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Koiso S, Gulbas E, Dike L, Mulroy NM, Ciaranello AL, Freedberg KA, Jalali MS, Walker AT, Ryan ET, LaRocque RC, Hyle EP. Modeling approaches to inform travel-related policies for COVID-19 containment: a scoping review and future directions. Travel Med Infect Dis 2024:102730. [PMID: 38830442 DOI: 10.1016/j.tmaid.2024.102730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 05/22/2024] [Accepted: 05/28/2024] [Indexed: 06/05/2024]
Abstract
BACKGROUND Travel-related strategies to reduce the spread of COVID-19 evolved rapidly in response to changes in the understanding of SARS-CoV-2 and newly available tools for prevention, diagnosis, and treatment. Modeling is an important methodology to investigate the range of outcomes that could occur from different disease containment strategies. METHODS We examined 43 articles published from December 2019 through September 2022 that used modeling to evaluate travel-related COVID-19 containment strategies. We extracted and synthesized data regarding study objectives, methods, outcomes, populations, settings, strategies, and costs. We used a standardized approach to evaluate each analysis according to 26 criteria for modeling quality and rigor. RESULTS The most frequent approaches included compartmental modeling to examine quarantine, isolation, or testing. Early in the pandemic, the goal was to prevent travel-related COVID-19 cases with a focus on individual-level outcomes and assessing strategies such as travel restrictions, quarantine without testing, social distancing, and on-arrival PCR testing. After the development of diagnostic tests and vaccines, modeling studies projected population-level outcomes and investigated these tools to limit COVID-19 spread. Very few published studies included rapid antigen screening strategies, costs, explicit model calibration, or critical evaluation of the modeling approaches. CONCLUSION Future modeling analyses should leverage open-source data, improve the transparency of modeling methods, incorporate newly available prevention, diagnostics, and treatments, and include costs and cost-effectiveness so that modeling analyses can be informative to address future SARS-CoV-2 variants of concern and other emerging infectious diseases (e.g., mpox and Ebola) for travel-related health policies.
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Affiliation(s)
- Satoshi Koiso
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA.
| | - Eren Gulbas
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA
| | - Lotanna Dike
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA
| | - Nora M Mulroy
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA
| | - Andrea L Ciaranello
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
| | - Kenneth A Freedberg
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA; Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA
| | - Mohammad S Jalali
- Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Institute for Technology Assessment, Massachusetts General Hospital, 101 Merrimac St., Suite 1010, Boston, MA, USA
| | - Allison T Walker
- Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, USA
| | - Edward T Ryan
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA; Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA; Travelers' Advice and Immunization Center, Massachusetts General Hospital, Cox Building, 5th Floor, 55 Fruit Street, Boston, MA, USA
| | - Regina C LaRocque
- Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA; Travelers' Advice and Immunization Center, Massachusetts General Hospital, Cox Building, 5th Floor, 55 Fruit Street, Boston, MA, USA
| | - Emily P Hyle
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA; Travelers' Advice and Immunization Center, Massachusetts General Hospital, Cox Building, 5th Floor, 55 Fruit Street, Boston, MA, USA.
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Balak N, Inan D, Ganau M, Zoia C, Sönmez S, Kurt B, Akgül A, Tez M. A simple mathematical tool to forecast COVID-19 cumulative case numbers. CLINICAL EPIDEMIOLOGY AND GLOBAL HEALTH 2021; 12:100853. [PMID: 34395949 PMCID: PMC8352661 DOI: 10.1016/j.cegh.2021.100853] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/20/2021] [Accepted: 08/04/2021] [Indexed: 02/07/2023] Open
Abstract
Objective Mathematical models are known to help determine potential intervention strategies by providing an approximate idea of the transmission dynamics of infectious diseases. To develop proper responses, not only are more accurate disease spread models needed, but also those that are easy to use. Materials and methods As of July 1, 2020, we selected the 20 countries with the highest numbers of COVID-19 cases in the world. Using the Verhulst–Pearl logistic function formula, we calculated estimates for the total number of cases for each country. We compared these estimates to the actual figures given by the WHO on the same dates. Finally, the formula was tested for longer-term reliability at t = 18 and t = 40 weeks. Results The Verhulst–Pearl logistic function formula estimated the actual numbers precisely, with only a 0.5% discrepancy on average for the first month. For all countries in the study and the world at large, the estimates for the 40th week were usually overestimated, although the estimates for some countries were still relatively close to the actual numbers in the forecasting long term. The estimated number for the world in general was about 8 times that actually observed for the long term. Conclusions The Verhulst–Pearl equation has the advantage of being very straightforward and applicable in clinical use for predicting the demand on hospitals in the short term of 4–6 weeks, which is usually enough time to reschedule elective procedures and free beds for new waves of the pandemic patients.
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Affiliation(s)
- Naci Balak
- Department of Neurosurgery, Istanbul Medeniyet University, Göztepe Education and Research Hospital, Istanbul, Turkey
- School of Applied Sciences, Marmara University, Istanbul, Turkey
| | - Deniz Inan
- Department of Statistics, Faculty of Arts and Sciences, Marmara University, Istanbul, Turkey
| | - Mario Ganau
- Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Cesare Zoia
- Department of Neurosurgery, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Sinan Sönmez
- School of Applied Sciences, Marmara University, Istanbul, Turkey
| | - Batuhan Kurt
- School of Applied Sciences, Marmara University, Istanbul, Turkey
| | - Ahmet Akgül
- School of Applied Sciences, Marmara University, