1
|
Yayan J, Saleh D, Franke KJ. Potential association between COVID-19 infections and the declining incidence of lung cancers. J Infect Public Health 2024; 17:102458. [PMID: 38823085 DOI: 10.1016/j.jiph.2024.05.046] [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: 03/22/2024] [Revised: 05/19/2024] [Accepted: 05/22/2024] [Indexed: 06/03/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic has significantly impacted global health and prompted studies on its effects across various diseases. Recent data suggest a potential correlation between COVID-19 and a decrease in lung cancer incidence. This study examines the association between COVID-19 infection and changes in lung cancer cases. MATERIAL AND METHODS We conducted a retrospective analysis of medical records from Clinic Lüdenscheid, Germany, from January 1, 2018, to December 31, 2021, comparing lung cancer cases before and during the pandemic. Demographic characteristics and cancer stages were also assessed. RESULTS We evaluated 523 patients; 269 pre-COVID and 254 during COVID. While the overall number of cases declined, a significant increase in advanced stage cancers was noted during COVID (P = 0.04). The adjusted incidence rates showed a nuanced decrease from approximately 33 cases per 100,000 pre-COVID to 31 during COVID. CONCLUSION This retrospective study suggests a modest decline in lung cancer incidence and an increase in advanced stages during COVID. Further comparisons with national data indicate a similar trend across Germany, with a decrease of about 3 % in lung cancer diagnoses post-2020, highlighting potential pandemic impacts on cancer detection.
Collapse
Affiliation(s)
- Josef Yayan
- Witten/Herdecke University, Witten, Märkische Clinics Health Holding Ltd., Clinic Lüdenscheid, Department of Internal Medicine, Pulmonary Division, Internal Intensive Care Medicine, Infectiology, and Sleep Medicine, Germany.
| | - Diana Saleh
- Witten/Herdecke University, Witten, Märkische Clinics Health Holding Ltd., Clinic Lüdenscheid, Department of Internal Medicine, Pulmonary Division, Internal Intensive Care Medicine, Infectiology, and Sleep Medicine, Germany
| | - Karl-Josef Franke
- Witten/Herdecke University, Witten, Märkische Clinics Health Holding Ltd., Clinic Lüdenscheid, Department of Internal Medicine, Pulmonary Division, Internal Intensive Care Medicine, Infectiology, and Sleep Medicine, Germany
| |
Collapse
|
2
|
Smith DRM, Chervet S, Pinettes T, Shirreff G, Jijón S, Oodally A, Jean K, Opatowski L, Kernéis S, Temime L. How have mathematical models contributed to understanding the transmission and control of SARS-CoV-2 in healthcare settings? A systematic search and review. J Hosp Infect 2023; 141:132-141. [PMID: 37734676 DOI: 10.1016/j.jhin.2023.07.028] [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: 03/24/2023] [Accepted: 07/04/2023] [Indexed: 09/23/2023]
Abstract
Since the onset of the COVID-19 pandemic, mathematical models have been widely used to inform public health recommendations regarding COVID-19 control in healthcare settings. The objective of this study was to systematically review SARS-CoV-2 transmission models in healthcare settings, and to summarize their contributions to understanding nosocomial COVID-19. A systematic search and review of published articles indexed in PubMed was carried out. Modelling studies describing dynamic inter-individual transmission of SARS-CoV-2 in healthcare settings, published by mid-February 2022 were included. Models have mostly focused on acute-care and long-term-care facilities in high-income countries. Models have quantified outbreak risk, showing great variation across settings and pandemic periods. Regarding surveillance, routine testing rather than symptom-based was highlighted as essential for COVID-19 prevention due to high rates of silent transmission. Surveillance impacts depended critically on testing frequency, diagnostic sensitivity, and turn-around time. Healthcare re-organization also proved to have large epidemiological impacts: beyond obvious benefits of isolating cases and limiting inter-individual contact, more complex strategies (staggered staff scheduling, immune-based cohorting) reduced infection risk. Finally, vaccination impact, while highly effective for limiting COVID-19 burden, varied substantially depending on assumed mechanistic impacts on infection acquisition, symptom onset and transmission. Modelling results form an extensive evidence base that may inform control strategies for future waves of SARS-CoV-2 and other viral respiratory pathogens. We propose new avenues for future models of healthcare-associated outbreaks, with the aim of enhancing their efficiency and contributions to decision-making.
Collapse
Affiliation(s)
- D R M Smith
- Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France; Institut Pasteur, Université Paris-Cité, Epidemiology and Modelling of Antibiotic Evasion (EMAE), F-75015 Paris, France; Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France
| | - S Chervet
- Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France; Institut Pasteur, Université Paris-Cité, Epidemiology and Modelling of Antibiotic Evasion (EMAE), F-75015 Paris, France; Université Paris-Cité, INSERM, IAME, F-75018, Paris, France
| | - T Pinettes
- Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France; Unité PACRI, Institut Pasteur, Conservatoire National des Arts et Métiers, Paris, France
| | - G Shirreff
- Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France; Institut Pasteur, Université Paris-Cité, Epidemiology and Modelling of Antibiotic Evasion (EMAE), F-75015 Paris, France; Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France
| | - S Jijón
- Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France; Unité PACRI, Institut Pasteur, Conservatoire National des Arts et Métiers, Paris, France
| | - A Oodally
- Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France; Institut Pasteur, Université Paris-Cité, Epidemiology and Modelling of Antibiotic Evasion (EMAE), F-75015 Paris, France; Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France
| | - K Jean
- Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France; Unité PACRI, Institut Pasteur, Conservatoire National des Arts et Métiers, Paris, France
| | - L Opatowski
- Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France; Institut Pasteur, Université Paris-Cité, Epidemiology and Modelling of Antibiotic Evasion (EMAE), F-75015 Paris, France
| | - S Kernéis
- Université Paris-Cité, INSERM, IAME, F-75018, Paris, France; Equipe de Prévention du Risque Infectieux (EPRI), AP-HP, Hôpital Bichat, F-75018 Paris, France.
