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Wong LP, Lee HY, Alias H, Zimet G, Liu T, Lin Y, Hu Z. Cost-based COVID-19 vaccination and willingness to pay: A post-pandemic review. Hum Vaccin Immunother 2024; 20:2313860. [PMID: 38359815 PMCID: PMC10877984 DOI: 10.1080/21645515.2024.2313860] [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: 10/31/2023] [Accepted: 01/31/2024] [Indexed: 02/17/2024] Open
Abstract
The primary objective of this paper is to serve as a valuable resource for policymakers who are confronted with the evolving landscape of the coronavirus disease 2019 (COVID-19), considering both free and cost-based vaccination approaches. The potential consequences of shifting from free to cost-based vaccination are explored, encompassing its impact on global vaccine equity and prioritization, economic well-being, healthcare systems and delivery, public health policies, and vaccine distribution strategies. Examining past studies on willingness to pay for the initial COVID-19 vaccine dose and booster shots provides insights into how individuals value COVID-19 vaccinations and underscores the significance of addressing issues related to affordability. If COVID-19 vaccinations incur expenses, using effective communication strategies that emphasize the importance of vaccination and personal health benefits can increase willingness to pay. Making COVID-19 vaccines accessible through public health programs or health insurance can help alleviate financial barriers and increase vaccination rates.
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Affiliation(s)
- Li Ping Wong
- Department of Epidemiology and Health Statistics, Fujian Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fuzhou, Fujian, China
- Centre for Epidemiology and Evidence-Based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Hai Yen Lee
- Tropical Infectious Diseases Research & Education Centre (TIDREC), Universiti Malaya, Kuala Lumpur, Malaysia
| | - Haridah Alias
- Centre for Epidemiology and Evidence-Based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Gregory Zimet
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tongyu Liu
- Department of Gynecology, Fujian Cancer Hospital, Clinical Oncology School of Fujian Medical University, Fuzhou, Fujian, China
| | - Yulan Lin
- Department of Epidemiology and Health Statistics, Fujian Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fuzhou, Fujian, China
| | - Zhijian Hu
- Department of Epidemiology and Health Statistics, Fujian Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fuzhou, Fujian, China
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2
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Kiing JS, Kang YQ, Mulay KV, Lim TS, Chong SC, Tan MY, Chan YH, Lim AS, Aishworiya R. Screen time and social-emotional skills in preschoolers with developmental, behavioural or emotional issues in Singapore. ANNALS OF THE ACADEMY OF MEDICINE, SINGAPORE 2024; 53:410-419. [PMID: 39132958 DOI: 10.47102/annals-acadmedsg.2023384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Introduction This study aimed to determine patterns of screen viewing time (SVT) in preschool children with developmental, behavioural or emotional (DBE) issues, and to identify its relationship with social-emotional development. Method This cross-sectional study involved children aged 0-5 years who were referred to a developmental paediatric clinic for DBE issues. Parents completed a screen time questionnaire, and the Devereux Early Childhood Assessment-Clinical (DECA-C) question-naire which assessed the social-emotional competence of the children. Data were analysed using logistic regression, correlational analyses and tests of comparison. Results Among 225 children (mean age: 32.4 months), mean daily SVT was 138 minutes. More than half (51.1%) of the children had clinical features of language delay, while 26.6% had features suggestive of autism spectrum disorder. Screen time was first introduced at a mean age of 13.8 months, with 32.4% of children previously experiencing higher SVT. Compared to SVT introduction after 1 year of age, SVT in the first 12 months was primarily to facilitate feeding (P<0.05). Children with higher past SVT had poorer attention, more aggression, and increased behavioural concerns. Children with DBE issues have significantly more screen time than same-aged peers. Conclusion Children with DBE issues are exposed to SVT at a very young age and have significantly more screen time than their peers. It is crucial to guide parents to reduce SVT in early childhood, particularly around mealtimes.
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Affiliation(s)
- Jennifer Sh Kiing
- Child Development Unit, Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, Singapore
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ying Qi Kang
- Child Development Unit, Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, Singapore
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kalyani Vijaykumar Mulay
- Child Development Unit, Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, Singapore
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tammy Sh Lim
- Child Development Unit, Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, Singapore
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Shang Chee Chong
- Child Development Unit, Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, Singapore
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Mae Yue Tan
- Child Development Unit, Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, Singapore
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Yiong Huak Chan
- Biostatistics Unit, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Alicia Sy Lim
- Women's Emotional Health Service, Department of Psychological Medicine, National University Hospital, Singapore
| | - Ramkumar Aishworiya
- Child Development Unit, Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, Singapore
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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3
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Oliveira MDS, Carvalho RF, Meurer CM, Rodrigues ÉM, Dias BP, Santos IGCD, Nascimento CA, Rodrigues YM, Santos AJFD, Almeida KDS, de Souza UJB, Campos FS, Ribeiro J, Soares CMDA, Ribeiro Júnior JC. Prevalence, under-reporting, and epidemiological surveillance of COVID-19 in the Araguaína City of Brazil. PLoS One 2024; 19:e0300191. [PMID: 38838042 DOI: 10.1371/journal.pone.0300191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 02/19/2024] [Indexed: 06/07/2024] Open
Abstract
Asymptomatic and underreported individuals remain a source of coronafig disease 2019 (COVID-19) transmission to others. Data on the prevalence and epidemiological factors influencing transmission are fundamental for establishing control measures, especially in vulnerable regions such as the Amazon. This study aimed to determine the point prevalence and active infection of COVID-19 among the population in Araguaína, a Brazilian city located in the Amazon region, analyzed the socioeconomic and behavioral variables of a statistically representative sample of this population using an epidemiological survey, and identify the viral genomic diversity in the region. During the sixth epidemiological week of 2021 (February 8 to 12), samples of 497 inhabitants of the municipality asymptomatic for respiratory syndromes underwent reverse transcription-quantitative polymerase chain reaction and serological tests (immunoglobulin M and immunoglobulin G). A questionnaire collated data on socioeconomic factors, prevention measures, and health status history. The active infection rate was 6.2%, and the prevalence was 13.5% of the study population. Active infection cases were under-reported; each reported positive case represented 14-28 under-reported cases. Lineages P.2, P.1, and B.1.1 were detected. Working from home was a protective factor against the infection, and clinical signs of fever, dry cough, and loss of taste or smell were associated with testing positive (p <0.05). A descriptive analysis of the indicators revealed that the entire population was susceptible to the disease. Intensified vaccination strategies are required regardless of socioeconomic factors, health conditions, and preventive measures. Implementation of objective, comprehensive, and efficient management tools to minimize the spread of COVID-19 in this municipality can serve as a model for other regions of Brazil.
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Affiliation(s)
- Monike da Silva Oliveira
- Molecular Biology Laboratory, Institute of Biological Sciences, Federal University of Goiás, Goiânia, Goiás, Brazil
- Microbiology Laboratory, Federal University of North Tocantins, Araguaína, Tocantins, Brazil
| | | | - Carolina Merlin Meurer
- Microbiology Laboratory, Federal University of North Tocantins, Araguaína, Tocantins, Brazil
| | - Ézio Machado Rodrigues
- Microbiology Laboratory, Federal University of North Tocantins, Araguaína, Tocantins, Brazil
| | - Bianca Pereira Dias
- Microbiology Laboratory, Federal University of North Tocantins, Araguaína, Tocantins, Brazil
| | | | | | - Yron Moreira Rodrigues
- Microbiology Laboratory, Federal University of North Tocantins, Araguaína, Tocantins, Brazil
| | | | | | | | - Fabrício Souza Campos
- Bioinformatics and Biotechnology Laboratory, Federal University of Tocantins, Gurupi, Tocantins, Brazil
- Institute of Basic Health Sciences, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Juliane Ribeiro
- Molecular Biology and Animal Virology Laboratory, State University of Londrina, Londrina, Paraná, Brazil
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4
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Jit M, Cook AR. Informing Public Health Policies with Models for Disease Burden, Impact Evaluation, and Economic Evaluation. Annu Rev Public Health 2024; 45:133-150. [PMID: 37871140 DOI: 10.1146/annurev-publhealth-060222-025149] [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] [Indexed: 10/25/2023]
Abstract
Conducting real-world public health experiments is often costly, time-consuming, and ethically challenging, so mathematical models have a long-standing history of being used to inform policy. Applications include estimating disease burden, performing economic evaluation of interventions, and responding to health emergencies such as pandemics. Models played a pivotal role during the COVID-19 pandemic, providing early detection of SARS-CoV-2's pandemic potential and informing subsequent public health measures. While models offer valuable policy insights, they often carry limitations, especially when they depend on assumptions and incomplete data. Striking a balance between accuracy and timely decision-making in rapidly evolving situations such as disease outbreaks is challenging. Modelers need to explore the extent to which their models deviate from representing the real world. The uncertainties inherent in models must be effectively communicated to policy makers and the public. As the field becomes increasingly influential, it needs to develop reporting standards that enable rigorous external scrutiny.
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Affiliation(s)
- Mark Jit
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom;
| | - Alex R Cook
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- National University Health System, Singapore
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5
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Njonnou SRS, Eloumba EAMS, Men-do E, Mboule RME, Sango AJF, Lekpa FK, Essomba MJN, Balti EV, Ouankou CN, Djotah J, Bangbang CFE, Sob-ze MS, Choukem SP. Knowledge, perceptions and practices of COVID19 among the came-roonian population. J Public Health Afr 2023; 14:2718. [PMID: 38523804 PMCID: PMC10959157 DOI: 10.4081/jphia.2023.2718] [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: 08/27/2023] [Accepted: 12/07/2023] [Indexed: 03/26/2024] Open
Abstract
COVID19 is an emerging infectious disease that has spread all over the world and became a pandemic. Cameroon is the 7th most affected country in Africa, with most of the cases in metropoles. The main objective was to assess the knowledge, perceptions and practices of the Cameroonian populations about COVID19 infection. A cross-sectional study was conducted from May 15th to July 15th, 2020; targeting all Cameroonians over 15 years old living in Cameroon. A standard Google Forms® questionnaire was submitted via social media (WhatsApp and Facebook particularly). The sampling was consecutive and not exhaustive. The data were processed on Excel 2016 and analyzed through EPI info 3.5. The questionnaire included knowledge, perceptions and practices about the disease's existence, its functional signs, prevention and control. Knowledge, perceptions and practices were considered good for a response score ≥75% and poor for a score ≤25%. Intermediate scores were considered average and insufficient. Univariate and multivariate analyzes were performed to identify the factors associated with a significance level (P#x003C;0.05). Of the 996 (507 males) participants, the median age was 26±8 years. Health workers represented 20.8% of participants. A large proportion of the population (78.6%) had good knowledge, while more than half (56.5%) had good perceptions, but only (23.1%) had good practices. Having reached higher education (P=0.007), and being a health worker (P=0.0008) were associated with a good level of knowledge. High school education (P=0.040) and being a health worker (P=0.049) were associated with positive perceptions. Being employed (P=0.003) and having secondary education level (P=0.033) were associated with good practices. Knowledge and perceptions were good among the study population, but the practice level is still insufficient to effectively fight the COVID19 pandemic. Educational level, being employed or being healthcare workers were associated with good knowledge, perceptions and practices.
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Affiliation(s)
- Sylvain Raoul Simeni Njonnou
- Department of Internal Medicine and Specialties, Faculty of Medicine and Pharmaceutical Sciences, University of Dschang, Dschang, Cameroon
| | | | - Edwige Men-do
- Departement of Internal Medicine and Specialties, Faculty of Medicine and Biomedical Sciences, University of Yaounde I, Yaounde
- Ebolowa Regional Hospital, Ebolowa
| | - Raissa Manuela Ewala Mboule
- Department of Public Health, Faculty of Medicine and Pharmaceutical Sciences, University of Dschang, Dschang
| | - Anne Juliette Flore Sango
- Yaounde Central Hospital
- Department of Internal Medicine and Pediatrics, Faculty of Health Sciences, Medicine and Biomedical Scienc-es, University of Buea, Buea
| | - Fernando Kemta Lekpa
- Department of Internal Medicine and specialties, Faculty of Medicine and Pharmaceutical Sciences, University of Dschang
- The University of Dschang Taskforce for The Elimination of COVID19 (UNITED#COVID19 ), Dschang
- Department of Internal Medicine, Douala General Hospital, Douala, Cameroon
| | - Marie-Josiane Ntsama Essomba
- Yaounde Central Hospital
- Departement of Internal Medicine and Specialties, Faculty of Medicine and Biomedical Sciences, University of Yaounde I, Yaounde
| | - Eric Vounsia Balti
- Department of Internal Medicine and specialties, Faculty of Medicine and Pharmaceutical Sciences, University of Dschang
- The University of Dschang Taskforce for The Elimination of COVID19 (UNITED#COVID19 ), Dschang
| | - Christian Ngongang Ouankou
- Department of Internal Medicine and specialties, Faculty of Medicine and Pharmaceutical Sciences, University of Dschang
- The University of Dschang Taskforce for The Elimination of COVID19 (UNITED#COVID19 ), Dschang
| | | | | | - Martin Sanou Sob-ze
- The University of Dschang Taskforce for The Elimination of COVID19 (UNITED#COVID19 ), Dschang
- Department of Public Health, Faculty of Medicine and Pharmaceutical Sciences, University of Dschang, Dschang
| | - Simeon Pierre Choukem
- Department of Internal Medicine and specialties, Faculty of Medicine and Pharmaceutical Sciences, University of Dschang
- The University of Dschang Taskforce for The Elimination of COVID19 (UNITED#COVID19 ), Dschang
- Department of Internal Medicine, Douala General Hospital, Douala, Cameroon
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6
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Matteson NL, Hassler GW, Kurzban E, Schwab MA, Perkins SA, Gangavarapu K, Levy JI, Parker E, Pride D, Hakim A, De Hoff P, Cheung W, Castro-Martinez A, Rivera A, Veder A, Rivera A, Wauer C, Holmes J, Wilson J, Ngo SN, Plascencia A, Lawrence ES, Smoot EW, Eisner ER, Tsai R, Chacón M, Baer NA, Seaver P, Salido RA, Aigner S, Ngo TT, Barber T, Ostrander T, Fielding-Miller R, Simmons EH, Zazueta OE, Serafin-Higuera I, Sanchez-Alavez M, Moreno-Camacho JL, García-Gil A, Murphy Schafer AR, McDonald E, Corrigan J, Malone JD, Stous S, Shah S, Moshiri N, Weiss A, Anderson C, Aceves CM, Spencer EG, Hufbauer EC, Lee JJ, King AJ, Ramesh KS, Nguyen KN, Saucedo K, Robles-Sikisaka R, Fisch KM, Gonias SL, Birmingham A, McDonald D, Karthikeyan S, Martin NK, Schooley RT, Negrete AJ, Reyna HJ, Chavez JR, Garcia ML, Cornejo-Bravo JM, Becker D, Isaksson M, Washington NL, Lee W, Garfein RS, Luna-Ruiz Esparza MA, Alcántar-Fernández J, Henson B, Jepsen K, Olivares-Flores B, Barrera-Badillo G, Lopez-Martínez I, Ramírez-González JE, Flores-León R, Kingsmore SF, Sanders A, Pradenas A, White B, Matthews G, Hale M, McLawhon RW, Reed SL, Winbush T, McHardy IH, Fielding RA, Nicholson L, Quigley MM, Harding A, Mendoza A, Bakhtar O, Browne SH, Olivas Flores J, Rincon Rodríguez DG, Gonzalez Ibarra M, Robles Ibarra LC, Arellano Vera BJ, Gonzalez Garcia J, Harvey-Vera A, Knight R, Laurent LC, Yeo GW, Wertheim JO, Ji X, Worobey M, Suchard MA, Andersen KG, Campos-Romero A, Wohl S, Zeller M. Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics. Cell 2023; 186:5690-5704.e20. [PMID: 38101407 PMCID: PMC10795731 DOI: 10.1016/j.cell.2023.11.024] [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: 03/12/2023] [Revised: 08/21/2023] [Accepted: 11/21/2023] [Indexed: 12/17/2023]
Abstract
The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of "local" when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.
