1
|
Small ML, Lennon RP, Dziak JJ, Smith RA, Sommerville G, Bharti N. College students' COVID-19 vaccine beliefs and intentions: Implications for interventions. JOURNAL OF AMERICAN COLLEGE HEALTH : J OF ACH 2024; 72:989-994. [PMID: 35622961 DOI: 10.1080/07448481.2022.2065205] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 03/03/2022] [Accepted: 03/29/2022] [Indexed: 06/15/2023]
Abstract
On college campuses, effective management of vaccine-preventable transmissible pathogens requires understanding student vaccination intentions. This is necessary for developing and tailoring health messaging to maximize uptake of health information and vaccines. The current study explored students' beliefs and attitudes about vaccines in general, and the new COVID-19 vaccines specifically. This study provides insights into effective health messaging needed to rapidly increase COVID-19 vaccination on college campuses-information that will continue to be informative in future academic years across a broad scope of pathogens. Data were collected from 696 undergraduate students ages 18-29 years old enrolled in a large public university in the Northeast during fall 2020. Data were collected via an online survey. Overall, we found COVID-19 vaccine hesitancy in college students correlated strongly with some concerns about vaccines in general as well as with concerns specific to COVID-19 vaccines. Taken together, these results provide further insight for message development and delivery and can inform more effective interventions to advance critical public health outcomes on college campuses beyond the current pandemic.
Collapse
Affiliation(s)
- Meg L Small
- College of Health and Human Development, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Robert P Lennon
- Department of Family and Community Medicine, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
| | - John J Dziak
- College of Health and Human Development, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Rachel A Smith
- Department of Communication Arts and Sciences, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Gillian Sommerville
- College of Health and Human Development, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Nita Bharti
- Department of Biology, Eberly College of Science, Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, Pennsylvania, USA
| |
Collapse
|
2
|
Willem L, Abrams S, Franco N, Coletti P, Libin PJK, Wambua J, Couvreur S, André E, Wenseleers T, Mao Z, Torneri A, Faes C, Beutels P, Hens N. The impact of quality-adjusted life years on evaluating COVID-19 mitigation strategies: lessons from age-specific vaccination roll-out and variants of concern in Belgium (2020-2022). BMC Public Health 2024; 24:1171. [PMID: 38671366 PMCID: PMC11047051 DOI: 10.1186/s12889-024-18576-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND When formulating and evaluating COVID-19 vaccination strategies, an emphasis has been placed on preventing severe disease that overburdens healthcare systems and leads to mortality. However, more conventional outcomes such as quality-adjusted life years (QALYs) and inequality indicators are warranted as additional information for policymakers. METHODS We adopted a mathematical transmission model to describe the infectious disease dynamics of SARS-COV-2, including disease mortality and morbidity, and to evaluate (non)pharmaceutical interventions. Therefore, we considered temporal immunity levels, together with the distinct transmissibility of variants of concern (VOCs) and their corresponding vaccine effectiveness. We included both general and age-specific characteristics related to SARS-CoV-2 vaccination. Our scenario study is informed by data from Belgium, focusing on the period from August 2021 until February 2022, when vaccination for children aged 5-11 years was initially not yet licensed and first booster doses were administered to adults. More specifically, we investigated the potential impact of an earlier vaccination programme for children and increased or reduced historical adult booster dose uptake. RESULTS Through simulations, we demonstrate that increasing vaccine uptake in children aged 5-11 years in August-September 2021 could have led to reduced disease incidence and ICU occupancy, which was an essential indicator for implementing non-pharmaceutical interventions and maintaining healthcare system functionality. However, an enhanced booster dose regimen for adults from November 2021 onward could have resulted in more substantial cumulative QALY gains, particularly through the prevention of elevated levels of infection and disease incidence associated with the emergence of Omicron VOC. In both scenarios, the need for non-pharmaceutical interventions could have decreased, potentially boosting economic activity and mental well-being. CONCLUSIONS When calculating the impact of measures to mitigate disease spread in terms of life years lost due to COVID-19 mortality, we highlight the impact of COVID-19 on the health-related quality of life of survivors. Our study underscores that disease-related morbidity could constitute a significant part of the overall health burden. Our quantitative findings depend on the specific setup of the interventions under review, which is open to debate or should be contextualised within future situations.
Collapse
Affiliation(s)
- Lander Willem
- Department of Family Medicine and Population Health, Antwerp, Belgium.
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium.
| | - Steven Abrams
- Department of Family Medicine and Population Health, Antwerp, Belgium
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Nicolas Franco
- Data Science Institute, Hasselt University, Hasselt, Belgium
- Namur Institute for Complex Systems (naXys) and Department of Mathematics, University of Namur, Namur, Belgium
| | - Pietro Coletti
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Pieter J K Libin
- Data Science Institute, Hasselt University, Hasselt, Belgium
- Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium
- Rega Institute for Medical Research, Clinical and Epidemiological Virology, University of Leuven, Leuven, Belgium
| | - James Wambua
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Simon Couvreur
- Department of Epidemiology and public health, Sciensano, Brussel, Belgium
| | - Emmanuel André
- National Reference Centre for Respiratory Pathogens, University Hospitals Leuven, Leuven, Belgium
- Department of Microbiology, Immunology and Transplantation, University of Leuven, Leuven, Belgium
| | - Tom Wenseleers
- Laboratory of Socioecology and Social Evolution, University of Leuven, Leuven, Belgium
| | - Zhuxin Mao
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
| | - Andrea Torneri
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Christel Faes
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Philippe Beutels
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia
| | - Niel Hens
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- Data Science Institute, Hasselt University, Hasselt, Belgium
| |
Collapse
|
3
|
Ma MZ, Ye S. Country's value priorities in health crisis: How dominant societal motivations shape COVID-19 severity. SSM Popul Health 2023; 24:101493. [PMID: 37664868 PMCID: PMC10474233 DOI: 10.1016/j.ssmph.2023.101493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/14/2023] [Accepted: 08/15/2023] [Indexed: 09/05/2023] Open
Abstract
This paper presents two comprehensive studies examining how Schwartz's human values dimensions at the country level predict COVID-19 pandemic severity. Study 1 aggregated survey data across 89 countries from the European Social Survey and World Values Survey to assess societal-level conservation versus openness to change (CON-OTC) and self-enhancement versus self-transcendence (SE-ST) value-continuums. Study 2 developed an innovative archival measurement approach using 10 indicators to estimate these value dimensions for over 180 countries. Both studies employed multilevel modeling to test the relationships between country-level values and COVID-19 severity, measured through epidemiological indicators of transmission speed, case fatality rate, infection prevalence and mortality burden. Results revealed that the CON-OTC and SE-ST value-continuums showed consistent, significant negative associations with transmission speed and infection prevalence before adjusting for modernization, latitude, historical pathogen prevalence and government stringency across both studies. However, after accounting for these socioecological and policy covariates, the CON-OTC value-continuum positively predicted case fatality rate in both studies, implying conservation values could increase COVID-19 lethality. In contrast, across both studies, the SE-ST value-continuum negatively predicted case fatality rate after adjusting for the covariates, suggesting countries prioritizing self-enhancement values exhibited relatively lower pandemic severity and lethality when accounting for developmental, ecological, and policy factors. Accordingly, the studies advance theoretical understanding of how country's value priorities shape COVID-19 impact. Methodologically, these studies contribute through multilevel techniques that account for spatial dependencies, as well as an innovative ecological measurement. Overall, this research demonstrates the value of applying Schwartz's framework at a societal level to predict global health crises and pandemics.
Collapse
Affiliation(s)
- Mac Zewei Ma
- Department of Applied Social Sciences, The Hong Kong Polytechnic University, PR China
| | - Shengquan Ye
- Department of Social and Behavioural Sciences, City University of Hong Kong, PR China
| |
Collapse
|
4
|
Fosch A, Aleta A, Moreno Y. Characterizing the role of human behavior in the effectiveness of contact-tracing applications. Front Public Health 2023; 11:1266989. [PMID: 38026393 PMCID: PMC10657191 DOI: 10.3389/fpubh.2023.1266989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Although numerous countries relied on contact-tracing (CT) applications as an epidemic control measure against the COVID-19 pandemic, the debate around their effectiveness is still open. Most studies indicate that very high levels of adoption are required to stop disease progression, placing the main interest of policymakers in promoting app adherence. However, other factors of human behavior, like delays in adherence or heterogeneous compliance, are often disregarded. Methods To characterize the impact of human behavior on the effectiveness of CT apps we propose a multilayer network model reflecting the co-evolution of an epidemic outbreak and the app adoption dynamics over a synthetic population generated from survey data. The model was initialized to produce epidemic outbreaks resembling the first wave of the COVID-19 pandemic and was used to explore the impact of different changes in behavioral features in peak incidence and maximal prevalence. Results The results corroborate the relevance of the number of users for the effectiveness of CT apps but also highlight the need for early adoption and, at least, moderate levels of compliance, which are factors often not considered by most policymakers. Discussion The insight obtained was used to identify a bottleneck in the implementation of several apps, such as the Spanish CT app, where we hypothesize that a simplification of the reporting system could result in increased effectiveness through a rise in the levels of compliance.
Collapse
Affiliation(s)
- Ariadna Fosch
- Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, Zaragoza, Spain
- CENTAI Institute, Turin, Italy
- Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | - Alberto Aleta
- Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, Zaragoza, Spain
- Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | - Yamir Moreno
- Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, Zaragoza, Spain
- CENTAI Institute, Turin, Italy
- Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| |
Collapse
|
5
|
Ma MZ, Ye S. The COVID-19 pandemic and seeking information about condoms online: an infodemiology approach. Psychol Health 2023; 38:1128-1147. [PMID: 34822308 DOI: 10.1080/08870446.2021.2005794] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 11/07/2021] [Accepted: 11/08/2021] [Indexed: 12/15/2022]
Abstract
Objectives: As condoms are effective tools for pathogen-avoidance in sexual intercourse, seeking information about condoms online may be a reactive response to the COVID-19 according to the behavioral immune system theory.Design: Taking an infodemiology perspective, this research employed multilevel analyses to examine how COVID-19 online query data (i.e., Google topic search terms Coronavirus and COVID-19) and coronavirus epidemiological data (i.e., COVID-19 cases per million and case fatality rate) would predict condom information seeking behavior online (i.e., Google topic search term Condom) throughout the pandemic across American states (Study 1) and 102 countries/territories (Study 2), after accounting for death-thought accessibility (i.e., illness-related searches), interest in birth control (i.e., birth-control-related searches), COVID-19 control policy, stay at home behavior, season, religious holidays, yearly trends, autocorrelation, and contextual variables such as HIV prevalence rate and socioeconomic development indicators (GINI index, urbanization, etc.).Results: When there were high levels of COVID-19 concerns in cyberspace in a given week, search volume for condoms increased from the previous week across American states and different countries/territories. By contrast, the effect of actual coronavirus threat was non-significant.Conclusion: Seeking information about condoms online could be a reactive response to high levels of COVID-19 concerns across different populations.
Collapse
Affiliation(s)
- Mac Zewei Ma
- Department of Social and Behavioural Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR
| | - Shengquan Ye
- Department of Social and Behavioural Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR
| |
Collapse
|
6
|
Rahaman H, Barik D. Investigation of airborne spread of COVID-19 using a hybrid agent-based model: a case study of the UK. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230377. [PMID: 37501658 PMCID: PMC10369033 DOI: 10.1098/rsos.230377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/04/2023] [Indexed: 07/29/2023]
Abstract
Agent-based models have been proven to be quite useful in understanding and predicting the SARS-CoV-2 virus-originated COVID-19 infection. Person-to-person contact was considered as the main mechanism of viral transmission in these models. However, recent understanding has confirmed that airborne transmission is the main route to infection spread of COVID-19. We have developed a computationally efficient agent-based hybrid model to study the aerial propagation of the virus and subsequent spread of infection. We considered virus, a continuous variable, spreads diffusively in air and members of populations as discrete agents possessing one of the eight different states at a particular time. The transition from one state to another is probabilistic and age linked. Recognizing that population movement is a key aspect of infection spread, the model allows unbiased movement of agents. We benchmarked the model to recapture the temporal stochastic infection count data of the UK. The model investigates various key factors such as movement, infection susceptibility, new variants, recovery rate and duration, incubation period and vaccination on the infection propagation over time. Furthermore, the model was applied to capture the infection spread in Italy and France.