| | - L Temime
- Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France; Unité PACRI, Institut Pasteur, Conservatoire National des Arts et Métiers, Paris, France
| |
Collapse
|
3
|
Hickey J, Rancourt DG. Predictions from standard epidemiological models of consequences of segregating and isolating vulnerable people into care facilities. PLoS One 2023; 18:e0293556. [PMID: 37903148 PMCID: PMC10615287 DOI: 10.1371/journal.pone.0293556] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 10/15/2023] [Indexed: 11/01/2023] Open
Abstract
OBJECTIVES Since the declaration of the COVID-19 pandemic, many governments have imposed policies to reduce contacts between people who are presumed to be particularly vulnerable to dying from respiratory illnesses and the rest of the population. These policies typically address vulnerable individuals concentrated in centralized care facilities and entail limiting social contacts with visitors, staff members, and other care home residents. We use a standard epidemiological model to investigate the impact of such circumstances on the predicted infectious disease attack rates, for interacting robust and vulnerable populations. METHODS We implement a general susceptible-infectious-recovered (SIR) compartmental model with two populations: robust and vulnerable. The key model parameters are the per-individual frequencies of within-group (robust-robust and vulnerable-vulnerable) and between-group (robust-vulnerable and vulnerable-robust) infectious-susceptible contacts and the recovery times of individuals in the two groups, which can be significantly longer for vulnerable people. RESULTS Across a large range of possible model parameters including degrees of segregation versus intermingling of vulnerable and robust individuals, we find that concentrating the most vulnerable into centralized care facilities virtually always increases the infectious disease attack rate in the vulnerable group, without significant benefit to the robust group. CONCLUSIONS Isolated care homes of vulnerable residents are predicted to be the worst possible mixing circumstances for reducing harm in epidemic or pandemic conditions.
Collapse
Affiliation(s)
- Joseph Hickey
- Correlation Research in the Public Interest, Ottawa, Ontario, Canada
| | - Denis G. Rancourt
- Correlation Research in the Public Interest, Ottawa, Ontario, Canada
| |
Collapse
|
4
|
Sullivan SG, Sadewo GRP, Brotherton JM, Kaufman C, Goldsmith JJ, Whiting S, Wu L, Canevari JT, Lusher D. The spread of coronavirus disease 2019 (COVID-19) via staff work and household networks in residential aged-care services in Victoria, Australia, May-October 2020. Infect Control Hosp Epidemiol 2023; 44:1334-1341. [PMID: 36263465 DOI: 10.1017/ice.2022.243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE Morbidity and mortality from coronavirus disease 2019 (COVID-19) have been significant among elderly residents of residential aged-care services (RACS). To prevent incursions of COVID-19 in RACS in Australia, visitors were banned and aged-care workers were encouraged to work at a single site. We conducted a review of case notes and a social network analysis to understand how workplace and social networks enabled the spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) among RACS. DESIGN Retrospective outbreak review. SETTING AND PARTICIPANTS Staff involved in COVID-19 outbreaks in RACS in Victoria, Australia, May-October 2020. METHODS The Victorian Department of Health COVID-19 case and contact data were reviewed to construct 2 social networks: (1) a work network connecting RACS through workers and (2) a household network connecting to RACS through households. Probable index cases were reviewed to estimate the number and size (number of resident cases and deaths) of outbreaks likely initiated by multisite work versus transmission via households. RESULTS Among 2,033 cases linked to an outbreak as staff, 91 (4.5%) were multisite staff cases. Forty-three outbreaks were attributed to multisite work and 35 were deemed potentially preventable had staff worked at a single site. In addition, 99 staff cases were linked to another RACS outbreak through their household contacts, and 21 outbreaks were attributed to staff-household transmission. CONCLUSIONS Limiting worker mobility through single-site policies could reduce the chances of SARS-CoV-2 spreading from one RACS to another. However, initiatives that reduce the chance of transmission via household networks would also be needed.
Collapse
Affiliation(s)
- Sheena G Sullivan
- Public Health Division, Victorian Department of Health, Melbourne, Victoria, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Giovanni Radhitio P Sadewo
- Social Network Research Laboratory, Centre for Transformative Innovation, Swinburne University of Technology, Melbourne, Victoria, Australia
| | - Julia M Brotherton
- Australian Centre for the Prevention of Cervical Cancer, Melbourne, Victoria, Australia
| | - Claire Kaufman
- Public Health Division, Victorian Department of Health, Melbourne, Victoria, Australia
| | - Jessie J Goldsmith
- Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | | | - Logan Wu
- Public Health Division, Victorian Department of Health, Melbourne, Victoria, Australia
| | - Jose T Canevari
- Public Health Division, Victorian Department of Health, Melbourne, Victoria, Australia
| | - Dean Lusher
- Social Network Research Laboratory, Centre for Transformative Innovation, Swinburne University of Technology, Melbourne, Victoria, Australia
| |
Collapse
|
5
|
Adams JW, Jones K, Preiss S, Hadley E, Segelman M. Evaluating Policies to Decrease the Risk of Introducing SARS-CoV-2 Infections to Nursing Home Facilities. J Appl Gerontol 2023:7334648231155873. [PMID: 36749786 PMCID: PMC10360919 DOI: 10.1177/07334648231155873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
We used an individual-based microsimulation model of North Carolina to determine what facility-level policies would result in the greatest reduction in the number of individuals with SARS-CoV-2 entering the nursing home environment from 12/15/2021 to 1/3/2022 (e.g., Omicron variant surge). On average, there were 14,287 (Credible Interval [CI]: 13,477-15,147) daily visitors and 17,168 (CI: 16,571-17,768) HCW coming from the community into 426 nursing home facilities. Policies requiring a negative rapid test or vaccinated status for visitors resulted in the greatest reduction in the number of individuals with SARS-CoV-2 infection entering the nursing home environment with a 29.6% (26.9%-32.0%) and 24.0% (CI: 22.2%-25.5%) reduction, respectively. Policies halving visits (21.2% [20.0%-28.2%]), requiring all vaccinated HCW to receive a booster (7.8% [CI: 7.4%-8.7%]), and limiting visitation to a primary visitor (6.5% [CI: 3.5%-9.7%]) reduced infectious contacts to a lesser degree.