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Affiliation(s)
| | - Gabriel W Hassler
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ezra Kurzban
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Madison A Schwab
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Sarah A Perkins
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Karthik Gangavarapu
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA; Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Joshua I Levy
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Edyth Parker
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - David Pride
- Department of Pathology, University of California, San Diego, La Jolla, CA, USA; Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Abbas Hakim
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond, CA, USA
| | - Peter De Hoff
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond, CA, USA
| | - Willi Cheung
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond, CA, USA
| | - Anelizze Castro-Martinez
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; Sanford Consortium of Regenerative Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Andrea Rivera
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Anthony Veder
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Ariana Rivera
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Cassandra Wauer
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Jacqueline Holmes
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Jedediah Wilson
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Shayla N Ngo
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Ashley Plascencia
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Elijah S Lawrence
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Elizabeth W Smoot
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Emily R Eisner
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Rebecca Tsai
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Marisol Chacón
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Nathan A Baer
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Phoebe Seaver
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Rodolfo A Salido
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Stefan Aigner
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Toan T Ngo
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Tom Barber
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Tyler Ostrander
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Rebecca Fielding-Miller
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA; Division of Infectious Disease and Global Public Health, University of California, San Diego, La Jolla, CA, USA
| | | | - Oscar E Zazueta
- Department of Epidemiology, Secretaria de Salud de Baja California, Tijuana, Baja California, Mexico
| | | | - Manuel Sanchez-Alavez
- Centro de Diagnostico COVID-19 UABC, Tijuana, Baja California, Mexico; Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA
| | | | - Abraham García-Gil
- Clinical Laboratory Department, Salud Digna, A.C, Tijuana, Baja California, Mexico
| | | | - Eric McDonald
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - Jeremy Corrigan
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - John D Malone
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - Sarah Stous
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - Seema Shah
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - Niema Moshiri
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Alana Weiss
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Catelyn Anderson
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Christine M Aceves
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Emily G Spencer
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Emory C Hufbauer
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Justin J Lee
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Alison J King
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Karthik S Ramesh
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Kelly N Nguyen
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Kieran Saucedo
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | | | - Kathleen M Fisch
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA, USA
| | - Steven L Gonias
- Department of Pathology, University of California, San Diego, La Jolla, CA, USA
| | - Amanda Birmingham
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Smruthi Karthikeyan
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Natasha K Martin
- Division of Infectious Disease and Global Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Robert T Schooley
- Division of Infectious Disease and Global Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Agustin J Negrete
- Facultad de Ciencias de la Salud Universidad Autonoma de Baja California Valle de Las Palmas, Tijuana, Baja California, Mexico
| | - Horacio J Reyna
- Facultad de Ciencias de la Salud Universidad Autonoma de Baja California Valle de Las Palmas, Tijuana, Baja California, Mexico
| | - Jose R Chavez
- Facultad de Ciencias de la Salud Universidad Autonoma de Baja California Valle de Las Palmas, Tijuana, Baja California, Mexico
| | - Maria L Garcia
- Facultad de Ciencias de la Salud Universidad Autonoma de Baja California Valle de Las Palmas, Tijuana, Baja California, Mexico
| | - Jose M Cornejo-Bravo
- Facultad de Ciencias Quimicas e Ingenieria, Universidad Autonoma de Baja California, Tijuana, Baja California, Mexico
| | | | | | | | | | - Richard S Garfein
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
| | | | | | - Benjamin Henson
- Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Kristen Jepsen
- Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Beatriz Olivares-Flores
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | - Gisela Barrera-Badillo
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | - Irma Lopez-Martínez
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | - José E Ramírez-González
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | - Rita Flores-León
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | | | - Alison Sanders
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Allorah Pradenas
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Benjamin White
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Gary Matthews
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Matt Hale
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Ronald W McLawhon
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Sharon L Reed
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Terri Winbush
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | | | | | | | | | | | | | | | - Sara H Browne
- Division of Infectious Disease and Global Public Health, University of California, San Diego, La Jolla, CA, USA; Specialist in Global Health, Encinitas, CA, USA
| | - Jocelyn Olivas Flores
- Facultad de Ciencias Quimicas e Ingenieria, Universidad Autonoma de Baja California, Tijuana, Baja California, Mexico; University of HealthMx, Tijuana, Baja California, Mexico
| | - Diana G Rincon Rodríguez
- University of HealthMx, Tijuana, Baja California, Mexico; Facultad de Medicina, Universidad Xochicalco, Tijuana, Baja California, Mexico
| | - Martin Gonzalez Ibarra
- University of HealthMx, Tijuana, Baja California, Mexico; Facultad de Medicina, Universidad Xochicalco, Tijuana, Baja California, Mexico
| | - Luis C Robles Ibarra
- University of HealthMx, Tijuana, Baja California, Mexico; Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, Tijuana, Baja California, Mexico
| | - Betsy J Arellano Vera
- University of HealthMx, Tijuana, Baja California, Mexico; Instituto Mexicano del Seguro Social, Tijuana, Baja California, Mexico
| | - Jonathan Gonzalez Garcia
- University of HealthMx, Tijuana, Baja California, Mexico; SIMNSA, Tijuana, Baja California, Mexico
| | | | - Rob Knight
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Louise C Laurent
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; Sanford Consortium of Regenerative Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Gene W Yeo
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Sanford Consortium of Regenerative Medicine, University of California, San Diego, La Jolla, CA, USA; Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Joel O Wertheim
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Xiang Ji
- Department of Mathematics, School of Science and Engineering, Tulane University, New Orleans, LA, USA
| | - Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Marc A Suchard
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kristian G Andersen
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA.
| | - Abraham Campos-Romero
- Innovation and Research Department, Salud Digna, A.C, Tijuana, Baja California, Mexico
| | - Shirlee Wohl
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Mark Zeller
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA.
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Begga A, Garibo-i-Orts Ò, de María-García S, Escolano F, Lozano MA, Oliver N, Conejero JA. Predicting COVID-19 pandemic waves including vaccination data with deep learning. Front Public Health 2023; 11:1279364. [PMID: 38162619 PMCID: PMC10757845 DOI: 10.3389/fpubh.2023.1279364] [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: 08/17/2023] [Accepted: 11/13/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction During the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect of vaccines and by infection, and also how this effect start to disappear. Methods We present a deep learning-based approach to predict the number of daily COVID-19 cases in 30 countries, considering the non-pharmaceutical interventions (NPIs) applied in those countries and including vaccination data of the most used vaccines. Results We empirically validate the proposed approach for 4 months between January and April 2021, once vaccination was available and applied to the population and the COVID-19 variants were closer to the one considered for developing the vaccines. With the predictions of new cases, we can prescribe NPIs plans that present the best trade-off between the expected number of COVID-19 cases and the social and economic cost of applying such interventions. Discussion Whereas, mathematical models which include the effect of vaccines in the spread of the SARS-COV-2 pandemic are available, to the best of our knowledge we are the first to propose a data driven method based on recurrent neural networks that considers the waning effect of the immunization acquired either by vaccine administration or by recovering from the illness. This work contributes with an accurate, scalable, data-driven approach to modeling the pandemic curves of cases when vaccination data is available.
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Affiliation(s)
- Ahmed Begga
- Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de València, València, Spain
| | - Òscar Garibo-i-Orts
- Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de València, València, Spain
| | - Sergi de María-García
- Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de València, València, Spain
| | - Francisco Escolano
- Departamento de Ciencia de la Computación e I.A., Universidad de Alicante, Alicante, Spain
| | - Miguel A. Lozano
- Departamento de Ciencia de la Computación e I.A., Universidad de Alicante, Alicante, Spain
| | | | - J. Alberto Conejero
- Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de València, València, Spain
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Filip F, Avramia R, Terteliu-Baitan M, Cocuz ME, Filip R. COVID-19 positive cases in a pediatric surgery department from Romania: Case series from 2 years of pandemics. Medicine (Baltimore) 2023; 102:e36235. [PMID: 38050253 PMCID: PMC10695600 DOI: 10.1097/md.0000000000036235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 10/30/2023] [Indexed: 12/06/2023] Open
Abstract
RATIONALE The COVID-19 pandemic had a dramatic effect on various health systems in terms of admissions and outcomes, including pediatric surgery activity. The aim of this paper was to analyze the outcome of SARS-CoV-2-positive patients admitted to our department during the regional COVID-19 pandemic in North-Eastern Romania. We also evaluated the changes generated in our daily practice by the COVID-19 pandemic and the dynamic response to this major challenge. PATIENT CONCERNS The patients presented with symptoms related to their primary diagnosis: local pain and deformity in case of fractures; pain, swelling, and erythema in case of abscess; pain and decreased range of motion (ROM) in case of intolerance to metal implants. Other specific concerns are mentioned on an individual basis. DIAGNOSES Eighteen patients (of which 4 had acute appendicitis and were included in a previous article), representing 1.18% of the total number of admissions, tested positive for SARS-CoV-2. There were 4 patients with fractures, 3 patients with soft tissue abscess or cellulitis, 2 patients with intolerance to metal implants, 1 patient with facial burn, 1 patient with thumb laceration, 1 patient with liver trauma, 1 patient with undescende testis, and 1 patient with symptomatic inguinal hernia, respectively. Boys represented 11/ 14 (78.57%) of the cases. The mean age of the patients was 9 years 11 months. There were only mild COVID-19 cases. INTERVENTIONS Surgery was performed in 13/ 14 (95.71%) of cases. The fractures were treated with open reduction internal fixation (ORIF); incision and drainage (I & D) were performed in case of soft tissue abscess; the metal implants were removed in case of local intolerance. Other conditions (burn, inguinal hernia, undescended testis, skin laceration) were treated specifically. Only 1 patient with liver laceration was treated conservatively under close hemodynamic monitoring. OUTCOMES The mean length of stay (LoS) was 2.71 days. The infection with the SARS-CoV-2 virus had no deleterious effect on the surgical outcome among the 14 patients included in the study. There were no surgical complications during admission and no patient returned for late complications related to their primary disease or SARS-CoV-2 infection. LESSONS The SARS-CoV-2 infection had no significant influence on the outcome of pediatric surgical cases included in the study. We noticed a significant (31.54%) decrease in the number of admissions compared to the previous 2-year interval before the COVID-19 pandemic. Fast and adequate adjustment of the daily activity imposed by the COVID-19 pandemic was feasible and may be used in the future should similar epidemiological emergencies occur.
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Affiliation(s)
- Florin Filip
- College of Medicine and Biological Sciences, Stefan cel Mare University of Suceava, Suceava, Romania
- Suceava Emergency County Hospital, Suceava, Romania
| | | | - Monica Terteliu-Baitan
- College of Medicine and Biological Sciences, Stefan cel Mare University of Suceava, Suceava, Romania
- Suceava Emergency County Hospital, Suceava, Romania
| | - Maria - Elena Cocuz
- Fundamental Prophylactic and Clinical Disciplines Department, Faculty of Medicine, Transilvania University of Brasov, Brașov, Romania
- Clinical Infectious Diseases Hospital of Brasov, Brasov, Romania
| | - Roxana Filip
- College of Medicine and Biological Sciences, Stefan cel Mare University of Suceava, Suceava, Romania
- Suceava Emergency County Hospital, Suceava, Romania
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Chae MK, Hwang DU, Nah K, Son WS. Evaluation of COVID-19 intervention policies in South Korea using the stochastic individual-based model. Sci Rep 2023; 13:18945. [PMID: 37919389 PMCID: PMC10622523 DOI: 10.1038/s41598-023-46277-8] [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: 07/31/2023] [Accepted: 10/30/2023] [Indexed: 11/04/2023] Open
Abstract
The COVID-19 pandemic has swept the globe, and countries have responded with various intervention policies to prevent its spread. In this study, we aim to analyze the effectiveness of intervention policies implemented in South Korea. We use a stochastic individual-based model (IBM) with a synthetic population to simulate the spread of COVID-19. Using statistical data, we make the synthetic population and assign sociodemographic attributes to each individual. Individuals go about their daily lives based on their assigned characteristics, and encountering infectors in their daily lives stochastically determines whether they are infected. We reproduce the transmission of COVID-19 using the IBM simulation from November 2020 to February 2021 when three phases of increasingly stringent intervention policies were implemented, and then assess their effectiveness. Additionally, we predict how the spread of infection would have been different if these policies had been implemented in January 2022. This study offers valuable insights into the effectiveness of intervention policies in South Korea, which can assist policymakers and public health officials in their decision-making process.
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Affiliation(s)
- Min-Kyung Chae
- Research Team for Transmission Dynamics of Infectious Diseases, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea
| | - Dong-Uk Hwang
- Research Team for Transmission Dynamics of Infectious Diseases, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea
| | - Kyeongah Nah
- Busan Center for Medical Mathematics, National Institute for Mathematical Sciences, Busan, 49241, Republic of Korea
| | - Woo-Sik Son
- Research Team for Transmission Dynamics of Infectious Diseases, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea.
