1
|
Zachreson C, Tobin R, Walker C, Conway E, Shearer FM, McVernon J, Geard N. A model-based assessment of social isolation practices for COVID-19 outbreak response in residential care facilities. BMC Infect Dis 2024; 24:880. [PMID: 39210276 PMCID: PMC11360480 DOI: 10.1186/s12879-024-09788-x] [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: 04/03/2024] [Accepted: 08/21/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Residential aged-care facilities (RACFs, also called long-term care facilities, aged care homes, or nursing homes) have elevated risks of respiratory infection outbreaks and associated disease burden. During the COVID-19 pandemic, social isolation policies were commonly used in these facilities to prevent and mitigate outbreaks. We refer specifically to general isolation policies that were intended to reduce contact between residents, without regard to confirmed infection status. Such policies are controversial because of their association with adverse mental and physical health indicators and there is a lack of modelling that assesses their effectiveness. METHODS In consultation with the Australian Government Department of Health and Aged Care, we developed an agent-based model of COVID-19 transmission in a structured population, intended to represent the salient characteristics of a residential care environment. Using our model, we generated stochastic ensembles of simulated outbreaks and compared summary statistics of outbreaks simulated under different mitigation conditions. Our study focuses on the marginal impact of general isolation (reducing social contact between residents), regardless of confirmed infection. For a realistic assessment, our model included other generic interventions consistent with the Australian Government's recommendations released during the COVID-19 pandemic: isolation of confirmed resident cases, furlough (mandatory paid leave) of staff members with confirmed infection, and deployment of personal protective equipment (PPE) after outbreak declaration. RESULTS In the absence of any asymptomatic screening, general isolation of residents to their rooms reduced median cumulative cases by approximately 27%. However, when conducted concurrently with asymptomatic screening and isolation of confirmed cases, general isolation reduced the median number of cumulative infections by only 12% in our simulations. CONCLUSIONS Under realistic sets of assumptions, our simulations showed that general isolation of residents did not provide substantial benefits beyond those achieved through screening, isolation of confirmed cases, and deployment of PPE. Our results also highlight the importance of effective case isolation, and indicate that asymptomatic screening of residents and staff may be warranted, especially if importation risk from the outside community is high. Our conclusions are sensitive to assumptions about the proportion of total contacts in a facility accounted for by casual interactions between residents.
Collapse
Affiliation(s)
- Cameron Zachreson
- School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia.
| | - Ruarai Tobin
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Camelia Walker
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Eamon Conway
- The Walter and Eliza Hall Institute, Parkville, Victoria, Australia
| | - Freya M Shearer
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jodie McVernon
- Victorian Infectious Disease Reference Laboratory Epidemiology Unit, The Royal Melbourne Hospital at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Nicholas Geard
- School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia
| |
Collapse
|
2
|
D'Souza GC, Kraschnewski JL, Francis E, Heilbrunn E, Kong L, Lehman E, Osevala N, Urso J, Chamberlain L, Suda KM, McNeil L, Calo WA. Implementation of COVID-19 infection control best practices in nursing homes amid the pandemic. BMC Health Serv Res 2024; 24:941. [PMID: 39154191 PMCID: PMC11329985 DOI: 10.1186/s12913-024-11407-z] [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: 07/19/2023] [Accepted: 08/06/2024] [Indexed: 08/19/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic has claimed around 170,000 lives among nursing home residents and staff in the United States through April 2023. In a cluster randomized controlled trial (RCT) with 136 nursing homes, we delivered training to improve COVID-19 infection control best practices. We sought to assess the implementation of infection control practices in participating nursing homes. METHODS Concurrent with the delivery of the RCT (January-November 2021), we surveyed nursing home administrators (NHAs, n = 38) at baseline and 6-month follow-up. Using validated items from the Centers for Disease Control and Prevention (CDC), the surveys inquired about 80 infection control best-practice activities (yes/no). The survey also asked seven scales corresponding to inner setting factors that may have impacted implementation. We assessed changes in infection control practices and inner setting factors between baseline and 6-month follow-up. RESULTS Overall, the implementation of 11 best practices changed over time. NHAs reported an increase in the availability of informational materials for residents and families (84% vs. 100%, p = 0.031), the use of alcohol-based hand sanitizer over soap (76% vs. 97%, p = 0.008), and the development of contingency plans for increased postmortem care (53% vs. 82%, p = 0.013). The implementation of four best-practice visitation policies and three communal restrictions decreased between baseline and 6-month follow-up (all p < 0.05). Regarding inner setting factors, only culture stress (perceived strain, stress, and role overload) increased between surveys (mean scores: 3.14 vs. 3.58, p = 0.004). CONCLUSION This study was among the first to report changes in implementing COVID-19 infection control best practices in nursing homes amid the pandemic. Culture stress was an important inner setting factor that may have impacted implementation activities. TRIAL REGISTRATION NCT04499391 DATE OF REGISTRATION: August 3rd, 2020.
Collapse
Affiliation(s)
- Gail C D'Souza
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, United States of America
| | - Jennifer L Kraschnewski
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, United States of America.