Collapse
Affiliation(s)
| | - Kasey Jones
- 6856RTI International, Research Triangle, NC, USA
| | - Sandy Preiss
- 6856RTI International, Research Triangle, NC, USA
| | - Emily Hadley
- 6856RTI International, Research Triangle, NC, USA
| | | |
Collapse
|
6
|
Maietti E, Reno C, Sanmarchi F, Montalti M, Fantini MP, Gori D. Are psychological status and trust in information related to vaccine hesitancy during COVID-19 pandemic? A latent class and mediation analyses in Italy. Hum Vaccin Immunother 2022; 18:2157622. [PMID: 36573024 PMCID: PMC9891681 DOI: 10.1080/21645515.2022.2157622] [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] [Indexed: 12/29/2022] Open
Abstract
Despite the recognized benefits of the COVID-19 vaccination, vaccine hesitancy (VH) remains one of the biggest challenges of the mass vaccination campaign. Most studies investigating VH determinants focused on socio-demographics and direct relationships. In this study, we aimed at: 1) identifying subgroups of people differently affected by the pandemic, in terms of psychological status; 2) investigating the role of psychological status and trust in information as possible mediators of the relationship between individual characteristics and VH. To this purpose, a latent class analysis (LCA) followed by a mediation analysis were carried out on data from a survey conducted in January 2021 on 1011 Italian citizens. LCA identified four different subgroups characterized by a differential psychological impact of the pandemic: the extremely affected (21.1%), the highly affected (49.1%), the moderately affected (21.8%) and the slightly affected (8%). We found that VH decreased with the increase of psychological impact (from 59.3% to 23.9%). In the mediation analysis, past vaccination refusal, age 45-54 years and lower-than-average income, were all indirectly related to higher VH through mistrust in COVID-19 information. Differently, the psychological impact counteracted the greater VH in females, the negative effect of social media among youngest (<35 years) and the negative effect of mistrust in the lower-than-average-income subgroup. Knowledge of psychological profile of hesitant individuals, their level of trust and the sources of information they access, together with their sociodemographic characteristics provides a more comprehensive picture of VH determinants that can be used by public health stakeholders to effectively design and adapt communication campaigns.
Collapse
Affiliation(s)
- Elisa Maietti
- Department of Biomedical and Nuromotor Sciences, Alma Mater Studiorum – Università di Bologna, Bologna, Italy
| | - Chiara Reno
- Department of Biomedical and Nuromotor Sciences, Alma Mater Studiorum – Università di Bologna, Bologna, Italy
| | - Francesco Sanmarchi
- Department of Biomedical and Nuromotor Sciences, Alma Mater Studiorum – Università di Bologna, Bologna, Italy,CONTACT Francesco Sanmarchi Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum – Università di Bologna, Via San Giacomo 12, Bologna, Italy
| | - Marco Montalti
- Department of Biomedical and Nuromotor Sciences, Alma Mater Studiorum – Università di Bologna, Bologna, Italy
| | - Maria Pia Fantini
- Department of Biomedical and Nuromotor Sciences, Alma Mater Studiorum – Università di Bologna, Bologna, Italy
| | - Davide Gori
- Department of Biomedical and Nuromotor Sciences, Alma Mater Studiorum – Università di Bologna, Bologna, Italy
| |
Collapse
|
7
|
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.
Collapse
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,
| |
Collapse
|
8
|
Rykovanov GN, Lebedev SN, Zatsepin OV, Kaminskii GD, Karamov EV, Romanyukha AA, Feigin AM, Chetverushkin BN. Agent-Based Simulation of the COVID-19 Epidemic in Russia. HERALD OF THE RUSSIAN ACADEMY OF SCIENCES 2022; 92:479-487. [PMID: 36091848 PMCID: PMC9447941 DOI: 10.1134/s1019331622040219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 02/20/2022] [Accepted: 03/10/2022] [Indexed: 06/15/2023]
Abstract
The COVID-19 pandemic has created a public health emergency in Russia and across the world. The wavelike spread of the new coronavirus infection, caused by newly emerging variants of the coronavirus, has led to a high incidence rate in all subjects of the Russian Federation. It is becoming extremely topical to get the opportunity to manage the development of the epidemic and assess the impact of certain regulatory measures on this process. This will help government agencies make informed decisions to control the burden on healthcare organizations. It is often impossible to obtain such assessments without using modern mathematical models.