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10
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Ni X, Sun B, Hu Z, Cui Q, Zhang Z, Zhang H. Dynamic variations in and prediction of COVID-19 with omicron in the four first-tier cities of mainland China, Hong Kong, and Singapore. Front Public Health 2023; 11:1228564. [PMID: 37881346 PMCID: PMC10597722 DOI: 10.3389/fpubh.2023.1228564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/11/2023] [Indexed: 10/27/2023] Open
Abstract
Background The COVID-19 pandemic, which began in late 2019, has resulted in the devastating collapse of the social economy and more than 10 million deaths worldwide. A recent study suggests that the pattern of COVID-19 cases will resemble a mini-wave rather than a seasonal surge. In general, COVID-19 has more severe impacts on cities than on rural areas, especially in cities with high population density. Methods In this study, the background situation of COVID-19 transmission is discussed, including the population number and population density. Moreover, a widely used time series autoregressive integrated moving average (ARIMA) model is applied to simulate and forecast the COVID-19 variations in the six cities. We comprehensively analyze the dynamic variations in COVID-19 in the four first-tier cities of mainland China (BJ: Beijing, SH: Shanghai, GZ: Guangzhou and SZ: Shenzhen), Hong Kong (HK), China and Singapore (SG) from 2020 to 2022. Results The major results show that the six cities have their own temporal characteristics, which are determined by the different control and prevention measures. The four first-tier cities of mainland China (i.e., BJ, SH, GZ, and SZ) have similar variations with one wave because of their identical "Dynamic COVID-19 Zero" strategy and strict Non-Pharmaceutical Interventions (NPIs). HK and SG have multiple waves primarily caused by the input cases. The ARIMA model has the ability to provide an accurate forecast of the COVID-19 pandemic trend for the six cities, which could provide a useful approach for predicting the short-term variations in infectious diseases.Accurate forecasting has significant value for implementing reasonable control and prevention measures. Conclusions Our main conclusions show that control and prevention measures should be dynamically adjusted and organically integrated for the COVID-19 pandemic. Moreover, the mathematical models are proven again to provide an important scientific basis for disease control.
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Affiliation(s)
- Xiaohua Ni
- College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Bo Sun
- Shenzhen Institute of Advanced Technology, Shenzhen University Town, Shenzhen, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen University Town, Shenzhen, China
| | - Zengyun Hu
- Shenzhen Institute of Advanced Technology, Shenzhen University Town, Shenzhen, China
- Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, China
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
| | - Qianqian Cui
- College of Mathematics and Statistics, Ningxia University, Yinchuan, China
| | - Zhuo Zhang
- Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, China
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
| | - Hua Zhang
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
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11
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Kreslake JM, Aarvig K, Muller-Tabanera H, Vallone DM, Hair EC. Checkpoint Travel Numbers as a Proxy Variable in Population-Based Studies During the COVID-19 Pandemic: Validation Study. JMIR Public Health Surveill 2023; 9:e44950. [PMID: 37191643 PMCID: PMC10467631 DOI: 10.2196/44950] [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: 12/09/2022] [Revised: 03/30/2023] [Accepted: 05/16/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic had wide-ranging systemic impacts, with implications for social and behavioral factors in human health. The pandemic may introduce history bias in population-level research studies of other health topics during the COVID-19 period. OBJECTIVE We sought to identify and validate an accessible, flexible measure to serve as a covariate in research spanning the COVID-19 pandemic period. METHODS Transportation Security Administration checkpoint travel numbers were used to calculate a weekly sum of daily passengers and validated against two measures with strong face validity: (1) a self-reported item on social distancing practices drawn from a continuous tracking survey among a national sample of youths and young adults (15-24 years) in the United States (N=45,080, approximately 280 unique respondents each week); and (2) Google's Community Mobility Reports, which calculate daily values at the national level to represent rates of change in visits and length of stays to public spaces. For the self-reported survey data, an aggregated week-level variable was calculated as the proportion of respondents who did not practice social distancing that week (January 1, 2019, to May 31, 2022). For the community mobility data, a weekly estimate of change was calculated using daily values compared to a 5-week prepandemic baseline period (January 3, 2020, to February 6, 2020). Spearman rank correlation coefficients were calculated for each comparison. RESULTS Checkpoint travel data ranged from 668,719 travelers in the week of April 8, 2020, to nearly 15.5 million travelers in the week of May 18, 2022. The weekly proportion of survey respondents who did not practice social distancing ranged from 18.1% (n=42; week of April 15, 2020) to 70.9% (n=213; week of May 25, 2022). The measures were strongly correlated from January 2019 to May 2022 (ρ=0.90, P<.001) and March 2020 to May 2022 (ρ=0.87, P<.001). Strong correlations were observed when analyses were restricted to age groups (15-17 years: ρ=0.90; P<.001; 18-20 years: ρ=0.87; P<.001; 21-24 years: ρ=0.88; P<.001), racial or ethnic minorities (ρ=0.86, P<.001), and respondents with lower socioeconomic status (ρ=0.88, P<.001). There were also strong correlations between the weekly change from the baseline period for checkpoint travel data and community mobility data for transit stations (ρ=0.92, P<.001) and retail and recreation (ρ=0.89, P<.001), and moderate significant correlations for grocery and pharmacy (ρ=0.68, P<.001) and parks (ρ=0.62, P<.001). A strong negative correlation was observed for places of residence (ρ=-0.78, P<.001), and a weak but significant positive correlation was found for workplaces (ρ=0.24, P<.001). CONCLUSIONS The Transportation Security Administration's travel checkpoint data provide a publicly available flexible time-varying metric to control for history bias introduced by the pandemic in research studies spanning the COVID-19 period in the United States.
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Affiliation(s)
| | - Kathleen Aarvig
- Schroeder Institute, Truth Initiative, Washington, DC, United States
| | | | - Donna M Vallone
- Schroeder Institute, Truth Initiative, Washington, DC, United States
- Department of Health, Behavior and Society, Johnks Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
- College of Global Public Health, New York University, New York, NY, United States
| | - Elizabeth C Hair
- Schroeder Institute, Truth Initiative, Washington, DC, United States
- Department of Health, Behavior and Society, Johnks Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
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12
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Cao W, Zhao S, Tong X, Dai H, Sun J, Xu J, Qiu G, Zhu J, Tian Y. Spatial-temporal diffusion model of aggregated infectious diseases based on population life characteristics: a case study of COVID-19. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:13086-13112. [PMID: 37501479 DOI: 10.3934/mbe.2023583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Outbreaks of infectious diseases pose significant threats to human life, and countries around the world need to implement more precise prevention and control measures to contain the spread of viruses. In this study, we propose a spatial-temporal diffusion model of infectious diseases under a discrete grid, based on the time series prediction of infectious diseases, to model the diffusion process of viruses in population. This model uses the estimated outbreak origin as the center of transmission, employing a tree-like structure of daily human travel to generalize the process of viral spread within the population. By incorporating diverse data, it simulates the congregation of people, thus quantifying the flow weights between grids for population movement. The model is validated with some Chinese cities with COVID-19 outbreaks, and the results show that the outbreak point estimation method could better estimate the virus transmission center of the epidemic. The estimated location of the outbreak point in Xi'an was only 0.965 km different from the actual one, and the results were more satisfactory. The spatiotemporal diffusion model for infectious diseases simulates daily newly infected areas, which effectively cover the actual patient infection zones on the same day. During the mid-stage of viral transmission, the coverage rate can increase to over 90%, compared to related research, this method has improved simulation accuracy by approximately 18%. This study can provide technical support for epidemic prevention and control, and assist decision-makers in developing more scientific and efficient epidemic prevention and control policies.
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Affiliation(s)
- Wen Cao
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
| | - Siqi Zhao
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
| | - Xiaochong Tong
- School of Geospatial Information, University of Information Engineering, Zhengzhou 450001, China
| | - Haoran Dai
- Northern Information Control Research Institute Group Co. Ltd, Nanjing 211106, China
| | - Jiang Sun
- Beijing QTMap Technology Co. Ltd, Beijing 100192, China
| | - Jiaqi Xu
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
| | | | - Jingwen Zhu
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
| | - Yuzhen Tian
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
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13
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Chen K, Jiang X, Li Y, Zhou R. A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility. NONLINEAR DYNAMICS 2023; 111:1-17. [PMID: 37361002 PMCID: PMC10148626 DOI: 10.1007/s11071-023-08489-5] [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: 11/01/2022] [Accepted: 03/20/2023] [Indexed: 06/28/2023]
Abstract
The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. A major challenge in forecasting the transmission of COVID-19 is the accurate assessment of the multiscale human mobility and how it impacts infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of geographical places, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behavior and individual health conditions on the disease outbreak and the probability of zero-COVID in the population. Specifically, individuals perform power law-type local movements within a container and global transport between different-level containers. It is revealed that frequent long-distance movements inside a small-level container (e.g., a road or a county) and a small population size reduce both the local crowdedness and disease transmission. It takes only half of the time to induce global disease outbreaks when the population increases from 150 to 500 (normalized unit). When the exponent c 1 of the long-tail distribution of distance k moved in the same-level container, p ( k ) ∼ k - c 1 · level , increases, the outbreak time decreases rapidly from 75 to 25 (normalized unit). In contrast, travel between large-level containers (e.g., cities and nations) facilitates global spread of the disease and outbreak. When the mean traveling distance across containers 1 d increases from 0.5 to 1 (normalized unit), the outbreak occurs almost twice as fast. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or to a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing population size and restricting global travel help achieve zero-COVID-19. Specifically, when c 1 is smaller than 0.2, the ratio of people with low levels of mobility is larger than 80% and the population size is smaller than 400, zero-COVID can be achieved within fewer than 1000 time steps. In summary, the Mob-Cov model considers more realistic human mobility at a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to apply when investigating pandemic dynamics and when planning actions against disease. Supplementary Information The online version contains supplementary material available at 10.1007/s11071-023-08489-5.
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Affiliation(s)
- Kejie Chen
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
| | - Xiaomo Jiang
- Provincial Key Lab of Digital Twin for Industrial Equipment, Dalian, 116024 China
- School of Energy and Power Engineering, Dalian, 116024 China
| | - Yanqing Li
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
| | - Rongxin Zhou
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
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Zhang Z, Yang YZ, Zhang HT, Xi Y, Wang CT, Liu DF, Mao JM, Lin HC, Tang WH, Zhao LM, Zhang XS, Dai YT, Jiang H. Psychological status of infertile men during the Coronavirus Disease 2019 Pandemic in China: a cross-sectional investigation. Basic Clin Androl 2023; 33:8. [PMID: 36792987 PMCID: PMC9931448 DOI: 10.1186/s12610-022-00177-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 11/24/2022] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) outbreak has had a widespread and profound impact on people's mental health. The factors associated with mental symptoms among men diagnosed with infertility, a disease closely related to psychological conditions, remain unclear. The aim of this study is to investigate the risk factors associated with mental symptoms among infertile Chinese men during the pandemic. RESULTS A total of 4,098 eligible participants were recruited in this cross-sectional, nationwide study, including 2,034 (49.6%) with primary infertility and 2,064 (50.4%) with secondary infertility. The prevalence of mental health conditions was 36.3%, 39.6%, and 6.7% for anxiety, depression, and post-pandemic stress, respectively. Sexual dysfunction is associated with a higher risk with adjusted odds ratios (ORs) of 1.40 for anxiety, 1.38 for depression, and 2.32 for stress. Men receiving infertility drug therapy displayed a higher risk for anxiety (adjusted OR, 1.31) and depression (adjusted OR, 1.28) symptoms, while those receiving intrauterine insemination had a lower risk of anxiety (adjusted OR, 0.56) and depression (adjusted OR, 0.55) symptoms. CONCLUSION The COVID-19 pandemic has had a significant psychological impact on infertile men. Several psychologically vulnerable populations were identified, including individuals with sexual dysfunction, respondents receiving infertility drug therapy, and those experiencing control measures for COVID-19. The findings provide a comprehensive profile of the mental health status of infertile Chinese men during the COVID-19 outbreak and provide potential psychological intervention strategies.
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Affiliation(s)
- Zhe Zhang
- grid.411642.40000 0004 0605 3760Department of Urology, Peking University Third Hospital, Beijing, China ,grid.411642.40000 0004 0605 3760Department of Reproductive Medicine Center, Peking University Third Hospital, Beijing, China
| | - Yu-Zhuo Yang
- grid.411642.40000 0004 0605 3760Department of Reproductive Medicine Center, Peking University Third Hospital, Beijing, China
| | - Hai-Tao Zhang
- grid.411642.40000 0004 0605 3760Department of Urology, Peking University Third Hospital, Beijing, China ,grid.411642.40000 0004 0605 3760Department of Reproductive Medicine Center, Peking University Third Hospital, Beijing, China
| | - Yu Xi
- grid.411642.40000 0004 0605 3760Department of Urology, Peking University Third Hospital, Beijing, China ,grid.411642.40000 0004 0605 3760Department of Reproductive Medicine Center, Peking University Third Hospital, Beijing, China
| | - Cun-Tong Wang
- grid.411054.50000 0000 9894 8211School of Social Development, Central University of Finance and Economics, Beijing, China
| | - De-Feng Liu
- grid.411642.40000 0004 0605 3760Department of Reproductive Medicine Center, Peking University Third Hospital, Beijing, China
| | - Jia-Ming Mao
- grid.411642.40000 0004 0605 3760Department of Reproductive Medicine Center, Peking University Third Hospital, Beijing, China
| | - Hao-Cheng Lin
- grid.411642.40000 0004 0605 3760Department of Urology, Peking University Third Hospital, Beijing, China ,grid.411642.40000 0004 0605 3760Department of Reproductive Medicine Center, Peking University Third Hospital, Beijing, China
| | - Wen-Hao Tang
- grid.411642.40000 0004 0605 3760Department of Urology, Peking University Third Hospital, Beijing, China ,grid.411642.40000 0004 0605 3760Department of Reproductive Medicine Center, Peking University Third Hospital, Beijing, China
| | - Lian-Ming Zhao
- grid.411642.40000 0004 0605 3760Department of Urology, Peking University Third Hospital, Beijing, China ,grid.411642.40000 0004 0605 3760Department of Reproductive Medicine Center, Peking University Third Hospital, Beijing, China
| | - Xian-Sheng Zhang
- Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Yu-Tian Dai
- Department of Andrology, Nanjing Drum Tower Hospital, Nanjing, China.
| | - Hui Jiang
- Department of Urology, Peking University Third Hospital, Beijing, China. .,Department of Reproductive Medicine Center, Peking University Third Hospital, Beijing, China.
