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Bobbio A, Campanile L, Gribaudo M, Iacono M, Marulli F, Mastroianni M. A cyber warfare perspective on risks related to health IoT devices and contact tracing. Neural Comput Appl 2022; 35:13823-13837. [PMID: 35075332 PMCID: PMC8769794 DOI: 10.1007/s00521-021-06720-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 10/30/2021] [Indexed: 11/26/2022]
Abstract
The wide use of IT resources to assess and manage the recent COVID-19 pandemic allows to increase the effectiveness of the countermeasures and the pervasiveness of monitoring and prevention. Unfortunately, the literature reports that IoT devices, a widely adopted technology for these applications, are characterized by security vulnerabilities that are difficult to manage at the state level. Comparable problems exist for related technologies that leverage smartphones, such as contact tracing applications, and non-medical health monitoring devices. In analogous situations, these vulnerabilities may be exploited in the cyber domain to overload the crisis management systems with false alarms and to interfere with the interests of target countries, with consequences on their economy and their political equilibria. In this paper we analyze the potential threat to an example subsystem to show how these influences may impact it and evaluate a possible consequence.
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Affiliation(s)
- Andrea Bobbio
- DiSit, Università del Piemonte Orientale, viale Teresa Michel 11, 15121 Alessandria, Italy
| | - Lelio Campanile
- Dipartimento di Matematica e Fisica, Università degli Studi della Campania “Luigi Vanvitelli”, viale Lincoln 5, 81100 Caserta, Italy
| | - Marco Gribaudo
- Dipartimento di Elettronica, Informatica e Bioingegneria, Politecnico di Milano, via Ponzio 34/5, 20133 Milano, Italy
| | - Mauro Iacono
- Dipartimento di Matematica e Fisica, Università degli Studi della Campania “Luigi Vanvitelli”, viale Lincoln 5, 81100 Caserta, Italy
| | - Fiammetta Marulli
- Dipartimento di Matematica e Fisica, Università degli Studi della Campania “Luigi Vanvitelli”, viale Lincoln 5, 81100 Caserta, Italy
| | - Michele Mastroianni
- Dipartimento di Matematica e Fisica, Università degli Studi della Campania “Luigi Vanvitelli”, viale Lincoln 5, 81100 Caserta, Italy
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Backhausz Á, Kiss IZ, Simon PL. The impact of spatial and social structure on an SIR epidemic on a weighted multilayer network. PERIODICA MATHEMATICA HUNGARICA 2022; 85:343-363. [PMID: 35013623 PMCID: PMC8733920 DOI: 10.1007/s10998-021-00440-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/18/2021] [Indexed: 06/14/2023]
Abstract
A key factor in the transmission of infectious diseases is the structure of disease transmitting contacts. In the context of the current COVID-19 pandemic and with some data based on the Hungarian population we develop a theoretical epidemic model (susceptible-infected-removed, SIR) on a multilayer network. The layers include the Hungarian household structure, with population divided into children, adults and elderly, as well as schools and workplaces, some spatial embedding and community transmission due to sharing communal spaces, service and public spaces. We investigate the sensitivity of the model (via the time evolution and final size of the epidemic) to the different contact layers and we map out the relation between peak prevalence and final epidemic size. When compared to the classic compartmental model and for the same final epidemic size, we find that epidemics on multilayer network lead to higher peak prevalence meaning that the risk of overwhelming the health care system is higher. Based on our model we found that keeping cliques/bubbles in school as isolated as possible has a major effect while closing workplaces had a mild effect as long as workplaces are of relatively small size.
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Affiliation(s)
- Ágnes Backhausz
- Institute of Mathematics, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/c, Budapest, 1117 Hungary
- Alfréd Rényi Institute of Matematics, Reáltanoda utca 13-15, Budapest, 1053 Hungary
| | - István Z. Kiss
- Department of Mathematics, School of Mathematical and Physical Sciences, University of Sussex, Falmer, Brighton, BN1 9QH United Kingdom
| | - Péter L. Simon
- Institute of Mathematics, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/c, Budapest, 1117 Hungary
- Numerical Analysis and Large Networks Research Group, Hungarian Academy of Sciences, Pázmány Péter sétány 1/c, Budapest, 1117 Hungary
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3
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Sadeghifar J, Jalilian H, Momeni K, Delam H, Sheleme T, Rashidi A, Hemmati F, Falahi S, Arab-Zozani M. Outcome evaluation of COVID-19 infected patients by disease symptoms: a cross-sectional study in Ilam Province, Iran. BMC Infect Dis 2021; 21:903. [PMID: 34479500 PMCID: PMC8414471 DOI: 10.1186/s12879-021-06613-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 08/24/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Novel coronavirus disease-19 (COVID-19) was declared as a global pandemic in 2020. With the spread of the disease, a better understanding of patient outcomes associated with their symptoms in diverse geographic levels is vital. This study aimed to evaluate clinical outcomes of COVID-19 patients by disease symptoms in Ilam province, Iran. METHODS This was a cross-sectional study. Data were collected from integrated health system records for all hospitals affiliated with the Ilam University of Medical Sciences between 26-Jan-2020 and 02-May-2020. All patients with a confirmed positive test were included in this study. Descriptive analyses, chi-square test, and binary logistic regression model were performed by using SPSS version 22. RESULTS The mean age of participants was 46.47 ± 18.24 years. Of the 3608 patients, 3477 (96.1%) were discharged, and 129 (3.9%) died. 54.2% of the patients were male and were in the age group of 30-40 years. Cough, sore throat, shortness of breath or difficulty breathing, and fever or chills were the most common symptoms. Patients with symptoms of shortness of breath, abnormal radiographic findings of the chest, and chest pain and pressure were relatively more likely to die. According to binary logistic regression results, the probability of death in patients with shortness of breath, abnormal chest radiographic findings, and chest pain was 1.34, 1.24, and 1.32 times higher, respectively, than for those without. CONCLUSION Our study provides evidence that the presentation of some symptoms significantly impacts outcomes of patients infected with SARS-CoV-2. Early detection of symptoms and proper management of outcomes can reduce mortality in patients with COVID-19.