Collapse
Affiliation(s)
- G. N. Rykovanov
- Russian Federal Nuclear Center‒Zababakhin All-Russia Scientific Research Institute of Technical Physics, Snezhinsk, Russia
| | - S. N. Lebedev
- Russian Federal Nuclear Center‒Zababakhin All-Russia Scientific Research Institute of Technical Physics, Snezhinsk, Russia
| | - O. V. Zatsepin
- Russian Federal Nuclear Center‒Zababakhin All-Russia Scientific Research Institute of Technical Physics, Snezhinsk, Russia
| | - G. D. Kaminskii
- National Medical Research Center of Phthisiopulmonology and Infectious Diseases, Moscow, Russia
| | - E. V. Karamov
- National Medical Research Center of Phthisiopulmonology and Infectious Diseases, Moscow, Russia
- Gamaleya National Center of Epidemiology and Microbiology, Moscow, Russia
| | - A. A. Romanyukha
- Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia
| | - A. M. Feigin
- Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod, Russia
| | - B. N. Chetverushkin
- Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Moscow, Russia
| |
Collapse
|
9
|
Zhang W, Liu S, Osgood N, Zhu H, Qian Y, Jia P. Using simulation modelling and systems science to help contain COVID-19: A systematic review. SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE 2022; 40:SRES2897. [PMID: 36245570 PMCID: PMC9538520 DOI: 10.1002/sres.2897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 05/23/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.
Collapse
Affiliation(s)
- Weiwei Zhang
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Shiyong Liu
- Institute of Advanced Studies in Humanities and Social SciencesBeijing Normal University at ZhuhaiZhuhaiChina
| | - Nathaniel Osgood
- Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada
- Department of Community Health and EpidemiologyUniversity of SaskatchewanSaskatoonCanada
| | - Hongli Zhu
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Ying Qian
- Business SchoolUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Peng Jia
- School of Resource and Environmental SciencesWuhan UniversityWuhanHubeiChina
- International Institute of Spatial Lifecourse HealthWuhan UniversityWuhanHubeiChina
| |
Collapse
|
10
|
Gómez Vázquez JP, García YE, Schmidt AJ, Martínez-López B, Nuño M. Testing and vaccination to reduce the impact of COVID-19 in nursing homes: an agent-based approach. BMC Infect Dis 2022; 22:477. [PMID: 35590305 PMCID: PMC9117861 DOI: 10.1186/s12879-022-07385-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 04/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Efforts to protect residents in nursing homes involve non-pharmaceutical interventions, testing, and vaccine. We sought to quantify the effect of testing and vaccine strategies on the attack rate, length of the epidemic, and hospitalization. METHODS We developed an agent-based model to simulate the dynamics of SARS-CoV-2 transmission among resident and staff agents in a nursing home. Interactions between 172 residents and 170 staff based on data from a nursing home in Los Angeles, CA. Scenarios were simulated assuming different levels of non-pharmaceutical interventions, testing frequencies, and vaccine efficacy to reduce transmission. RESULTS Under the hypothetical scenario of widespread SARS-CoV-2 in the community, 3-day testing frequency minimized the attack rate and the time to eradicate an outbreak. Prioritization of vaccine among staff or staff and residents minimized the cumulative number of infections and hospitalization, particularly in the scenario of high probability of an introduction. Reducing the probability of a viral introduction eased the demand on testing and vaccination rate to decrease infections and hospitalizations. CONCLUSIONS Improving frequency of testing from 7-days to 3-days minimized the number of infections and hospitalizations, despite widespread community transmission. Vaccine prioritization of staff provides the best protection strategy when the risk of viral introduction is high.
Collapse
Affiliation(s)
- José P. Gómez Vázquez
- Center for Animal Disease Modeling and Surveillance, University of California Davis, Davis, CA USA
| | - Yury E. García
- Department of Public Health Sciences, University of California Davis, Davis, CA USA
| | - Alec J. Schmidt
- Department of Public Health Sciences, University of California Davis, Davis, CA USA
| | - Beatriz Martínez-López
- Center for Animal Disease Modeling and Surveillance, University of California Davis, Davis, CA USA
| | - Miriam Nuño
- Department of Public Health Sciences, University of California Davis, Davis, CA USA
| |
Collapse
|
11
|
Evaluating intervention strategies in controlling coronavirus disease 2019 (COVID-19) spread in care homes: An agent-based model - CORRIGENDUM. Infect Control Hosp Epidemiol 2022; 43:685. [PMID: 33583469 PMCID: PMC9981471 DOI: 10.1017/ice.2021.36] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
12
|
Asgary A, Blue H, Solis AO, McCarthy Z, Najafabadi M, Tofighi MA, Wu J. Modeling COVID-19 Outbreaks in Long-Term Care Facilities Using an Agent-Based Modeling and Simulation Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19052635. [PMID: 35270344 PMCID: PMC8910468 DOI: 10.3390/ijerph19052635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/15/2022] [Accepted: 02/20/2022] [Indexed: 02/01/2023]
Abstract
The elderly, especially those individuals with pre-existing health problems, have been disproportionally at a higher risk during the COVID-19 pandemic. Residents of long-term care facilities have been gravely affected by the pandemic and resident death numbers have been far above those of the general population. To better understand how infectious diseases such as COVID-19 can spread through long-term care facilities, we developed an agent-based simulation tool that uses a contact matrix adapted from previous infection control research in these types of facilities. This matrix accounts for the average distinct daily contacts between seven different agent types that represent the roles of individuals in long-term care facilities. The simulation results were compared to actual COVID-19 outbreaks in some of the long-term care facilities in Ontario, Canada. Our analysis shows that this simulation tool is capable of predicting the number of resident deaths after 50 days with a less than 0.1 variation in death rate. We modeled and predicted the effectiveness of infection control measures by utilizing this simulation tool. We found that to reduce the number of resident deaths, the effectiveness of personal protective equipment must be above 50%. We also found that daily random COVID-19 tests for as low as less than 10% of a long-term care facility’s population will reduce the number of resident deaths by over 75%. The results further show that combining several infection control measures will lead to more effective outcomes.