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15
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Suzuki T, Yamamoto H, Ogawa Y, Umetani R. Effects of media on preventive behaviour during the COVID-19 pandemic. HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS 2023; 10:58. [PMID: 36818040 PMCID: PMC9926457 DOI: 10.1057/s41599-023-01554-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
The novel coronavirus 2019 (COVID-19) pandemic required implementation of a variety of measures. In addition to pharmaceutical measures, such as vaccines, changing individuals' nonpharmaceutical preventive behaviour is essential to prevent the spread of infection. In uncertain situations, such as a pandemic, media sources are important for guiding individuals' decision-making behaviour. In this study, we examined the effects of media use on preventive behaviour during COVID-19. Earlier studies have shown that social networking service (SNS) browsing promotes preventive behaviour. However, those studies only assessed a single point during the early stages of the pandemic; therefore, the effects on ongoing preventive behaviour are unclear. Thus, a two-wave panel survey was conducted in 2020 and 2021 for an exploratory analysis of changes in the effects of media on individuals' preventive behaviour over time. The results show that the effect of SNS browsing on preventing going out was confirmed only during the early stage of the pandemic and was not observed 1 year later. It is also shown that those who shifted from self-restraint to going out within 1 year were not affected by the type of media use, but by cognitive factors. As the situation changes during a pandemic, analyses that consider time-series changes are essential for gaining insights about the effects of media on the promotion and maintenance of continuous prevention behaviours.
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16
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Fu J, Liu T, Binte Touhid SS, Fu F, Liu X. Functional Textile Materials for Blocking COVID-19 Transmission. ACS NANO 2023; 17:1739-1763. [PMID: 36683285 PMCID: PMC9885531 DOI: 10.1021/acsnano.2c08894] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 01/17/2023] [Indexed: 06/17/2023]
Abstract
The outbreak of COVID-19 provided a warning sign for society worldwide: that is, we urgently need to explore effective strategies for combating unpredictable viral pandemics. Protective textiles such as surgery masks have played an important role in the mitigation of the COVID-19 pandemic, while revealing serious challenges in terms of supply, cross-infection risk, and environmental pollution. In this context, textiles with an antivirus functionality have attracted increasing attention, and many innovative proposals with exciting commercial possibilities have been reported over the past three years. In this review, we illustrate the progress of textile filtration for pandemics and summarize the recent development of antiviral textiles for personal protective purposes by cataloging them into three classes: metal-based, carbon-based, and polymer-based materials. We focused on the preparation routes of emerging antiviral textiles, providing a forward-looking perspective on their opportunities and challenges, to evaluate their efficacy, scale up their manufacturing processes, and expand their high-volume applications. Based on this review, we conclude that ideal antiviral textiles are characterized by a high filtration efficiency, reliable antiviral effect, long storage life, and recyclability. The expected manufacturing processes should be economically feasible, scalable, and quickly responsive.
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Affiliation(s)
- Jiajia Fu
- School of Materials Science and Engineering,
Zhejiang Sci-Tech University, Xiasha Higher Education Zone,
Hangzhou310018, People’s Republic of China
| | - Tianxing Liu
- Department of Cell and Systems Biology,
University of Toronto, Toronto, OntarioM5S1A1,
Canada
| | - S Salvia Binte Touhid
- School of Materials Science and Engineering,
Zhejiang Sci-Tech University, Xiasha Higher Education Zone,
Hangzhou310018, People’s Republic of China
| | - Feiya Fu
- School of Materials Science and Engineering,
Zhejiang Sci-Tech University, Xiasha Higher Education Zone,
Hangzhou310018, People’s Republic of China
| | - Xiangdong Liu
- School of Materials Science and Engineering,
Zhejiang Sci-Tech University, Xiasha Higher Education Zone,
Hangzhou310018, People’s Republic of China
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17
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Wang P, Liu H, Zheng X, Ma R. A new method for spatio-temporal transmission prediction of COVID-19. CHAOS, SOLITONS, AND FRACTALS 2023; 167:112996. [PMID: 36589549 PMCID: PMC9792945 DOI: 10.1016/j.chaos.2022.112996] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 11/06/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
COVID-19 is the most serious public health event of the 21st century and has had a huge impact across the world. The spatio-temporal pattern analysis and simulation of epidemic spread have become the focus of current research. LSTM model has made a lot of achievements in the prediction of infectious diseases by virtue of its advantages in time prediction, but lacks the spatial expression. CA model plays an important role in epidemic spatial propagation modeling due to its unique evolution characteristics from local to global. However, no existing studies of CA have considered long-term dependence due to the impact of time changes on the evolution of the epidemic, and few have modeled using location data from actual diagnosed patients. Therefore, we proposed a LSTM-CA model to solve above mentioned problems. Base on the advantages of LSTM in temporal level and CA in spatial level, LSTM and CA are integrated from the spatio-temporal perspective of geography based on the fine-grained characteristics of epidemic data. The method divides the study area into regular grids, simulates the spatial interactions between neighborhood cells with the help of CA model, and extracts the parameters affecting the transition probability in CA with the help of LSTM model to assist evolution. Simulations are conducted in Python 3.4 to model the propagation of COVID-19 between Feb, 6 to Mar 20, 2020 in China. Experimental results show that, LSTM-CA performs a higher statistical accuracy than LSTM and spatial accuracy than CA, which could demonstrate the effectiveness of the proposed model. This method could be universal for the temporal and spatial transmission of major public health events. Especially in the early stage of the epidemic, we can quickly understand its development trend and cycle, so as to provide an important reference for epidemic prevention and control and public sentiment counseling.
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Affiliation(s)
- Peipei Wang
- School of Information Engineering, China University of Geosciences, Beijing, China
| | - Haiyan Liu
- School of Economic and Management, China University of Geosciences, Beijing, China
| | - Xinqi Zheng
- School of Information Engineering, China University of Geosciences, Beijing, China
- Technology Innovation Center for Territory Spatial Big-data, MNR of China, Beijing, China
| | - Ruifang Ma
- School of Information Engineering, China University of Geosciences, Beijing, China
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18
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Zammit AP, Brown I, Hooper JD, Clark DA, Riddell AD. Malignant polyps in the COVID-19 era: a population-based analysis. ANZ J Surg 2023; 93:932-938. [PMID: 36692251 DOI: 10.1111/ans.18253] [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: 10/01/2022] [Revised: 12/19/2022] [Accepted: 12/23/2022] [Indexed: 01/25/2023]
Abstract
BACKGROUND Malignant polyps represent the early development of colorectal adenocarcinoma. During 2020, there was widescale rationing of health-care resources in response to the COVID-19 pandemic. In particular there was deferral of some colonoscopy procedures required for timely malignant polyp detection. This study sought to assess how these deferrals affected the diagnosis of malignant polyps. METHODS A population wide analysis was performed of 2079 malignant polyps, diagnosed in Queensland, Australia from 2011 to 2020. A regression analysis, with 95% prediction intervals, was produced to determine whether there was a significant impact on the number of malignant polyps diagnosed in 2020 compared to previous years. Univariate statistical analysis of patient, procedural, and pathological variables was also performed. RESULTS In 2020 there were 211 malignant polyps diagnosed, which was significantly lower than was predicted by the univariate regression analysis (r2 = 0.85, 95% prediction interval: 255.07-323.91, P < 0.001). These malignant polyps were less likely to be diagnosed in a private setting (P < 0.001), and exhibited significantly less depth of submucosal invasion (P = 0.017). There was no significant difference in the management strategy (polypectomy, resection or trans-anal resection) between 2011 and 2019 and 2020. CONCLUSION Because of the significant decrease in the number of malignant polyps, and the natural history of the disease, it is expected that there will be an increase in more advanced colorectal adenocarcinomas presenting in 2021 and beyond. This has implications for healthcare resources, particularly in light of the ongoing strain on health departments as a result of the COVID-19 pandemic.
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Affiliation(s)
- Andrew P Zammit
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Ian Brown
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia.,Envoi Specialist Pathologists, Brisbane, Queensland, Australia.,Department of Surgical and Perioperitive Services, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - John D Hooper
- Mater Research Institute, The University of Queensland, Translational Research Institute, Brisbane, Queensland, Australia
| | - David A Clark
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia.,Department of Surgical and Perioperitive Services, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia.,Mater Research Institute, The University of Queensland, Translational Research Institute, Brisbane, Queensland, Australia.,Faculty of Medicine and Health, University of Sydney and Surgical Outcomes Research Centre (SOuRCe), Sydney, New South Wales, Australia.,Department of Surgery, St Vincent's Private Hospital Northside, Brisbane, Queensland, Australia
| | - Andrew D Riddell
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia.,Department of Surgery, Redcliffe Hospital, Redcliffe, Queensland, Australia
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19
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Singh CK, Sodhi KK. The emerging significance of nanomedicine-based approaches to fighting COVID-19 variants of concern: A perspective on the nanotechnology’s role in COVID-19 diagnosis and treatment. FRONTIERS IN NANOTECHNOLOGY 2023. [DOI: 10.3389/fnano.2022.1084033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
COVID-19, one of the worst-hit pandemics, has quickly spread like fire across nations with very high mortality rates. Researchers all around the globe are making consistent efforts to address the main challenges faced due to COVID-19 infection including prompt diagnosis and therapeutics to reduce mortality. Conventional medical technology does not effectively contain the havoc caused by deadly COVID-19. This signals a crucial mandate for innovative and novel interventions in diagnostics and therapeutics to combat this ongoing pandemic and counter its successor or disease if it were ever to arise. The expeditious solutions can spring from promising areas such as nanomedicine and nanotechnology. Nanomedicine is a dominant tool that has a huge potential to alleviate the disease burden by providing nanoparticle-based vaccines and carriers. Nanotechnology encompasses multidisciplinary aspects including artificial intelligence, chemistry, biology, material science, physical science, and medicine. Nanoparticles offer many advantages compared to larger particles, including better magnetic properties and a multiplied surface-to-volume ratio. Given this, the present review focuses on promising nanomedicine-based solutions to combat COVID-19 and their utility to control a broad range of pathogens and viruses, along with understanding their role in the therapy, diagnosis, and prevention of COVID-19. Various studies, reports, and recent research and development from the nanotechnology perspective are discussed in this article.
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20
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Stefanou CK, Papathanakos G, Stefanou SK, Tepelenis K, Kitsouli A, Barbouti A, Flindris S, Tsoumanis P, Kanavaros P, Kitsoulis P. Thyroid surgery during the COVID-19 pandemic: difficulties - how to improve. Innov Surg Sci 2022; 7:125-132. [PMID: 36561505 PMCID: PMC9742267 DOI: 10.1515/iss-2022-0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 08/22/2022] [Indexed: 12/14/2022] Open
Abstract
In December 2019, the new coronavirus infection (COVID-19) was declared a pandemic by the World Health Organization after rapidly spreading over the world in just a few months. All elective operations and nonemergency treatments have been postponed worldwide. However, some patients require surgical therapy as well, and the time spent waiting should not have a negative impact on the surgical outcome or disease course. Following the initial onset of the COVID-19 epidemic, instructions for proper and safe surgery for healthcare staff and patients should develop. Thyroid surgeries have decreased during the COVID-19 pandemic. Most of them can be postponed for a long time. Assessment of thyroid nodules recommends clinical examination, imaging studies, fine needle aspiration (FNA) and vocal cord examination. All these procedures are necessary, and sometimes they cannot be postponed. To determine the best timing, a thorough preoperative assessment should be undertaken, taking into account both oncological and anatomical features. Furthermore, COVID-19 status must be negative prior to any intervention, and hospital infrastructure must be ready to deal with the demanding situation.
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Affiliation(s)
| | | | | | - Kostas Tepelenis
- Department of Surgery, University Hospital of Ioannina, Ioannina, Greece
| | | | - Alexandra Barbouti
- Department of Anatomy-Histology-Embryology, University of Ioannina, Ioannina, Greece
| | - Stefanos Flindris
- Department of Obstetrics and Gynecology, University Hospital ofIoannina, Greece
| | - Periklis Tsoumanis
- Department of Ophthalmology, University Hospital of Ioannina, Ioannina, Greece
| | - Panagiotis Kanavaros
- Department of Anatomy-Histology-Embryology, University of Ioannina, Ioannina, Greece
| | - Panagiotis Kitsoulis
- Department of Anatomy-Histology-Embryology, University of Ioannina, Ioannina, Greece
- Department of Orthopaedics, University of Ioannina, Ioannina, Greece
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21
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Future behaviours decision-making regarding travel avoidance during COVID-19 outbreaks. Sci Rep 2022; 12:19780. [PMID: 36396687 PMCID: PMC9671889 DOI: 10.1038/s41598-022-24323-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
Abstract
Human behavioural changes are poorly understood, and this limitation has been a serious obstacle to epidemic forecasting. It is generally understood that people change their respective behaviours to reduce the risk of infection in response to the status of an epidemic or government interventions. We must first identify the factors that lead to such decision-making to predict these changes. However, due to an absence of a method to observe decision-making for future behaviour, understanding the behavioural responses to disease is limited. Here, we show that accommodation reservation data could reveal the decision-making process that underpins behavioural changes, travel avoidance, for reducing the risk of COVID-19 infections. We found that the motivation to avoid travel with respect to only short-term future behaviours dynamically varied and was associated with the outbreak status and/or the interventions of the government. Our developed method can quantitatively measure and predict a large-scale population's behaviour to determine the future risk of COVID-19 infections. These findings enable us to better understand behavioural changes in response to disease spread, and thus, contribute to the development of reliable long-term forecasting of disease spread.
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22
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Giner-Soriano M, de Dios V, Ouchi D, Vilaplana-Carnerero C, Monteagudo M, Morros R. Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Primary Health Care Electronic Records. JMIR Public Health Surveill 2022; 8:e36712. [PMID: 36265160 PMCID: PMC9662290 DOI: 10.2196/36712] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 05/11/2022] [Accepted: 10/18/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND A possible link between influenza immunization and susceptibility to the complications of COVID-19 infection has been previously suggested owing to a boost in the immunity against SARS-CoV-2. OBJECTIVE This study aimed to investigate whether individuals with COVID-19 could have benefited from vaccination against influenza. We hypothesized that the immunity resulting from the previous influenza vaccination would boost part of the immunity against SARS-CoV-2. METHODS We performed a population-based cohort study including all patients with COVID-19 with registered entries in the primary health care (PHC) electronic records during the first wave of the COVID-19 pandemic (March 1 to June 30, 2020) in Catalonia, Spain. We compared individuals who took an influenza vaccine before being infected with COVID-19, with those who had not taken one. Data were obtained from Information System for Research in Primary Care, capturing PHC information of 5.8 million people from Catalonia. The main outcomes assessed during follow-up were a diagnosis of pneumonia, hospital admission, and mortality. RESULTS We included 309,039 individuals with COVID-19 and compared them on the basis of their influenza immunization status, with 114,181 (36.9%) having been vaccinated at least once and 194,858 (63.1%) having never been vaccinated. In total, 21,721 (19%) vaccinated individuals and 11,000 (5.7%) unvaccinated individuals had at least one of their outcomes assessed. Those vaccinated against influenza at any time (odds ratio [OR] 1.14, 95% CI 1.10-1.19), recently (OR 1.13, 95% CI 1.10-1.18), or recurrently (OR 1.10, 95% CI 1.05-1.15) before being infected with COVID-19 had a higher risk of presenting at least one of the outcomes than did unvaccinated individuals. When we excluded people living in long-term care facilities, the results were similar. CONCLUSIONS We could not establish a protective role of the immunity conferred by the influenza vaccine on the outcomes of COVID-19 infection, as the risk of COVID-19 complications was higher in vaccinated than in unvaccinated individuals. Our results correspond to the first wave of the COVID-19 pandemic, where more complications and mortalities due to COVID-19 had occurred. Despite that, our study adds more evidence for the analysis of a possible link between the quality of immunity and COVID-19 outcomes, particularly in the PHC setting.
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Affiliation(s)
- Maria Giner-Soriano
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain
| | - Vanessa de Dios
- Department of Clinical Pharmacology, Medicines Area, Hospital Clinic of Barcelona, Barcelona, Spain
| | - Dan Ouchi
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain
| | - Carles Vilaplana-Carnerero
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain
| | - Mònica Monteagudo
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain
| | - Rosa Morros
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain
- Plataforma Spanish Clinical Research Network, Unidad de Investigación Clínica, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Institut Català de la Salut, Barcelona, Spain
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23
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Azizi A, Kazanci C, Komarova NL, Wodarz D. Effect of Human Behavior on the Evolution of Viral Strains During an Epidemic. Bull Math Biol 2022; 84:144. [PMID: 36334172 PMCID: PMC9638455 DOI: 10.1007/s11538-022-01102-7] [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: 06/25/2022] [Accepted: 10/17/2022] [Indexed: 11/08/2022]
Abstract
It is well known in the literature that human behavior can change as a reaction to disease observed in others, and that such behavioral changes can be an important factor in the spread of an epidemic. It has been noted that human behavioral traits in disease avoidance are under selection in the presence of infectious diseases. Here, we explore a complementary trend: the pathogen itself might experience a force of selection to become less “visible,” or less “symptomatic,” in the presence of such human behavioral trends. Using a stochastic SIR agent-based model, we investigated the co-evolution of two viral strains with cross-immunity, where the resident strain is symptomatic while the mutant strain is asymptomatic. We assumed that individuals exercised self-regulated social distancing (SD) behavior if one of their neighbors was infected with a symptomatic strain. We observed that the proportion of asymptomatic carriers increased over time with a stronger effect corresponding to higher levels of self-regulated SD. Adding mandated SD made the effect more significant, while the existence of a time-delay between the onset of infection and the change of behavior reduced the advantage of the asymptomatic strain. These results were consistent under random geometric networks, scale-free networks, and a synthetic network that represented the social behavior of the residents of New Orleans.
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Affiliation(s)
- Asma Azizi
- Department of Mathematics, Kennesaw State University, Marietta, GA, 30060, USA.
| | - Caner Kazanci
- Department of Mathematics, University of Georgia, Athens, GA, 30602, USA.,College of Engineering, University of Georgia, Athens, GA, 30602, USA
| | - Natalia L Komarova
- Department of Mathematics, University of California Irvine, Irvine, CA, 92697, USA
| | - Dominik Wodarz
- Department of Population Health and Disease Prevention Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, CA, 92697, USA
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24
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Balzarini RN, Muise A, Zoppolat G, Gesselman AN, Lehmiller JJ, Garcia JR, Slatcher RB, Mark KP. Sexual Desire in the Time of COVID-19: How COVID-Related Stressors Are Associated with Sexual Desire in Romantic Relationships. ARCHIVES OF SEXUAL BEHAVIOR 2022; 51:3823-3838. [PMID: 36100726 PMCID: PMC9469826 DOI: 10.1007/s10508-022-02365-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/15/2022] [Accepted: 06/16/2022] [Indexed: 06/15/2023]
Abstract
The COVID-19 pandemic and the resulting social distancing measures have caused widespread social and economic disruptions, resulting in spikes in unemployment and financial instability, along with drastic changes to people's ability to feel socially connected. Many of the changes resulting from the COVID-19 pandemic are risk factors for depressive symptoms, which are associated with lower levels of sexual desire. The current research (N = 4,993) examined whether responses to external stressors brought on by COVID-19 (i.e., financial concern, worry, loneliness, stress) were associated with sexual desire among a multi-national sample of people in relationships (Studies 1-2), and whether this association was, in part, due to reports of depressive symptoms (Study 2). In the period immediately following the onset of the pandemic, more financial concern (Study 1) and worry (Study 2) were associated with higher sexual desire, while other factors, like stress (Studies 1-2), were associated with lower desire. We also followed a subset of participants every two weeks during the initial stages of the pandemic and at times when people reported greater stress, loneliness, financial strain, or worry than their average, they reported greater depressive symptoms, which was, in turn, associated with lower sexual desire. Results suggest that the social isolation and stress resulting from the COVID-19 pandemic have mixed associations with sexual desire at the onset of the pandemic. But over time, when people report heightened COVID-related stressors, they tend to report lower sexual desire for their partner, in part because these stressors are associated with more depressive symptoms.
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Affiliation(s)
- Rhonda N Balzarini
- Department of Psychology, Texas State University, San Marcos, TX, 78666, USA.
- The Kinsey Institute, Indiana University, Bloomington, IN, USA.
| | - Amy Muise
- Department of Psychology, York University, Toronto, ON, Canada
| | - Giulia Zoppolat
- Department of Experimental and Applied Psychology, Vrije University, Amsterdam, Netherlands
| | | | | | - Justin R Garcia
- The Kinsey Institute, Indiana University, Bloomington, IN, USA
- Department of Gender Studies, Indiana University, Bloomington, IN, USA
| | | | - Kristen P Mark
- The Kinsey Institute, Indiana University, Bloomington, IN, USA
- Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN, USA
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25
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Park D, Boudier-Revéret M, Chang MC. The pros and cons of entry restrictions: are entry restrictions really effective in preventing the spread of SARS-CoV-2? JOURNAL OF YEUNGNAM MEDICAL SCIENCE 2022; 39:344-346. [PMID: 35108763 PMCID: PMC9580053 DOI: 10.12701/yujm.2021.01599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/09/2021] [Indexed: 11/28/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide, leading the World Health Organization to declare coronavirus disease 2019 (COVID-19) a pandemic. To curb the unchecked spread of SARS-CoV-2 infection, most countries have enforced travel restrictions. However, it is debatable whether such restrictions are effective in containing infections and preventing pandemics. Rather, they may negatively impact economies and diplomatic relationships. Each government should conduct an extensive and appropriate analysis of its national economy, diplomatic status, and COVID-19 preparedness to decide whether it is best to restrict entering travelers. Even if travelers from other countries are allowed entry, extensive contact tracing is required to prevent the spread of COVID-19. In addition, governments can implement “travel bubbles,” which allow the quarantine-free flow of people among countries with relatively low levels of community transmission. An accurate evaluation of the benefits and losses due to entry restrictions during the COVID-19 pandemic would be helpful in determining whether entry restrictions are an effective measure to reduce the spread of infection in future pandemics.
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Affiliation(s)
- Donghwi Park
- Department of Physical Medicine and Rehabilitation, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea
| | - Mathieu Boudier-Revéret
- Department of Physical Medicine and Rehabilitation, Centre Hospitalier de l’Université de Montréal, Montréal, QC, Canada
| | - Min Cheol Chang
- Department of Physical Medicine and Rehabilitation, Yeungnam University College of Medicine, Daegu, Korea
- Corresponding author: Min Cheol Chang, MD Department of Physical Medicine and Rehabilitation, Yeungnam University College of Medicine, 170 Hyeonchung-ro, Nam-gu, Daegu 42415, Korea Tel: +82-53-620-4682 Fax: 0504-231-8694 E-mail:
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Deng B, Niu Y, Xu J, Rui J, Lin S, Zhao Z, Yu S, Guo Y, Luo L, Chen T, Li Q. Mathematical Models Supporting Control of COVID-19. China CDC Wkly 2022; 4:895-901. [PMID: 36285321 PMCID: PMC9579983 DOI: 10.46234/ccdcw2022.186] [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: 09/05/2022] [Accepted: 10/03/2022] [Indexed: 12/13/2022] Open
Abstract
Mathematical models have played an important role in the management of the coronavirus disease 2019 (COVID-19) pandemic. The aim of this review is to describe the use of COVID-19 mathematical models, their classification, and the advantages and disadvantages of different types of models. We conducted subject heading searches of PubMed and China National Knowledge Infrastructure with the terms "COVID-19," "Mathematical Statistical Model," "Model," "Modeling," "Agent-based Model," and "Ordinary Differential Equation Model" and classified and analyzed the scientific literature retrieved in the search. We categorized the models as data-driven or mechanism-driven. Data-driven models are mainly used for predicting epidemics, and have the advantage of rapid assessment of disease instances. However, their ability to determine transmission mechanisms is limited. Mechanism-driven models include ordinary differential equation (ODE) and agent-based models. ODE models are used to estimate transmissibility and evaluate impact of interventions. Although ODE models are good at determining pathogen transmission characteristics, they are less suitable for simulation of early epidemic stages and rely heavily on availability of first-hand field data. Agent-based models consider influences of individual differences, but they require large amounts of data and can take a long time to develop fully. Many COVID-19 mathematical modeling studies have been conducted, and these have been used for predicting trends, evaluating interventions, and calculating pathogen transmissibility. Successful infectious disease modeling requires comprehensive considerations of data, applications, and purposes.
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Affiliation(s)
- Bin Deng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Yan Niu
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jingwen Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Shengnan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Shanshan Yu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Yichao Guo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Li Luo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China,Tianmu Chen,
| | - Qun Li
- Chinese Center for Disease Control and Prevention, Beijing, China,Qun Li,
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Robin N, Dominique L, Hue O. Influence of Face Mask and Tropical Climate on Subjective States: Affect, Motivation, and Selective Attention. AMERICAN JOURNAL OF PSYCHOLOGY 2022. [DOI: 10.5406/19398298.135.3.05] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Abstract
We investigated whether face mask wearing in a hot and humid environment would influence subjective states: motivation, affect, and the performance of cognitive tasks requiring attentional processes. Forty volunteers performed the tasks under 4 repeated conditions: in an air-conditioned (AC) room or a tropical climate (TC; hot and wet environment) while wearing a mask or not (counterbalanced). For each condition, they completed questionnaires on subjective states (affect, motivation, and attentional processes) by performing the Bells and d2 tests, and a comparison of men and women was an indirect focus of this study. Results showed that the participants had higher sensations of fatigue, thermal discomfort, and a negative affect when wearing a mask in the TC condition. Additionally, lower performance scores were noted in the Bells test when participants wore a mask in a TC compared with all other conditions. Lastly, the participants’ performance on the d2 test involving selective and sustained attention improved without a mask in AC and in TC conditions. Consequently, the results revealed that wearing a mask in a TC can induce anxiety, headaches, discomfort, and lower motivation.
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Affiliation(s)
| | | | - Olivier Hue
- Université des Antilles, Laboratoire ACTES, France
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THE COVID-19 PANDEMIC AND ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN HEALTH: HOW MUCH ARE WE INTERESTED IN? JOURNAL OF BASIC AND CLINICAL HEALTH SCIENCES 2022. [DOI: 10.30621/jbachs.984596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Objective New viruses have emerged, causing global damage and mass deaths that can spread to international borders, the latest of which is the new coronavirus (COVID-19). After the Second International Congress on Artificial Intelligence in Health, themed "Artificial Intelligence in Health During COVID-19 Pandemic Process" organized online by İzmir Bakırçay University and İzmir Provincial Health Directorate with the contributions of the International Association of Artificial Intelligence in Health, a questionnaire was conducted to evaluate the knowledge of the participants about artificial intelligence applications.
Materials and Methods: This study aimed to evaluate the interest of the congress participants in this field with the questions which form the questionnaire such as the duration of the interest of the participants in the field of artificial intelligence in health, their publication status, the development of studies on artificial intelligence with the COVID-19 pandemic, demographic structures such as age and gender, and educational level. 130 participants answered the questionnaire consisting of 23 questions. Questionnaire responses were analyzed in a statistical setting.
Results: We found that 130 people filled out the questionnaire and the majority of the participants were female, with participation from many organizations, but university staff showed more interest. We have seen that the 30-39 age group is more interested in artificial intelligence than the other age groups, but the majority of the participants do not have academic studies in this field. We found that the technical terms related to artificial intelligence were not well known by the participants, and that the number of participants who tended to this field, especially in the recent year, was high. Another important point was that people working in this field stated that they would definitely follow up if scientific activities continued.
Conclusion: We know how important congresses, symposiums, courses and other meetings are, especially for scientist candidates, which will be held to raise awareness about the usage areas of artificial intelligence-based health technologies, to develop new communication and work networks by bringing together different disciplines, to create an agenda and to lay the groundwork for new studies, and we think that there is a need for many repetitive activities in this field and that these activities should be continued.
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Dong T, Dong W, Xu Q. Agent Simulation Model of COVID-19 Epidemic Agent-Based on GIS: A Case Study of Huangpu District, Shanghai. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10242. [PMID: 36011877 PMCID: PMC9407715 DOI: 10.3390/ijerph191610242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/14/2022] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Abstract
Since the COVID-19 outbreak was detected and reported at the end of 2019, the pandemic continues worldwide, with public health authorities and the general public in each country struggling to balance safety and normal travel activities. However, the complex public health environment and the complexity of human behaviors, as well as the constant mutation of the COVID-19 virus, requires the development of theoretical and simulation tools to accurately model all segments of society. In this paper, an agent-based model is proposed, the model constructs the real geographical environment of Shanghai Huangpu District based on the building statistics data of Shanghai Huangpu District, and the real population data of Shanghai Huangpu District based on the data of China's seventh Population census in 2020. After incorporating the detailed elements of COVID-19 transmission and the real data of WHO, the model forms various impact parameters. Finally, the model was validated according to the COVID-19 data reported by the official, and the model is applied to a hypothetical scenario. Shanghai is one of the places hardest hit by the current outbreak, Huangpu District is the "heart, window and name card" of Shanghai, and its importance to Shanghai is self-evident. so we used one-to-one population modeling to simulate the spread of COVID-19 in Huangpu District of Shanghai, In addition to the conventional functions of crowd movement, detection and treatment, the model also takes into account the burden of nucleic acid detection on the model caused by diseases similar to COVID-19, such as seasonal cold. The model validation results show that we have constructed a COVID-19 epidemic agent risk assessment system suitable for the individual epidemiological characteristics of COVID-19 in China, which can adjust and reflect on the existing COVID-19 epidemic intervention strategies and individual health behaviors. To provide scientific theoretical basis and information decision-making tools for effective prevention and control of COVID-19 and public health intervention in China.