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Affiliation(s)
- Jamil Sadeghifar
- Department of Public Health and Health Education, School of Health, Ilam University of Medical Sciences, Ilam, Iran
| | - Habib Jalilian
- Social Determinants of Health Research Center (SDHRC), Department of Health Services Management, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Khalil Momeni
- Department of Public Health and Health Education, School of Health, Ilam University of Medical Sciences, Ilam, Iran
| | - Hamed Delam
- Student Research Committee, Larestan University of Medical Sciences, Larestan, Iran
| | - Tadesse Sheleme
- Department of Pharmacy, College of Health Science, Mettu University, Metu, Ethiopia
| | | | - Fariba Hemmati
- Emergency Department, Imam Khomeini Hospital, Ilam University of Medical Sciences, Ilam, Iran
| | - Shahab Falahi
- Zoonotic Diseases Research Center, Ilam University of Medical Sciences, Ilam, Iran
| | - Morteza Arab-Zozani
- Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, Iran
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Batabyal S, Batabyal A. Public healthcare system capacity during COVID-19: A computational case study of SARS-CoV-2. Health Sci Rep 2021; 4:e305. [PMID: 34136660 PMCID: PMC8177899 DOI: 10.1002/hsr2.305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/18/2021] [Accepted: 04/25/2021] [Indexed: 01/10/2023] Open
Abstract
AIM Coronavirus Disease (COVID-19) is spreading typically to the human population all over the world and the report suggests that scientists have been trying to map the pattern of the early transmission of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) since it has been reported as an epidemic. Our main aim is to show if the rise-in-cases proceeds in a gradual and staggered manner instead of soaring quickly then we can suppress the burden of the health system. In this new case study, we are attempting to show how to control the outbreak of the infectious disease COVID-19 via mathematical modeling. We have examined that the method of flattening the curve of the coronavirus, which increases the recovery rate of the infected individuals and also helps to decrease the number of deaths. In this pandemic situation, the countries like Russia, India, the United States of America (USA), South Africa, and the United Kingdom (UK) are leading in front where the virus is spreading in an unprecedented way. From our point of view, we establish that if these countries are following the method of flattening the curve like China and South Korea then these countries can also overcome this pandemic situation. METHOD We propose a Susceptible, Infected, and Recovered (SIR) mathematical model of infectious disease with onset data of COVID-19 in Wuhan and international cases, which has been propagated in Wuhan City to calculate the transmission rate of the infectious virus COVID-19 until now. To understand the whole dynamics of the transmission rate of coronavirus, we portray time series diagrams such as growth rate diagram, flattening the pandemic curve diagram, infected and recovered rate diagram, prediction of the transmission of the disease from the available dataset in Wuhan, and internationally exported cases from Wuhan. RESULTS We have observed that the basic reproduction number in Wuhan declined from 2.2 (95% Confidence Interval [CI] 1.15-4.77) to 1.05 (0.41-2.39) and the mean incubation period was 5.2 days (95% [CI], 4.1-7.0). Interestingly the mean value lies between 2 and 2.5 for COVID-19. The doubling time of COVID-19 was registered 7.4 days (95% CI, 5.3-19) in the early stages and now the value decreases to -4.9 days. Similarly, we have observed the doubling time of the epidemic in South Korea decreased to -9.6 days. Currently, the doubling time of the epidemic in Russia, India, and the USA are 19.4 days, 16.4 days, and 41 days, respectively. We have investigated the growth rate of COVID-19 and plotted the curve flattening diagram against time. CONCLUSION Via flattening the curve method, China and South Korea control the transmission of the fatal disease COVID-19 in the human population. Our results show that these two countries initially sustained pandemics in a large portion of the human population in the form of virus outbreaks that basically prevented the virus from spreading further and created ways to prevent community transmission. The majority portion of the people are perfectly fine, who are quarantined strictly and never get sick, but the portion of people who have developed symptoms is quickly isolated further.
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Affiliation(s)
- Saikat Batabyal
- Department of Mathematics & SRM Research InstituteSRM Institute of Science and TechnologyKattankulathurIndia
| | - Arthita Batabyal
- Department of Mathematics & SRM Research InstituteSRM Institute of Science and TechnologyKattankulathurIndia
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5
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Zhan C, Chen J, Zhang H. An investigation of testing capacity for evaluating and modeling the spread of coronavirus disease. Inf Sci (N Y) 2021; 561:211-229. [PMID: 33612854 PMCID: PMC7884244 DOI: 10.1016/j.ins.2021.01.084] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 01/26/2021] [Accepted: 01/31/2021] [Indexed: 02/08/2023]
Abstract
Despite the consistent recommendation to scale-up the testing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), comprehensive analysis on determining the desirable testing capacity (TC) is limited. This study aims to investigate the daily TC and the percentage of positive cases over the tested population (PPCTP) to evaluate the novel coronavirus disease 2019 (COVID-19) trajectory phase and generate benchmarks on desirable TC. Data were retrieved from government facilities, including 101 countries and 55 areas in the USA. We have divided the pandemic situations of investigated areas into four phases, i.e., low-level, suppressing, widespread, or uncertain transmission phase. Findings indicate each country should increase TC to roughly two tests per thousand people each day. Additionally, based on TC, a susceptible-unconfirmed-confirmed-recovered (SUCR) model, which can capture the dynamic growth of confirmed cases and estimate the group size of unconfirmed cases in a country or area, is proposed. We examined our proposed SUCR model for 55 areas in the USA. Results show that the SUCR model can accurately capture the dynamic growth of confirmed cases in each area. By increasing TC by five times and applying strict control measures, the total number of COVID-19 patients would reduce to 33%.
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Affiliation(s)
- Choujun Zhan
- School of Computing, South China Normal University, Guangzhou 510641, China
| | - Jiaqi Chen
- Department of Computer Science, Harbin Institute of Technology, Shenzhen 518055, China
| | - Haijun Zhang
- Department of Computer Science, Harbin Institute of Technology, Shenzhen 518055, China
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Danon L, Brooks-Pollock E, Bailey M, Keeling M. A spatial model of COVID-19 transmission in England and Wales: early spread, peak timing and the impact of seasonality. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200272. [PMID: 34053261 PMCID: PMC8165591 DOI: 10.1098/rstb.2020.0272] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
An outbreak of a novel coronavirus was first reported in China on 31 December 2019. As of 9 February 2020, cases have been reported in 25 countries, including probable human-to-human transmission in England. We adapted an existing national-scale metapopulation model to capture the spread of COVID-19 in England and Wales. We used 2011 census data to inform population sizes and movements, together with parameter estimates from the outbreak in China. We predict that the epidemic will peak 126 to 147 days (approx. 4 months) after the start of person-to-person transmission in the absence of controls. Assuming biological parameters remain unchanged and transmission persists from February, we expect the peak to occur in June. Starting location and model stochasticity have a minimal impact on peak timing. However, realistic parameter uncertainty leads to peak time estimates ranging from 78 to 241 days following sustained transmission. Seasonal changes in transmission rate can substantially impact the timing and size of the epidemic. We provide initial estimates of the epidemic potential of COVID-19. These results can be refined with more precise parameters. Seasonal changes in transmission could shift the timing of the peak into winter, with important implications for healthcare capacity planning. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK.
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Affiliation(s)
- Leon Danon
- Department of Engineering Mathematics, Population Health Sciences, University of Bristol, Bristol BS8 1QU, UK
| | - Ellen Brooks-Pollock
- Bristol Veterinary School, Population Health Sciences, University of Bristol, Bristol BS8 1QU, UK.,NIHR Health Protection Research Unit (HPRU) in Behavioural Science and Evaluation, Population Health Sciences, University of Bristol, Bristol BS8 1QU, UK
| | - Mick Bailey
- Bristol Veterinary School, Population Health Sciences, University of Bristol, Bristol BS8 1QU, UK
| | - Matt Keeling
- Mathematics Institute, and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
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Abdulkareem M, Petersen SE. The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype. Front Artif Intell 2021; 4:652669. [PMID: 34056579 PMCID: PMC8160471 DOI: 10.3389/frai.2021.652669] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/13/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19 has created enormous suffering, affecting lives, and causing deaths. The ease with which this type of coronavirus can spread has exposed weaknesses of many healthcare systems around the world. Since its emergence, many governments, research communities, commercial enterprises, and other institutions and stakeholders around the world have been fighting in various ways to curb the spread of the disease. Science and technology have helped in the implementation of policies of many governments that are directed toward mitigating the impacts of the pandemic and in diagnosing and providing care for the disease. Recent technological tools, artificial intelligence (AI) tools in particular, have also been explored to track the spread of the coronavirus, identify patients with high mortality risk and diagnose patients for the disease. In this paper, areas where AI techniques are being used in the detection, diagnosis and epidemiological predictions, forecasting and social control for combating COVID-19 are discussed, highlighting areas of successful applications and underscoring issues that need to be addressed to achieve significant progress in battling COVID-19 and future pandemics. Several AI systems have been developed for diagnosing COVID-19 using medical imaging modalities such as chest CT and X-ray images. These AI systems mainly differ in their choices of the algorithms for image segmentation, classification and disease diagnosis. Other AI-based systems have focused on predicting mortality rate, long-term patient hospitalization and patient outcomes for COVID-19. AI has huge potential in the battle against the COVID-19 pandemic but successful practical deployments of these AI-based tools have so far been limited due to challenges such as limited data accessibility, the need for external evaluation of AI models, the lack of awareness of AI experts of the regulatory landscape governing the deployment of AI tools in healthcare, the need for clinicians and other experts to work with AI experts in a multidisciplinary context and the need to address public concerns over data collection, privacy, and protection. Having a dedicated team with expertise in medical data collection, privacy, access and sharing, using federated learning whereby AI scientists hand over training algorithms to the healthcare institutions to train models locally, and taking full advantage of biomedical data stored in biobanks can alleviate some of problems posed by these challenges. Addressing these challenges will ultimately accelerate the translation of AI research into practical and useful solutions for combating pandemics.