Collapse
Affiliation(s)
- Ali Asgary
- Disaster and Emergency Management Area, School of Administrative Studies, York University, Toronto, ON M3J 1P3, Canada
- Correspondence: (A.A.); (H.B.)
| | - Hudson Blue
- Disaster and Emergency Management Area, School of Administrative Studies, York University, Toronto, ON M3J 1P3, Canada
- Correspondence: (A.A.); (H.B.)
| | - Adriano O. Solis
- Decision Sciences Area, School of Administrative Studies, York University, Toronto, ON M3J 1P3, Canada;
| | - Zachary McCarthy
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada; (Z.M.); (J.W.)
| | - Mahdi Najafabadi
- Advanced Disaster, Emergency, and Rapid Response Simulation (ADERSIM), York University, Toronto, ON M3J 1P3, Canada; (M.N.); (M.A.T.)
| | - Mohammad Ali Tofighi
- Advanced Disaster, Emergency, and Rapid Response Simulation (ADERSIM), York University, Toronto, ON M3J 1P3, Canada; (M.N.); (M.A.T.)
| | - Jianhong Wu
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada; (Z.M.); (J.W.)
| |
Collapse
|
13
|
Smith DRM, Duval A, Zahar JR, Opatowski L, Temime L. Rapid antigen testing as a reactive response to surges in nosocomial SARS-CoV-2 outbreak risk. Nat Commun 2022; 13:236. [PMID: 35017499 PMCID: PMC8752617 DOI: 10.1038/s41467-021-27845-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 12/07/2021] [Indexed: 11/24/2022] Open
Abstract
Healthcare facilities are vulnerable to SARS-CoV-2 introductions and subsequent nosocomial outbreaks. Antigen rapid diagnostic testing (Ag-RDT) is widely used for population screening, but its health and economic benefits as a reactive response to local surges in outbreak risk are unclear. We simulate SARS-CoV-2 transmission in a long-term care hospital with varying COVID-19 containment measures in place (social distancing, face masks, vaccination). Across scenarios, nosocomial incidence is reduced by up to 40-47% (range of means) with routine symptomatic RT-PCR testing, 59-63% with the addition of a timely round of Ag-RDT screening, and 69-75% with well-timed two-round screening. For the latter, a delay of 4-5 days between the two screening rounds is optimal for transmission prevention. Screening efficacy varies depending on test sensitivity, test type, subpopulations targeted, and community incidence. Efficiency, however, varies primarily depending on underlying outbreak risk, with health-economic benefits scaling by orders of magnitude depending on the COVID-19 containment measures in place.
Collapse
Affiliation(s)
- David R M Smith
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France.
- Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, France.
- Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire national des arts et métiers, Paris, France.
| | - Audrey Duval
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France
- Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, France
- IAME, UMR 1137, Université Paris 13, Sorbonne Paris Cité, Paris, France
| | - Jean Ralph Zahar
- IAME, UMR 1137, Université Paris 13, Sorbonne Paris Cité, Paris, France
- Service de Microbiologie Clinique et Unité de Contrôle et de Prévention du Risque Infectieux, Groupe Hospitalier Paris Seine Saint-Denis, AP-HP, Bobigny, France
| | - Lulla Opatowski
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France
- Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, France
| | - Laura Temime
- Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire national des arts et métiers, Paris, France
- PACRI unit, Institut Pasteur, Conservatoire national des arts et métiers, Paris, France
| |
Collapse
|
14
|
How can risk of COVID-19 transmission be minimised in domiciliary care for older people: development, parameterisation and initial results of a simple mathematical model. Epidemiol Infect 2021. [PMCID: PMC8755531 DOI: 10.1017/s0950268821002727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
This paper proposes and analyses a stochastic model for the spread of an infectious disease transmitted between clients and care workers in the UK domiciliary (home) care setting. Interactions between clients and care workers are modelled using specially generated networks, with network parameters reflecting realistic patterns of care needs and visit allocation. These networks are then used to simulate a susceptible-exposed-infected-recovered/dead (SEIR/D)-type epidemic dynamics with different numbers of infectious and recovery stages. The results indicate that with the same overall capacity provided by care workers, the minimum peak proportion of infection and the smallest overall size of infection are achieved for the highest proportion of overlap between visit allocation, i.e. when care workers have the highest chances of being allocated a visit to the same client they have visited before. An intuitive explanation of this is that while providing the required care coverage, maximising overlap in visit allocation reduces the possibility of an infectious care worker inadvertently spreading the infection to other clients. The model is generic and can be adapted to any directly transmitted infectious disease, such as, more recently, corona virus disease 2019, provided accurate estimates of disease parameters can be obtained from real data.