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Affiliation(s)
- Tao Dong
- School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
- Faculty of Geography, Yunnan Normal University, Kunming 650500, China
| | - Wen Dong
- Faculty of Geography, Yunnan Normal University, Kunming 650500, China
- GIS Technology Engineering Research Centre for West-China Resources and Environment of Educational Ministry, Yunnan Normal University, Kunming 650500, China
| | - Quanli Xu
- Faculty of Geography, Yunnan Normal University, Kunming 650500, China
- GIS Technology Engineering Research Centre for West-China Resources and Environment of Educational Ministry, Yunnan Normal University, Kunming 650500, China
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Nie R, Abdelrahman Z, Liu Z, Wang X. Evaluation of the role of vaccination in the COVID-19 pandemic based on the data from the 50 U.S. States. Comput Struct Biotechnol J 2022; 20:4138-4145. [PMID: 35971518 PMCID: PMC9359589 DOI: 10.1016/j.csbj.2022.08.009] [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: 03/19/2022] [Revised: 08/04/2022] [Accepted: 08/04/2022] [Indexed: 11/04/2022] Open
Abstract
The use of vaccines can significantly reduce COVID-19 deaths and mortality. The use of vaccines cannot reduce the risk of COVID-19 infection. The SARS-CoV-2 Delta variant may weaken the effect of vaccines on lessening the severity of COVID-19. Vaccine usage remains effective in reducing the severity of COVID-19 caused by the SARS-CoV-2 Delta variant infection.
Vaccination is considered as the ultimate weapon to end the pandemic. However, the role of vaccines in the pandemic remains controversial. To explore the impact of vaccination on the COVID-19 pandemic, we used logistic regression models to predict numbers of population-adjusted confirmed cases, deaths, intensive care unit (ICU) cases, case fatality rates and ICU admission rates of COVID-19 in the 50 U.S. states, based on 17 related variables. The logistic regression analysis showed that percentages of people vaccinated correlated inversely with the numbers of COVID-19 deaths and case fatality rates but showed no significant correlation with numbers of confirmed cases or ICU cases, or ICU admission rates. The Spearman correlation analysis showed that the percentages of people vaccinated correlated inversely with the numbers of COVID-19 deaths, ICU cases, ICU case rates, and case fatality rates but showed no significant correlation with numbers of confirmed cases. The number of deaths and mortality in the group after the vaccine usage were significantly lower than those in the group before the vaccine usage. However, after delta became the dominant strain, there were no longer significant differences in the number of deaths and the mortality rate between before and after delta became the dominant strain, although vaccines were used in both periods. Vaccination can significantly reduce COVID-19 deaths and mortality, while it cannot reduce the risk of COVID-19 infection. In addition to vaccination, other measures, such as social distancing, remain important in containing COVID-19 transmission and lower the risk of COVID-19 severe outcomes.
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Affiliation(s)
- Rongfang Nie
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Big Data Research Institute, China Pharmaceutical University, Nanjing 211198, China
| | - Zeinab Abdelrahman
- Department of Neurobiology and Department of Orthopedics, 2nd Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310009, China.,NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang Province 310003, China
| | - Zhixian Liu
- Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
| | - Xiaosheng Wang
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Big Data Research Institute, China Pharmaceutical University, Nanjing 211198, China
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Ben-Nun M, Riley P, Turtle J, Riley S. Consistent pattern of epidemic slowing across many geographies led to longer, flatter initial waves of the COVID-19 pandemic. PLoS Comput Biol 2022; 18:e1010375. [PMID: 35969627 PMCID: PMC9410547 DOI: 10.1371/journal.pcbi.1010375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/25/2022] [Accepted: 07/11/2022] [Indexed: 11/22/2022] Open
Abstract
To define appropriate planning scenarios for future pandemics of respiratory pathogens, it is important to understand the initial transmission dynamics of COVID-19 during 2020. Here, we fit an age-stratified compartmental model with a flexible underlying transmission term to daily COVID-19 death data from states in the contiguous U.S. and to national and sub-national data from around the world. The daily death data of the first months of the COVID-19 pandemic was qualitatively categorized into one of four main profile types: "spring single-peak", "summer single-peak", "spring/summer two-peak" and "broad with shoulder". We estimated a reproduction number R as a function of calendar time tc and as a function of time since the first death reported in that population (local pandemic time, tp). Contrary to the diversity of categories and range of magnitudes in death incidence profiles, the R(tp) profiles were much more homogeneous. We found that in both the contiguous U.S. and globally, the initial value of both R(tc) and R(tp) was substantial: at or above two. However, during the early months, pandemic time R(tp) decreased exponentially to a value that hovered around one. This decrease was accompanied by a reduction in the variance of R(tp). For calendar time R(tc), the decrease in magnitude was slower and non-exponential, with a smaller reduction in variance. Intriguingly, similar trends of exponential decrease and reduced variance were not observed in raw death data. Our findings suggest that the combination of specific government responses and spontaneous changes in behaviour ensured that transmissibility dropped, rather than remaining constant, during the initial phases of a pandemic. Future pandemic planning scenarios should include models that assume similar decreases in transmissibility, which lead to longer epidemics with lower peaks when compared with models based on constant transmissibility.
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Affiliation(s)
- Michal Ben-Nun
- Predictive Science Inc., San Diego California United States of America
- * E-mail:
| | - Pete Riley
- Predictive Science Inc., San Diego California United States of America
| | - James Turtle
- Predictive Science Inc., San Diego California United States of America
| | - Steven Riley
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
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Investigating the Effectiveness of Government Public Health Systems against COVID-19 by Hybrid MCDM Approaches. MATHEMATICS 2022. [DOI: 10.3390/math10152678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
To elucidate the effectiveness of the containment strategies against the pandemic, a Multi-Criteria Decision Making (MCDM) model is established to evaluate the government’s performance against COVID-19. In this study, the Analytic Hierarchy Process (AHP), Entropy, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method are used in determining the performance of the public health system. We adopt both subjective and objective weighting methods for a more accurate evaluation. In addition, the evaluation of performance against COVID-19 is conducted in various aspects and divided into different periods. Data Envelopment Analysis (DEA) is applied to evaluate the sustainability of the public health system. Composite scores of the public health system are determined based on the performance and sustainability assessment. The five countries, South Korea, Japan, Germany, Australia, and China are rated with higher composite scores. On the country, the US, Indonesia, Egypt, South Africa, and Brazil receive lower rating scores among the countries for evaluation. This modeling study can provide a practical quantitative justification for developing containment policies and suggestions for improving the public health system in more countries or areas.
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Jo H. Effects of Psychological Discomfort on Social Networking Site (SNS) Usage Intensity During COVID-19. Front Psychol 2022; 13:939726. [PMID: 35936310 PMCID: PMC9354781 DOI: 10.3389/fpsyg.2022.939726] [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: 05/09/2022] [Accepted: 06/20/2022] [Indexed: 12/04/2022] Open
Abstract
To cope with the COVID-19 pandemic, many countries are implementing social measures. Social distancing, working from home, and non-face-to-face lectures have led to major changes in people's activities. Since face-to-face classes are restricted, students in higher education become to feel psychological and cognitive discomforts such as isolation and risk perception. The purpose of this study is to explore the effects of psychological discomforts on the social network site (SNS) usage intensity of University students. Using structural equation modeling (SEM), this study applied SmartPLS 3.3.9 to analyze 271 valid samples. The results show that the affective risk perception significantly affects social distancing attitude. Cognitive risk perception is positively related to social distancing intention. In addition, cabin fever syndrome influences SNS usage intensity, affective risk perception, and cognitive risk perception. In conclusion, psychological discomfort partially affects the intensity of SNS use. Therefore, the government should set policies by reflecting citizens' mental difficulties and SNS activities together. Moreover, companies are needed to carefully consider the risk of the sense of isolation when marketing to SNS users.
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Affiliation(s)
- Hyeon Jo
- Department of Strategic Planning, RealSecu, Busan, South Korea
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From Policy to Prediction: Forecasting COVID-19 Dynamics Under Imperfect Vaccination. Bull Math Biol 2022; 84:90. [PMID: 35857207 PMCID: PMC9297284 DOI: 10.1007/s11538-022-01047-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 06/23/2022] [Indexed: 12/14/2022]
Abstract
Understanding the joint impact of vaccination and non-pharmaceutical interventions on COVID-19 development is important for making public health decisions that control the pandemic. Recently, we created a method in forecasting the daily number of confirmed cases of infectious diseases by combining a mechanistic ordinary differential equation (ODE) model for infectious classes and a generalized boosting machine learning model (GBM) for predicting how public health policies and mobility data affect the transmission rate in the ODE model (Wang et al. in Bull Math Biol 84:57, 2022). In this paper, we extend the method to the post-vaccination period, accordingly obtain a retrospective forecast of COVID-19 daily confirmed cases in the US, and identify the relative influence of the policies used as the predictor variables. In particular, our ODE model contains both partially and fully vaccinated compartments and accounts for the breakthrough cases, that is, vaccinated individuals can still get infected. Our results indicate that the inclusion of data on non-pharmaceutical interventions can significantly improve the accuracy of the predictions. With the use of policy data, the model predicts the number of daily infected cases up to 35 days in the future, with an average mean absolute percentage error of \documentclass[12pt]{minimal}
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\begin{document}$$14.88\%$$\end{document}14.88% if combined with human mobility data. Moreover, the most influential predictor variables are the policies of restrictions on gatherings, testing and school closing. The modeling approach used in this work can help policymakers design control measures as variant strains threaten public health in the future.
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Yesuf EA, Riad A, Sofi-Mahmudi A, Sudhakar M, Mekonnen A, Endalkachew S, Mama F, Muhidin S, Ayele B, Yahya M, Usman A, Abafita J, Klugar M. Self-reported side effects of the Oxford AstraZeneca COVID-19 vaccine among healthcare workers in Ethiopia, Africa: A cross-sectional study. Front Public Health 2022; 10:937794. [PMID: 35928493 PMCID: PMC9343757 DOI: 10.3389/fpubh.2022.937794] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 06/27/2022] [Indexed: 01/14/2023] Open
Abstract
Introduction Ethiopia is the second most populous country in Africa. Ethiopia received most of its COVID-19 vaccines through donations. The Oxford AstraZeneca vaccine is the first to be donated to Ethiopia by the COVAX facility. Healthcare workers were the priority population that received the Oxford AstraZeneca COVID-19 vaccine. However, there was no nationwide study on the safety of the vaccine in Ethiopia. This study aimed to measure the prevalence and predictors of self-reported side effects of the Oxford AstraZeneca vaccine. Materials and methods The study employed a cross-sectional design. A sample of healthcare workers who took Oxford AstraZeneca COVID-19 vaccine was drawn from four regions of Ethiopia; namely, Amhara, Oromia, Somali, and Southwest. Data were collected on sociodemographic characteristics, medical anamnesis, COVID-19 related anamnesis, and COVID-19 vaccine anamnesis via telephone interview. Descriptive and inferential analyses were done. The software, IBM SPSS Statistics v21.0, was used for analyses of data. Results Out of 384 people, 346 responded (response rate: 90.1%). Female accounted for 34.1% of the respondents. The mean age of the respondents was 31.0 years (Standard Deviation (SD) = 7.4). Nurses accounted for 43.7% of the respondents. The prevalence of at least one local- and systemic-side effect was 50.6 and 44.5%, respectively. The most frequent local- and systemic- side effect were injection site pain and headache, respectively. Both types of side effects mostly subsided in the first 3 days. A third of healthcare workers with side effects took at least one medication. Paracetamol followed by diclofenac sodium were taken by healthcare workers to overcome side effects. There was no independent predictor of local side effect. After controlling for age and chronic diseases, the odds of healthcare workers with COVID-19 like symptoms to experience systemic side effects was 1.38 (Confidence Interval (CI): 1.04–1.82) times more than that of healthcare workers without COVID-19 like symptoms. Conclusions The prevalence of local- and systemic-side effects of the Oxford AstraZeneca COVID-19 vaccine was modest. As the symptoms were mostly common in the first 3 days, it is preferable to monitor healthcare workers at least in the first 3 days following the administration of the vaccine.
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Affiliation(s)
- Elias Ali Yesuf
- Department of Health Policy and Management, Jimma University, Jimma, Ethiopia
- *Correspondence: Elias Ali Yesuf ;
| | - Abanoub Riad
- Department of Public Health, Masaryk University, Brno, Czechia
- Czech National Center for Evidence-Based Healthcare and Knowledge Translation (Cochrane Czech Republic, Czech EBHC: JBI Center of Excellence, Masaryk University GRADE Center), Institute of Biostatistics and Analyses, Masaryk University, Brno, Czechia
- Abanoub Riad
| | - Ahmad Sofi-Mahmudi
- Cochrane Iran Associate Center, National Institute for Medical Research Development, Tehran, Iran
- Seqiz Health Network, Kurdistan University of Medical Sciences, Seqiz, Iran
| | - Morankar Sudhakar
- Department of Health, Behavior and Society, Jimma University, Jimma, Ethiopia
| | | | | | - Feyissa Mama
- Jimma University Medical Center, Jimma University, Jimma, Ethiopia
| | - Semira Muhidin
- Department of Public Health, Woldia University, Woldia, Ethiopia
| | - Bethelhem Ayele
- Gebretsadik Shawo Hospital, Southwest Health Bureau, Bonga, Ethiopia
| | - Mohammed Yahya
- Jigjiga University Specialized Hospital, Jigjiga University, Jigjiga, Ethiopia
| | - Abduselam Usman
- Department of Gynecology and Obstetrics, Jigjiga University, Jigjiga, Ethiopia
| | - Jemal Abafita
- Department of Economics, Jimma University, Jimma, Ethiopia
| | - Miloslav Klugar
- Czech National Center for Evidence-Based Healthcare and Knowledge Translation (Cochrane Czech Republic, Czech EBHC: JBI Center of Excellence, Masaryk University GRADE Center), Institute of Biostatistics and Analyses, Masaryk University, Brno, Czechia
- Institute of Health Information and Statistics of the Czech Republic, Prague, Czechia
- Miloslav Klugar
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Su W, Wang H, Sun C, Li N, Guo X, Song Q, Liang Q, Liang M, Ding X, Sun Y. The Association Between Previous Influenza Vaccination and COVID-19 Infection Risk and Severity: A Systematic Review and Meta-analysis. Am J Prev Med 2022; 63:121-130. [PMID: 35410774 PMCID: PMC8920881 DOI: 10.1016/j.amepre.2022.02.008] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 11/30/2022]
Abstract
INTRODUCTION The association between influenza vaccination and COVID-19 remains controversial. This meta-analysis aimed to investigate whether influenza vaccination reduces the susceptibility and severity of SARS-CoV-2 infection. METHODS A systematic literature search of PubMed, Web of Science, the Cochrane Library, Embase, China National Knowledge Infrastructure, SinoMed, Wanfang Data Knowledge Service Platform, and China Science and Technology Journal VIP Database was conducted from database inception to August 2021. The pooled RR with 95% CI was used to estimate the effect of influenza vaccination on COVID-19. The I2 value was used to assess heterogeneity. If I2>50%, the random-effects model was used as the pooling method. RESULTS A total of 23 published articles with 1,037,445 participants were identified. This meta-analysis showed that influenza vaccination was associated with reduced risk of COVID-19 infection (RR=0.83, 95% CI=0.76, 0.90) and hospitalization (RR=0.71, 95% CI=0.59, 0.84), although not significantly associated with intensive care unit admission and death (risk of intensive care unit admission: RR=0.93, 95% CI=0.64, 1.36; risk of death: RR=0.83, 95% CI=0.68, 1.01). Further analysis suggested that the tetravalent influenza vaccine may be associated with a reduced risk of COVID-19 infection (RR=0.74, 95% CI=0.65, 0.84). DISCUSSION The results suggest that influenza vaccination is associated with reduced susceptibility to or disease severity of COVID-19 and that influenza vaccination may reduce the risk of COVID-19 and improve clinical outcomes.
