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Yang L, Iwami M, Chen Y, Wu M, van Dam KH. Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review. PROGRESS IN PLANNING 2023; 168:100657. [PMID: 35280114 PMCID: PMC8904142 DOI: 10.1016/j.progress.2022.100657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, impelling behaviour change and facilitating the construction of lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, have been used to support responses to the current pandemic as well as to the spread of previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. We selected 109 out of 8,737 studies retrieved from databases and analysed them based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design, as well as computational modelling support, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches for evaluating design decisions depending on the target disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for fighting against COVID-19, or be incorporated into broader frameworks to help cities become more resilient to future disasters.
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Affiliation(s)
- Liu Yang
- School of Architecture, Southeast University, Nanjing, China
- Research Center of Urban Design, Southeast University, Nanjing, China
| | - Michiyo Iwami
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, UK
| | - Yishan Chen
- Architecture and Urban Design Research Center, China IPPR International Engineering CO., LTD, Beijing, China
| | - Mingbo Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Koen H van Dam
- Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, UK
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Marion G, Hadley L, Isham V, Mollison D, Panovska-Griffiths J, Pellis L, Tomba GS, Scarabel F, Swallow B, Trapman P, Villela D. Modelling: Understanding pandemics and how to control them. Epidemics 2022; 39:100588. [PMID: 35679714 DOI: 10.1016/j.epidem.2022.100588] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 03/22/2022] [Accepted: 05/26/2022] [Indexed: 12/11/2022] Open
Abstract
New disease challenges, societal demands and better or novel types of data, drive innovations in the structure, formulation and analysis of epidemic models. Innovations in modelling can lead to new insights into epidemic processes and better use of available data, yielding improved disease control and stimulating collection of better data and new data types. Here we identify key challenges for the structure, formulation, analysis and use of mathematical models of pathogen transmission relevant to current and future pandemics.
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Affiliation(s)
- Glenn Marion
- Biomathematics and Statistics Scotland, Edinburgh, UK; Scottish COVID-19 Response Consortium, UK.
| | - Liza Hadley
- Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, UK
| | - Valerie Isham
- Department of Statistical Science, University College London, UK
| | - Denis Mollison
- Department of Actuarial Mathematics and Statistics, Heriot-Watt University, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; The Queen's College, Oxford University, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, UK; The Alan Turing Institute, London, UK; Joint UNIversities Pandemic and Epidemiological Research, UK
| | | | - Francesca Scarabel
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; CDLab - Computational Dynamics Laboratory, Department of Mathematics, Computer Science and Physics, University of Udine, Italy
| | - Ben Swallow
- Scottish COVID-19 Response Consortium, UK; School of Mathematics and Statistics, University of Glasgow, UK
| | - Pieter Trapman
- Department of Mathematics, Stockholm University, Stockholm, Sweden
| | - Daniel Villela
- Program of Scientific Computing, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
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Bou-Karroum L, Khabsa J, Jabbour M, Hilal N, Haidar Z, Abi Khalil P, Khalek RA, Assaf J, Honein-AbouHaidar G, Samra CA, Hneiny L, Al-Awlaqi S, Hanefeld J, El-Jardali F, Akl EA, El Bcheraoui C. Public health effects of travel-related policies on the COVID-19 pandemic: A mixed-methods systematic review. J Infect 2021; 83:413-423. [PMID: 34314737 PMCID: PMC8310423 DOI: 10.1016/j.jinf.2021.07.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 07/21/2021] [Indexed: 12/22/2022]
Abstract
OBJECTIVES To map travel policies implemented due to COVID-19 during 2020, and conduct a mixed-methods systematic review of health effects of such policies, and related contextual factors. DESIGN Policy mapping and systematic review. DATA SOURCES AND ELIGIBILITY CRITERIA: for the policy mapping, we searched websites of relevant government bodies and used data from the Oxford COVID-19 Government Response Tracker for a convenient sample of 31 countries across different regions. For the systematic review, we searched Medline (Ovid), PubMed, EMBASE, the Cochrane Central Register of Controlled Trials and COVID-19 specific databases. We included randomized controlled trial, non-randomized studies, modeling studies, and qualitative studies. Two independent reviewers selected studies, abstracted data and assessed risk of bias. RESULTS Most countries adopted a total border closure at the start of the pandemic. For the remainder of the year, partial border closure banning arrivals from some countries or regions was the most widely adopted measure, followed by mandatory quarantine and screening of travelers. The systematic search identified 69 eligible studies, including 50 modeling studies. Both observational and modeling evidence suggest that border closure may reduce the number of COVID-19 cases, disease spread across countries and between regions, and slow the progression of the outbreak. These effects are likely to be enhanced when implemented early, and when combined with measures reducing transmission rates in the community. Quarantine of travelers may decrease the number of COVID-19 cases but its effectiveness depends on compliance and enforcement and is more effective if followed by testing, especially when less than 14 day-quarantine is considered. Screening at departure and/or arrival is unlikely to detect a large proportion of cases or to delay an outbreak. Effectiveness of screening may be improved with increased sensitivity of screening tests, awareness of travelers, asymptomatic screening, and exit screening at country source. While four studies on contextual evidence found that the majority of the public is supportive of travel restrictions, they uncovered concerns about the unintended harms of those policies. CONCLUSION Most countries adopted full or partial border closure in response to COVID-19 in 2020. Evidence suggests positive effects on controlling the COVID-19 pandemic for border closure (particularly when implemented early), as well as quarantine of travelers (particularly with higher levels of compliance). While these positive effects are enhanced when implemented in combination with other public health measures, they are associated with concerns by the public regarding some unintended effects.