Collapse
Affiliation(s)
- Hafijur Rahaman
- School of Chemistry, University of Hyderabad, Central University PO, Hyderabad 500046, Telangana, India
| | - Debashis Barik
- School of Chemistry, University of Hyderabad, Central University PO, Hyderabad 500046, Telangana, India
| |
Collapse
|
7
|
Zou K, Hayashi M, Simon S, Eisenberg JN. Trade-off Between Quarantine Length and Compliance to Optimize COVID-19 Control. Epidemiology 2023; 34:589-600. [PMID: 37255265 PMCID: PMC10231873 DOI: 10.1097/ede.0000000000001619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 03/22/2023] [Indexed: 06/01/2023]
Abstract
BACKGROUND Guidance on COVID-19 quarantine duration is often based on the maximum observed incubation periods assuming perfect compliance. However, the impact of longer quarantines may be subject to diminishing returns; the largest benefits of quarantine occur over the first few days. Additionally, the financial and psychological burdens of quarantine may motivate increases in noncompliance behavior. METHODS We use a deterministic transmission model to identify the optimal length of quarantine to minimize transmission. We modeled the relation between noncompliance behavior and disease risk using a time-varying function of leaving quarantine based on studies from the literature. RESULTS The first few days in quarantine were more crucial to control the spread of COVID-19; even when compliance is high, a 10-day quarantine was as effective in lowering transmission as a 14-day quarantine; under certain noncompliance scenarios a 5-day quarantine may become nearly protective as 14-day quarantine. CONCLUSION Data to characterize compliance dynamics will help select optimal quarantine strategies that balance the trade-offs between social forces governing behavior and transmission dynamics.
Collapse
Affiliation(s)
- Kaiyue Zou
- From the Department of Epidemiology, Johns Hopkins University, Baltimore, MD
| | - Michael Hayashi
- Department of Epidemiology, University of Michigan, Ann Arbor, MI
| | - Sophia Simon
- Department of Environmental Science and Policy, University of California, Davis, Davis, CA
| | | |
Collapse
|
8
|
Ridenti MA, Teles LK, Maranhão A, Teles VK. Mathematical modeling and investigation on the role of demography and contact patterns in social distancing measures effectiveness in COVID-19 dissemination. MATHEMATICAL MEDICINE AND BIOLOGY : A JOURNAL OF THE IMA 2023; 40:73-95. [PMID: 36373595 DOI: 10.1093/imammb/dqac015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 08/30/2022] [Accepted: 10/03/2022] [Indexed: 11/15/2022]
Abstract
In this article, we investigate the importance of demography and contact patterns in determining the spread of COVID-19 and to the effectiveness of social distancing policies. We investigate these questions proposing an augmented epidemiological model with an age-structured model, with the population divided into susceptible (S), exposed (E), asymptomatic infectious (A), hospitalized (H), symptomatic infectious (I) and recovered individuals (R), to simulate COVID-19 dissemination. The simulations were carried out using six combinations of four types of isolation policies (work restrictions, isolation of the elderly, community distancing and school closures) and four representative fictitious countries generated over alternative demographic transition stage patterns (aged developed, developed, developing and least developed countries). We concluded that the basic reproduction number depends on the age profile and the contact patterns. The aged developed country had the lowest basic reproduction number ($R0=1.74$) due to the low contact rate among individuals, followed by the least developed country ($R0=2.00$), the developing country ($R0=2.43$) and the developed country ($R0=2.64$). Because of these differences in the basic reproduction numbers, the same intervention policies had higher efficiencies in the aged and least developed countries. Of all intervention policies, the reduction in work contacts and community distancing were the ones that produced the highest decrease in the $R0$ value, prevalence, maximum hospitalization demand and fatality rate. The isolation of the elderly was more effective in the developed and aged developed countries. The school closure was the less effective intervention policy, though its effects were not negligible in the least developed and developing countries.
Collapse
Affiliation(s)
- Marco A Ridenti
- Physics Department, Aeronautics Institute of Technology, Marechal Eduardo Gomes, 50 Vila das Acácias, 12228-900, SP, Brazil
| | - Lara K Teles
- Physics Department, Aeronautics Institute of Technology, Marechal Eduardo Gomes, 50 Vila das Acácias, 12228-900, SP, Brazil
| | - Alexandre Maranhão
- Physics Department, Aeronautics Institute of Technology, Marechal Eduardo Gomes, 50 Vila das Acácias, 12228-900, SP, Brazil
| | - Vladimir K Teles
- Sao Paulo School of Economics, FGV-SP, Rua Itapeva, 474 Bela Vista, 01332-000, SP, Brazil
| |
Collapse
|
9
|
Lamkiewicz K, Esquivel Gomez LR, Kühnert D, Marz M. Genome Structure, Life Cycle, and Taxonomy of Coronaviruses and the Evolution of SARS-CoV-2. Curr Top Microbiol Immunol 2023; 439:305-339. [PMID: 36592250 DOI: 10.1007/978-3-031-15640-3_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Coronaviruses have a broad host range and exhibit high zoonotic potential. In this chapter, we describe their genomic organization in terms of encoded proteins and provide an introduction to the peculiar discontinuous transcription mechanism. Further, we present evolutionary conserved genomic RNA secondary structure features, which are involved in the complex replication mechanism. With a focus on computational methods, we review the emergence of SARS-CoV-2 starting with the 2019 strains. In that context, we also discuss the debated hypothesis of whether SARS-CoV-2 was created in a laboratory. We focus on the molecular evolution and the epidemiological dynamics of this recently emerged pathogen and we explain how variants of concern are detected and characterised. COVID-19, the disease caused by SARS-CoV-2, can spread through different transmission routes and also depends on a number of risk factors. We describe how current computational models of viral epidemiology, or more specifically, phylodynamics, have facilitated and will continue to enable a better understanding of the epidemic dynamics of SARS-CoV-2.
Collapse
Affiliation(s)
- Kevin Lamkiewicz
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743, Jena, Germany
- European Virus Bioinformatics Center, Leutragraben 1, 07743, Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103, Leipzig, Germany
| | - Luis Roger Esquivel Gomez
- Transmission, Infection, Diversification and Evolution Group, Max Planck Institute for the Science of Human History, Kahlaische Straße 10, 07745, Jena, Germany
| | - Denise Kühnert
- Transmission, Infection, Diversification and Evolution Group, Max Planck Institute for the Science of Human History, Kahlaische Straße 10, 07745, Jena, Germany
- European Virus Bioinformatics Center, Leutragraben 1, 07743, Jena, Germany
| | - Manja Marz
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743, Jena, Germany.
- European Virus Bioinformatics Center, Leutragraben 1, 07743, Jena, Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103, Leipzig, Germany.
- FLI Leibniz Institute for Age Research, Beutenbergstraße 11, 07745, Jena, Germany.
| |
Collapse
|
10
|
Heneghan CJ, Jefferson T. Why COVID-19 modelling of progression and prevention fails to translate to the real-world. Adv Biol Regul 2022; 86:100914. [PMID: 36182545 PMCID: PMC9508693 DOI: 10.1016/j.jbior.2022.100914] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/25/2022] [Accepted: 09/06/2022] [Indexed: 01/25/2023]
Abstract
Mathematical models were used widely to inform policy during the COVID pandemic. However, there is a poor understanding of their limitations and how they influence decision-making. We used systematic review search methods to find early modelling studies that determined the reproduction number and analysed its use and application to interventions and policy in the UK. Up to March 2020, we found 42 reproduction number estimates (39 based on Chinese data: R0 range 2.1-6.47). Several biases affect the quality of modelling studies that are infrequently discussed, and many factors contribute to significant differences in the results of individual studies that go beyond chance. The sources of effect estimates incorporated into mathematical models are unclear. There is often a lack of a relationship between transmission estimates and the timing of imposed restrictions, which is further affected by the lag in reporting. Modelling studies lack basic evidence-based methods that aid their quality assessment, reporting and critical appraisal. If used judiciously, models may be helpful, especially if they openly present the uncertainties and use sensitivity analyses extensively, which need to consider and explicitly discuss the limitations of the evidence. However, until the methodological and ethical issues are resolved, predictive models should be used cautiously.
Collapse
|
11
|
Zakariaee SS, Naderi N, Rezaee D. Prognostic accuracy of visual lung damage computed tomography score for mortality prediction in patients with COVID-19 pneumonia: a systematic review and meta-analysis. EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [PMCID: PMC8907554 DOI: 10.1186/s43055-022-00741-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background Chest computed tomography (CT) findings provide great added value in characterizing the extent of disease and severity of pulmonary involvements. Chest CT severity score (CT-SS) could be considered as an appropriate prognostic factor for mortality prediction in patients with COVID-19 pneumonia. In this study, we performed a meta-analysis evaluating the prognostic accuracy of CT-SS for mortality prediction in patients with COVID-19 pneumonia. Methods A systematic search was conducted on Web of Science, PubMed, Embase, Scopus, and Google Scholar databases between December 2019 and September 2021. The meta-analysis was performed using the random-effects model, and sensitivity and specificity (with 95%CIs) of CT-SS were calculated using the study authors’ pre-specified threshold. Results Sensitivity estimates ranged from 0.32 to 1.00, and the pooled estimate of sensitivity was 0.67 [95%CI (0.59–0.75)]. Specificity estimates ranged from 0.53 to 0.95 and the pooled estimate of specificity was 0.79 [95%CI (0.74–0.84)]. Results of meta-regression analysis showed that radiologist experiences did not affect the sensitivity and specificity of CT-SS to predict mortality in COVID-19 patients (P = 0.314 and 0.283, respectively). The test for subgroup differences suggests that study location significantly modifies sensitivity and specificity of CT-SS to predict mortality in COVID-19 patients. The area under the summary receiver operator characteristic (ROC) curve was 0.8248. Conclusion Our results have shown that CT-SS has acceptable prognostic accuracy for mortality prediction in COVID-19 patients. This simple scoring method could help to improve the management of high-risk patients with COVID-19.
Collapse
|
12
|
Tan Y, Zhang Y, Cheng X, Zhou XH. Statistical inference using GLEaM model with spatial heterogeneity and correlation between regions. Sci Rep 2022; 12:16630. [PMID: 36198691 PMCID: PMC9534028 DOI: 10.1038/s41598-022-18775-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 08/18/2022] [Indexed: 11/09/2022] Open
Abstract
A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models.
Collapse
Affiliation(s)
- Yixuan Tan
- Department of Mathematics, Duke University, Durham, USA
| | - Yuan Zhang
- School of Statistics, Renmin University of China, Beijing, China
| | - Xiuyuan Cheng
- Department of Mathematics, Duke University, Durham, USA.
| | - Xiao-Hua Zhou
- Center for Statistical Sciences, Peking University, Beijing, China. .,Beijing International Center for Mathematical Research, Peking University, Beijing, China. .,Department of Biostatistics, School of Public Health, Peking University, Beijing, China.
| |
Collapse
|
13
|
Li X, Liang H. Blockchain solution benefits for controlling pandemics: Bottom-up decentralization, automation with real-time update, and immutability with privacy preservation. COMPUTERS & INDUSTRIAL ENGINEERING 2022; 172:108602. [PMID: 36061978 PMCID: PMC9420009 DOI: 10.1016/j.cie.2022.108602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 07/06/2022] [Accepted: 08/21/2022] [Indexed: 06/15/2023]
Abstract
The current COVID-19 pandemic has created turmoil around the world. To fight this ongoing global crisis and future ones, all stakeholders must collaborate and share timely and truthful information. This paper proposes a blockchain solution based on its inherent technological advantages. We posit that benefits can be derived from three unique blockchain features: bottom-up decentralization, automation with real-time update, and immutability with privacy preservation. A decentralized common platform provides easy access and increases participation in disease surveillance, which reduces the estimation errors of the compartmental model parameters. Automation with real-time update facilitates prompt detection and diagnosis, accurate contact tracing, and targeted mitigation and containment, achieving faster recovery and slower transmission. Being immutable while preserving privacy, the blockchain solution enhances respondents' willingness to truthfully report their contact history, avoiding false and erroneous data that will cause wrong estimates on pandemic transmission and recovery. Thus, the blockchain solution mitigates three types of risks: sample variance, delay, and bias. Through simulation, we quantify the value of the blockchain solution in these three aspects. Accordingly, we provide specific action plans based on our research findings: before building blockchain solutions for controlling COVID-19, governments and organizations can calculate the blockchain benefits and decide whether or not they should invest in such blockchain solutions by conducting a cost-benefit analysis.