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Affiliation(s)
- Musa Abdulkareem
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Steffen E. Petersen
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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Yin S, Zhang N. Prevention schemes for future pandemic cases: mathematical model and experience of interurban multi-agent COVID-19 epidemic prevention. NONLINEAR DYNAMICS 2021; 104:2865-2900. [PMID: 33814725 PMCID: PMC7998090 DOI: 10.1007/s11071-021-06385-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 03/17/2021] [Indexed: 05/07/2023]
Abstract
To enhance the effectiveness of epidemic prevention (EP) in urban sustainability transformation, joint prevention and control mechanism should be established to prevent and control the COVID-19 epidemic. The interurban multi-agent EP strategy, as a key component of this mechanism, includes the spontaneous EP model, the superior leading EP model, and the collaborative EP model. In this study, firstly, the theoretical mechanism of the interurban multi-agent EP strategy was analyzed. Then, we proposed a three-party differential game model including factors such as the risk coefficient for the virus infection and EP experience teaching. Finally, prevention strategies, prevention efficiency, and prevention losses were compared under the three models based on theoretical analysis and numerical analysis. The results of this study are as follows. COVID-19 EP should be guided by a model of central government (CG) leadership, interurban collaboration, and social participation. The CG and urban governments (UGs) should comprehensively carry out COVID-19 EP from various aspects, including EP experience teaching, mass EP comfort, the utilization rate of EP funds, and the ability to implement strategies. During the course of the COVID-19 EP, when the CG and UGs transition from spontaneous EP model to a higher-level EP model, the UG's EP efforts will be enhanced. Under the collaborative EP model, the CG and UGs undergo the highest levels of EP effort. Compared with spontaneous EP model, the superior leading EP model can promote a Pareto improvement for all parties. From the perspective of total loss, the collaborative EP model is superior to the other two EP models. This study not only provides practical guidance for coordinating interurban relationships and enabling multi-agents to fully form joint forces, but also provides theoretical support for the establishment of an interurban joint EP mechanism under unified leadership.
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Affiliation(s)
- Shi Yin
- College of Economics and Management, Hebei Agricultural University, Baoding, 071000 China
- School of Economics and Management, Harbin Engineering University, Harbin, 150001 China
| | - Nan Zhang
- College of Economics and Management, Hebei Agricultural University, Baoding, 071000 China
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9
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Libin PJK, Willem L, Verstraeten T, Torneri A, Vanderlocht J, Hens N. Assessing the feasibility and effectiveness of household-pooled universal testing to control COVID-19 epidemics. PLoS Comput Biol 2021; 17:e1008688. [PMID: 33690626 PMCID: PMC7943003 DOI: 10.1371/journal.pcbi.1008688] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 01/09/2021] [Indexed: 12/19/2022] Open
Abstract
Outbreaks of SARS-CoV-2 are threatening the health care systems of several countries around the world. The initial control of SARS-CoV-2 epidemics relied on non-pharmaceutical interventions, such as social distancing, teleworking, mouth masks and contact tracing. However, as pre-symptomatic transmission remains an important driver of the epidemic, contact tracing efforts struggle to fully control SARS-CoV-2 epidemics. Therefore, in this work, we investigate to what extent the use of universal testing, i.e., an approach in which we screen the entire population, can be utilized to mitigate this epidemic. To this end, we rely on PCR test pooling of individuals that belong to the same households, to allow for a universal testing procedure that is feasible with the limited testing capacity. We evaluate two isolation strategies: on the one hand pool isolation, where we isolate all individuals that belong to a positive PCR test pool, and on the other hand individual isolation, where we determine which of the individuals that belong to the positive PCR pool are positive, through an additional testing step. We evaluate this universal testing approach in the STRIDE individual-based epidemiological model in the context of the Belgian COVID-19 epidemic. As the organisation of universal testing will be challenging, we discuss the different aspects related to sample extraction and PCR testing, to demonstrate the feasibility of universal testing when a decentralized testing approach is used. We show through simulation, that weekly universal testing is able to control the epidemic, even when many of the contact reductions are relieved. Finally, our model shows that the use of universal testing in combination with stringent contact reductions could be considered as a strategy to eradicate the virus.
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Affiliation(s)
- Pieter J. K. Libin
- Interuniversity Institute of Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
- Artificial Intelligence Lab, Department of computer science, Vrije Universiteit Brussel, Brussels, Belgium
- KU Leuven – University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Leuven, Belgium
| | - Lander Willem
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Timothy Verstraeten
- Artificial Intelligence Lab, Department of computer science, Vrije Universiteit Brussel, Brussels, Belgium
| | - Andrea Torneri
- Interuniversity Institute of Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Joris Vanderlocht
- Interuniversity Institute of Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Niel Hens
- Interuniversity Institute of Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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Ganiny S, Nisar O. Mathematical modeling and a month ahead forecast of the coronavirus disease 2019 (COVID-19) pandemic: an Indian scenario. MODELING EARTH SYSTEMS AND ENVIRONMENT 2021; 7:29-40. [PMID: 33490366 PMCID: PMC7813670 DOI: 10.1007/s40808-020-01080-6] [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: 09/04/2020] [Accepted: 12/31/2020] [Indexed: 02/07/2023]
Abstract
India, the second-most populous country in the world is witnessing a daily surge in the COVID-19 infected cases. India is currently among the worst-hit nations worldwide due to the COVID-19 pandemic and ranks just behind Brazil and the USA. The prediction of the future course of the pandemic is thus of utmost importance in order to prevent further worsening of the situation. In this paper, we develop models for the past trajectory (March 01, 2020-July 25, 2020) and also make a month-long (July 26, 2020-August 24, 2020) forecast of the future evolution of the COVID-19 pandemic in India by using an autoregressive integrated moving average (ARIMA) model. We determine the most optimal ARIMA model (ARIMA(7,2,2)) based on the statistical parameters viz. root-mean-squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and the coefficient of determination ( R 2 ). Subsequently, the developed model is used to obtain a one month-long forecast for the cumulative cases, active cases, recoveries, and the number of fatalities. According to our forecasting results, India is likely to have 3800,989 cumulative infected cases, 1634,142 cumulative active cases, 2110,697 cumulative recoveries, and 56,150 cumulative deaths by August 24, 2020, if the current trend of the pandemic continues to prevail. The implications of these forecasts are that in the upcoming month, the infection rate of COVID-19 in India is going to escalate, while the rate of recovery and the case-fatality rate is likely to reduce. In order to avert these possible scenarios, the administration and health-care personnel need to formulate and implement robust control measures, while the general public needs to be more responsible and strictly adhere to the established and newly formulated guidelines in order to slow down the spread of the pandemic and prevent it from transforming into a catastrophe.
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Affiliation(s)
- Suhail Ganiny
- Mechanical Engineering Department, National Institute of Technology Srinagar, Hazratbal, Srinagar, J&K 190006 India
| | - Owais Nisar
- College of Agricultural Engineering and Technology, Sher-e-Kashmir University of Agricultural Science and Technology, Shalimar, Srinagar, J&K 190025 India
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Yin S, Zhang N. Prevention schemes for future pandemic cases: mathematical model and experience of interurban multi-agent COVID-19 epidemic prevention. NONLINEAR DYNAMICS 2021. [PMID: 33814725 DOI: 10.1007/s11071-021-06385-4.(0123456789)] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
To enhance the effectiveness of epidemic prevention (EP) in urban sustainability transformation, joint prevention and control mechanism should be established to prevent and control the COVID-19 epidemic. The interurban multi-agent EP strategy, as a key component of this mechanism, includes the spontaneous EP model, the superior leading EP model, and the collaborative EP model. In this study, firstly, the theoretical mechanism of the interurban multi-agent EP strategy was analyzed. Then, we proposed a three-party differential game model including factors such as the risk coefficient for the virus infection and EP experience teaching. Finally, prevention strategies, prevention efficiency, and prevention losses were compared under the three models based on theoretical analysis and numerical analysis. The results of this study are as follows. COVID-19 EP should be guided by a model of central government (CG) leadership, interurban collaboration, and social participation. The CG and urban governments (UGs) should comprehensively carry out COVID-19 EP from various aspects, including EP experience teaching, mass EP comfort, the utilization rate of EP funds, and the ability to implement strategies. During the course of the COVID-19 EP, when the CG and UGs transition from spontaneous EP model to a higher-level EP model, the UG's EP efforts will be enhanced. Under the collaborative EP model, the CG and UGs undergo the highest levels of EP effort. Compared with spontaneous EP model, the superior leading EP model can promote a Pareto improvement for all parties. From the perspective of total loss, the collaborative EP model is superior to the other two EP models. This study not only provides practical guidance for coordinating interurban relationships and enabling multi-agents to fully form joint forces, but also provides theoretical support for the establishment of an interurban joint EP mechanism under unified leadership.
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Affiliation(s)
- Shi Yin
- College of Economics and Management, Hebei Agricultural University, Baoding, 071000 China
- School of Economics and Management, Harbin Engineering University, Harbin, 150001 China
| | - Nan Zhang
- College of Economics and Management, Hebei Agricultural University, Baoding, 071000 China
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Marrazzo F, Spina S, Pepe PE, D'Ambrosio A, Bernasconi F, Manzoni P, Graci C, Frigerio C, Sacchi M, Stucchi R, Teruzzi M, Baraldi S, Lovisari F, Langer T, Sforza A, Migliari M, Sechi G, Sangalli F, Fumagalli R. Rapid reorganization of the Milan metropolitan public safety answering point operations during the initial phase of the COVID-19 outbreak in Italy. J Am Coll Emerg Physicians Open 2020; 1:1240-1249. [PMID: 33043317 PMCID: PMC7537156 DOI: 10.1002/emp2.12245] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/03/2020] [Accepted: 08/19/2020] [Indexed: 01/10/2023] Open
Abstract
Objective To quantify how the first public announcement of confirmed coronavirus disease 2019 (COVID-19) in Italy affected a metropolitan region's emergency medical services (EMS) call volume and how rapid introduction of alternative procedures at the public safety answering point (PSAP) managed system resources. Methods PSAP processes were modified over several days including (1) referral of non-ill callers to public health information call centers; (2) algorithms for detection, isolation, or hospitalization of suspected COVID-19 patients; and (3) specialized medical teams sent to the PSAP for triage and case management, including ambulance dispatches or alternative dispositions. Call volumes, ambulance dispatches, and response intervals for the 2 weeks after announcement were compared to 2017-2019 data and the week before. Results For 2 weeks following outbreak announcement, the primary-level PSAP (police/fire/EMS) averaged 56% more daily calls compared to prior years and recorded 9281 (106% increase) on Day 4, averaging ∼400/hour. The secondary-level (EMS) PSAP recorded an analogous 63% increase with 3863 calls (∼161/hour; 264% increase) on Day 3. The COVID-19 response team processed the more complex cases (n = 5361), averaging 432 ± 110 daily (∼one-fifth of EMS calls). Although community COVID-19 cases increased exponentially, ambulance response intervals and dispatches (averaging 1120 ± 46 daily) were successfully contained, particularly compared with the week before (1174 ± 40; P = 0.02). Conclusion With sudden escalating EMS call volumes, rapid reorganization of dispatch operations using tailored algorithms and specially assigned personnel can protect EMS system resources by optimizing patient dispositions, controlling ambulance allocations and mitigating hospital impact. Prudent population-based disaster planning should strongly consider pre-establishing similar highly coordinated medical taskforce contingencies.
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Affiliation(s)
- Francesco Marrazzo
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
| | - Stefano Spina
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
| | - Paul E. Pepe
- Metropolitan Emergency Medical Services Medical Directors Global CoalitionDallasTexasUSA
| | | | - Filippo Bernasconi
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
| | - Paola Manzoni
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
| | - Carmela Graci
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
| | - Cristina Frigerio
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
| | - Marco Sacchi
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
| | - Riccardo Stucchi
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
| | - Mario Teruzzi
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
| | - Sara Baraldi
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
| | - Federica Lovisari
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
| | - Thomas Langer
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
- School of Medicine and SurgeryUniversity of Milano‐BicoccaMilanItaly
| | | | - Maurizio Migliari
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
| | - Giuseppe Sechi
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
| | - Fabio Sangalli
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
| | - Roberto Fumagalli
- Azienda Regionale Emergenza Urgenza (AREU)MilanLombardyItaly
- Department of Anesthesia and Critical Care MedicineNiguarda HospitalMilanItaly
- School of Medicine and SurgeryUniversity of Milano‐BicoccaMilanItaly
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Tinôco D, Borschiver S. Development of research on COVID-19 by the World Scientific Community in the first half of 2020. BIONATURA 2020. [DOI: 10.21931/rb/2020.05.04.23] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
The World Scientific Community has carried out several studies on the novel coronavirus, responsible for the current COVID-19 pandemic. This study aimed to verify the development level and research evolution on COVID-19, summarizing experts' main trends in the first half of 2020. The most cited articles focused on understanding the disease, addressing aspects of its transmission, viral activity period, symptoms, health complications, risk factors, and the estimate of new cases. These papers also focused on the treatment/prevention and management of COVID-19. Several drugs and alternative treatments have been investigated, such as the convalescent plasma transfusion and stem cell transplantation, while an efficient vaccine is developed. Prevention and control measures, such as social isolation and immediate case identification, were also investigated. Therefore, the main COVID-19 trends were identified and classified in disease, treatment/prevention, and pandemic management, contributing to scientific understanding and future studies.
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Affiliation(s)
- Daniel Tinôco
- Department of Biochemical Engineering, School of Chemistry, Federal University of Rio de Janeiro, Av. Athos da Silveira Ramos, 149, Rio de Janeiro, 21941-909 RJ, Brazil
| | - Suzana Borschiver
- Department of Organic Processes, School of Chemistry, Federal University of Rio de Janeiro, Av. Athos da Silveira Ramos, 149, Rio de Janeiro, 21941-909 RJ, Brazil
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Choucair F, Younis N, Hourani A. IVF laboratory COVID-19 pandemic response plan: a roadmap. MIDDLE EAST FERTILITY SOCIETY JOURNAL 2020; 25:31. [PMID: 33046958 PMCID: PMC7542571 DOI: 10.1186/s43043-020-00043-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 09/22/2020] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND The potential of COVID-19 severe pandemic necessitates the development of an organized and well-reasoned plan for the management of embryology/andrology laboratories while safeguarding the wellbeing of patients and IVF staff. MAIN BODY A COVID-19 pandemic response plan was proposed for embryology and andrology laboratories for pre-pandemic preparedness and pandemic management in anticipation of a possible second coronavirus wave. Preparation involves many plans and logistics before a pandemic risk rises. Many operational changes can be considered during the pandemic. This plan includes logistical arrangements, reducing labor needs, conserving supplies, and protective measures for embryologists and gametes/embryos. CONCLUSION The unpredictable emergence of the COVID-19 pandemic dictates the need for a preparedness plan for embryology/andrology laboratories, which includes an action-oriented plan to secure the safety of all stakeholders.