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Affiliation(s)
- Wanying Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Anhui, P. R. China
| | - Hao Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Anhui, P. R. China
| | - Chenyu Sun
- Internal Medicine, AMITA Health Saint Joseph Hospital Chicago, Chicago, Illinois
| | - Ning Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Anhui, P. R. China
| | - Xianwei Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Anhui, P. R. China
| | - Qiuxia Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Anhui, P. R. China
| | - Qiwei Liang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Anhui, P. R. China
| | - Mingming Liang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Anhui, P. R. China
| | - Xiuxiu Ding
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Anhui, P. R. China
| | - Yehuan Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Anhui, P. R. China; Center for Evidence-Based Practice, Anhui Medical University, Anhui, P. R. China.
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Jin C, Zhang H, Yin L, Zhang Y, Feng SZ. Optimize data-driven multi-agent simulation for COVID-19 transmission. BMC Bioinformatics 2022; 23:260. [PMID: 35778688 PMCID: PMC9250248 DOI: 10.1186/s12859-022-04799-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 06/15/2022] [Indexed: 12/24/2022] Open
Abstract
Background Multi-Agent Simulation is an essential technique for exploring complex systems. In research of contagious diseases, it is widely exploited to analyze their spread mechanisms, especially for preventing COVID-19. Nowadays, transmission dynamics and interventions of COVID-19 have been elaborately established by this method, but its computation performance is seldomly concerned. As it usually suffers from inadequate CPU utilization and poor data locality, optimizing the performance is challenging and important for real-time analyzing its spreading. Results This paper explores approaches to optimize multi-agent simulation for COVID-19 disease. The focus of this work is on the algorithm and data structure designs for improving performance, as well as its parallelization strategies. We propose two successive methods to optimize the computation. We construct a case-focused iteration algorithm to improve data locality, and propose a fast data-mapping scheme called hierarchical hash table to accelerate hash operations. As a result, The case-focused method degrades \documentclass[12pt]{minimal}
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\begin{document}$$\sim 90 \%$$\end{document}∼90% cache references and achieves \documentclass[12pt]{minimal}
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\begin{document}$$\times 4.3$$\end{document}×4.3 speedup. Hierarchical hash table can further boost computation speed by 47%. And parallel implementation with 20 threads on CPU achieves \documentclass[12pt]{minimal}
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\begin{document}$$\times 80$$\end{document}×80 speedup consequently. Conclusions In this work, we propose optimizations for multi-agent simulation of COVID-19 transmission from aspects of algorithm and data structure. Benefit from improvement of locality and multi-thread implementation, our methods can significantly accelerate the simulation computation. It is promising in supporting real-time prevention of COVID-19 and other infectious diseases in the future.
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Affiliation(s)
- Chao Jin
- National Supercomputing Center in Shenzhen, Shenzhen, 518055, Guangdong, People's Republic of China.
| | - Hao Zhang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, People's Republic of China.,University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Ling Yin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, People's Republic of China
| | - Yong Zhang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, People's Republic of China
| | - Sheng-Zhong Feng
- National Supercomputing Center in Shenzhen, Shenzhen, 518055, Guangdong, People's Republic of China
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Gupta R, Rathore B, Srivastava A, Biswas B. Decision-making framework for identifying regions vulnerable to transmission of COVID-19 pandemic. COMPUTERS & INDUSTRIAL ENGINEERING 2022; 169:108207. [PMID: 35529174 PMCID: PMC9052709 DOI: 10.1016/j.cie.2022.108207] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
At the beginning of 2020, the World Health Organization (WHO) identified an unusual coronavirus and declared the associated COVID-19 disease as a global pandemic. We proposed a novel hybrid fuzzy decision-making framework to identify and analyze these transmission factors and conduct proactive decision-making in this context. We identified thirty factors from the extant literature and classified them into six major clusters (climate, hygiene and safety, responsiveness to decision-making, social and demographic, economic, and psychological) with the help of domain experts. We chose the most relevant twenty-five factors using the Fuzzy Delphi Method (FDM) screening from the initial thirty. We computed the weights of those clusters and their constituting factors and ranked them based on their criticality, applying the Fuzzy Analytic Hierarchy Process (FAHP). We found that the top five factors were global travel, delay in travel restriction, close contact, social cohesiveness, and asymptomatic. To evaluate our framework, we chose ten different geographically located cities and analyzed their exposure to COVID-19 pandemic by ranking them based on their vulnerability of transmission using Fuzzy Technique for Order of Preference by Similarity To Ideal Solution (FTOPSIS). Our study contributes to the disciplines of decision analytics and healthcare risk management during a pandemic through these novel findings. Policymakers and healthcare officials will benefit from our study by formulating and improving existing preventive measures to mitigate future global pandemics. Finally, we performed a sequence of sensitivity analyses to check for the robustness and generalizability of our proposed hybrid decision-making framework.
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Affiliation(s)
- Rohit Gupta
- Operations Management Area, Indian Institute of Management Ranchi, 834008, India
| | - Bhawana Rathore
- Institute of Business Management, GLA university, Mathura, 281406, India
| | - Abhishek Srivastava
- Operations Management Area, Indian Institute of Management Kashipur, 244713, India
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Jo T, Sato M, Minamoto T, Ushimaru A. Valuing the cultural services from urban blue‐space ecosystems in Japanese megacities during the
COVID
‐19 pandemic. PEOPLE AND NATURE 2022. [DOI: 10.1002/pan3.10366] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Affiliation(s)
- Toshiaki Jo
- Graduate School of Human Development and Environment Kobe University Kobe City Japan
- Faculty of Advanced Science and Technology Ryukoku University Otsu City Japan
- Ryukoku University Center for Biodiversity Science Otsu City Japan
| | - Masayuki Sato
- Graduate School of Human Development and Environment Kobe University Kobe City Japan
- Division of Life, Medical, Natural Sciences and Technology, Organization for Advanced and Integrated Research Kobe University Kobe City Japan
| | - Toshifumi Minamoto
- Graduate School of Human Development and Environment Kobe University Kobe City Japan
- Division of Life, Medical, Natural Sciences and Technology, Organization for Advanced and Integrated Research Kobe University Kobe City Japan
| | - Atushi Ushimaru
- Graduate School of Human Development and Environment Kobe University Kobe City Japan
- Division of Life, Medical, Natural Sciences and Technology, Organization for Advanced and Integrated Research Kobe University Kobe City Japan
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40
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Balzarini RN, Muise A, Zoppolat G, Di Bartolomeo A, Rodrigues DL, Alonso-Ferres M, Urganci B, Debrot A, Bock Pichayayothin N, Dharma C, Chi P, Karremans JC, Schoebi D, Slatcher RB. Love in the Time of COVID: Perceived Partner Responsiveness Buffers People From Lower Relationship Quality Associated With COVID-Related Stressors. SOCIAL PSYCHOLOGICAL AND PERSONALITY SCIENCE 2022. [DOI: 10.1177/19485506221094437] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
External stressors can erode relationship quality, though little is known about what can mitigate these effects. We examined whether COVID-related stressors were associated with lower relationship quality, and whether perceived partner responsiveness—the extent to which people believe their partner understands, validates, and cares for them—buffers these effects. When people in relationships reported more COVID-related stressors they reported poorer relationship quality at the onset of the pandemic ( N = 3,593 from 57 countries) and over the subsequent 3 months ( N = 1,125). At the onset of the pandemic, most associations were buffered by perceived partner responsiveness, such that people who perceived their partners to be low in responsiveness reported poorer relationship quality when they experienced COVID-related stressors, but these associations were reduced among people who perceived their partners to be highly responsive. In some cases, these associations were buffered over the ensuing weeks of the pandemic.
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Affiliation(s)
- Rhonda N. Balzarini
- Texas State University, San Marcos, TX, United States
- The Kinsey Institute, Indiana University, Bloomington, IN, United States
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41
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Song X, Zhu J, Tan X, Yu W, Wang Q, Shen D, Chen W. XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers. Front Public Health 2022; 10:926069. [PMID: 35812523 PMCID: PMC9256927 DOI: 10.3389/fpubh.2022.926069] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 05/31/2022] [Indexed: 12/13/2022] Open
Abstract
In December 2019, an outbreak of novel coronavirus pneumonia spread over Wuhan, Hubei Province, China, which then developed into a significant global health public event, giving rise to substantial economic losses. We downloaded throat swab expression profiling data of COVID-19 positive and negative patients from the Gene Expression Omnibus (GEO) database to mine novel diagnostic biomarkers. XGBoost was used to construct the model and select feature genes. Subsequently, we constructed COVID-19 classifiers such as MARS, KNN, SVM, MIL, and RF using machine learning methods. We selected the KNN classifier with the optimal MCC value from these classifiers using the IFS method to identify 24 feature genes. Finally, we used principal component analysis to classify the samples and found that the 24 feature genes could effectively be used to classify COVID-19-positive and negative patients. Additionally, we analyzed the possible biological functions and signaling pathways in which the 24 feature genes were involved by GO and KEGG enrichment analyses. The results demonstrated that these feature genes were primarily enriched in biological functions such as viral transcription and viral gene expression and pathways such as Coronavirus disease-COVID-19. In summary, the 24 feature genes we identified were highly effective in classifying COVID-19 positive and negative patients, which could serve as novel markers for COVID-19.
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Affiliation(s)
- Xianbin Song
- Department of Critical Care Medicine, Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Jiangang Zhu
- Department of Critical Care Medicine, Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Xiaoli Tan
- Department of Respiration, Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Wenlong Yu
- Department of Critical Care Medicine, Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Qianqian Wang
- Department of Critical Care Medicine, Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Dongfeng Shen
- Department of Critical Care Medicine, Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Wenyu Chen
- Department of Respiration, Affiliated Hospital of Jiaxing University, Jiaxing, China
- *Correspondence: Wenyu Chen
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Liu J, Gao B, Bao HX, Shi Z. Isolating the net effect of multiple government interventions with an extended Susceptible-Exposed-Infectious-Recovered (SEIR) framework: empirical evidence from the second wave of COVID-19 pandemic in China. BMJ Open 2022; 12:e060996. [PMID: 35725257 PMCID: PMC9213777 DOI: 10.1136/bmjopen-2022-060996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE By using a data-driven statistical approach, we isolated the net effect of multiple government interventions that were simultaneously implemented during the second wave of COVID-19 pandemic in China. DESIGN, DATA SOURCES AND ELIGIBILITY CRITERIA We gathered epidemiological data and government interventions data of nine cities with local outbreaks during the second wave of COVID-19 pandemic in China. We employed the Susceptible-Exposed-Infectious-Recovered (SEIR) framework model to analyse the different pathways of transmission between cities with government interventions implementation and those without. We introduced new components to the standard SEIR model and investigated five themes of government interventions against COVID-19 pandemic. DATA EXTRACTION AND SYNTHESIS We extracted information including study objective, design, methods, main findings and implications. These were tabulated and a narrative synthesis was undertaken given the diverse research designs, methods and implications. RESULTS Supported by extensive empirical validation, our results indicated that the net effect of some specific government interventions (including masks, environmental cleaning and disinfection, tracing, tracking and 14-day centralised quarantining close contacts) had been significantly underestimated in the previous investigation. We also identified important moderators and mediators for the effect of certain government interventions, such as closure of shopping mall and restaurant in the medium-risk level areas, etc. Linking the COVID-19 epidemiological dynamics with the implementation timing of government interventions, we detected that the earlier implementation of some specific government interventions (including targeted partial lockdown, tracing, tracking and 14-day centralised quarantining close contacts) achieved the strongest and most timely effect on controlling COVID-19, especially at the early period of local outbreak. CONCLUSIONS These findings provide important scientific information for decisions regarding which and when government interventions should be implemented to fight against COVID-19 in China and beyond. The proposed analytical framework is useful for policy-making in future endemic and pandemic as well.
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Affiliation(s)
- Jie Liu
- School of Civil Engineering, Northeast Forestry University, Harbin, China
| | - Boya Gao
- School of Civil Engineering, Northeast Forestry University, Harbin, China
| | | | - Zhenwu Shi
- School of Civil Engineering, Northeast Forestry University, Harbin, China
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43
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Risk Assessment and Prevention Strategy of Virus Infection in the Context of University Resumption. BUILDINGS 2022. [DOI: 10.3390/buildings12060806] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The risk assessment system of virus infection probability and the prevention measures for virus transmission are keys to controlling epidemics. In the context of university resumption, this study identifies the risk elements in terms of the mechanism of virus transmission. The effect of two recognized effective measures, i.e., occupancy constraints and ventilation intervention, on the infection risk are quantified and compared using the improved Wells–Riley model. Considering the priority of these two measures, the controlling quantity are determined, and the optimal schemes are proposed based on the targeted infection risk. The results show that the effect of reducing infection risk by constraining occupancy within 25% of all public campus buildings is better than that achieved by increasing the ventilation rate alone. If the ventilation system of the building type is operated by occupiers, it is a priority to prevent the risk of virus infection by restricting occupancy and ensuring the distance between occupants, while if the ventilation system of the building type is centrally controlled, it is a priority to increase the ventilation rate and then limit the occupancy rate during peak periods to 75%.