Collapse
Affiliation(s)
- Xiaoming Li
- Department of Business Administration, Tennessee State University, 330 10 Ave. N, Nashville, TN 37203, USA
| | - Huigang Liang
- Department of Business Information and Technology, University of Memphis, 3675 Central Avenue, Memphis, TN 38152, USA
| |
Collapse
|
14
|
Novakovic A, Marshall AH. The CP-ABM approach for modelling COVID-19 infection dynamics and quantifying the effects of non-pharmaceutical interventions. PATTERN RECOGNITION 2022; 130:108790. [PMID: 35601479 PMCID: PMC9107333 DOI: 10.1016/j.patcog.2022.108790] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/04/2022] [Accepted: 05/11/2022] [Indexed: 05/16/2023]
Abstract
The motivation for this research is to develop an approach that reliably captures the disease dynamics of COVID-19 for an entire population in order to identify the key events driving change in the epidemic through accurate estimation of daily COVID-19 cases. This has been achieved through the new CP-ABM approach which uniquely incorporates Change Point detection into an Agent Based Model taking advantage of genetic algorithms for calibration and an efficient infection centric procedure for computational efficiency. The CP-ABM is applied to the Northern Ireland population where it successfully captures patterns in COVID-19 infection dynamics over both waves of the pandemic and quantifies the significant effects of non-pharmaceutical interventions (NPI) on a national level for lockdowns and mask wearing. To our knowledge, there is no other approach to date that has captured NPI effectiveness and infection spreading dynamics for both waves of the COVID-19 pandemic for an entire country population.
Collapse
Affiliation(s)
- Aleksandar Novakovic
- School of Mathematics and Physics, Queen's University Belfast, University Road, Belfast, BT7 1NN, Northern Ireland, United Kingdom
- Joint Research Centre in AI for Health and Wellness, Faculty of Business and IT, Ontario Tech University, 2000 Simcoe Street North, Oshawa, Ontario L1G 0C5, Canada
| | - Adele H Marshall
- School of Mathematics and Physics, Queen's University Belfast, University Road, Belfast, BT7 1NN, Northern Ireland, United Kingdom
- Joint Research Centre in AI for Health and Wellness, Faculty of Business and IT, Ontario Tech University, 2000 Simcoe Street North, Oshawa, Ontario L1G 0C5, Canada
| |
Collapse
|
15
|
Hilton J, Riley H, Pellis L, Aziza R, Brand SPC, K. Kombe I, Ojal J, Parisi A, Keeling MJ, Nokes DJ, Manson-Sawko R, House T. A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic. PLoS Comput Biol 2022; 18:e1010390. [PMID: 36067212 PMCID: PMC9481179 DOI: 10.1371/journal.pcbi.1010390] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 09/16/2022] [Accepted: 07/14/2022] [Indexed: 11/18/2022] Open
Abstract
The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts.
Collapse
Affiliation(s)
- Joe Hilton
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
- * E-mail:
| | - Heather Riley
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- The Alan Turing Institute for Data Science and Artificial Intelligence, London, United Kingdom
| | - Rabia Aziza
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
| | - Samuel P. C. Brand
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
- Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya
| | - Ivy K. Kombe
- Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya
| | - John Ojal
- Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Andrea Parisi
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
| | - Matt J. Keeling
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
| | - D. James Nokes
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
- Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya
| | | | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- The Alan Turing Institute for Data Science and Artificial Intelligence, London, United Kingdom
- IBM Research Europe, Hartree Centre, Daresbury, United Kingdom
| |
Collapse
|
16
|
Ortuso F, Mercatelli D, Guzzi PH, Giorgi FM. Structural genetics of circulating variants affecting the SARS-CoV-2 spike/human ACE2 complex. J Biomol Struct Dyn 2022; 40:6545-6555. [PMID: 33583326 PMCID: PMC7885719 DOI: 10.1080/07391102.2021.1886175] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 02/01/2021] [Indexed: 01/08/2023]
Abstract
SARS-CoV-2 entry in human cells is mediated by the interaction between the viral Spike protein and the human ACE2 receptor. This mechanism evolved from the ancestor bat coronavirus and is currently one of the main targets for antiviral strategies. However, there currently exist several Spike protein variants in the SARS-CoV-2 population as the result of mutations, and it is unclear if these variants may exert a specific effect on the affinity with ACE2 which, in turn, is also characterized by multiple alleles in the human population. In the current study, the GBPM analysis, originally developed for highlighting host-guest interaction features, has been applied to define the key amino acids responsible for the Spike/ACE2 molecular recognition, using four different crystallographic structures. Then, we intersected these structural results with the current mutational status, based on more than 295,000 sequenced cases, in the SARS-CoV-2 population. We identified several Spike mutations interacting with ACE2 and mutated in at least 20 distinct patients: S477N, N439K, N501Y, Y453F, E484K, K417N, S477I and G476S. Among these, mutation N501Y in particular is one of the events characterizing SARS-CoV-2 lineage B.1.1.7, which has recently risen in frequency in Europe. We also identified five ACE2 rare variants that may affect interaction with Spike and susceptibility to infection: S19P, E37K, M82I, E329G and G352V.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Francesco Ortuso
- Department of Health Sciences, University “Magna Graecia” of Catanzaro, Catanzaro, Italy
- Net4Science srl, c/o University “Magna Graecia” of Catanzaro, Catanzaro, Italy
| | - Daniele Mercatelli
- Department of Surgical and Medical Sciences, University “Magna Graecia” of Catanzaro, Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University “Magna Graecia” of Catanzaro, Catanzaro, Italy
| | | |
Collapse
|
17
|
Estimating the basic reproduction number at the beginning of an outbreak. PLoS One 2022; 17:e0269306. [PMID: 35714080 PMCID: PMC9205483 DOI: 10.1371/journal.pone.0269306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 05/18/2022] [Indexed: 11/19/2022] Open
Abstract
We compare several popular methods of estimating the basic reproduction number, R0, focusing on the early stages of an epidemic, and assuming weekly reports of new infecteds. We study the situation when data is generated by one of three standard epidemiological compartmental models: SIR, SEIR, and SEAIR; and examine the sensitivity of the estimators to the model structure. As some methods are developed assuming specific epidemiological models, our work adds a study of their performance in both a well-specified (data generating model and method model are the same) and miss-specified (data generating model and method model differ) settings. We also study R0 estimation using Canadian COVID-19 case report data. In this study we focus on examples of influenza and COVID-19, though the general approach is easily extendable to other scenarios. Our simulation study reveals that some estimation methods tend to work better than others, however, no singular best method was clearly detected. In the discussion, we provide recommendations for practitioners based on our results.
Collapse
|
18
|
Association of national and regional lockdowns with COVID-19 infection rates in Pune, India. Sci Rep 2022; 12:10446. [PMID: 35729206 PMCID: PMC9212203 DOI: 10.1038/s41598-022-14674-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 05/16/2022] [Indexed: 12/18/2022] Open
Abstract
Assessing the impact of lockdowns on COVID-19 incidence may provide important lessons for management of pandemic in resource-limited settings. We examined growth of incident confirmed COVID-19 patients before, during and after lockdowns during the first wave in Pune city that reported the largest COVID-19 burden at the peak of the pandemic. Using anonymized individual-level data captured by Pune’s public health surveillance program between February 1st and September 15th 2020, we assessed weekly incident COVID-19 patients, infection rates, and epidemic curves by lockdown status (overall and by sex, age, and population density) and modelled the natural epidemic using the compartmental model. Effect of lockdown on incident patients was assessed using multilevel Poisson regression. We used geospatial mapping to characterize regional spread. Of 241,629 persons tested for SARS-CoV-2, 64,526 (26%) were positive, contributing to an overall rate of COVID-19 disease of 267·0 (95% CI 265·3–268·8) per 1000 persons. The median age of COVID-19 patients was 36 (interquartile range [IQR] 25–50) years, 36,180 (56%) were male, and 9414 (15%) were children < 18 years. Epidemic curves and geospatial mapping showed delayed peak of the patients by approximately 8 weeks during the lockdowns as compared to modelled natural epidemic. Compared to a subsequent unlocking period, incident COVID-19 patients were 43% lower (IRR 0·57, 95% CI 0·53–0·62) during India’s nationwide lockdown and were 22% lower (IRR 0·78, 95% CI 0.73–0.84) during Pune’s regional lockdown and was uniform across age groups and population densities. Both national and regional lockdowns slowed the COVID-19 infection rates in population dense, urban region in India, underscoring its impact on COVID-19 control efforts.
Collapse
|
19
|
Alser M, Kim JS, Almadhoun Alserr N, Tell SW, Mutlu O. COVIDHunter: COVID-19 Pandemic Wave Prediction and Mitigation via Seasonality Aware Modeling. Front Public Health 2022; 10:877621. [PMID: 35784219 PMCID: PMC9247408 DOI: 10.3389/fpubh.2022.877621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/20/2022] [Indexed: 11/25/2022] Open
Abstract
Early detection and isolation of COVID-19 patients are essential for successful implementation of mitigation strategies and eventually curbing the disease spread. With a limited number of daily COVID-19 tests performed in every country, simulating the COVID-19 spread along with the potential effect of each mitigation strategy currently remains one of the most effective ways in managing the healthcare system and guiding policy-makers. We introduce COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that evaluates the current mitigation measures that are applied to a region, predicts COVID-19 statistics (the daily number of cases, hospitalizations, and deaths), and provides suggestions on what strength the upcoming mitigation measure should be. The key idea of COVIDHunter is to quantify the spread of COVID-19 in a geographical region by simulating the average number of new infections caused by an infected person considering the effect of external factors, such as environmental conditions (e.g., climate, temperature, humidity), different variants of concern, vaccination rate, and mitigation measures. Using Switzerland as a case study, COVIDHunter estimates that we are experiencing a deadly new wave that will peak on 26 January 2022, which is very similar in numbers to the wave we had in February 2020. The policy-makers have only one choice that is to increase the strength of the currently applied mitigation measures for 30 days. Unlike existing models, the COVIDHunter model accurately monitors and predicts the daily number of cases, hospitalizations, and deaths due to COVID-19. Our model is flexible to configure and simple to modify for modeling different scenarios under different environmental conditions and mitigation measures. We release the source code of the COVIDHunter implementation at https://github.com/CMU-SAFARI/COVIDHunter and show how to flexibly configure our model for any scenario and easily extend it for different measures and conditions than we account for.
Collapse
|
20
|
Zuo C, Meng Z, Zhu F, Zheng Y, Ling Y. Assessing Vaccination Prioritization Strategies for COVID-19 in South Africa Based on Age-Specific Compartment Model. Front Public Health 2022; 10:876551. [PMID: 35784231 PMCID: PMC9240634 DOI: 10.3389/fpubh.2022.876551] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/11/2022] [Indexed: 02/05/2023] Open
Abstract
The vaccines are considered to be important for the prevention and control of coronavirus disease 2019 (COVID-19). However, considering the limited vaccine supply within an extended period of time in many countries where COVID-19 vaccine booster shot are taken and new vaccines are developed to suppress the mutation of virus, designing an effective vaccination strategy is extremely important to reduce the number of deaths and infections. Then, the simulations were implemented to study the relative reduction in morbidity and mortality of vaccine allocation strategies by using the proposed model and actual South Africa's epidemiological data. Our results indicated that in light of South Africa's demographics, vaccinating older age groups (>60 years) largely reduced the cumulative deaths and the "0-20 first" strategy was the most effective way to reduce confirmed cases. In addition, "21-30 first" and "31-40 first" strategies have also had a positive effect. Partial vaccination resulted in lower numbers of infections and deaths under different control measures compared with full vaccination in low-income countries. In addition, we analyzed the sensitivity of daily testing volume and infection rate, which are critical to optimize vaccine allocation. However, comprehensive reduction in infections was mainly affected by the vaccine proportion of the target age group. An increase in the proportion of vaccines given priority to "0-20" groups always had a favorable effect, and the prioritizing vaccine allocation among the "60+" age group with 60% of the total amount of vaccine consistently resulted in the greatest reduction in deaths. Meanwhile, we observed a significant distinction in the effect of COVID-19 vaccine allocation policies under varying priority strategies on relative reductions in the effective reproduction number. Our results could help evaluate to control measures performance and the improvement of vaccine allocation strategy for COVID-19 epidemic.
Collapse
Affiliation(s)
- Chao Zuo
- School of Management Engineering and E-Commerce, Zhejiang Gongshang University, Hangzhou, China
| | | | | | | | - Yuting Ling
- School of Management Engineering and E-Commerce, Zhejiang Gongshang University, Hangzhou, China
| |
Collapse
|
21
|
Wu JW, Jiao XK, Du XH, Jiao ZT, Liang ZR, Pang MF, Ji HR, Cheng ZD, Cai KN, Qi XP. Assessment of the Benefits of Targeted Interventions for Pandemic Control in China Based on Machine Learning Method and Web Service for COVID-19 Policy Simulation. BIOMEDICAL AND ENVIRONMENTAL SCIENCES : BES 2022; 35:412-418. [PMID: 35676812 PMCID: PMC9187338 DOI: 10.3967/bes2022.057] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 03/29/2022] [Indexed: 06/15/2023]
Abstract
Taking the Chinese city of Xiamen as an example, simulation and quantitative analysis were performed on the transmissions of the Coronavirus Disease 2019 (COVID-19) and the influence of intervention combinations to assist policymakers in the preparation of targeted response measures. A machine learning model was built to estimate the effectiveness of interventions and simulate transmission in different scenarios. The comparison was conducted between simulated and real cases in Xiamen. A web interface with adjustable parameters, including choice of intervention measures, intervention weights, vaccination, and viral variants, was designed for users to run the simulation. The total case number was set as the outcome. The cumulative number was 4,614,641 without restrictions and 78 under the strictest intervention set. Simulation with the parameters closest to the real situation of the Xiamen outbreak was performed to verify the accuracy and reliability of the model. The simulation model generated a duration of 52 days before the daily cases dropped to zero and the final cumulative case number of 200, which were 25 more days and 36 fewer cases than the real situation, respectively. Targeted interventions could benefit the prevention and control of COVID-19 outbreak while safeguarding public health and mitigating impacts on people's livelihood.
Collapse
Affiliation(s)
- Jie Wen Wu
- Center for Global Public Health, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Xiao Kang Jiao
- Yidu Cloud (Beijing) Technology Co., Ltd., Beijing 100083, China
| | - Xin Hui Du
- Yidu Cloud (Beijing) Technology Co., Ltd., Beijing 100083, China
| | - Zeng Tao Jiao
- Yidu Cloud (Beijing) Technology Co., Ltd., Beijing 100083, China
| | - Zuo Ru Liang
- Center for Global Public Health, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Ming Fan Pang
- Center for Global Public Health, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Han Ran Ji
- Center for Global Public Health, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Zhi Da Cheng
- Yidu Cloud (Beijing) Technology Co., Ltd., Beijing 100083, China
| | - Kang Ning Cai
- Yidu Cloud (Beijing) Technology Co., Ltd., Beijing 100083, China
| | - Xiao Peng Qi
- Center for Global Public Health, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| |
Collapse
|
22
|
Gomes MGM, Ferreira MU, Corder RM, King JG, Souto-Maior C, Penha-Gonçalves C, Gonçalves G, Chikina M, Pegden W, Aguas R. Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold. J Theor Biol 2022; 540:111063. [PMID: 35189135 PMCID: PMC8855661 DOI: 10.1016/j.jtbi.2022.111063] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 12/21/2022]
Abstract
Individual variation in susceptibility and exposure is subject to selection by natural infection, accelerating the acquisition of immunity, and reducing herd immunity thresholds and epidemic final sizes. This is a manifestation of a wider population phenomenon known as "frailty variation". Despite theoretical understanding, public health policies continue to be guided by mathematical models that leave out considerable variation and as a result inflate projected disease burdens and overestimate the impact of interventions. Here we focus on trajectories of the coronavirus disease (COVID-19) pandemic in England and Scotland until November 2021. We fit models to series of daily deaths and infer relevant epidemiological parameters, including coefficients of variation and effects of non-pharmaceutical interventions which we find in agreement with independent empirical estimates based on contact surveys. Our estimates are robust to whether the analysed data series encompass one or two pandemic waves and enable projections compatible with subsequent dynamics. We conclude that vaccination programmes may have contributed modestly to the acquisition of herd immunity in populations with high levels of pre-existing naturally acquired immunity, while being crucial to protect vulnerable individuals from severe outcomes as the virus becomes endemic.
Collapse
Affiliation(s)
- M Gabriela M Gomes
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK; Centro de Matemática e Aplicações, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
| | - Marcelo U Ferreira
- Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil; Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, Nova University of Lisbon, Lisbon, Portugal
| | - Rodrigo M Corder
- Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Jessica G King
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Caetano Souto-Maior
- Laboratory of Systems Genetics, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Guilherme Gonçalves
- Unidade Multidisciplinar de Investigação Biomédica, Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal
| | - Maria Chikina
- Department of Computational and Systems Biology, University of Pittsburgh, Pittburgh, PA, USA
| | - Wesley Pegden
- Department of Mathematical Sciences, Carnegie Mellon University, Pittburgh, PA, USA
| | - Ricardo Aguas
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| |
Collapse
|
23
|
Kasis A, Timotheou S, Monshizadeh N, Polycarpou M. Optimal intervention strategies to mitigate the COVID-19 pandemic effects. Sci Rep 2022; 12:6124. [PMID: 35414076 PMCID: PMC9004223 DOI: 10.1038/s41598-022-09857-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 03/22/2022] [Indexed: 12/14/2022] Open
Abstract
Governments across the world are currently facing the task of selecting suitable intervention strategies to cope with the effects of the COVID-19 pandemic. This is a highly challenging task, since harsh measures may result in economic collapse while a relaxed strategy might lead to a high death toll. Motivated by this, we consider the problem of forming intervention strategies to mitigate the impact of the COVID-19 pandemic that optimize the trade-off between the number of deceases and the socio-economic costs. We demonstrate that the healthcare capacity and the testing rate highly affect the optimal intervention strategies. Moreover, we propose an approach that enables practical strategies, with a small number of policies and policy changes, that are close to optimal. In particular, we provide tools to decide which policies should be implemented and when should a government change to a different policy. Finally, we consider how the presented results are affected by uncertainty in the initial reproduction number and infection fatality rate and demonstrate that parametric uncertainty has a more substantial effect when stricter strategies are adopted.
Collapse
Affiliation(s)
- Andreas Kasis
- Department of Electrical and Computer Engineering, KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus.
| | - Stelios Timotheou
- Department of Electrical and Computer Engineering, KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus
| | - Nima Monshizadeh
- Engineering and Technology Institute, University of Groningen, Nijenborgh 4, 9747AG, Groningen, The Netherlands
| | - Marios Polycarpou
- Department of Electrical and Computer Engineering, KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus
| |
Collapse
|
24
|
Yoo DS, Hwang M, Chun BC, Kim SJ, Son M, Seo NK, Ki M. Socioeconomic Inequalities in COVID-19 Incidence During Different Epidemic Phases in South Korea. Front Med (Lausanne) 2022; 9:840685. [PMID: 35345769 PMCID: PMC8957264 DOI: 10.3389/fmed.2022.840685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 01/31/2022] [Indexed: 12/02/2022] Open
Abstract
Objective Area-level socioeconomic status (SES) is associated with coronavirus disease 2019 (COVID-19) incidence. However, the underlying mechanism of the association is context-specific, and the choice of measure is still important. We aimed to evaluate the socioeconomic gradient regarding COVID-19 incidence in Korea based on several area-level SES measures. Methods COVID-19 incidence and area-level SES measures across 229 Korean municipalities were derived from various administrative regional data collected between 2015 and 2020. The Bayesian negative binomial model with a spatial autocorrelation term was used to estimate the incidence rate ratio (IRR) and relative index of inequality (RII) of each SES factor, with adjustment for covariates. The magnitude of association was compared between two epidemic phases: a low phase (<100 daily cases, from May 6 to August 14, 2020) and a rebound phase (>100 daily cases, from August 15 to December 31, 2020). Results Area-level socioeconomic inequalities in COVID-19 incidence between the most disadvantaged region and the least disadvantaged region were observed for nonemployment rates [RII = 1.40, 95% credible interval (Crl) = 1.01–1.95] and basic livelihood security recipients (RII = 2.66, 95% Crl = 1.12–5.97), but were not observed for other measures in the low phase. However, the magnitude of the inequalities of these SES variables diminished in the rebound phase. A higher area-level mobility showed a higher risk of COVID-19 incidence in both the low (IRR = 1.67, 95% Crl = 1.26–2.17) and rebound phases (IRR = 1.28, 95% Crl = 1.14–1.44). When SES and mobility measures were simultaneously adjusted, the association of SES with COVID-19 incidence remained significant but only in the low phase, indicating they were mutually independent in the low phase. Conclusion The level of basic livelihood benefit recipients and nonemployment rate showed social stratification of COVID-19 incidence in Korea. Explanation of area-level inequalities in COVID-19 incidence may not be derived only from mobility differences in Korea but, instead, from the country's own context.
Collapse
Affiliation(s)
- Dae-Sung Yoo
- Department of Public Health, Korea University Graduate School, Seoul, South Korea.,Veterinary Epidemiology Division, Animal and Plant Quarantine Agency, Gimcheon, South Korea
| | - Minji Hwang
- Department of Public Health, Korea University Graduate School, Seoul, South Korea.,BK21FOUR R&E Center for Learning Health Systems, Korea University, Seoul, South Korea
| | - Byung Chul Chun
- Department of Public Health, Korea University Graduate School, Seoul, South Korea.,BK21FOUR R&E Center for Learning Health Systems, Korea University, Seoul, South Korea.,Department of Preventive Medicine, College of Medicine, Korea University, Seoul, South Korea
| | - Su Jin Kim
- Department of Emergency Medicine, College of Medicine, Korea University, Seoul, South Korea
| | - Mia Son
- Department of Preventive Medicine, School of Medicine, Kangwon National University, Chuncheon, South Korea
| | - Nam-Kyu Seo
- Department of Non-Benefits Management, National Health Insurance Service/Health Insurance Policy Research Institute, Wonju, South Korea
| | - Myung Ki
- Department of Public Health, Korea University Graduate School, Seoul, South Korea.,BK21FOUR R&E Center for Learning Health Systems, Korea University, Seoul, South Korea.,Department of Preventive Medicine, College of Medicine, Korea University, Seoul, South Korea
| |
Collapse
|
25
|
Gomes MGM, Ferreira MU, Corder RM, King JG, Souto-Maior C, Penha-Gonçalves C, Gonçalves G, Chikina M, Pegden W, Aguas R. Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2020.04.27.20081893. [PMID: 32511451 PMCID: PMC7239079 DOI: 10.1101/2020.04.27.20081893] [Citation(s) in RCA: 128] [Impact Index Per Article: 64.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Individual variation in susceptibility and exposure is subject to selection by natural infection, accelerating the acquisition of immunity, and reducing herd immunity thresholds and epidemic final sizes. This is a manifestation of a wider population phenomenon known as "frailty variation". Despite theoretical understanding, public health policies continue to be guided by mathematical models that leave out considerable variation and as a result inflate projected disease burdens and overestimate the impact of interventions. Here we focus on trajectories of the coronavirus disease (COVID-19) pandemic in England and Scotland until November 2021. We fit models to series of daily deaths and infer relevant epidemiological parameters, including coefficients of variation and effects of non-pharmaceutical interventions which we find in agreement with independent empirical estimates based on contact surveys. Our estimates are robust to whether the analysed data series encompass one or two pandemic waves and enable projections compatible with subsequent dynamics. We conclude that vaccination programmes may have contributed modestly to the acquisition of herd immunity in populations with high levels of pre-existing naturally acquired immunity, while being critical to protect vulnerable individuals from severe outcomes as the virus becomes endemic.
Collapse
Affiliation(s)
- M Gabriela M Gomes
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
- Centro de Matemática e Aplicações, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
| | - Marcelo U Ferreira
- Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
- Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, Nova University of Lisbon, Lisbon, Portugal
| | - Rodrigo M Corder
- Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Jessica G King
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Caetano Souto-Maior
- Laboratory of Systems Genetics, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Guilherme Gonçalves
- Unidade Multidisciplinar de Investigação Biomédica, Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal
| | - Maria Chikina
- Department of Computational and Systems Biology, University of Pittsburgh, Pittburgh, PA, USA
| | - Wesley Pegden
- Department of Mathematical Sciences, Carnegie Mellon University, , Pittburgh" , PA, USA
| | - Ricardo Aguas
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| |
Collapse
|
26
|
Boraldi F, Lofaro FD, Cossarizza A, Quaglino D. The "Elastic Perspective" of SARS-CoV-2 Infection and the Role of Intrinsic and Extrinsic Factors. Int J Mol Sci 2022; 23:ijms23031559. [PMID: 35163482 PMCID: PMC8835950 DOI: 10.3390/ijms23031559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/20/2022] [Accepted: 01/28/2022] [Indexed: 02/07/2023] Open
Abstract
Elastin represents the structural component of the extracellular matrix providing elastic recoil to tissues such as skin, blood vessels and lungs. Elastogenic cells secrete soluble tropoelastin monomers into the extracellular space where these monomers associate with other matrix proteins (e.g., microfibrils and glycoproteins) and are crosslinked by lysyl oxidase to form insoluble fibres. Once elastic fibres are formed, they are very stable, highly resistant to degradation and have an almost negligible turnover. However, there are circumstances, mainly related to inflammatory conditions, where increased proteolytic degradation of elastic fibres may lead to consequences of major clinical relevance. In severely affected COVID-19 patients, for instance, the massive recruitment and activation of neutrophils is responsible for the profuse release of elastases and other proteolytic enzymes which cause the irreversible degradation of elastic fibres. Within the lungs, destruction of the elastic network may lead to the permanent impairment of pulmonary function, thus suggesting that elastases can be a promising target to preserve the elastic component in COVID-19 patients. Moreover, intrinsic and extrinsic factors additionally contributing to damaging the elastic component and to increasing the spread and severity of SARS-CoV-2 infection are reviewed.