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Affiliation(s)
- Fadi Choucair
- Middle East Fertility Society Embryology Specialty Interest Group, Beirut, Lebanon
- American University of Beirut Medical Center, Beirut, Lebanon
| | - Nagham Younis
- Middle East Fertility Society Embryology Specialty Interest Group, Beirut, Lebanon
- University of Jordan, Amman, Jordan
| | - Alia Hourani
- Middle East Fertility Society Embryology Specialty Interest Group, Beirut, Lebanon
- Quttainah Medical Center, Kuwait City, Kuwait
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15
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Yan Q, Tang Y, Yan D, Wang J, Yang L, Yang X, Tang S. Impact of media reports on the early spread of COVID-19 epidemic. J Theor Biol 2020; 502:110385. [PMID: 32593679 PMCID: PMC7316072 DOI: 10.1016/j.jtbi.2020.110385] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 06/15/2020] [Accepted: 06/17/2020] [Indexed: 11/16/2022]
Abstract
Media reports can modify people's knowledge of emerging infectious diseases, and thus changing the public attitudes and behaviors. However, how the media reports affect the development of COVID-19 epidemic is a key public health issue. Here the Pearson correlation and cross-correlation analyses are conducted to find the statistically significant correlations between the number of new hospital notifications for COVID-19 and the number of daily news items for twelve major websites in China from January 11th to February 6th 2020. To examine the implication for transmission dynamics of these correlations, we proposed a novel model, which embeds the function of individual behaviour change (media impact) into the intensity of infection. The nonlinear least squares estimation is used to identify the best-fit parameter values in the model from the observed data. To determine impact of key parameters with media impact and control measures for the later outcome of the outbreak, we also carried out the uncertainty and sensitivity analyses. These findings confirm the importance of the responses of individuals to the media reports, and the crucial role of experts and governments in promoting the public under self-quarantine. Therefore, for mitigating epidemic COVID-19, the media publicity should be focused on how to guide people's behavioral changes by experts, and the management departments and designated hospitals of the COVID-19 should take effective quarantined measures, which are critical for the control of the disease.
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Affiliation(s)
- Qinling Yan
- School of Science, Chang'an University, Xi'an 710064, PR China
| | - Yingling Tang
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China
| | - Dingding Yan
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China
| | - Jiaying Wang
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China
| | - Linqian Yang
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China
| | - Xinpei Yang
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China
| | - Sanyi Tang
- College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China.
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Abstract
In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.
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Affiliation(s)
- Junaid Shuja
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Islamabad, Pakistan
- Department of Computer Engineering, Umm Al-Qura University, Makkah, Saudi Arabia
- Center of Innovation and Development in Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Eisa Alanazi
- Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia
- Center of Innovation and Development in Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Waleed Alasmary
- Department of Computer Engineering, Umm Al-Qura University, Makkah, Saudi Arabia
- Center of Innovation and Development in Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Abdulaziz Alashaikh
- Computer Engineering and Networks Department, University of Jeddah, Jeddah, Saudi Arabia
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17
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Abebe A, Mekuria A, Balchut A. Awareness of Health Professionals on COVID-19 and Factors Affecting It Before and During Index Case in North Shoa Zone, Ethiopia, 2020. Infect Drug Resist 2020; 13:2979-2988. [PMID: 32904682 PMCID: PMC7457574 DOI: 10.2147/idr.s268033] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 08/06/2020] [Indexed: 01/22/2023] Open
Abstract
Background COVID-19 is a disease caused by a SARS-COV2. The main way of transmission is from person to person through droplet nuclei. In this time, this disease has no treatment and vaccination. Hence, the WHO recommends countries to work intensively on prevention and control measures. Objective This study aimed to assess the level of awareness on clinical and epidemiological spectrum of COVID-19 and factors affecting it in the North Shoa zone, Amhara Regional State, Ethiopia, 2020. Methods A facility-based cross-sectional study design was used to assess awareness of health professionals on COVID-19 and associated factors affecting it before and during index case. A total of 384 participants selected from 10 hospitals participated in this study. The data were entered and coded using EPI-INFO version 3.5.4 and then transferred to SPSS version 20 for analysis. Bivariable and multivariable logistic regression were computed. Variables with a p-value less than 0.05 were taken as predictor variables. Results A total of 384 respondents with a response rate of 91% participated in this study. The proportion of participants with an awareness of COVID-19 was 305 (79.4%). The types of profession (AOR=6.9, 95% CI=1.6–29.8) and level of a profession (AOR=2.3, 95% CI=1.4–4.4) of the profession, availability of television at home (AOR=2.1, 95% CI=1.1, 3.9) and hearing of the emerging diseases in the past (AOR=2.7, 95% CI=1.5–5) were factors that determine the awareness of health professionals on COVID-19 clinical and epidemiological spectrum. Conclusion and Recommendations The level of the health professional’s awareness on the clinical and epidemiological spectrum of COVID-19 was promising. There is a need for a coordinated effort from stakeholders and health professionals to increase awareness.
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Affiliation(s)
- Ayele Abebe
- Department of Pediatrics Nursing, Debre Berhan University, Amhara, Ethiopia
| | - Abinet Mekuria
- Public Health Department, Debre Berhan University, Amhara,Ethiopia
| | - Awraris Balchut
- Public Health Department, Debre Berhan University, Amhara,Ethiopia
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18
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Jiang X, Chang L, Shi Y. A retrospective analysis of the dynamic transmission routes of the COVID-19 in mainland China. Sci Rep 2020; 10:14015. [PMID: 32814822 PMCID: PMC7438497 DOI: 10.1038/s41598-020-71023-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 08/04/2020] [Indexed: 11/12/2022] Open
Abstract
The fourth outbreak of the Coronaviruses, known as the COVID-19, has occurred in Wuhan city of Hubei province in China in December 2019. We propose a time-varying sparse vector autoregressive (VAR) model to retrospectively analyze and visualize the dynamic transmission routes of this outbreak in mainland China over January 31-February 19, 2020. Our results demonstrate that the influential inter-location routes from Hubei have become unidentifiable since February 4, 2020, whereas the self-transmission in each provincial-level administrative region (location, hereafter) was accelerating over February 4-15, 2020. From February 16, 2020, all routes became less detectable, and no influential transmissions could be identified on February 18 and 19, 2020. Such evidence supports the effectiveness of government interventions, including the travel restrictions in Hubei. Implications of our results suggest that in addition to the origin of the outbreak, virus preventions are of crucial importance in locations with the largest migrant workers percentages (e.g., Jiangxi, Henan and Anhui) to controlling the spread of COVID-19.
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Affiliation(s)
- Xiandeng Jiang
- School of Public Finance and Taxation, Southwestern University of Finance and Economics, Chengdu, 611130, Sichuan, People's Republic of China
| | - Le Chang
- Research School of Finance, Actuarial Studies, and Statistics, Australian National University, Canberra, ACT, 2601, Australia
| | - Yanlin Shi
- Department of Actuarial Studies and Business Analytics, Macquarie University, Sydney, NSW, 2109, Australia.