- Department of Medicine, Penn State College of Medicine, 90 Hope Drive, Mail Code A145, Hershey, PA, 17033, United States of America.
| | - Erica Francis
- Department of Medicine, Penn State College of Medicine, 90 Hope Drive, Mail Code A145, Hershey, PA, 17033, United States of America
| | - Emily Heilbrunn
- Department of Medicine, Penn State College of Medicine, 90 Hope Drive, Mail Code A145, Hershey, PA, 17033, United States of America
| | - Lan Kong
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, United States of America
| | - Erik Lehman
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, United States of America
| | - Nicole Osevala
- Department of Medicine, Penn State College of Medicine, 90 Hope Drive, Mail Code A145, Hershey, PA, 17033, United States of America
| | - Jennifer Urso
- Guardian Healthcare Pharmacy Services, Brockway, PA, United States of America
| | - Linda Chamberlain
- Pennsylvania Department of Health, Division of Home Health, Harrisburg, PA, United States of America
| | - Kim M Suda
- Helion, A division of Highmark Health, Harrisburg, PA, United States of America
| | - Laura McNeil
- Helion, A division of Highmark Health, Harrisburg, PA, United States of America
| | - William A Calo
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, United States of America
| |
Collapse
|
3
|
Duong KN, Nguyen DT, Kategeaw W, Liang X, Khaing W, Visnovsky LD, Veettil SK, McFarland MM, Nelson RE, Jones BE, Pavia AT, Coates E, Khader K, Love J, Vega Yon GG, Zhang Y, Willson T, Dorsan E, Toth DJ, Jones MM, Samore MH, Chaiyakunapruk N. Incorporating social determinants of health into transmission modeling of COVID-19 vaccine in the US: a scoping review. LANCET REGIONAL HEALTH. AMERICAS 2024; 35:100806. [PMID: 38948323 PMCID: PMC11214325 DOI: 10.1016/j.lana.2024.100806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 05/22/2024] [Accepted: 05/23/2024] [Indexed: 07/02/2024]
Abstract
During COVID-19 in the US, social determinants of health (SDH) have driven health disparities. However, the use of SDH in COVID-19 vaccine modeling is unclear. This review aimed to summarize the current landscape of incorporating SDH into COVID-19 vaccine transmission modeling in the US. Medline and Embase were searched up to October 2022. We included studies that used transmission modeling to assess the effects of COVID-19 vaccine strategies in the US. Studies' characteristics, factors incorporated into models, and approaches to incorporate these factors were extracted. Ninety-two studies were included. Of these, 11 studies incorporated SDH factors (alone or combined with demographic factors). Various sets of SDH factors were integrated, with occupation being the most common (8 studies), followed by geographical location (5 studies). The results show that few studies incorporate SDHs into their models, highlighting the need for research on SDH impact and approaches to incorporating SDH into modeling. Funding This research was funded by the Centers for Disease Control and Prevention (CDC).
Collapse
Affiliation(s)
- Khanh N.C. Duong
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Danielle T. Nguyen
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Warittakorn Kategeaw
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Xi Liang
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Win Khaing
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Lindsay D. Visnovsky
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Sajesh K. Veettil
- International Medical University, School of Pharmacy, Department of Pharmacy Practice, Kuala Lumpur, Malaysia
| | - Mary M. McFarland
- Spencer S. Eccles Health Sciences Library, University of Utah, Salt Lake City, UT, USA
| | - Richard E. Nelson
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Barbara E. Jones
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
- Division of Pulmonary & Critical Care, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Andrew T. Pavia
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
- Division of Pediatric Infectious Diseases, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Emma Coates
- Department of Mathematics & Statistics, McMaster University, Ontario, Canada
| | - Karim Khader
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Jay Love
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - George G. Vega Yon
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Yue Zhang
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Tina Willson
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Egenia Dorsan
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Damon J.A. Toth
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA
| | - Makoto M. Jones
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Matthew H. Samore
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Nathorn Chaiyakunapruk
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| |
Collapse
|
4
|
Espinosa O, Mora L, Sanabria C, Ramos A, Rincón D, Bejarano V, Rodríguez J, Barrera N, Álvarez-Moreno C, Cortés J, Saavedra C, Robayo A, Franco OH. Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review. Syst Rev 2024; 13:30. [PMID: 38229123 PMCID: PMC10790449 DOI: 10.1186/s13643-023-02411-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 12/04/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). METHODOLOGY PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. FINDINGS In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. INTERPRETATION The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.
Collapse
Affiliation(s)
- Oscar Espinosa
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia.
| | - Laura Mora
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Cristian Sanabria
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Antonio Ramos
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Duván Rincón
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Valeria Bejarano
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Jhonathan Rodríguez
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Nicolás Barrera
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | | | - Jorge Cortés
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Carlos Saavedra
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Adriana Robayo
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Oscar H Franco
- University Medical Center Utrecht, Utrecht University & Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, USA
| |
Collapse
|
5
|
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
|