Collapse
Affiliation(s)
- Federica Boraldi
- Department of Life Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy; (F.B.); (F.D.L.)
| | - Francesco Demetrio Lofaro
- Department of Life Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy; (F.B.); (F.D.L.)
| | - Andrea Cossarizza
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, 41125 Modena, Italy;
| | - Daniela Quaglino
- Department of Life Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy; (F.B.); (F.D.L.)
- Correspondence:
| |
Collapse
|
27
|
A COVINDEX based on a GAM beta regression model with an application to the COVID-19 pandemic in Italy. STAT METHOD APPL-GER 2022; 31:881-900. [PMID: 35035344 PMCID: PMC8743080 DOI: 10.1007/s10260-021-00617-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2021] [Indexed: 11/04/2022]
Abstract
Detecting changes in COVID-19 disease transmission over time is a key indicator of epidemic growth. Near real-time monitoring of the pandemic growth is crucial for policy makers and public health officials who need to make informed decisions about whether to enforce lockdowns or allow certain activities. The effective reproduction number \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$R_t$$\end{document}Rt is the standard index used in many countries for this goal. However, it is known that due to the delays between infection and case registration, its use for decision making is somewhat limited. In this paper a near real-time COVINDEX is proposed for monitoring the evolution of the pandemic. The index is computed from predictions obtained from a GAM beta regression for modelling the test positive rate as a function of time. The proposal is illustrated using data on COVID-19 pandemic in Italy and compared with \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$R_t$$\end{document}Rt. A simple chart is also proposed for monitoring local and national outbreaks by policy makers and public health officials.
Collapse
|
28
|
Wu JT, Mei S, Luo S, Leung K, Liu D, Lv Q, Liu J, Li Y, Prem K, Jit M, Weng J, Feng T, Zheng X, Leung GM. A global assessment of the impact of school closure in reducing COVID-19 spread. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210124. [PMID: 34802277 PMCID: PMC8607143 DOI: 10.1098/rsta.2021.0124] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Prolonged school closure has been adopted worldwide to control COVID-19. Indeed, UN Educational, Scientific and Cultural Organization figures show that two-thirds of an academic year was lost on average worldwide due to COVID-19 school closures. Such pre-emptive implementation was predicated on the premise that school children are a core group for COVID-19 transmission. Using surveillance data from the Chinese cities of Shenzhen and Anqing together, we inferred that compared with the elderly aged 60 and over, children aged 18 and under and adults aged 19-59 were 75% and 32% less susceptible to infection, respectively. Using transmission models parametrized with synthetic contact matrices for 177 jurisdictions around the world, we showed that the lower susceptibility of school children substantially limited the effectiveness of school closure in reducing COVID-19 transmissibility. Our results, together with recent findings that clinical severity of COVID-19 in children is lower, suggest that school closure may not be ideal as a sustained, primary intervention for controlling COVID-19. This article is part of the theme issue 'Data science approach to infectious disease surveillance'.
Collapse
Affiliation(s)
- Joseph T. Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, New Territories, Hong Kong
| | - Shujiang Mei
- Department of Communicable Diseases Control and Prevention, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, People's Republic of China
| | - Sihui Luo
- The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, People's Republic of China
- Clinical Research Hospital (Hefei) of Chinese Academy of Science, Hefei, People's Republic of China
| | - Kathy Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, New Territories, Hong Kong
| | - Di Liu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, New Territories, Hong Kong
| | - Qiuying Lv
- Department of Communicable Diseases Control and Prevention, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, People's Republic of China
| | - Jian Liu
- Anqing Hospital Affiliated to Anhui Medical University (Anqing Municipal Hospital), Anqing, People's Republic of China
| | - Yuan Li
- Department of Communicable Diseases Control and Prevention, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, People's Republic of China
| | - Kiesha Prem
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Mark Jit
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, New Territories, Hong Kong
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Jianping Weng
- The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, People's Republic of China
- Clinical Research Hospital (Hefei) of Chinese Academy of Science, Hefei, People's Republic of China
| | - Tiejian Feng
- Department of Communicable Diseases Control and Prevention, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, People's Republic of China
| | - Xueying Zheng
- The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, People's Republic of China
- Clinical Research Hospital (Hefei) of Chinese Academy of Science, Hefei, People's Republic of China
| | - Gabriel M. Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science Park, New Territories, Hong Kong
| |
Collapse
|
29
|
Stockdale JE, Anderson SC, Edwards AM, Iyaniwura SA, Mulberry N, Otterstatter MC, Janjua NZ, Coombs D, Colijn C, Irvine MA. Quantifying transmissibility of SARS-CoV-2 and impact of intervention within long-term healthcare facilities. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211710. [PMID: 35242355 PMCID: PMC8753163 DOI: 10.1098/rsos.211710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 12/02/2021] [Indexed: 05/03/2023]
Abstract
Estimates of the basic reproduction number (R 0) for COVID-19 are particularly variable in the context of transmission within locations such as long-term healthcare (LTHC) facilities. We sought to characterize the heterogeneity of R 0 across known outbreaks within these facilities. We used a unique comprehensive dataset of all outbreaks that occurred within LTHC facilities in British Columbia, Canada as of 21 September 2020. We estimated R 0 in 18 LTHC outbreaks with a novel Bayesian hierarchical dynamic model of susceptible, exposed, infected and recovered individuals, incorporating heterogeneity of R 0 between facilities. We further compared these estimates to those obtained with standard methods that use the exponential growth rate and maximum likelihood. The total size of outbreaks varied dramatically, with range of attack rates 2%-86%. The Bayesian analysis provided an overall estimate of R 0 = 2.51 (90% credible interval 0.47-9.0), with individual facility estimates ranging between 0.56 and 9.17. Uncertainty in these estimates was more constrained than standard methods, particularly for smaller outbreaks informed by the population-level model. We further estimated that intervention led to 61% (52%-69%) of all potential cases being averted within the LTHC facilities, or 75% (68%-79%) when using a model with multi-level intervention effect. Understanding of transmission risks and impact of intervention are essential in planning during the ongoing global pandemic, particularly in high-risk environments such as LTHC facilities.
Collapse
Affiliation(s)
| | - Sean C. Anderson
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
- Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, Canada
| | - Andrew M. Edwards
- Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, Canada
- Department of Biology, University of Victoria, Victoria, Canada
| | - Sarafa A. Iyaniwura
- Department of Mathematics and Institute of Applied Mathematics, University of British Columbia, Vancouver, Canada
- British Columbia Centre for Disease Control, Vancouver, Canada
| | - Nicola Mulberry
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
| | - Michael C. Otterstatter
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
- British Columbia Centre for Disease Control, Vancouver, Canada
| | - Naveed Z. Janjua
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
- British Columbia Centre for Disease Control, Vancouver, Canada
| | - Daniel Coombs
- Department of Mathematics and Institute of Applied Mathematics, University of British Columbia, Vancouver, Canada
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
| | - Michael A. Irvine
- Faculty of Health Sciences, Simon Fraser University, Burnaby, Canada
- British Columbia Centre for Disease Control, Vancouver, Canada
- British Columbia Children’s Hospital Research Institute, Vancouver, Canada
| |
Collapse
|
30
|
The impact of non-pharmaceutical interventions during COVID-19 pandemic on three children infectious diseases in China. J Infect 2021; 84:e13-e15. [PMID: 34953908 PMCID: PMC8694816 DOI: 10.1016/j.jinf.2021.12.023] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 12/19/2021] [Indexed: 12/28/2022]
|
31
|
Zardini A, Galli M, Tirani M, Cereda D, Manica M, Trentini F, Guzzetta G, Marziano V, Piccarreta R, Melegaro A, Ajelli M, Poletti P, Merler S. A quantitative assessment of epidemiological parameters required to investigate COVID-19 burden. Epidemics 2021; 37:100530. [PMID: 34826786 PMCID: PMC8595250 DOI: 10.1016/j.epidem.2021.100530] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 10/07/2021] [Accepted: 11/12/2021] [Indexed: 01/08/2023] Open
Abstract
Solid estimates describing the clinical course of SARS-CoV-2 infections are still lacking due to under-ascertainment of asymptomatic and mild-disease cases. In this work, we quantify age-specific probabilities of transitions between stages defining the natural history of SARS-CoV-2 infection from 1965 SARS-CoV-2 positive individuals identified in Italy between March and April 2020 among contacts of confirmed cases. Infected contacts of cases were confirmed via RT-PCR tests as part of contact tracing activities or retrospectively via IgG serological tests and followed-up for symptoms and clinical outcomes. In addition, we provide estimates of time intervals between key events defining the clinical progression of cases as obtained from a larger sample, consisting of 95,371 infections ascertained between February and July 2020. We found that being older than 60 years of age was associated with a 39.9% (95%CI: 36.2–43.6%) likelihood of developing respiratory symptoms or fever ≥ 37.5 °C after SARS-CoV-2 infection; the 22.3% (95%CI: 19.3–25.6%) of the infections in this age group required hospital care and the 1% (95%CI: 0.4–2.1%) were admitted to an intensive care unit (ICU). The corresponding proportions in individuals younger than 60 years were estimated at 27.9% (95%CI: 25.4–30.4%), 8.8% (95%CI: 7.3–10.5%) and 0.4% (95%CI: 0.1–0.9%), respectively. The infection fatality ratio (IFR) ranged from 0.2% (95%CI: 0.0–0.6%) in individuals younger than 60 years to 12.3% (95%CI: 6.9–19.7%) for those aged 80 years or more; the case fatality ratio (CFR) in these two age classes was 0.6% (95%CI: 0.1–2%) and 19.2% (95%CI: 10.9–30.1%), respectively. The median length of stay in hospital was 10 (IQR: 3–21) days; the length of stay in ICU was 11 (IQR: 6–19) days. The obtained estimates provide insights into the epidemiology of COVID-19 and could be instrumental to refine mathematical modeling work supporting public health decisions.
Collapse
Affiliation(s)
| | - Margherita Galli
- Bruno Kessler Foundation, Trento, Italy; Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
| | - Marcello Tirani
- Directorate General for Health, Lombardy Region, Milan, Italy; Health Protection Agency of the Metropolitan Area of Milan, Milano, Italy
| | - Danilo Cereda
- Directorate General for Health, Lombardy Region, Milan, Italy
| | | | - Filippo Trentini
- Bruno Kessler Foundation, Trento, Italy; Dondena Centre for Research on Social Dynamics and Public Policy, and CovidCrisisLab, Bocconi University, Milan, Italy
| | | | | | - Raffaella Piccarreta
- Dondena Centre for Research on Social Dynamics and Public Policy, and CovidCrisisLab, Bocconi University, Milan, Italy; Department of Decision Sciences, Bocconi University, Milan, Italy
| | - Alessia Melegaro
- Dondena Centre for Research on Social Dynamics and Public Policy, and CovidCrisisLab, Bocconi University, Milan, Italy; Department of Social and Political Sciences, Bocconi University, Milan, Italy
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Indiana University School of Public Health, Bloomington, United States
| | | | | |
Collapse
|
32
|
Using GAM functions and Markov-Switching models in an evaluation framework to assess countries' performance in controlling the COVID-19 pandemic. BMC Public Health 2021; 21:2173. [PMID: 34837982 PMCID: PMC8626735 DOI: 10.1186/s12889-021-11891-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 09/30/2021] [Indexed: 12/24/2022] Open
Abstract
Background The COVID-19 pandemic has initiated several initiatives to better understand its behavior, and some projects are monitoring its evolution across countries, which naturally leads to comparisons made by those using the data. However, most “at a glance” comparisons may be misleading because the curve that should explain the evolution of COVID-19 is different across countries, as a result of the underlying geopolitical or socio-economic characteristics. Therefore, this paper contributes to the scientific endeavour by creating a new evaluation framework to help stakeholders adequately monitor and assess the evolution of COVID-19 in countries, considering the occurrence of spikes, "secondary waves" and structural breaks in the time series. Methods Generalized Additive Models were used to model cumulative and daily curves for confirmed cases and deaths. The Root Relative Squared Error and the Percentage Deviance Explained measured how well the models fit the data. A local min-max function was used to identify all local maxima in the fitted values. The pure Markov-Switching and the family of Markov-Switching GARCH models were used to identify structural breaks in the COVID-19 time series. Finally, a quadrants system to identify countries that are more/less efficient in the short/long term in controlling the spread of the virus and the number of deaths was developed. Such methods were applied in the time series of 189 countries, collected from the Centre for Systems Science and Engineering at Johns Hopkins University. Results Our methodology proves more effective in explaining the evolution of COVID-19 than growth functions worldwide, in addition to standardizing the entire estimation process in a single type of function. Besides, it highlights several inflection points and regime-switching moments, as a consequence of people’s diminished commitment to fighting the pandemic. Although Europe is the most developed continent in the world, it is home to most countries with an upward trend and considered inefficient, for confirmed cases and deaths. Conclusions The new outcomes presented in this research will allow key stakeholders to check whether or not public policies and interventions in the fight against COVID-19 are having an effect, easily identifying examples of best practices and promote such policies more widely around the world. Supplementary Information The online version contains supplementary material available at (10.1186/s12889-021-11891-6).
