - Martin A. Buttaro
- Italian Hospital in Buenos Aires, Potosi 4247, Buenos Aires, Argentina
| | - Antonia F. Chen
- Department of Orthopaedic Surgery, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA USA
| | - Claudio Diaz-Ledezma
- Jefe Subrogante Unidad de Ortopeda y Traumatologia, Hospital El Carmen and Clinica Redsalud Santiago, Santiago, Chile
| | | | - Michael T. Hirschmann
- Department of Orthopaedic Surgery and Traumatology, Kantonsspital Baselland, (Bruderholz, Liestal, Laufen), Bruderholz, 4101 Basel, Switzerland
| | - Theofilos Karachalios
- Orthopaedic Department, Faculty of Medicine, School of Health Sciences, University General Hospital of Larissa, University of Thessalia, Volos, Greece
| | - Alexey Karpukhin
- The Federal Centre of Traumatology, Orthopedics and Arthroplasty, Cheboksary, Russia
| | | | - Hongyi Shao
- Department of Joint Surgery, Beijing Jishuitan Hospital, Beijing, China
| | | | - Bruno Violante
- Orthopaedic Department, Istituto Clinico Sant’Ambrogio IRCCS Galeazzi, Milan, Italy
| | - Luigi Zagra
- Hip Department IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
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38
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Friston KJ, Parr T, Zeidman P, Razi A, Flandin G, Daunizeau J, Hulme OJ, Billig AJ, Litvak V, Price CJ, Moran RJ, Costello A, Pillay D, Lambert C. Effective immunity and second waves: a dynamic causal modelling study. Wellcome Open Res 2020; 5:204. [PMID: 33088924 PMCID: PMC7549178 DOI: 10.12688/wellcomeopenres.16253.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/25/2020] [Indexed: 08/15/2023] Open
Abstract
This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.
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Affiliation(s)
- Karl J. Friston
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Thomas Parr
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Peter Zeidman
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Adeel Razi
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, 3800, Australia
| | - Guillaume Flandin
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Jean Daunizeau
- Institut du Cerveau et de la Moelle épinière, INSERM UMRS 1127, Paris, France
| | - Oliver J. Hulme
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Hvidovre, Denmark
- London Mathematical Laboratory, Hammersmith, London, UK
| | | | - Vladimir Litvak
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Cathy J. Price
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Rosalyn J. Moran
- Centre for Neuroimaging Science, Department of Neuroimaging, IoPPN, King's College London, London, UK
| | - Anthony Costello
- UCL Institute for Global Health, Institute of Child Health, University College London, London, UK
| | - Deenan Pillay
- UCL Division of Infection and Immunity, University College London, London, UK
| | - Christian Lambert
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
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39
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Davis JT, Chinazzi M, Perra N, Mu K, Piontti APY, Ajelli M, Dean NE, Gioannini C, Litvinova M, Merler S, Rossi L, Sun K, Xiong X, Halloran ME, Longini IM, Viboud C, Vespignani A. Estimating the establishment of local transmission and the cryptic phase of the COVID-19 pandemic in the USA. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.07.06.20140285. [PMID: 32676609 PMCID: PMC7359534 DOI: 10.1101/2020.07.06.20140285] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
We use a global metapopulation transmission model to study the establishment of sustained and undetected community transmission of the COVID-19 pandemic in the United States. The model is calibrated on international case importations from mainland China and takes into account travel restrictions to and from international destinations. We estimate widespread community transmission of SARS-CoV-2 in February, 2020. Modeling results indicate international travel as the key driver of the introduction of SARS-CoV-2 in the West and East Coast metropolitan areas that could have been seeded as early as late-December, 2019. For most of the continental states the largest contribution of imported infections arrived through domestic travel flows.
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Affiliation(s)
- Jessica T. Davis
- laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA
| | - Matteo Chinazzi
- laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA
| | - Nicola Perra
- laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA
- Networks and Urban Systems Centre, University of Greenwich, London, UK
| | - Kunpeng Mu
- laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA
| | - Ana Pastore y Piontti
- laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA
| | - Marco Ajelli
- Bruno Kessler Foundation, Trento Italy
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Natalie E. Dean
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, USA
| | | | | | | | | | | | - Xinyue Xiong
- laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA
| | - M. Elizabeth Halloran
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA. USA
| | - Ira M. Longini
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, USA
| | | | - Alessandro Vespignani
- laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA
- ISI Foundation, Turin, Italy
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40
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Linka K, Goriely A, Kuhl E. Global and local mobility as a barometer for COVID-19 dynamics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.06.13.20130658. [PMID: 32817955 PMCID: PMC7430597 DOI: 10.1101/2020.06.13.20130658] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
. The spreading of infectious diseases including COVID-19 depends on human interactions. In an environment where behavioral patterns and physical contacts are constantly evolving according to new governmental regulations, measuring these interactions is a major challenge. Mobility has emerged as an indicator for human activity and, implicitly, for human interactions. Here we study the coupling between mobility and COVID-19 dynamics and show that variations in global air traffic and local driving mobility can be used to stratify different disease phases. For ten European countries, our study shows maximal correlation between driving mobility and disease dynamics with a time lag of 14.6 +/- 5.6 days. Our findings suggests that local mobility can serve as a quantitative metric to forecast future reproduction numbers and identify the stages of the pandemic when mobility and reproduction become decorrelated.