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Affiliation(s)
- Lama Bou-Karroum
- Center for Systematic Reviews for Health Policy and Systems Research, American University of Beirut, P.O.Box 11-0236, Riad El-Solh, Beirut 1107 2020, Lebanon
| | - Joanne Khabsa
- Clinical Research Institute, American University of Beirut Medical Center, Clinical Research Institute, P.O.Box 11-0236, Riad El-Solh, Beirut 1107 2020, Lebanon
| | - Mathilda Jabbour
- Knowledge to Policy (K2P) Center, Faculty of Health Sciences, American University of Beirut, P.O.Box 11-0236, Riad El-Solh, Beirut 1107 2020, Lebanon
| | - Nadeen Hilal
- Knowledge to Policy (K2P) Center, Faculty of Health Sciences, American University of Beirut, P.O.Box 11-0236, Riad El-Solh, Beirut 1107 2020, Lebanon
| | - Zeinab Haidar
- Clinical Research Institute, American University of Beirut Medical Center, Clinical Research Institute, P.O.Box 11-0236, Riad El-Solh, Beirut 1107 2020, Lebanon
| | - Pamela Abi Khalil
- Clinical Research Institute, American University of Beirut Medical Center, Clinical Research Institute, P.O.Box 11-0236, Riad El-Solh, Beirut 1107 2020, Lebanon
| | - Rima Abdul Khalek
- Economic and Social Commission of Western Asia, P.O. Box 11-8575, Riad el-Solh Square, Beirut, Lebanon
| | - Jana Assaf
- Department of Health Management and Policy, Faculty of Health Sciences, American University of Beirut, P.O.Box 11-0236, Riad El-Solh, Beirut 1107 2020, Lebanon
| | - Gladys Honein-AbouHaidar
- Rafic Hariri School of Nursing, American University of Beirut, P.O.Box 11-0236, Riad El-Solh, Beirut 1107 2020, Lebanon
| | - Clara Abou Samra
- Department of Health Management and Policy, Faculty of Health Sciences, American University of Beirut, P.O.Box 11-0236, Riad El-Solh, Beirut 1107 2020, Lebanon
| | - Layal Hneiny
- Saab Medical Library, American University of Beirut, P.O.Box 11-0236, Riad El-Solh, Beirut 1107 2020, Lebanon
| | - Sameh Al-Awlaqi
- Evidence-Based Public Health Unit, Center for International Health Protection, Robert Koch Institute, Nordufer. 20, Berlin 13353, Germany
| | - Johanna Hanefeld
- Center for International Health Protection, Robert Koch Institute, Nordufer. 20, Berlin 13353, Germany
| | - Fadi El-Jardali
- Center for Systematic Reviews for Health Policy and Systems Research, American University of Beirut, P.O.Box 11-0236, Riad El-Solh, Beirut 1107 2020, Lebanon
| | - Elie A Akl
- Department of Internal Medicine, American University of Beirut Medical Center, P.O.Box 11-0236, Riad El-Solh, Beirut 1107 2020, Lebanon.
| | - Charbel El Bcheraoui
- Evidence-Based Public Health Unit, Center for International Health Protection, Robert Koch Institute, Nordufer. 20, Berlin 13353, Germany.