Collapse
|
33
|
Impact of the Innate Inflammatory Response on ICU Admission and Death in Hospitalized Patients with COVID-19. Biomedicines 2021; 9:biomedicines9111675. [PMID: 34829906 PMCID: PMC8615389 DOI: 10.3390/biomedicines9111675] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/29/2021] [Accepted: 11/10/2021] [Indexed: 12/24/2022] Open
Abstract
Objective: To describe the capacity of a broad spectrum of cytokines and growth factors to predict ICU admission and/or death in patients with severe COVID-19. Design: An observational, analytical, retrospective cohort study with longitudinal follow-up. Setting: Hospital Universitario Príncipe de Asturias (HUPA). Participants: 287 patients diagnosed with COVID-19 admitted to our hospital from 24 March to 8 May 2020, followed until 31 August 2020. Main outcome measures: Profiles of immune response (IR) mediators were determined using the Luminex Multiplex technique in hospitalized patients within six days of admission by examining serum levels of 62 soluble molecules classified into the three groups: adaptive IR-related cytokines (n = 19), innate inflammatory IR-related cytokines (n = 27), and growth factors (n = 16). Results: A statistically robust link with ICU admission and/or death was detected for increased serum levels of interleukin (IL)-6, IL-15, soluble (s) RAGE, IP10, MCP3, sIL1RII, IL-8, GCSF and MCSF and IL-10. The greatest prognostic value was observed for the marker combination IL-10, IL-6 and GCSF. Conclusions: When severe COVID-19 progresses to ICU admission and/or death there is a marked increase in serum levels of several cytokines and chemokines, mainly related to the patient’s inflammatory IR. Serum levels of IL-10, IL-6 and GCSF were most prognostic of the outcome measure.
Collapse
|
34
|
Caro JJ, Möller J, Santhirapala V, Gill H, Johnston J, El-Boghdadly K, Santhirapala R, Kelly P, McGuire A. Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2021; 24:1570-1577. [PMID: 34711356 PMCID: PMC8339677 DOI: 10.1016/j.jval.2021.05.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 04/22/2021] [Accepted: 05/26/2021] [Indexed: 06/07/2023]
Abstract
OBJECTIVES To assist with planning hospital resources, including critical care (CC) beds, for managing patients with COVID-19. METHODS An individual simulation was implemented in Microsoft Excel using a discretely integrated condition event simulation. Expected daily cases presented to the emergency department were modeled in terms of transitions to and from ward and CC and to discharge or death. The duration of stay in each location was selected from trajectory-specific distributions. Daily ward and CC bed occupancy and the number of discharges according to care needs were forecast for the period of interest. Face validity was ascertained by local experts and, for the case study, by comparing forecasts with actual data. RESULTS To illustrate the use of the model, a case study was developed for Guy's and St Thomas' Trust. They provided inputs for January 2020 to early April 2020, and local observed case numbers were fit to provide estimates of emergency department arrivals. A peak demand of 467 ward and 135 CC beds was forecast, with diminishing numbers through July. The model tended to predict higher occupancy in Level 1 than what was eventually observed, but the timing of peaks was quite close, especially for CC, where the model predicted at least 120 beds would be occupied from April 9, 2020, to April 17, 2020, compared with April 7, 2020, to April 19, 2020, in reality. The care needs on discharge varied greatly from day to day. CONCLUSIONS The DICE simulation of hospital trajectories of patients with COVID-19 provides forecasts of resources needed with only a few local inputs. This should help planners understand their expected resource needs.
Collapse
Affiliation(s)
- J Jaime Caro
- Department of Health Policy, London School of Economics and Political Science, London, England, UK; Evidera, London, England, UK.
| | | | - Vatshalan Santhirapala
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Harpreet Gill
- Department of Health Policy, London School of Economics and Political Science, London, England, UK; Department of Theatres, Anaesthesia, and Perioperative Care, Guy's and St Thomas' NHS Foundation Trust, London, England, UK
| | - Jessica Johnston
- Department of Theatres, Anaesthesia, and Perioperative Care, Guy's and St Thomas' NHS Foundation Trust, London, England, UK
| | - Kariem El-Boghdadly
- Department of Theatres, Anaesthesia, and Perioperative Care, Guy's and St Thomas' NHS Foundation Trust, London, England, UK
| | - Ramai Santhirapala
- Department of Theatres, Anaesthesia, and Perioperative Care, Guy's and St Thomas' NHS Foundation Trust, London, England, UK
| | - Paul Kelly
- Department of Theatres, Anaesthesia, and Perioperative Care, Guy's and St Thomas' NHS Foundation Trust, London, England, UK
| | - Alistair McGuire
- Department of Health Policy, London School of Economics and Political Science, London, England, UK
| |
Collapse
|
35
|
Bel G, Gasulla Ó, Mazaira-Font FA. The Effect of Health and Economic Costs on Governments' Policy Responses to COVID-19 Crisis under Incomplete Information. PUBLIC ADMINISTRATION REVIEW 2021; 81:1131-1146. [PMID: 34226767 PMCID: PMC8242661 DOI: 10.1111/puar.13394] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 04/23/2021] [Accepted: 04/28/2021] [Indexed: 05/06/2023]
Abstract
The COVID-19 pandemic has become an unprecedented health, economic, and social crisis. The present study has built a theoretical model and used it to develop an empirical strategy, analyzing the drivers of policy-response agility during the outbreak. Our empirical results show that national policy responses were delayed, both by government expectations of the healthcare system capacity and by expectations that any hard measures used to manage the crisis would entail severe economic costs. With decision-making based on incomplete information, the agility of national policy responses increased as knowledge increased and uncertainty decreased in relation to the epidemic's evolution and the policy responses of other countries.
Collapse
|
36
|
Fotsa-Mbogne DJ, Tchoumi SY, Kouakep-Tchaptchie Y, Kamla VC, Kamgang JC, Houpa-Danga DE, Bowong-Tsakou S, Bekolle D. Estimation and optimal control of the multiscale dynamics of Covid-19: a case study from Cameroon. NONLINEAR DYNAMICS 2021; 106:2703-2738. [PMID: 34697521 PMCID: PMC8528969 DOI: 10.1007/s11071-021-06920-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/18/2021] [Indexed: 05/31/2023]
Abstract
This work aims at a better understanding and the optimal control of the spread of the new severe acute respiratory corona virus 2 (SARS-CoV-2). A multi-scale model giving insights on the virus population dynamics, the transmission process and the infection mechanism is proposed first. Indeed, there are human to human virus transmission, human to environment virus transmission, environment to human virus transmission and self-infection by susceptible individuals. The global stability of the disease-free equilibrium is shown when a given threshold T 0 is less or equal to 1 and the basic reproduction number R 0 is calculated. A convergence index T 1 is also defined in order to estimate the speed at which the disease extincts and an upper bound to the time of infectious extinction is given. The existence of the endemic equilibrium is conditional and its description is provided. Using Partial Rank Correlation Coefficient with a three levels fractional experimental design, the sensitivity of R 0 , T 0 and T 1 to control parameters is evaluated. Following this study, the most significant parameter is the probability of wearing mask followed by the probability of mobility and the disinfection rate. According to a functional cost taking into account economic impacts of SARS-CoV-2, optimal fighting strategies are determined and discussed. The study is applied to real and available data from Cameroon with a model fitting. After several simulations, social distancing and the disinfection frequency appear as the main elements of the optimal control strategy against SARS-CoV-2.
Collapse
Affiliation(s)
- David Jaurès Fotsa-Mbogne
- Department of Mathematics and Computer Science, ENSAI, The University of Ngaoundere, P.O. Box 455, Ngaoundere, Cameroon
| | - Stéphane Yanick Tchoumi
- Department of Mathematics and Computer Science, ENSAI, The University of Ngaoundere, P.O. Box 455, Ngaoundere, Cameroon
| | - Yannick Kouakep-Tchaptchie
- Department of Fundamental Science and Engineering, EGCIM, The University of Ngaoundere, P.O. Box 454, Ngaoundere, Cameroon
| | - Vivient Corneille Kamla
- Department of Mathematics and Computer Science, ENSAI, The University of Ngaoundere, P.O. Box 455, Ngaoundere, Cameroon
| | - Jean-Claude Kamgang
- Department of Mathematics and Computer Science, ENSAI, The University of Ngaoundere, P.O. Box 455, Ngaoundere, Cameroon
| | - Duplex Elvis Houpa-Danga
- Department of Mathematics and Computer Science, FS, The University of Ngaoundere, P.O. Box 454, Ngaoundere, Cameroon
| | - Samuel Bowong-Tsakou
- Department of Mathematics and Computer Science, FS, The University of Douala, P.O. Box 24157, Douala, Cameroon
| | - David Bekolle
- Department of Mathematics and Computer Science, FS, The University of Ngaoundere, P.O. Box 454, Ngaoundere, Cameroon
| |
Collapse
|
37
|
Faes C, Hens N, Gilbert M. On the timing of interventions to preserve hospital capacity: lessons to be learned from the Belgian SARS-CoV-2 pandemic in 2020. Arch Public Health 2021; 79:164. [PMID: 34517923 PMCID: PMC8436011 DOI: 10.1186/s13690-021-00685-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 08/29/2021] [Indexed: 01/22/2023] Open
Abstract
Using publicly available data on the number of new hospitalisations we use a newly developed statistical model to produce a phase portrait to monitor the epidemic allowing for assessing whether or not intervention measures are needed to keep hospital capacity under control. The phase portrait is called a cliquets' diagram, referring to the discrete alarm phases it points to. Using this cliquets' diagram we show that intervention measures were associated with an effective mitigation of a Summer resurgence but that too little too late was done to prevent a large autumn wave in Belgium.
Collapse
Affiliation(s)
- Christel Faes
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium.
| | - Niel Hens
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Marius Gilbert
- Spatial Epidemiology Laboratory, Université Libre de Bruxelles, Brussels, Belgium
- Fonds National de la Recherche Scientifiques, Brussels, Belgium
| |
Collapse
|
38
|
Yang S, Senapati P, Wang D, Bauch CT, Fountoulakis K. Targeted pandemic containment through identifying local contact network bottlenecks. PLoS Comput Biol 2021; 17:e1009351. [PMID: 34460813 PMCID: PMC8432902 DOI: 10.1371/journal.pcbi.1009351] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 09/10/2021] [Accepted: 08/13/2021] [Indexed: 01/24/2023] Open
Abstract
Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts–between individuals or between population centres–are increasingly used for these purposes. Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another. In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods. During the COVID-19 pandemic decision makers frequently face questions like where to impose a lockdown, which traffic to close, and whom to quarantine, all required to be carried out at minimal costs. Establishing cost-effective pandemic control policies requires identifying good targets. New computational models from network theory and epidemic simulations over real contact networks provide a valuable tool for finding the right bottlenecks to target upon. Here we study a computationally efficient network centrality measure that enables us to detect local transmission bottlenecks, i.e., contact edges that are especially important for the spread of disease among small communities or local network structures inside large networks. We find that pandemic intervention strategies that target at local network structures significantly outperform interventions that solely focus on the entire network structure as a whole, which are traditionally believed to be the most effective.