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Mbuvha R, Marwala T. Bayesian inference of COVID-19 spreading rates in South Africa. PLoS One 2020; 15:e0237126. [PMID: 32756608 PMCID: PMC7406053 DOI: 10.1371/journal.pone.0237126] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 07/20/2020] [Indexed: 11/25/2022] Open
Abstract
The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for performing accurate inference with limited data. Fundamental to the design of rapid state responses is the ability to perform epidemiological model parameter inference for localised trajectory predictions. In this work, we perform Bayesian parameter inference using Markov Chain Monte Carlo (MCMC) methods on the Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological models with time-varying spreading rates for South Africa. The results find two change points in the spreading rate of COVID-19 in South Africa as inferred from the confirmed cases. The first change point coincides with state enactment of a travel ban and the resultant containment of imported infections. The second change point coincides with the start of a state-led mass screening and testing programme which has highlighted community-level disease spread that was not well represented in the initial largely traveller based and private laboratory dominated testing data. The results further suggest that due to the likely effect of the national lockdown, community level transmissions are slower than the original imported case driven spread of the disease.
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Affiliation(s)
- Rendani Mbuvha
- School of Statistics and Actuarial Science, University of Witwatersrand, Johannesburg, South Africa
- Institute of Intelligent Systems, University of Johannesburg, Johannesburg, South Africa
| | - Tshilidzi Marwala
- Institute of Intelligent Systems, University of Johannesburg, Johannesburg, South Africa
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Gill BS, Jayaraj VJ, Singh S, Mohd Ghazali S, Cheong YL, Md Iderus NH, Sundram BM, Aris TB, Mohd Ibrahim H, Hong BH, Labadin J. Modelling the Effectiveness of Epidemic Control Measures in Preventing the Transmission of COVID-19 in Malaysia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E5509. [PMID: 32751669 PMCID: PMC7432794 DOI: 10.3390/ijerph17155509] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 06/14/2020] [Accepted: 07/02/2020] [Indexed: 01/10/2023]
Abstract
Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, infectious, and recovered (SEIR) model by incorporating isolation, quarantine, and movement control order (MCO) taken in Malaysia. The simulations were fitted into the Malaysian COVID-19 active case numbers, allowing approximation of parameters consisting of probability of transmission per contact (β), average number of contacts per day per case (ζ), and proportion of close-contact traced per day (q). The effective reproduction number (Rt) was also determined through this model. Our model calibration estimated that (β), (ζ), and (q) were 0.052, 25 persons, and 0.23, respectively. The (Rt) was estimated to be 1.68. MCO measures reduce the peak number of active COVID-19 cases by 99.1% and reduce (ζ) from 25 (pre-MCO) to 7 (during MCO). The flattening of the epidemic curve was also observed with the implementation of these control measures. We conclude that isolation, quarantine, and MCO measures are essential to break the transmission of COVID-19 in Malaysia.
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Affiliation(s)
- Balvinder Singh Gill
- Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur 50588, Malaysia; (B.S.G.); (S.S.); (S.M.G.); (Y.L.C.); (N.H.M.I.); (B.M.S.); (T.B.A.)
| | - Vivek Jason Jayaraj
- Department of Social and Preventive Medicine, Medical Faculty, University Malaya, Kuala Lumpur 50603, Malaysia;
- Ministry of Health, Malaysia, Putrajaya 62590, Malaysia;
| | - Sarbhan Singh
- Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur 50588, Malaysia; (B.S.G.); (S.S.); (S.M.G.); (Y.L.C.); (N.H.M.I.); (B.M.S.); (T.B.A.)
| | - Sumarni Mohd Ghazali
- Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur 50588, Malaysia; (B.S.G.); (S.S.); (S.M.G.); (Y.L.C.); (N.H.M.I.); (B.M.S.); (T.B.A.)
| | - Yoon Ling Cheong
- Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur 50588, Malaysia; (B.S.G.); (S.S.); (S.M.G.); (Y.L.C.); (N.H.M.I.); (B.M.S.); (T.B.A.)
| | - Nuur Hafizah Md Iderus
- Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur 50588, Malaysia; (B.S.G.); (S.S.); (S.M.G.); (Y.L.C.); (N.H.M.I.); (B.M.S.); (T.B.A.)
| | - Bala Murali Sundram
- Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur 50588, Malaysia; (B.S.G.); (S.S.); (S.M.G.); (Y.L.C.); (N.H.M.I.); (B.M.S.); (T.B.A.)
| | - Tahir Bin Aris
- Institute for Medical Research (IMR), Ministry of Health, Kuala Lumpur 50588, Malaysia; (B.S.G.); (S.S.); (S.M.G.); (Y.L.C.); (N.H.M.I.); (B.M.S.); (T.B.A.)
| | | | - Boon Hao Hong
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia;
| | - Jane Labadin
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia;
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MALIK YASHPALSINGH, SIRCAR SHUBHANKAR, BHAT SUDIPTA, R VINODHKUMARO, TIWARI RUCHI, SAH RANJIT, RABAAN ALIA, RODRIGUEZ-MORALES ALFONSOJ, DHAMA KULDEEP. Emerging Coronavirus Disease (COVID-19), a pandemic public health emergency with animal linkages: Current status update. THE INDIAN JOURNAL OF ANIMAL SCIENCES 2020. [DOI: 10.56093/ijans.v90i3.102316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
After the appearance of first cases of ‘pneumonia of unknown origin’ in the Wuhan city, China, during late 2019, the disease progressed fast. Its cause was identified as a novel coronavirus, named provisionally 2019-nCoV. Subsequently, an official name was given as SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) by the International Committee on Taxonomy of Viruses (ICTV) study group. The World Health Organization (WHO) named the Coronavirus disease-2019 as COVID-19. The epidemics of COVID-2019 have been recorded over 113 countries/territories/areas apart from China and filched more than 4,292 humans, affecting severely around 1,18,326 cases in a short span. The status of COVID-2019 emergency revised by the WHO within 42 days from Public Health International Emergency (January 30, 2020) to a pandemic (March 11, 2020). Nonetheless, the case fatality rate (CFR) of the current epidemic is on the rise (between 2–4%), relatively is lower than the previous SARS-CoV (2002/2003) and MERS-CoV (2012) outbreaks. Even though investigations are on its way, the researchers across the globe have assumptions of animal-origin of current SARS-CoV-2. A recent case report provides evidence of mild COVID-2019 infection in a pet dog that acquired COVID-2019 infection from his owner in Hong Kong. The news on travellers associated spread across the globe have also put many countries on alert with the cancellation of tourist visa to all affected countries and postponement of events where international visits were required. A few diagnostic approaches, including quantitative and differential real-time polymerase chain reaction assays, have been recommended for the screening of the individuals at risk. In the absence of any selective vaccine against SARS-CoV-2, re-purposed drugs are advocated in many studies. This article discourse the current worldwide situation of COVID-2019 with information on virus, epidemiology, host, the role of animals, effective diagnosis, therapeutics, preventive and control approaches making people aware on the disease outcomes.
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Patiño-Lugo DF, Vélez M, Velásquez Salazar P, Vera-Giraldo CY, Vélez V, Marín IC, Ramírez PA, Quintero SP, Castrillón Martínez E, Pineda Higuita DA, Henandez G. Non-pharmaceutical interventions for containment, mitigation and suppression of COVID-19 infection. Colomb Med (Cali) 2020; 51:e4266. [PMID: 33012884 PMCID: PMC7518730 DOI: 10.25100/cm.v51i2.4266] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 04/25/2020] [Accepted: 05/04/2020] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND The best scientific evidence is required to design effective Non-pharmaceutical interventions to help policymakers to contain COVID-19. AIM To describe which Non-pharmaceutical interventions used different countries and a when they use them. It also explores how Non-pharmaceutical interventions impact the number of cases, the mortality, and the capacity of health systems. METHODS We consulted eight web pages of transnational organizations, 17 of international media, 99 of government institutions in the 19 countries included, and besides, we included nine studies (out of 34 identified) that met inclusion criteria. RESULT Some countries are focused on establishing travel restrictions, isolation of identified cases, and high-risk people. Others have a combination of mandatory quarantine and other drastic social distancing measures. The timing to implement the interventions varied from the first fifteen days after detecting the first case to more than 30 days. The effectiveness of isolated non-pharmaceutical interventions may be limited, but combined interventions have shown to be effective in reducing the transmissibility of the disease, the collapse of health care services, and mortality. When the number of new cases has been controlled, it is necessary to maintain social distancing measures, self-isolation, and contact tracing for several months. The policy decision-making in this time should be aimed to optimize the opportunities of saving lives, reducing the collapse of health services, and minimizing the economic and social impact over the general population, but principally over the most vulnerable. The timing of implementing and lifting interventions could have a substantial effect on those objectives.