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Goldberg LA, Jorritsma J, Komjáthy J, Lapinskas J. Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining. PLoS One 2021; 16:e0250435. [PMID: 34010333 PMCID: PMC8133478 DOI: 10.1371/journal.pone.0250435] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 04/07/2021] [Indexed: 12/22/2022] Open
Abstract
We study the effects of two mechanisms which increase the efficacy of contact-tracing applications (CTAs) such as the mobile phone contact-tracing applications that have been used during the COVID-19 epidemic. The first mechanism is the introduction of user referrals. We compare four scenarios for the uptake of CTAs-(1) the p% of individuals that use the CTA are chosen randomly, (2) a smaller initial set of randomly-chosen users each refer a contact to use the CTA, achieving p% in total, (3) a small initial set of randomly-chosen users each refer around half of their contacts to use the CTA, achieving p% in total, and (4) for comparison, an idealised scenario in which the p% of the population that uses the CTA is the p% with the most contacts. Using agent-based epidemiological models incorporating a geometric space, we find that, even when the uptake percentage p% is small, CTAs are an effective tool for mitigating the spread of the epidemic in all scenarios. Moreover, user referrals significantly improve efficacy. In addition, it turns out that user referrals reduce the quarantine load. The second mechanism for increasing the efficacy of CTAs is tuning the severity of quarantine measures. Our modelling shows that using CTAs with mild quarantine measures is effective in reducing the maximum hospital load and the number of people who become ill, but leads to a relatively high quarantine load, which may cause economic disruption. Fortunately, under stricter quarantine measures, the advantages are maintained but the quarantine load is reduced. Our models incorporate geometric inhomogeneous random graphs to study the effects of the presence of super-spreaders and of the absence of long-distant contacts (e.g., through travel restrictions) on our conclusions.
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Affiliation(s)
- Leslie Ann Goldberg
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Joost Jorritsma
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Júlia Komjáthy
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - John Lapinskas
- Department of Computer Science, University of Bristol, Bristol, United Kingdom
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Burns J, Movsisyan A, Stratil JM, Biallas RL, Coenen M, Emmert-Fees KM, Geffert K, Hoffmann S, Horstick O, Laxy M, Klinger C, Kratzer S, Litwin T, Norris S, Pfadenhauer LM, von Philipsborn P, Sell K, Stadelmaier J, Verboom B, Voss S, Wabnitz K, Rehfuess E. International travel-related control measures to contain the COVID-19 pandemic: a rapid review. Cochrane Database Syst Rev 2021; 3:CD013717. [PMID: 33763851 PMCID: PMC8406796 DOI: 10.1002/14651858.cd013717.pub2] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND In late 2019, the first cases of coronavirus disease 2019 (COVID-19) were reported in Wuhan, China, followed by a worldwide spread. Numerous countries have implemented control measures related to international travel, including border closures, travel restrictions, screening at borders, and quarantine of travellers. OBJECTIVES To assess the effectiveness of international travel-related control measures during the COVID-19 pandemic on infectious disease transmission and screening-related outcomes. SEARCH METHODS We searched MEDLINE, Embase and COVID-19-specific databases, including the Cochrane COVID-19 Study Register and the WHO Global Database on COVID-19 Research to 13 November 2020. SELECTION CRITERIA We considered experimental, quasi-experimental, observational and modelling studies assessing the effects of travel-related control measures affecting human travel across international borders during the COVID-19 pandemic. In the original review, we also considered evidence on severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). In this version we decided to focus on COVID-19 evidence only. Primary outcome categories were (i) cases avoided, (ii) cases detected, and (iii) a shift in epidemic development. Secondary outcomes were other infectious disease transmission outcomes, healthcare utilisation, resource requirements and adverse effects if identified in studies assessing at least one primary outcome. DATA COLLECTION AND ANALYSIS Two review authors independently screened titles and abstracts and subsequently full texts. For studies included in the analysis, one review author extracted data and appraised the study. At least one additional review author checked for correctness of data. To assess the risk of bias and quality of included studies, we used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for observational studies concerned with screening, and a bespoke tool for modelling studies. We synthesised findings narratively. One review author assessed the certainty of evidence with GRADE, and several review authors discussed these GRADE judgements. MAIN RESULTS Overall, we included 62 unique studies in the analysis; 49 were modelling studies and 13 were observational studies. Studies covered a variety of settings and levels of community transmission. Most studies compared travel-related control measures against a counterfactual scenario in which the measure was not implemented. However, some modelling studies described additional comparator scenarios, such as different levels of stringency of the measures (including relaxation of restrictions), or a combination of measures. Concerns with the quality of modelling studies related to potentially inappropriate assumptions about the structure and input parameters, and an inadequate assessment of model uncertainty. Concerns with risk of bias in observational studies related to the selection of travellers and the reference test, and unclear reporting of certain methodological aspects. Below we outline the results for each intervention category by illustrating the findings from selected outcomes. Travel restrictions reducing or stopping cross-border travel (31 modelling studies) The studies assessed cases avoided and shift in epidemic development. We found very low-certainty evidence for a reduction in COVID-19 cases in the community (13 