Collapse
|
15
|
Lasser J, Zuber J, Sorger J, Dervic E, Ledebur K, Lindner SD, Klager E, Kletečka-Pulker M, Willschke H, Stangl K, Stadtmann S, Haslinger C, Klimek P, Wochele-Thoma T. Agent-based simulations for protecting nursing homes with prevention and vaccination strategies. J R Soc Interface 2021; 18:20210608. [PMID: 34932931 PMCID: PMC8692030 DOI: 10.1098/rsif.2021.0608] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/08/2021] [Indexed: 12/27/2022] Open
Abstract
Due to its high lethality among older people, the safety of nursing homes has been of central importance during the COVID-19 pandemic. With test procedures and vaccines becoming available at scale, nursing homes might relax prohibitory measures while controlling the spread of infections. By control we mean that each index case infects less than one other person on average. Here, we develop an agent-based epidemiological model for the spread of SARS-CoV-2 calibrated to Austrian nursing homes to identify optimal prevention strategies. We find that the effectiveness of mitigation testing depends critically on test turnover time (time until test result), the detection threshold of tests and mitigation testing frequencies. Under realistic conditions and in absence of vaccinations, we find that mitigation testing of employees only might be sufficient to control outbreaks if tests have low turnover times and detection thresholds. If vaccines that are 60% effective against high viral load and transmission are available, control is achieved if 80% or more of the residents are vaccinated, even without mitigation testing and if residents are allowed to have visitors. Since these results strongly depend on vaccine efficacy against infection, retention of testing infrastructures, regular testing and sequencing of virus genomes is advised to enable early identification of new variants of concern.
Collapse
Affiliation(s)
- Jana Lasser
- Institute for Interactive Systems and Data Science, Graz University of Technology, Graz, Steierermark, Austria
- Institute for Interactive Systems and Data Science, Graz University of Technology, Graz, Austria
- Complexity Science Hub Vienna, Wien, Austria
| | - Johannes Zuber
- Research Institute of Molecular Pathology, Wien, Austria
| | | | - Elma Dervic
- Medical University Vienna, Section for Science of Complex Systems, Wien, Austria
| | - Katharina Ledebur
- Medical University Vienna, Section for Science of Complex Systems, Wien, Austria
| | - Simon David Lindner
- Medical University Vienna, Section for Science of Complex Systems, Wien, Austria
| | - Elisabeth Klager
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Vienna, Austria
| | - Maria Kletečka-Pulker
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Vienna, Austria
- University Vienna, Institut für Ethik und Recht in der Medizin, Wien, Austria
| | - Harald Willschke
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Vienna, Austria
| | | | | | | | - Peter Klimek
- Complexity Science Hub Vienna, Wien, Austria
- Medical University Vienna, Section for Science of Complex Systems, Wien, Austria
| | - Thomas Wochele-Thoma
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Vienna, Austria
- Caritas Erzdiözese Wien, Wien, Austria
| |
Collapse
|
16
|
Hüttel FB, Iversen AM, Bo Hansen M, Kjær Ersbøll B, Ellermann-Eriksen S, Lundtorp Olsen N. Analysis of social interactions and risk factors relevant to the spread of infectious diseases at hospitals and nursing homes. PLoS One 2021; 16:e0257684. [PMID: 34543324 PMCID: PMC8452062 DOI: 10.1371/journal.pone.0257684] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 09/07/2021] [Indexed: 02/05/2023] Open
Abstract
Ensuring the safety of healthcare workers is vital to overcome the ongoing COVID-19 pandemic. We here present an analysis of the social interactions between the healthcare workers at hospitals and nursing homes. Using data from an automated hand hygiene system, we inferred social interactions between healthcare workers to identify transmission paths of infection in hospitals and nursing homes. A majority of social interactions occurred in medication rooms and kitchens emphasising that health-care workers should be especially aware of following the infection prevention guidelines in these places. Using epidemiology simulations of disease at the locations, we found no need to quarantine all healthcare workers at work with a contagious colleague. Only 14.1% and 24.2% of the health-care workers in the hospitals and nursing homes are potentially infected when we disregard hand sanitization and assume the disease is very infectious. Based on our simulations, we observe a 41% and 26% reduction in the number of infected healthcare workers at the hospital and nursing home, when we assume that hand sanitization reduces the spread by 20% from people to people and 99% from people to objects. The analysis and results presented here forms a basis for future research to explore the potential of a fully automated contact tracing systems.
Collapse
|
17
|
Stratil JM, Biallas RL, Burns J, Arnold L, Geffert K, Kunzler AM, Monsef I, Stadelmaier J, Wabnitz K, Litwin T, Kreutz C, Boger AH, Lindner S, Verboom B, Voss S, Movsisyan A. Non-pharmacological measures implemented in the setting of long-term care facilities to prevent SARS-CoV-2 infections and their consequences: a rapid review. Cochrane Database Syst Rev 2021; 9:CD015085. [PMID: 34523727 PMCID: PMC8442144 DOI: 10.1002/14651858.cd015085.pub2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Starting in late 2019, COVID-19, caused by the novel coronavirus SARS-CoV-2, spread around the world. Long-term care facilities are at particularly high risk of outbreaks, and the burden of morbidity and mortality is very high among residents living in these facilities. OBJECTIVES To assess the effects of non-pharmacological measures implemented in long-term care facilities to prevent or reduce the transmission of SARS-CoV-2 infection among residents, staff, and visitors. SEARCH METHODS On 22 January 2021, we searched the Cochrane COVID-19 Study Register, WHO COVID-19 Global literature on coronavirus disease, Web of Science, and CINAHL. We also conducted backward citation searches of existing reviews. SELECTION CRITERIA We considered experimental, quasi-experimental, observational and modelling studies that assessed the effects of the measures implemented in long-term care facilities to protect residents and staff against SARS-CoV-2 infection. Primary outcomes were infections, hospitalisations and deaths due to COVID-19, contaminations of and outbreaks in long-term care facilities, and adverse health effects. DATA COLLECTION AND ANALYSIS Two review authors independently screened titles, abstracts and full texts. One review author performed data extractions, risk of bias assessments and quality appraisals, and at least one other author checked their accuracy. Risk of bias and quality assessments were conducted using the ROBINS-I tool for cohort and interrupted-time-series studies, the Joanna Briggs Institute (JBI) checklist for case-control studies, and a bespoke tool for modelling studies. We synthesised findings narratively, focusing on the direction of effect. One review author assessed certainty of evidence with GRADE, with the author team critically discussing the ratings. MAIN RESULTS