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Affiliation(s)
- Daniel F. Patiño-Lugo
- Universidad de Antioquia, Unidad de Evidencia y Deliberación para la toma de Decisiones-UNED, Facultad de Medicina, Medellin Colombia
| | - Marcela Vélez
- Universidad de Antioquia, Unidad de Evidencia y Deliberación para la toma de Decisiones-UNED, Facultad de Medicina, Medellin Colombia
| | - Pamela Velásquez Salazar
- Universidad de Antioquia, Unidad de Evidencia y Deliberación para la toma de Decisiones-UNED, Facultad de Medicina, Medellin Colombia
| | - Claudia Yaneth Vera-Giraldo
- Universidad de Antioquia, Unidad de Evidencia y Deliberación para la toma de Decisiones-UNED, Facultad de Medicina, Medellin Colombia
| | - Viviana Vélez
- Universidad de Antioquia, Unidad de Evidencia y Deliberación para la toma de Decisiones-UNED, Facultad de Medicina, Medellin Colombia
| | - Isabel Cristina Marín
- Universidad de Antioquia, Unidad de Evidencia y Deliberación para la toma de Decisiones-UNED, Facultad de Medicina, Medellin Colombia
| | - Paola Andrea Ramírez
- Universidad de Antioquia, Unidad de Evidencia y Deliberación para la toma de Decisiones-UNED, Facultad de Medicina, Medellin Colombia
| | - Sebastián Pemberthy Quintero
- Universidad de Antioquia, Unidad de Evidencia y Deliberación para la toma de Decisiones-UNED, Facultad de Medicina, Medellin Colombia
| | - Esteban Castrillón Martínez
- Universidad de Antioquia, Unidad de Evidencia y Deliberación para la toma de Decisiones-UNED, Facultad de Medicina, Medellin Colombia
| | - Daniel Andrés Pineda Higuita
- Universidad de Antioquia, Unidad de Evidencia y Deliberación para la toma de Decisiones-UNED, Facultad de Medicina, Medellin Colombia
| | - Gilma Henandez
- Universidad de Antioquia, Unidad de Evidencia y Deliberación para la toma de Decisiones-UNED, Facultad de Medicina, Medellin Colombia
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Jung G, Lee H, Kim A, Lee U. Too Much Information: Assessing Privacy Risks of Contact Trace Data Disclosure on People With COVID-19 in South Korea. Front Public Health 2020; 8:305. [PMID: 32626681 PMCID: PMC7314957 DOI: 10.3389/fpubh.2020.00305] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 06/05/2020] [Indexed: 11/13/2022] Open
Abstract
Introduction: With the COVID-19 outbreak, South Korea has been making contact trace data public to help people self-check if they have been in contact with a person infected with the coronavirus. Despite its benefits in suppressing the spread of the virus, publicizing contact trace data raises concerns about individuals' privacy. In view of this tug-of-war between one's privacy and public safety, this work aims to deepen the understanding of privacy risks of contact trace data disclosure practices in South Korea. Method: In this study, publicly available contact trace data of 970 confirmed patients were collected from seven metropolitan cities in South Korea (20th Jan-20th Apr 2020). Then, an ordinal scale of relative privacy risk levels was introduced for evaluation, and the assessment was performed on the personal information included in the contact trace data, such as demographics, significant places, sensitive information, social relationships, and routine behaviors. In addition, variance of privacy risk levels was examined across regions and over time to check for differences in policy implementation. Results: It was found that most of the contact trace data showed the gender and age of the patients. In addition, it disclosed significant places (home/work) ranging across different levels of privacy risks in over 70% of the cases. Inference on sensitive information (hobby, religion) was made possible, and 48.7% of the cases exposed the patient's social relationships. In terms of regional differences, a considerable discrepancy was found in the privacy risk for each category. Despite the recent release of government guidelines on data disclosure, its effects were still limited to a few factors (e.g., workplaces, routine behaviors). Discussion: Privacy risk assessment showed evidence of superfluous information disclosure in the current practice. This study discusses the role of "identifiability" in contact tracing to provide new directions for minimizing disclosure of privacy infringing information. Analysis of real-world data can offer potential stakeholders, such as researchers, service developers, and government officials with practical protocols/guidelines in publicizing information of patients and design implications for future systems (e.g., automatic privacy sensitivity checking) to strike a balance between one's privacy and the public benefits with data disclosure.
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Affiliation(s)
- Gyuwon Jung
- Graduate School of Knowledge Service Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Hyunsoo Lee
- Graduate School of Knowledge Service Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Auk Kim
- Graduate School of Knowledge Service Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Uichin Lee
- Graduate School of Knowledge Service Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.,Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
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Shao N, Zhong M, Yan Y, Pan H, Cheng J, Chen W. Dynamic models for Coronavirus Disease 2019 and data analysis. MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2020; 43:4943-4949. [PMID: 32327866 PMCID: PMC7168448 DOI: 10.1002/mma.6345] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Revised: 06/06/2016] [Accepted: 06/06/2016] [Indexed: 05/18/2023]
Abstract
In this letter, two time delay dynamic models, a Time Delay Dynamical-Novel Coronavirus Pneumonia (TDD-NCP) model and Fudan-Chinese Center for Disease Control and Prevention (CCDC) model, are introduced to track the data of Coronavirus Disease 2019 (COVID-19). The TDD-NCP model was developed recently by Chengąŕs group in Fudan and Shanghai University of Finance and Economics (SUFE). The TDD-NCP model introduced the time delay process into the differential equations to describe the latent period of the epidemic. The Fudan-CDCC model was established when Wenbin Chen suggested to determine the kernel functions in the TDD-NCP model by the public data from CDCC. By the public data of the cumulative confirmed cases in different regions in China and different countries, these models can clearly illustrate that the containment of the epidemic highly depends on early and effective isolations.
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Affiliation(s)
- Nian Shao
- School of Mathematical SciencesFudan UniversityShanghaiChina
| | - Min Zhong
- School of MathematicsSoutheast UniversityNanjingChina
| | - Yue Yan
- School of MathematicsShanghai University of Finance and EconomicsShanghaiChina
| | - HanShuang Pan
- School of Mathematical SciencesFudan UniversityShanghaiChina
| | - Jin Cheng
- School of Mathematical SciencesFudan UniversityShanghaiChina
- Shanghai Key Laboratory for Contemporary Applied MathematicsFudan UniversityShanghaiChina
| | - Wenbin Chen
- School of Mathematical SciencesFudan UniversityShanghaiChina
- Shanghai Key Laboratory for Contemporary Applied MathematicsFudan UniversityShanghaiChina
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Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, Eggo RM. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. THE LANCET. INFECTIOUS DISEASES 2020. [PMID: 32171059 DOI: 10.1016/s1473-3099(20)3014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
BACKGROUND An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced. METHODS We combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020. FINDINGS We estimated that the median daily reproduction number (Rt) in Wuhan declined from 2·35 (95% CI 1·15-4·77) 1 week before travel restrictions were introduced on Jan 23, 2020, to 1·05 (0·41-2·39) 1 week after. Based on our estimates of Rt, assuming SARS-like variation, we calculated that in locations with similar transmission potential to Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population. INTERPRETATION Our results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually. FUNDING Wellcome Trust, Health Data Research UK, Bill & Melinda Gates Foundation, and National Institute for Health Research.
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Affiliation(s)
- Adam J Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.
| | - Timothy W Russell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Charlie Diamond
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Yang Liu
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
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Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, Eggo RM. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. THE LANCET. INFECTIOUS DISEASES 2020; 20:553-558. [PMID: 32171059 PMCID: PMC7158569 DOI: 10.1016/s1473-3099(20)30144-4] [Citation(s) in RCA: 1328] [Impact Index Per Article: 332.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 02/14/2020] [Accepted: 02/19/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced. METHODS We combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020. FINDINGS We estimated that the median daily reproduction number (Rt) in Wuhan declined from 2·35 (95% CI 1·15-4·77) 1 week before travel restrictions were introduced on Jan 23, 2020, to 1·05 (0·41-2·39) 1 week after. Based on our estimates of Rt, assuming SARS-like variation, we calculated that in locations with similar transmission potential to Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population. INTERPRETATION Our results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually. FUNDING Wellcome Trust, Health Data Research UK, Bill & Melinda Gates Foundation, and National Institute for Health Research.
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Affiliation(s)
- Adam J Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.
| | - Timothy W Russell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Charlie Diamond
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Yang Liu
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
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Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, Eggo RM. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. THE LANCET. INFECTIOUS DISEASES 2020; 20:553-558. [PMID: 32171059 DOI: 10.1101/2020.01.31.20019901] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 02/14/2020] [Accepted: 02/19/2020] [Indexed: 05/27/2023]
Abstract
BACKGROUND An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced. METHODS We combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020. FINDINGS We estimated that the median daily reproduction number (Rt) in Wuhan declined from 2·35 (95% CI 1·15-4·77) 1 week before travel restrictions were introduced on Jan 23, 2020, to 1·05 (0·41-2·39) 1 week after. Based on our estimates of Rt, assuming SARS-like variation, we calculated that in locations with similar transmission potential to Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population. INTERPRETATION Our results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually. FUNDING Wellcome Trust, Health Data Research UK, Bill & Melinda Gates Foundation, and National Institute for Health Research.
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Affiliation(s)
- Adam J Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.
| | - Timothy W Russell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Charlie Diamond
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Yang Liu
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
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Ganyani T, Kremer C, Chen D, Torneri A, Faes C, Wallinga J, Hens N. Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. Euro Surveill 2020; 25. [PMID: 32372755 DOI: 10.1101/2020.03.05.20031815v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023] Open
Abstract
BackgroundEstimating key infectious disease parameters from the coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies.AimWe estimate the generation interval, serial interval, proportion of pre-symptomatic transmission and effective reproduction number of COVID-19. We illustrate that reproduction numbers calculated based on serial interval estimates can be biased.MethodsWe used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates, we obtained the serial interval, proportions of pre-symptomatic transmission and reproduction numbers.ResultsThe mean generation interval was 5.20 days (95% credible interval (CrI): 3.78-6.78) for Singapore and 3.95 days (95% CrI: 3.01-4.91) for Tianjin. The proportion of pre-symptomatic transmission was 48% (95% CrI: 32-67) for Singapore and 62% (95% CrI: 50-76) for Tianjin. Reproduction number estimates based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Sensitivity analyses showed that estimating these quantities from outbreak data requires detailed contact tracing information.ConclusionHigh estimates of the proportion of pre-symptomatic transmission imply that case finding and contact tracing need to be supplemented by physical distancing measures in order to control the COVID-19 outbreak. Notably, quarantine and other containment measures were already in place at the time of data collection, which may inflate the proportion of infections from pre-symptomatic individuals.
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Affiliation(s)
- Tapiwa Ganyani
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Cécile Kremer
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Dongxuan Chen
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
- Leiden University Medical Center, Leiden, the Netherlands
| | - Andrea Torneri
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Christel Faes
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Jacco Wallinga
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
- Leiden University Medical Center, Leiden, the Netherlands
| | - Niel Hens
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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Ganyani T, Kremer C, Chen D, Torneri A, Faes C, Wallinga J, Hens N. Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. Euro Surveill 2020; 25:2000257. [PMID: 32372755 PMCID: PMC7201952 DOI: 10.2807/1560-7917.es.2020.25.17.2000257] [Citation(s) in RCA: 333] [Impact Index Per Article: 83.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 04/07/2020] [Indexed: 12/29/2022] Open
Abstract
BackgroundEstimating key infectious disease parameters from the coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies.AimWe estimate the generation interval, serial interval, proportion of pre-symptomatic transmission and effective reproduction number of COVID-19. We illustrate that reproduction numbers calculated based on serial interval estimates can be biased.MethodsWe used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates, we obtained the serial interval, proportions of pre-symptomatic transmission and reproduction numbers.ResultsThe mean generation interval was 5.20 days (95% credible interval (CrI): 3.78-6.78) for Singapore and 3.95 days (95% CrI: 3.01-4.91) for Tianjin. The proportion of pre-symptomatic transmission was 48% (95% CrI: 32-67) for Singapore and 62% (95% CrI: 50-76) for Tianjin. Reproduction number estimates based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Sensitivity analyses showed that estimating these quantities from outbreak data requires detailed contact tracing information.ConclusionHigh estimates of the proportion of pre-symptomatic transmission imply that case finding and contact tracing need to be supplemented by physical distancing measures in order to control the COVID-19 outbreak. Notably, quarantine and other containment measures were already in place at the time of data collection, which may inflate the proportion of infections from pre-symptomatic individuals.
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Affiliation(s)
- Tapiwa Ganyani
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Cécile Kremer
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Dongxuan Chen
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
- Leiden University Medical Center, Leiden, the Netherlands
| | - Andrea Torneri
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Christel Faes
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Jacco Wallinga
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
- Leiden University Medical Center, Leiden, the Netherlands
| | - Niel Hens
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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Affiliation(s)
- Zhenjian He
- School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, China.
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Rohith G, Devika KB. Dynamics and control of COVID-19 pandemic with nonlinear incidence rates. NONLINEAR DYNAMICS 2020; 101:2013-2026. [PMID: 32836807 PMCID: PMC7315126 DOI: 10.1007/s11071-020-05774-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 06/17/2020] [Indexed: 05/03/2023]
Abstract
World Health Organization (WHO) has declared COVID-19 a pandemic on March 11, 2020. As of May 23, 2020, according to WHO, there are 213 countries, areas or territories with COVID-19 positive cases. To effectively address this situation, it is imperative to have a clear understanding of the COVID-19 transmission dynamics and to concoct efficient control measures to mitigate/contain the spread. In this work, the COVID-19 dynamics is modelled using susceptible-exposed-infectious-removed model with a nonlinear incidence rate. In order to control the transmission, the coefficient of nonlinear incidence function is adopted as the Governmental control input. To adequately understand the COVID-19 dynamics, bifurcation analysis is performed and the effect of varying reproduction number on the COVID-19 transmission is studied. The inadequacy of an open-loop approach in controlling the disease spread is validated via numerical simulations and a robust closed-loop control methodology using sliding mode control is also presented. The proposed SMC strategy could bring the basic reproduction number closer to 1 from an initial value of 2.5, thus limiting the exposed and infected individuals to a controllable threshold value. The model and the proposed control strategy are then compared with real-time data in order to verify its efficacy.
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Affiliation(s)
- G. Rohith
- Department of Engineering Design, IIT Madras, Chennai, 600036 India
| | - K. B. Devika
- Department of Engineering Design, IIT Madras, Chennai, 600036 India
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