Collapse
Affiliation(s)
- Shenghao Yang
- School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
- * E-mail:
| | - Priyabrata Senapati
- School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Di Wang
- Google Research, Mountain View, California, United States
| | - Chris T. Bauch
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada
| | - Kimon Fountoulakis
- School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
| |
Collapse
|
39
|
Ghosh S, Senapati A, Mishra A, Chattopadhyay J, Dana SK, Hens C, Ghosh D. Reservoir computing on epidemic spreading: A case study on COVID-19 cases. Phys Rev E 2021; 104:014308. [PMID: 34412296 DOI: 10.1103/physreve.104.014308] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 06/23/2021] [Indexed: 12/19/2022]
Abstract
A reservoir computing based echo state network (ESN) is used here for the purpose of predicting the spread of a disease. The current infection trends of a disease in some targeted locations are efficiently captured by the ESN when it is fed with the infection data for other locations. The performance of the ESN is first tested with synthetic data generated by numerical simulations of independent uncoupled patches, each governed by the classical susceptible-infected-recovery model for a choice of distributed infection parameters. From a large pool of synthetic data, the ESN predicts the current trend of infection in 5% patches by exploiting the uncorrelated infection trend of 95% patches. The prediction remains consistent for most of the patches for approximately 4 to 5 weeks. The machine's performance is further tested with real data on the current COVID-19 pandemic collected for different countries. We show that our proposed scheme is able to predict the trend of the disease for up to 3 weeks for some targeted locations. An important point is that no detailed information on the epidemiological rate parameters is needed; the success of the machine rather depends on the history of the disease progress represented by the time-evolving data sets of a large number of locations. Finally, we apply a modified version of our proposed scheme for the purpose of future forecasting.
Collapse
Affiliation(s)
- Subrata Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
| | - Abhishek Senapati
- Agricultural and Ecological Research Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India.,Center for Advanced Systems Understanding (CASUS), Goerlitz, Germany
| | - Arindam Mishra
- Department of Mathematics, Jadavpur University, Kolkata 700032, India
| | - Joydev Chattopadhyay
- Agricultural and Ecological Research Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
| | - Syamal K Dana
- Department of Mathematics, Jadavpur University, Kolkata 700032, India
| | - Chittaranjan Hens
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
| | - Dibakar Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
| |
Collapse
|
40
|
Zimmerman PA, King CL, Ghannoum M, Bonomo RA, Procop GW. Molecular Diagnosis of SARS-CoV-2: Assessing and Interpreting Nucleic Acid and Antigen Tests. Pathog Immun 2021; 6:135-156. [PMID: 34405126 PMCID: PMC8360705 DOI: 10.20411/pai.v6i1.422] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 04/23/2021] [Indexed: 12/11/2022] Open
Abstract
In this review, we summarize the current status of nucleic acid and antigen testing required for diagnosing SARS-CoV-2 infection and COVID-19 disease. Nucleic acid amplification (NAAT) and antigen-detection (Ag) tests occupy a critically important frontline of defense against SARS-CoV-2 in clinical and public health settings. In early stages of this outbreak, we observed that identifying the causative agent of a new illness of unknown origin was greatly accelerated by characterizing the nucleic acid signature of the novel coronavirus. Results from nucleic acid sequencing led to the development of highly sensitive RT-PCR testing for use in clinical settings and to informing best practices for patient care, and in public health settings to the development of strategies for protecting populations. As the current COVID-19 pandemic has evolved, we have seen how NAAT performance has been used to guide and optimize specimen collection, inform patient triage decisions, reveal unexpected clinical symptoms, clarify risks of transmission within patient care facilities, and guide appropriate treatment strategies. For public health settings during the earliest stages of the pandemic, NAATs served as the only tool available for studying the epidemiology of this new disease by identifying infected individuals, studying transmission patterns, modeling population impacts, and enabling disease control organizations and governments to make challenging disease mitigation recommendations to protect the expanding breadth of populations at risk. With time, the nucleic acid signature has provided the information necessary to understand SARS-CoV-2 protein expression for further development of antigen-based point-of-care (POC) diagnostic tests. The advent of massive parallel sequencing (ie, next generation sequencing) has afforded the characterization of this novel pathogen, informed the sequences best adapted for RT-PCR assays, guided vaccine production, and is currently used for tracking and monitoring SARS-CoV-2 variants.
Collapse
Affiliation(s)
- Peter A Zimmerman
- Center for Global Health and Diseases, Case Western Reserve University, Cleveland, Ohio
| | - Christopher L King
- Center for Global Health and Diseases, Case Western Reserve University, Cleveland, Ohio
| | - Mahmoud Ghannoum
- Center for Medical Mycology and Integrated Microbiome Core, Case Western Reserve University and University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Robert A Bonomo
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio; Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, Ohio; Departments of Pharmacology, Molecular Biology and Microbiology, Biochemistry, and Proteomics and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland, Ohio; and the CWRU-Cleveland VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA CARES) Cleveland, Ohio
| | | |
Collapse
|
41
|
Dutta R, Gomes SN, Kalise D, Pacchiardi L. Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic. PLoS Comput Biol 2021; 17:e1009236. [PMID: 34383756 PMCID: PMC8360388 DOI: 10.1371/journal.pcbi.1009236] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 07/02/2021] [Indexed: 01/29/2023] Open
Abstract
A mathematical model for the COVID-19 pandemic spread, which integrates age-structured Susceptible-Exposed-Infected-Recovered-Deceased dynamics with real mobile phone data accounting for the population mobility, is presented. The dynamical model adjustment is performed via Approximate Bayesian Computation. Optimal lockdown and exit strategies are determined based on nonlinear model predictive control, constrained to public-health and socio-economic factors. Through an extensive computational validation of the methodology, it is shown that it is possible to compute robust exit strategies with realistic reduced mobility values to inform public policy making, and we exemplify the applicability of the methodology using datasets from England and France. In many countries, the COVID-19 pandemic has revealed a gap between public policy making and the use of advanced technological tools to inform such a process. In the big data era, decisions concerning the implementation of quarantines and travel restrictions are still being taken based on incomplete public health data, despite the myriad of information our society provides in real time, such as mobility data, commuting network structures, and financial patterns, to name a few. To advance towards an effective data-driven, quantitative policy making, we propose a computational framework where a predictive epidemiological model is fitted by feeding both public health and Google mobility data. The resulting model is then used as a basis for designing mobility reduction strategies which are optimised taking into account both the healthcare system capacity, and the economic impact of an extended lockdown. For the COVID-19 pandemic in England and France, we show that it is possible to design lockdown policies allowing a partial return to workplaces and schools, while maintaining the epidemic under control.
Collapse
Affiliation(s)
- Ritabrata Dutta
- Department of Statistics, Warwick University, Coventry, United Kingdom
- * E-mail:
| | - Susana N. Gomes
- Department of Mathematics, Warwick University, Coventry, United Kingdom
| | - Dante Kalise
- School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | | |
Collapse
|
42
|
Farshbafnadi M, Kamali Zonouzi S, Sabahi M, Dolatshahi M, Aarabi MH. Aging & COVID-19 susceptibility, disease severity, and clinical outcomes: The role of entangled risk factors. Exp Gerontol 2021; 154:111507. [PMID: 34352287 PMCID: PMC8329427 DOI: 10.1016/j.exger.2021.111507] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 02/07/2023]
Abstract
The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) in late 2019 has been associated with a high rate of mortality and morbidity. It has been determined that the old population are not only at an increased risk for affliction with COVID-19 infection, but also atypical presentations, severe forms of the disease, and mortality are more common in this population. A plethora of mechanisms and risk factors contribute to the higher risk of infection in the old population. For instance, aging is associated with an increment in the expression of Angiotensin-Converting Enzyme-2 (ACE-2), the receptor for SARS-CoV-2 spike protein, which precipitates replication of the virus in the old population. On the other hand, immune dysregulation and changes in gut microbiota as a result of aging can contribute to the cytokine storm, one of the main indicators of disease severity. Decrement in sex steroids, especially in women, as well as growth hormone, both of which have crucial roles in immune regulation, is a key contributor to disease severity in old age. Senescence-associated oxidative stress and mitochondrial dysfunction in both pneumocytes and immune cells contribute to the severity of infection in an exacerbative manner. In addition, lifestyle-associated factors such as nutrition and physical activity, which are compromised in old age, are known as important factors in COVID-19 infection. Aging-associated comorbidities, especially cardiovascular diseases and diabetes mellitus, also put older adults at an increased risk of complications, and disease severity.
Collapse
Affiliation(s)
| | - Sara Kamali Zonouzi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; NeuroImaging Network (NIN), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
| | - Mohammadmahdi Sabahi
- NeuroImaging Network (NIN), Universal Scientific Education and Research Network (USERN), Tehran, Iran; Neurosurgery Research Group (NRG), Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran.
| | - Mahsa Dolatshahi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; NeuroImaging Network (NIN), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
| | - Mohammad Hadi Aarabi
- Padova Neuroscience Center (PNC), Department of Neuroscience, University of Padova, Padova, Italy.
| |
Collapse
|
43
|
Jentsch PC, Anand M, Bauch CT. Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study. THE LANCET. INFECTIOUS DISEASES 2021; 21:1097-1106. [PMID: 33811817 DOI: 10.1101/2020.09.25.20201889] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 01/07/2021] [Accepted: 01/22/2021] [Indexed: 05/21/2023]
Abstract
BACKGROUND During the COVID-19 pandemic, authorities must decide which groups to prioritise for vaccination in a shifting social-epidemiological landscape in which the success of large-scale non-pharmaceutical interventions requires broad social acceptance. We aimed to compare projected COVID-19 mortality under four different strategies for the prioritisation of SARS-CoV-2 vaccines. METHODS We developed a coupled social-epidemiological model of SARS-CoV-2 transmission in which social and epidemiological dynamics interact with one another. We modelled how population adherence to non-pharmaceutical interventions responds to case incidence. In the model, schools and workplaces are also closed and reopened on the basis of reported cases. The model was parameterised with data on COVID-19 cases and mortality, SARS-CoV-2 seroprevalence, population mobility, and demography from Ontario, Canada (population 14·5 million). Disease progression parameters came from the SARS-CoV-2 epidemiological literature. We assumed a vaccine with 75% efficacy against disease and transmissibility. We compared vaccinating those aged 60 years and older first (oldest-first strategy), vaccinating those younger than 20 years first (youngest-first strategy), vaccinating uniformly by age (uniform strategy), and a novel contact-based strategy. The latter three strategies interrupt transmission, whereas the first targets a vulnerable group to reduce disease. Vaccination rates ranged from 0·5% to 5% of the population per week, beginning on either Jan 1 or Sept 1, 2021. FINDINGS Case notifications, non-pharmaceutical intervention adherence, and lockdown undergo successive waves that interact with the timing of the vaccine programme to determine the relative effectiveness of the four strategies. Transmission-interrupting strategies become relatively more effective with time as herd immunity builds. The model predicts that, in the absence of vaccination, 72 000 deaths (95% credible interval 40 000-122 000) would occur in Ontario from Jan 1, 2021, to March 14, 2025, and at a vaccination rate of 1·5% of the population per week, the oldest-first strategy would reduce COVID-19 mortality by 90·8% on average (followed by 89·5% in the uniform, 88·9% in the contact-based, and 88·2% in the youngest-first strategies). 60 000 deaths (31 000-108 000) would occur from Sept 1, 2021, to March 14, 2025, in the absence of vaccination, and the contact-based strategy would reduce COVID-19 mortality by 92·6% on average (followed by 92·1% in the uniform, 91·0% in the oldest-first, and 88·3% in the youngest-first strategies) at a vaccination rate of 1·5% of the population per week. INTERPRETATION The most effective vaccination strategy for reducing mortality due to COVID-19 depends on the time course of the pandemic in the population. For later vaccination start dates, use of SARS-CoV-2 vaccines to interrupt transmission might prevent more deaths than prioritising vulnerable age groups. FUNDING Ontario Ministry of Colleges and Universities.