We included 11 observational studies and 11 modelling studies in the analysis. All studies were conducted in high-income countries. Most studies compared outcomes in long-term care facilities that implemented the measures with predicted or observed control scenarios without the measure (but often with baseline infection control measures also in place). Several modelling studies assessed additional comparator scenarios, such as comparing higher with lower rates of testing. There were serious concerns regarding risk of bias in almost all observational studies and major or critical concerns regarding the quality of many modelling studies. Most observational studies did not adequately control for confounding. Many modelling studies used inappropriate assumptions about the structure and input parameters of the models, and failed to adequately assess uncertainty. Overall, we identified five intervention domains, each including a number of specific measures. Entry regulation measures (4 observational studies; 4 modelling studies) Self-confinement of staff with residents may reduce the number of infections, probability of facility contamination, and number of deaths. Quarantine for new admissions may reduce the number of infections. Testing of new admissions and intensified testing of residents and of staff after holidays may reduce the number of infections, but the evidence is very uncertain. The evidence is very uncertain regarding whether restricting admissions of new residents reduces the number of infections, but the measure may reduce the probability of facility contamination. Visiting restrictions may reduce the number of infections and deaths. Furthermore, it may increase the probability of facility contamination, but the evidence is very uncertain. It is very uncertain how visiting restrictions may adversely affect the mental health of residents. Contact-regulating and transmission-reducing measures (6 observational studies; 2 modelling studies) Barrier nursing may increase the number of infections and the probability of outbreaks, but the evidence is very uncertain. Multicomponent cleaning and environmental hygiene measures may reduce the number of infections, but the evidence is very uncertain. It is unclear how contact reduction measures affect the probability of outbreaks. These measures may reduce the number of infections, but the evidence is very uncertain. Personal hygiene measures may reduce the probability of outbreaks, but the evidence is very uncertain. Mask and personal protective equipment usage may reduce the number of infections, the probability of outbreaks, and the number of deaths, but the evidence is very uncertain. Cohorting residents and staff may reduce the number of infections, although evidence is very uncertain. Multicomponent contact -regulating and transmission -reducing measures may reduce the probability of outbreaks, but the evidence is very uncertain. Surveillance measures (2 observational studies; 6 modelling studies) Routine testing of residents and staff independent of symptoms may reduce the number of infections. It may reduce the probability of outbreaks, but the evidence is very uncertain. Evidence from one observational study suggests that the measure may reduce, while the evidence from one modelling study suggests that it probably reduces hospitalisations. The measure may reduce the number of deaths among residents, but the evidence on deaths among staff is unclear. Symptom-based surveillance testing may reduce the number of infections and the probability of outbreaks, but the evidence is very uncertain. Outbreak control measures (4 observational studies; 3 modelling studies) Separating infected and non-infected residents or staff caring for them may reduce the number of infections. The measure may reduce the probability of outbreaks and may reduce the number of deaths, but the evidence for the latter is very uncertain. Isolation of cases may reduce the number of infections and the probability of outbreaks, but the evidence is very uncertain. Multicomponent measures (2 observational studies; 1 modelling study) A combination of multiple infection-control measures, including various combinations of the above categories, may reduce the number of infections and may reduce the number of deaths, but the evidence for the latter is very uncertain. AUTHORS' CONCLUSIONS This review provides a comprehensive framework and synthesis of a range of non-pharmacological measures implemented in long-term care facilities. These may prevent SARS-CoV-2 infections and their consequences. However, the certainty of evidence is predominantly low to very low, due to the limited availability of evidence and the design and quality of available studies. Therefore, true effects may be substantially different from those reported here. Overall, more studies producing stronger evidence on the effects of non-pharmacological measures are needed, especially in low- and middle-income countries and on possible unintended consequences of these measures. Future research should explore the reasons behind the paucity of evidence to guide pandemic research priority setting in the future.
Collapse
Affiliation(s)
- Jan M Stratil
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Renke L Biallas
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Jacob Burns
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Laura Arnold
- Academy of Public Health Services, Duesseldorf, Germany
| | - Karin Geffert
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Angela M Kunzler
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Ina Monsef
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Julia Stadelmaier
- Institute for Evidence in Medicine, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Katharina Wabnitz
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Tim Litwin
- Institute of Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Anna Helen Boger
- Institute of Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Saskia Lindner
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Ben Verboom
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
- Department of Social Policy and Intervention, University of Oxford, Oxford, UK
| | - Stephan Voss
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Ani Movsisyan
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| |
Collapse
|
18
|
Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data. ELECTRONICS 2021. [DOI: 10.3390/electronics10141626] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Research on SARS-CoV-2 and its social implications have become a major focus to interdisciplinary teams worldwide. As interest in more direct solutions, such as mass testing and vaccination grows, several studies appear to be dedicated to the operationalization of those solutions, leveraging both traditional and new methodologies, and, increasingly, the combination of both. This research examines the challenges anticipated for preventative testing of SARS-CoV-2 in schools and proposes an artificial intelligence (AI)-powered agent-based model crafted specifically for school scenarios. This research shows that in the absence of real data, simulation-based data can be used to develop an artificial intelligence model for the application of rapid assessment of school testing policies.
Collapse
|
19
|
Hu Y, Guo J, Li G, Lu X, Li X, Zhang Y, Cong L, Kang Y, Jia X, Shi X, Xie G, Zhang L. Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study. BMJ Open 2021; 11:e045886. [PMID: 34233974 PMCID: PMC8266432 DOI: 10.1136/bmjopen-2020-045886] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVES This study quantified how the efficiency of testing and contact tracing impacts the spread of COVID-19. The average time interval between infection and quarantine, whether asymptomatic cases are tested or not, and initial delays to beginning a testing and tracing programme were investigated. SETTING We developed a novel individual-level network model, called CoTECT (Testing Efficiency and Contact Tracing model for COVID-19), using key parameters from recent studies to quantify the impacts of testing and tracing efficiency. The model distinguishes infection from confirmation by integrating a 'T' compartment, which represents infections confirmed by testing and quarantine. The compartments of presymptomatic (E), asymptomatic (I), symptomatic (Is), and death with (F) or without (f) test confirmation were also included in the model. Three scenarios were evaluated in a closed population of 3000 individuals to mimic community-level dynamics. Real-world data from four Nordic countries were also analysed. PRIMARY AND SECONDARY OUTCOME MEASURES Simulation result: total/peak daily infections and confirmed cases, total deaths (confirmed/unconfirmed by testing), fatalities and the case fatality rate. Real-world analysis: confirmed cases and deaths per million people. RESULTS (1) Shortening the duration between Is and T from 12 to 4 days reduces infections by 85.2% and deaths by 88.8%. (2) Testing and tracing regardless of symptoms reduce infections by 35.7% and deaths by 46.2% compared with testing only symptomatic cases. (3) Reducing the delay to implementing a testing and tracing programme from 50 to 10 days reduces infections by 35.2% and deaths by 44.6%. These results were robust to sensitivity analysis. An analysis of real-world data showed that tests per case early in the pandemic are critical for reducing confirmed cases and the fatality rate. CONCLUSIONS Reducing testing delays will help to contain outbreaks. These results provide policymakers with quantitative evidence of efficiency as a critical value in developing testing and contact tracing strategies.
Collapse
Affiliation(s)
- Yiying Hu
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Jianying Guo
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Guanqiao Li
- School of Medicine and Vanke School of Public Health Beijing, Tsinghua University, Beijing, China
- Tsinghua Clinical Research Institute (TCRI), School of Medicine, Tsinghua University, Beijing, China
| | - Xi Lu
- School of Medicine and Vanke School of Public Health Beijing, Tsinghua University, Beijing, China
| | - Xiang Li
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Yuan Zhang
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Lin Cong
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Yanni Kang
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Xiaoyu Jia
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Xuanling Shi
- School of Medicine and Vanke School of Public Health Beijing, Tsinghua University, Beijing, China
| | - Guotong Xie
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
- Ping An Health Cloud Company, Ping An Insurance Group Company of China, Shenzhen, China
- Ping An International Smart City Technology Co., Ltd, Ping An Insurance Group Company of China, Shenzhen, China
| | - Linqi Zhang
- School of Medicine and Vanke School of Public Health Beijing, Tsinghua University, Beijing, China
| |
Collapse
|
20
|
Alfawaz HA, Khan N, Aljumah GA, Hussain SD, Al-Daghri NM. Dietary Intake and Supplement Use Among Saudi Residents during COVID-19 Lockdown. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18126435. [PMID: 34198578 PMCID: PMC8296224 DOI: 10.3390/ijerph18126435] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/12/2021] [Accepted: 06/13/2021] [Indexed: 12/30/2022]
Abstract
Healthy diet and supplement use may prove as sustainable strategies to lower COVID-19 infection. Our study investigated the dietary changes before and during lockdown and observed dietary supplements (DS) use among residents in Saudi Arabia. This cross-sectional study collected data via an online electronic survey questionnaire among males (N = 921) and females (N = 1044) residing in Saudi Arabia, 15 years of age and above. There was a significant decrease in the prevalence of males (before vs. during lockdown) having improved changes in dietary habit (68.6% vs. 65.8%; p = 0.004), which was similar in female participants (69 vs. 73.4% vs. 69%; p < 0.001). The frequency of multivitamin users among COVID-19 participants was significantly lower than non-users (44.4 vs. 55.6; p < 0.003). Male respondents within 26-35 years of age were more likely to use multivitamin supplements than females (30.1 vs. 22.6%; p < 0.05) of same age group. Predictors for DS use were increased age group, income, education level and COVID-19 status. In conclusion, an increase in unhealthy diet behavior was observed among Saudi males and females during the pandemic lockdown and the predictors of DS use included increased age, income, education level and COVID-19 status.
Collapse
Affiliation(s)
- Hanan A. Alfawaz
- Department of Food Science & Nutrition, College of Food & Agriculture Science, King Saud University, Riyadh 11495, Saudi Arabia;
- Biomarkers of Chronic Diseases, Biochemistry Department, King Saud University, Riyadh 11451, Saudi Arabia;
| | - Nasiruddin Khan
- Department of Food Science and Human Nutrition, College of Applied and Health Sciences, A’ Sharqiyah University, Ibra 400, Oman;
| | | | - Syed D. Hussain
- Biomarkers of Chronic Diseases, Biochemistry Department, King Saud University, Riyadh 11451, Saudi Arabia;
| | - Nasser M. Al-Daghri
- Biomarkers of Chronic Diseases, Biochemistry Department, King Saud University, Riyadh 11451, Saudi Arabia;
- Correspondence: ; Tel.: +966-(11)-467-5939
| |
Collapse
|