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Lam SSW, Pourghaderi AR, Abdullah HR, Nguyen FNHL, Siddiqui FJ, Ansah JP, Low JG, Matchar DB, Ong MEH. An Agile Systems Modeling Framework for Bed Resource Planning During COVID-19 Pandemic in Singapore. Front Public Health 2022; 10:714092. [PMID: 35664119 PMCID: PMC9157760 DOI: 10.3389/fpubh.2022.714092] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
Background The COVID-19 pandemic has had a major impact on health systems globally. The sufficiency of hospitals' bed resource is a cornerstone for access to care which can significantly impact the public health outcomes. Objective We describe the development of a dynamic simulation framework to support agile resource planning during the COVID-19 pandemic in Singapore. Materials and Methods The study data were derived from the Singapore General Hospital and public domain sources over the period from 1 January 2020 till 31 May 2020 covering the period when the initial outbreak and surge of COVID-19 cases in Singapore happened. The simulation models and its variants take into consideration the dynamic evolution of the pandemic and the rapidly evolving policies and processes in Singapore. Results The models were calibrated against historical data for the Singapore COVID-19 situation. Several variants of the resource planning model were rapidly developed to adapt to the fast-changing COVID-19 situation in Singapore. Conclusion The agility in adaptable models and robust collaborative management structure enabled the quick deployment of human and capital resources to sustain the high level of health services delivery during the COVID-19 surge.
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Affiliation(s)
- Sean Shao Wei Lam
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.,Health Services Research Centre, Singapore Health Services, Singapore, Singapore.,SingHealth Duke NUS Academic Medical Centre, Health Services Research Institute, Singapore, Singapore.,Lee Kong Chian School of Business, School of Computing and Information Systems, Singapore Management University, Singapore, Singapore
| | - Ahmad Reza Pourghaderi
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.,Health Services Research Centre, Singapore Health Services, Singapore, Singapore.,SingHealth Duke NUS Academic Medical Centre, Health Services Research Institute, Singapore, Singapore
| | | | - Francis Ngoc Hoang Long Nguyen
- Health Services Research Centre, Singapore Health Services, Singapore, Singapore.,SingHealth Duke NUS Academic Medical Centre, Health Services Research Institute, Singapore, Singapore
| | | | - John Pastor Ansah
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.,Residential College 4, National University of Singapore, Singapore, Singapore
| | - Jenny G Low
- Department of Infectious Diseases, Singapore General Hospital, Singapore, Singapore.,Programme in Emerging Infectious Diseases, Duke-NUS Medical School, Singapore, Singapore
| | - David Bruce Matchar
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.,Department of Internal Medicine (General Internal Medicine), Duke University Medical School, Durham, NC, United States.,Department of Internal Medicine, Singapore General Hospital, Singapore, Singapore
| | - Marcus Eng Hock Ong
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.,Health Services Research Centre, Singapore Health Services, Singapore, Singapore.,SingHealth Duke NUS Academic Medical Centre, Health Services Research Institute, Singapore, Singapore.,Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
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45
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Skinner A, Flannery K, Nocka K, Bor J, Dean LT, Jay J, Lipson SK, Cole MB, Benfer EA, Scheckman R, Raderman W, Jones DK, Raifman J. A database of US state policies to mitigate COVID-19 and its economic consequences. BMC Public Health 2022; 22:1124. [PMID: 35659285 PMCID: PMC9166228 DOI: 10.1186/s12889-022-13487-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/23/2022] [Indexed: 11/18/2022] Open
Abstract
Background Since COVID-19 first appeared in the United States (US) in January 2020, US states have pursued a wide range of policies to mitigate the spread of the virus and its economic ramifications. Without unified federal guidance, states have been the front lines of the policy response. Main text We created the COVID-19 US State Policy (CUSP) database (https://statepolicies.com/) to document the dates and components of economic relief and public health measures issued at the state level in response to the COVID-19 pandemic. Documented interventions included school and business closures, face mask mandates, directives on vaccine eligibility, eviction moratoria, and expanded unemployment insurance benefits. By providing continually updated information, CUSP was designed to inform rapid-response, policy-relevant research in the context of the COVID-19 pandemic and has been widely used to investigate the impact of state policies on population health and health equity. This paper introduces the CUSP database and highlights how it is already informing the COVID-19 pandemic response in the US. Conclusion CUSP is the most comprehensive publicly available policy database of health, social, and economic policies in response to the COVID-19 pandemic in the US. CUSP documents widespread variation in state policy decisions and implementation dates across the US and serves as a freely available and valuable resource to policymakers and researchers. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-13487-0.
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Contreras DA, Colosi E, Bassignana G, Colizza V, Barrat A. Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases. J R Soc Interface 2022; 19:20220164. [PMID: 35730172 PMCID: PMC9214285 DOI: 10.1098/rsif.2022.0164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/31/2022] [Indexed: 11/12/2022] Open
Abstract
Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation.
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Affiliation(s)
- Diego Andrés Contreras
- Aix Marseille University, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
| | - Elisabetta Colosi
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Giulia Bassignana
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
- Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
| | - Alain Barrat
- Aix Marseille University, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
- Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
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Leung KK, Zhang R, Hashim MJ, Fang M, Xu J, Sun D, Li X, Liu Y, Deng H, Zeng D, Lin Z, He P, Zhang Y, Zhu X, Liang D, Xing A, Lee SS, Memish ZA, Jiang G, Khan G. Effectiveness of containment strategies in preventing SARS-CoV-2 transmission. J Infect Public Health 2022; 15:609-614. [PMID: 35537237 PMCID: PMC9052634 DOI: 10.1016/j.jiph.2022.04.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/14/2022] [Accepted: 04/18/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Despite substantial resources deployed to curb SARS-CoV-2 transmission, controlling the COVID-19 pandemic has been a major challenge. New variants of the virus are frequently emerging leading to new waves of infection and re-introduction of control measures. In this study, we assessed the effectiveness of containment strategies implemented in the early phase of the pandemic. METHODS Real-world data for COVID-19 cases was retrieved for the period Jan 1 to May 1, 2020 from a number of different sources, including PubMed, MEDLINE, Facebook, Epidemic Forecasting and Google Mobility Reports. We analyzed data for 18 countries/regions that deployed containment strategies such as travel restrictions, lockdowns, stay-at-home requests, school/public events closure, social distancing, and exposure history information management (digital contact tracing, DCT). Primary outcome measure was the change in the number of new cases over 30 days before and after deployment of a control measure. We also compared the effectiveness of centralized versus decentralized DCT. Time series data for COVID-19 were analyzed using Mann-Kendall (M-K) trend tests to investigate the impact of these measures on changes in the number of new cases. The rate of change in the number of new cases was compared using M-K z-values and Sen's slope. RESULTS In spite of the widespread implementation of conventional strategies such as lockdowns, travel restrictions, social distancing, school closures, and stay-at-home requests, analysis revealed that these measures could not prevent the spread of the virus. However, countries which adopted DCT with centralized data storage were more likely to contain the spread. CONCLUSIONS Centralized DCT was more effective in containing the spread of COVID-19. Early implementation of centralized DCT should be considered in future outbreaks. However, challenges such as public acceptance, data security and privacy concerns will need to be addressed.
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Affiliation(s)
- Ka Kit Leung
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China; Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Rusheng Zhang
- Changsha Center for Disease Control and Prevention, Changsha, 410005, Hunan, China
| | - Muhammad Jawad Hashim
- Department of Family Medicine, College of Medicine & Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Mingying Fang
- Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Jing Xu
- Department of Pathology, School of Basic Medical Sciences, Shanghai University of Medicine and Health, 279 Zhou Zhu Gong Road, Shanghai 201318, China
| | - Derek Sun
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China
| | - Xiang Li
- Sophia Technologies, Hunan 410001, China
| | - Yanhui Liu
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China
| | - Haohui Deng
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China
| | - Dingyuan Zeng
- Liuzhou Maternal and Child Health Care Hospital, Liuzhou, Guangxi 545001, China
| | - Zhong Lin
- Reproductive Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi 530021, China
| | - Peiqing He
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China
| | - Yu Zhang
- Liuzhou Maternal and Child Health Care Hospital, Liuzhou, Guangxi 545001, China
| | - Xuehong Zhu
- Reproductive Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi 530021, China
| | - Dachao Liang
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China
| | - Abao Xing
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China
| | - Shui-Shan Lee
- Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Ziad A Memish
- Research & Innovation Centre, King Saud Medical City, Ministry of Health & College of Medicine, AlFaisal University, Riyadh, Saudi Arabia
| | - Guozhi Jiang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Gulfaraz Khan
- Department of Microbiology and Immunology, College of Medicine & Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
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An Efficient Numerical Simulation for the Fractional COVID-19 Model Using the GRK4M Together with the Fractional FDM. FRACTAL AND FRACTIONAL 2022. [DOI: 10.3390/fractalfract6060304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
In this research, we studied a mathematical model formulated with six fractional differential equations to characterize a COVID-19 outbreak. For the past two years, the disease transmission has been increasing all over the world. We included the considerations of people with infections who were both asymptomatic and symptomatic as well as the fact that an individual who has been exposed is either quarantined or moved to one of the diseased classes, with the chance that a susceptible individual could also migrate to the quarantined class. The suggested model is solved numerically by implementing the generalized Runge–Kutta method of the fourth order (GRK4M). We discuss the stability analysis of the GRK4M as a general study. The acquired findings are compared with those obtained using the fractional finite difference method (FDM), where we used the Grünwald–Letnikov approach to discretize the fractional differentiation operator. The FDM is mostly reliant on correctly converting the suggested model into a system of algebraic equations. By applying the proposed methods, the numerical results reveal that these methods are straightforward to apply and computationally very effective at presenting a numerical simulation of the behavior of all components of the model under study.
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Zhang H, Yin L, Mao L, Mei S, Chen T, Liu K, Feng S. Combinational Recommendation of Vaccinations, Mask-Wearing, and Home-Quarantine to Control Influenza in Megacities: An Agent-Based Modeling Study With Large-Scale Trajectory Data. Front Public Health 2022; 10:883624. [PMID: 35719665 PMCID: PMC9204335 DOI: 10.3389/fpubh.2022.883624] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/31/2022] [Indexed: 11/15/2022] Open
Abstract
The outbreak of COVID-19 stimulated a new round of discussion on how to deal with respiratory infectious diseases. Influenza viruses have led to several pandemics worldwide. The spatiotemporal characteristics of influenza transmission in modern cities, especially megacities, are not well-known, which increases the difficulty of influenza prevention and control for populous urban areas. For a long time, influenza prevention and control measures have focused on vaccination of the elderly and children, and school closure. Since the outbreak of COVID-19, the public's awareness of measures such as vaccinations, mask-wearing, and home-quarantine has generally increased in some regions of the world. To control the influenza epidemic and reduce the proportion of infected people with high mortality, the combination of these three measures needs quantitative evaluation based on the spatiotemporal transmission characteristics of influenza in megacities. Given that the agent-based model with both demographic attributes and fine-grained mobility is a key planning tool in deploying intervention strategies, this study proposes a spatially explicit agent-based influenza model for assessing and recommending the combinations of influenza control measures. This study considers Shenzhen city, China as the research area. First, a spatially explicit agent-based influenza transmission model was developed by integrating large-scale individual trajectory data and human response behavior. Then, the model was evaluated across multiple intra-urban spatial scales based on confirmed influenza cases. Finally, the model was used to evaluate the combined effects of the three interventions (V: vaccinations, M: mask-wearing, and Q: home-quarantining) under different compliance rates, and their optimal combinations for given control objectives were recommended. This study reveals that adults were a high-risk population with a low reporting rate, and children formed the lowest infected proportion and had the highest reporting rate in Shenzhen. In addition, this study systematically recommended different combinations of vaccinations, mask-wearing, and home-quarantine with different compliance rates for different control objectives to deal with the influenza epidemic. For example, the “V45%-M60%-Q20%” strategy can maintain the infection percentage below 5%, while the “V20%-M60%-Q20%” strategy can maintain the infection percentage below 15%. The model and policy recommendations from this study provide a tool and intervention reference for influenza epidemic management in the post-COVID-19 era.
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Affiliation(s)
- Hao Zhang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ling Yin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- *Correspondence: Ling Yin
| | - Liang Mao
- Department of Geography, University of Florida, Gainesville, FL, United States
| | - Shujiang Mei
- Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Kang Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Wang X, Wu T, Oliveira LFS, Zhang D. Sheet, Surveillance, Strategy, Salvage and Shield in global biodefense system to protect the public health and tackle the incoming pandemics. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 822:153469. [PMID: 35093353 PMCID: PMC8799268 DOI: 10.1016/j.scitotenv.2022.153469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/23/2022] [Accepted: 01/23/2022] [Indexed: 06/14/2023]
Abstract
The pandemic of COVID-19 challenges the global health system and raises our concerns on the next waves of other emerging infectious diseases. Considering the lessons from the failure of world's pandemic warning system against COVID-19, many scientists and politicians have mentioned different strategies to improve global biodefense system, among which Sheet, Surveillance, Strategy, Salvage and Shield (5S) are frequently discussed. Nevertheless, the current focus is mainly on the optimization and management of individual strategy, and there are limited attempts to combine the five strategies as an integral global biodefense system. Sheet represents the biosafety datasheet for biohazards in natural environment and human society, which helps our deeper understanding on the geographical pattern, transmission routes and infection mechanism of pathogens. Online surveillance and prognostication network is an environmental Surveillance tool for monitoring the outbreak of pandemic diseases and alarming the risks to take emergency actions, targeting aerosols, waters, soils and animals. Strategy is policies and legislations for social distancing, lockdown and personal protective equipment to block the spread of infectious diseases in communities. Clinical measures are Salvage on patients by innovating appropriate medicines and therapies. The ultimate defensive Shield is vaccine development to protect healthy crowds from infection. Fighting against COVID-19 and other emerging infectious diseases is a long rocky journey, requiring the common endeavors of scientists and politicians from all countries around the world. 5S in global biodefense system bring a ray of light to the current darkest and future road from environmental and geographical perspectives.
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Affiliation(s)
- Xinzi Wang
- School of Environment, Tsinghua University, Beijing 100084, PR China
| | - Tianyun Wu
- Research Institute for Environmental Innovation (Tsinghua-Suzhou), Suzhou 215163, PR China
| | - Luis F S Oliveira
- Departamento de Ingeniería Civil y Arquitectura, Universidad de Lima, Avenida Javier Prado Este 4600, Santiago de Surco 1503, Peru; Department of Civil and Environmental, Universidad de la Costa, Calle 58 #55-66, 080002 Barranquilla, Atlántico, Colombia
| | - Dayi Zhang
- College of New Energy and Environment, Jilin University, Changchun 130021, PR China.
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