Collapse
Affiliation(s)
- Peter C Jentsch
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada; School of Environmental Sciences, University of Guelph, Guelph, ON, Canada
| | - Madhur Anand
- School of Environmental Sciences, University of Guelph, Guelph, ON, Canada
| | - Chris T Bauch
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada.
| |
Collapse
|
44
|
Rella SA, Kulikova YA, Dermitzakis ET, Kondrashov FA. Rates of SARS-CoV-2 transmission and vaccination impact the fate of vaccine-resistant strains. Sci Rep 2021; 11:15729. [PMID: 34330988 PMCID: PMC8324827 DOI: 10.1038/s41598-021-95025-3] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 07/20/2021] [Indexed: 12/21/2022] Open
Abstract
Vaccines are thought to be the best available solution for controlling the ongoing SARS-CoV-2 pandemic. However, the emergence of vaccine-resistant strains may come too rapidly for current vaccine developments to alleviate the health, economic and social consequences of the pandemic. To quantify and characterize the risk of such a scenario, we created a SIR-derived model with initial stochastic dynamics of the vaccine-resistant strain to study the probability of its emergence and establishment. Using parameters realistically resembling SARS-CoV-2 transmission, we model a wave-like pattern of the pandemic and consider the impact of the rate of vaccination and the strength of non-pharmaceutical intervention measures on the probability of emergence of a resistant strain. As expected, we found that a fast rate of vaccination decreases the probability of emergence of a resistant strain. Counterintuitively, when a relaxation of non-pharmaceutical interventions happened at a time when most individuals of the population have already been vaccinated the probability of emergence of a resistant strain was greatly increased. Consequently, we show that a period of transmission reduction close to the end of the vaccination campaign can substantially reduce the probability of resistant strain establishment. Our results suggest that policymakers and individuals should consider maintaining non-pharmaceutical interventions and transmission-reducing behaviours throughout the entire vaccination period.
Collapse
Affiliation(s)
- Simon A Rella
- Institute of Science and Technology Austria, 1 Am Campus, 3400, Klosterneuburg, Austria
| | | | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.
| | - Fyodor A Kondrashov
- Institute of Science and Technology Austria, 1 Am Campus, 3400, Klosterneuburg, Austria.
| |
Collapse
|
45
|
Morando N, Sanfilippo M, Herrero F, Iturburu M, Torti A, Gutson D, Pando MA, Rabinovich RD. [Evaluation of interventions during the COVID-19 pandemic: development of a model based on subpopulations with different contact rates]. Rev Argent Microbiol 2021; 54:81-94. [PMID: 34509309 PMCID: PMC8302851 DOI: 10.1016/j.ram.2021.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 04/01/2021] [Accepted: 04/26/2021] [Indexed: 12/15/2022] Open
Abstract
Si bien se han realizado múltiples intentos de modelar matemáticamente la pandemia de la enfermedad por coronavirus 2019 (COVID-19), causada por SARS-CoV-2, pocos modelos han sido pensados como herramientas interactivas accesibles para usuarios de distintos ámbitos. El objetivo de este trabajo fue desarrollar un modelo que tuviera en cuenta la heterogeneidad de las tasas de contacto de la población e implementarlo en una aplicación accesible, que permitiera estimar el impacto de posibles intervenciones a partir de información disponible. Se desarrolló una versión ampliada del modelo susceptible-expuesto-infectado-resistente (SEIR), denominada SEIR-HL, que asume una población dividida en dos subpoblaciones, con tasas de contacto diferentes. Asimismo, se desarrolló una fórmula para calcular el número básico de reproducción (R0) para una población dividida en n subpoblaciones, discriminando las tasas de contacto de cada subpoblación según el tipo o contexto de contacto. Se compararon las predicciones del SEIR-HL con las del SEIR y se demostró que la heterogeneidad en las tasas de contacto puede afectar drásticamente la dinámica de las simulaciones, aun partiendo de las mismas condiciones iniciales y los mismos parámetros. Se empleó el SEIR-HL para mostrar el efecto sobre la evolución de la pandemia del desplazamiento de individuos desde posiciones de alto contacto hacia posiciones de bajo contacto. Finalmente, a modo de ejemplo, se aplicó el SEIR-HL al análisis de la pandemia de COVID-19 en Argentina; también se desarrolló un ejemplo de uso de la fórmula del R0. Tanto el SEIR-HL como una calculadora del R0 fueron implementados informáticamente y puestos a disposición de la comunidad.
Collapse
Affiliation(s)
- Nicolás Morando
- CONICET-Universidad de Buenos Aires. Instituto de Investigaciones Biomédicas en Retrovirus y Sida (INBIRS), Buenos Aires, Argentina
| | - Mauricio Sanfilippo
- Fundación para el Desarrollo de la Programación en Acidos Nucleicos (FuDePAN), Córdoba, Argentina
| | - Francisco Herrero
- Fundación para el Desarrollo de la Programación en Acidos Nucleicos (FuDePAN), Córdoba, Argentina
| | - Matías Iturburu
- Fundación para el Desarrollo de la Programación en Acidos Nucleicos (FuDePAN), Córdoba, Argentina
| | - Ariel Torti
- Fundación para el Desarrollo de la Programación en Acidos Nucleicos (FuDePAN), Córdoba, Argentina
| | - Daniel Gutson
- Fundación para el Desarrollo de la Programación en Acidos Nucleicos (FuDePAN), Córdoba, Argentina
| | - María A Pando
- CONICET-Universidad de Buenos Aires. Instituto de Investigaciones Biomédicas en Retrovirus y Sida (INBIRS), Buenos Aires, Argentina.
| | - Roberto Daniel Rabinovich
- CONICET-Universidad de Buenos Aires. Instituto de Investigaciones Biomédicas en Retrovirus y Sida (INBIRS), Buenos Aires, Argentina
| |
Collapse
|
46
|
Tsinda EK, Mmbando GS. Recent updates on the possible reasons for the low incidence and morbidity of COVID-19 cases in Africa. BULLETIN OF THE NATIONAL RESEARCH CENTRE 2021; 45:133. [PMID: 34335014 PMCID: PMC8300982 DOI: 10.1186/s42269-021-00589-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 07/11/2021] [Indexed: 05/03/2023]
Abstract
BACKGROUND The COVID-19 respiratory illness caused by the SARS-CoV-2 has been a major cause of morbidity and mortality worldwide since the first reported case in Wuhan, China. A year has passed since pandemic began, and the reasons for different COVID-19 burden variation across continents keep puzzling the general public. MAIN BODY OF THE ABSTRACT Since the COVID-19 pandemic started, published research articles have addressed the epidemiological risk factors, host factors, susceptibility and immunity. To ascertain possible reasons for the different rates of COVID-19 infections between Africa and other continents, we summarized the up-to-date scientific literature to identify possible arguments in this regard. Available literature suggests that demographic, epidemiological, sociological, genetic and immunological factors contribute in the COVID-19 severity and the susceptibly to SARS-CoV-2. SHORT CONCLUSION This review summarizes existing data and discusses reasons for differential COVID-19 burden across continents. The arguments mentioned herein will be helpful to guide future experimental studies to test different hypotheses.
Collapse
|
47
|
Spatially resolved simulations of the spread of COVID-19 in three European countries. PLoS Comput Biol 2021; 17:e1009090. [PMID: 34283832 PMCID: PMC8323901 DOI: 10.1371/journal.pcbi.1009090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 07/30/2021] [Accepted: 05/18/2021] [Indexed: 01/16/2023] Open
Abstract
We explore the spatial and temporal spread of the novel SARS-CoV-2 virus under containment measures in three European countries based on fits to data of the early outbreak. Using data from Spain and Italy, we estimate an age dependent infection fatality ratio for SARS-CoV-2, as well as risks of hospitalization and intensive care admission. We use them in a model that simulates the dynamics of the virus using an age structured, spatially detailed agent based approach, that explicitly incorporates governmental interventions and changes in mobility and contact patterns occurred during the COVID-19 outbreak in each country. Our simulations reproduce several of the features of its spatio-temporal spread in the three countries studied. They show that containment measures combined with high density are responsible for the containment of cases within densely populated areas, and that spread to less densely populated areas occurred during the late stages of the first wave. The capability to reproduce observed features of the spatio-temporal dynamics of SARS-CoV-2 makes this model a potential candidate for forecasting the dynamics of SARS-CoV-2 in other settings, and we recommend its application in low and lower-middle income countries which remain understudied.
Collapse
|
48
|
Spouge JL. A comprehensive estimation of country-level basic reproduction numbers R0 for COVID-19: Regime regression can automatically estimate the end of the exponential phase in epidemic data. PLoS One 2021; 16:e0254145. [PMID: 34255772 PMCID: PMC8277067 DOI: 10.1371/journal.pone.0254145] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 06/18/2021] [Indexed: 12/30/2022] Open
Abstract
In a compartmental epidemic model, the initial exponential phase reflects a fixed interaction between an infectious agent and a susceptible population in steady state, so it determines the basic reproduction number R0 on its own. After the exponential phase, dynamic complexities like societal responses muddy the practical interpretation of many estimated parameters. The computer program ARRP, already available from sequence alignment applications, automatically estimated the end of the exponential phase in COVID-19 and extracted the exponential growth rate r for 160 countries. By positing a gamma-distributed generation time, the exponential growth method then yielded R0 estimates for COVID-19 in 160 countries. The use of ARRP ensured that the R0 estimates were largely freed from any dependency outside the exponential phase. The Prem matrices quantify rates of effective contact for infectious disease. Without using any age-stratified COVID-19 data, but under strong assumptions about the homogeneity of susceptibility, infectiousness, etc., across different age-groups, the Prem contact matrices also yielded theoretical R0 estimates for COVID-19 in 152 countries, generally in quantitative conflict with the R0 estimates derived from the exponential growth method. An exploratory analysis manipulating only the Prem contact matrices reduced the conflict, suggesting that age-groups under 20 years did not promote the initial exponential growth of COVID-19 as much as other age-groups. The analysis therefore supports tentatively and tardily, but independently of age-stratified COVID-19 data, the low priority given to vaccinating younger age groups. It also supports the judicious reopening of schools. The exploratory analysis also supports the possibility of suspecting differences in epidemic spread among different age-groups, even before substantial amounts of age-stratified data become available.
Collapse
Affiliation(s)
- John L Spouge
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America
| |
Collapse
|
49
|
El-Rashidy N, Abdelrazik S, Abuhmed T, Amer E, Ali F, Hu JW, El-Sappagh S. Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic. Diagnostics (Basel) 2021; 11:1155. [PMID: 34202587 PMCID: PMC8303306 DOI: 10.3390/diagnostics11071155] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/29/2021] [Accepted: 05/31/2021] [Indexed: 12/11/2022] Open
Abstract
Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.
Collapse
Affiliation(s)
- Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt
| | - Samir Abdelrazik
- Information System Department, Faculty of Computer Science and Information Systems, Mansoura University, Mansoura 13518, Egypt;
| | - Tamer Abuhmed
- College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Korea
| | - Eslam Amer
- Faculty of Computer Science, Misr International University, Cairo 11828, Egypt;
| | - Farman Ali
- Department of Software, Sejong University, Seoul 05006, Korea;
| | - Jong-Wan Hu
- Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| |
Collapse
|
50
|
Kong JD, Tekwa EW, Gignoux-Wolfsohn SA. Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries. PLoS One 2021; 16:e0252373. [PMID: 34106993 PMCID: PMC8189449 DOI: 10.1371/journal.pone.0252373] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/15/2021] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To assess whether the basic reproduction number (R0) of COVID-19 is different across countries and what national-level demographic, social, and environmental factors other than interventions characterize initial vulnerability to the virus. METHODS We fit logistic growth curves to reported daily case numbers, up to the first epidemic peak, for 58 countries for which 16 explanatory covariates are available. This fitting has been shown to robustly estimate R0 from the specified period. We then use a generalized additive model (GAM) to discern both linear and nonlinear effects, and include 5 random effect covariates to account for potential differences in testing and reporting that can bias the estimated R0. FINDINGS We found that the mean R0 is 1.70 (S.D. 0.57), with a range between 1.10 (Ghana) and 3.52 (South Korea). We identified four factors-population between 20-34 years old (youth), population residing in urban agglomerates over 1 million (city), social media use to organize offline action (social media), and GINI income inequality-as having strong relationships with R0, across countries. An intermediate level of youth and GINI inequality are associated with high R0, (n-shape relationships), while high city population and high social media use are associated with high R0. Pollution, temperature, and humidity did not have strong relationships with R0 but were positive. CONCLUSION Countries have different characteristics that predispose them to greater intrinsic vulnerability to COVID-19. Studies that aim to measure the effectiveness of interventions across locations should account for these baseline differences in social and demographic characteristics.
Collapse
Affiliation(s)
- Jude Dzevela Kong
- Centre for Diseases Modeling (CDM), York University, Toronto, ON, Canada
- Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Edward W. Tekwa
- Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States of America
- Department of Zoology, University of British Columbia, BC, Canada
| | | |
Collapse
|