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Affiliation(s)
- Kevin Linka
- Mechanical Engineering, Stanford University, USA
| | | | - Ellen Kuhl
- Mechanical Engineering, Stanford University, USA
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41
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Chams N, Chams S, Badran R, Shams A, Araji A, Raad M, Mukhopadhyay S, Stroberg E, Duval EJ, Barton LM, Hajj Hussein I. COVID-19: A Multidisciplinary Review. Front Public Health 2020; 8:383. [PMID: 32850602 PMCID: PMC7403483 DOI: 10.3389/fpubh.2020.00383] [Citation(s) in RCA: 159] [Impact Index Per Article: 39.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/01/2020] [Indexed: 01/08/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel coronavirus that is responsible for the 2019-2020 pandemic. In this comprehensive review, we discuss the current published literature surrounding the SARS-CoV-2 virus. We examine the fundamental concepts including the origin, virology, pathogenesis, clinical manifestations, diagnosis, laboratory, radiology, and histopathologic findings, complications, and treatment. Given that much of the information has been extrapolated from what we know about other coronaviruses including severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), we identify and provide insight into controversies and research gaps for the current pandemic to assist with future research ideas. Finally, we discuss the global response to the coronavirus disease-2019 (COVID-19) pandemic and provide thoughts regarding lessons for future pandemics.
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Affiliation(s)
- Nour Chams
- Geriatric Division, Department of Internal Medicine, Beaumont Health System, Royal Oak, MI, United States
| | - Sana Chams
- Geriatric Division, Department of Internal Medicine, Beaumont Health System, Royal Oak, MI, United States
| | - Reina Badran
- Department of Internal Medicine, Wayne State University School of Medicine, Detroit, MI, United States
| | - Ali Shams
- Department of Emergency Medicine, Beaumont Health System, Royal Oak, MI, United States
| | - Abdallah Araji
- Department of Diagnostic Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Mohamad Raad
- Department of Cardiology, Henry Ford Health System, Detroit, MI, United States
| | | | - Edana Stroberg
- Office of the Chief Medical Examiner, Oklahoma City, OK, United States
| | - Eric J. Duval
- Office of the Chief Medical Examiner, Oklahoma City, OK, United States
| | - Lisa M. Barton
- Office of the Chief Medical Examiner, Oklahoma City, OK, United States
| | - Inaya Hajj Hussein
- Department of Foundational Medical Studies, Oakland University William Beaumont School of Medicine, Rochester, MI, United States
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42
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Estrada E. COVID-19 and SARS-CoV-2. Modeling the present, looking at the future. PHYSICS REPORTS 2020; 869:1-51. [PMID: 32834430 PMCID: PMC7386394 DOI: 10.1016/j.physrep.2020.07.005] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 07/27/2020] [Indexed: 05/21/2023]
Abstract
Since December 2019 the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has produced an outbreak of pulmonary disease which has soon become a global pandemic, known as COronaVIrus Disease-19 (COVID-19). The new coronavirus shares about 82% of its genome with the one which produced the 2003 outbreak (SARS CoV-1). Both coronaviruses also share the same cellular receptor, which is the angiotensin-converting enzyme 2 (ACE2) one. In spite of these similarities, the new coronavirus has expanded more widely, more faster and more lethally than the previous one. Many researchers across the disciplines have used diverse modeling tools to analyze the impact of this pandemic at global and local scales. This includes a wide range of approaches - deterministic, data-driven, stochastic, agent-based, and their combinations - to forecast the progression of the epidemic as well as the effects of non-pharmaceutical interventions to stop or mitigate its impact on the world population. The physical complexities of modern society need to be captured by these models. This includes the many ways of social contacts - (multiplex) social contact networks, (multilayers) transport systems, metapopulations, etc. - that may act as a framework for the virus propagation. But modeling not only plays a fundamental role in analyzing and forecasting epidemiological variables, but it also plays an important role in helping to find cures for the disease and in preventing contagion by means of new vaccines. The necessity for answering swiftly and effectively the questions: could existing drugs work against SARS CoV-2? and can new vaccines be developed in time? demands the use of physical modeling of proteins, protein-inhibitors interactions, virtual screening of drugs against virus targets, predicting immunogenicity of small peptides, modeling vaccinomics and vaccine design, to mention just a few. Here, we review these three main areas of modeling research against SARS CoV-2 and COVID-19: (1) epidemiology; (2) drug repurposing; and (3) vaccine design. Therefore, we compile the most relevant existing literature about modeling strategies against the virus to help modelers to navigate this fast-growing literature. We also keep an eye on future outbreaks, where the modelers can find the most relevant strategies used in an emergency situation as the current one to help in fighting future pandemics.
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Affiliation(s)
- Ernesto Estrada
- Instituto Universitario de Matemáticas y Aplicaciones, Universidad de Zaragoza, 50009 Zaragoza, Spain
- ARAID Foundation, Government of Aragón, 50018 Zaragoza, Spain
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43
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Friston KJ, Parr T, Zeidman P, Razi A, Flandin G, Daunizeau J, Hulme OJ, Billig AJ, Litvak V, Price CJ, Moran RJ, Lambert C. Testing and tracking in the UK: A dynamic causal modelling study. Wellcome Open Res 2020. [DOI: 10.12688/wellcomeopenres.16004.1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
By equipping a previously reported dynamic causal modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a dynamic causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.
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44
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Abstract
There is an urgent necessity of effective medication against severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is producing the COVID-19 pandemic across the world. Its main protease (Mpro) represents an attractive pharmacological target due to its involvement in essential viral functions. The crystal structure of free Mpro shows a large structural resemblance with the main protease of SARS CoV (nowadays known as SARS CoV-1). Here, we report that average SARS CoV-2 Mpro is 1900% more sensitive than SARS CoV-1 Mpro in transmitting tiny structural changes across the whole protein through long-range interactions. The largest sensitivity of Mpro to structural perturbations is located exactly around the catalytic site Cys-145 and coincides with the binding site of strong inhibitors. These findings, based on a simplified representation of the protein as a residue network, may help in designing potent inhibitors of SARS CoV-2 Mpro.
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