studies) and cases exported or imported (9 studies). Most studies reported positive effects, with effect sizes varying widely; only a few studies showed no effect. There was very low-certainty evidence that cross-border travel controls can slow the spread of COVID-19. Most studies predicted positive effects, however, results from individual studies varied from a delay of less than one day to a delay of 85 days; very few studies predicted no effect of the measure. Screening at borders (13 modelling studies; 13 observational studies) Screening measures covered symptom/exposure-based screening or test-based screening (commonly specifying polymerase chain reaction (PCR) testing), or both, before departure or upon or within a few days of arrival. Studies assessed cases avoided, shift in epidemic development and cases detected. Studies generally predicted or observed some benefit from screening at borders, however these varied widely. For symptom/exposure-based screening, one modelling study reported that global implementation of screening measures would reduce the number of cases exported per day from another country by 82% (95% confidence interval (CI) 72% to 95%) (moderate-certainty evidence). Four modelling studies predicted delays in epidemic development, although there was wide variation in the results between the studies (very low-certainty evidence). Four modelling studies predicted that the proportion of cases detected would range from 1% to 53% (very low-certainty evidence). Nine observational studies observed the detected proportion to range from 0% to 100% (very low-certainty evidence), although all but one study observed this proportion to be less than 54%. For test-based screening, one modelling study provided very low-certainty evidence for the number of cases avoided. It reported that testing travellers reduced imported or exported cases as well as secondary cases. Five observational studies observed that the proportion of cases detected varied from 58% to 90% (very low-certainty evidence). Quarantine (12 modelling studies) The studies assessed cases avoided, shift in epidemic development and cases detected. All studies suggested some benefit of quarantine, however the magnitude of the effect ranged from small to large across the different outcomes (very low- to low-certainty evidence). Three modelling studies predicted that the reduction in the number of cases in the community ranged from 450 to over 64,000 fewer cases (very low-certainty evidence). The variation in effect was possibly related to the duration of quarantine and compliance. Quarantine and screening at borders (7 modelling studies; 4 observational studies) The studies assessed shift in epidemic development and cases detected. Most studies predicted positive effects for the combined measures with varying magnitudes (very low- to low-certainty evidence). Four observational studies observed that the proportion of cases detected for quarantine and screening at borders ranged from 68% to 92% (low-certainty evidence). The variation may depend on how the measures were combined, including the length of the quarantine period and days when the test was conducted in quarantine. AUTHORS' CONCLUSIONS With much of the evidence derived from modelling studies, notably for travel restrictions reducing or stopping cross-border travel and quarantine of travellers, there is a lack of 'real-world' evidence. The certainty of the evidence for most travel-related control measures and outcomes is very low and the true effects are likely to be substantially different from those reported here. Broadly, travel restrictions may limit the spread of disease across national borders. Symptom/exposure-based screening measures at borders on their own are likely not effective; PCR testing at borders as a screening measure likely detects more cases than symptom/exposure-based screening at borders, although if performed only upon arrival this will likely also miss a meaningful proportion of cases. Quarantine, based on a sufficiently long quarantine period and high compliance is likely to largely avoid further transmission from travellers. Combining quarantine with PCR testing at borders will likely improve effectiveness. Many studies suggest that effects depend on factors, such as levels of community transmission, travel volumes and duration, other public health measures in place, and the exact specification and timing of the measure. Future research should be better reported, employ a range of designs beyond modelling and assess potential benefits and harms of the travel-related control measures from a societal perspective.
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Affiliation(s)
- 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
| | - 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
| | - 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 Lars 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
| | - Michaela Coenen
- 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
| | - Karl Mf Emmert-Fees
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Munich, 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
| | - Sabine Hoffmann
- 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
| | - Olaf Horstick
- Heidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany
| | - Michael Laxy
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Munich, Germany
- Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
| | - Carmen Klinger
- 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
| | - Suzie Kratzer
- 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 for Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Susan Norris
- 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
- Oregon Health & Science University, Portland, OR, USA
| | - Lisa M Pfadenhauer
- 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
| | - Peter von Philipsborn
- 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
| | - Kerstin Sell
- 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
| | - Julia Stadelmaier
- Institute for Evidence in Medicine, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, 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
| | - 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
| | - 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
| | - Eva Rehfuess
- 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
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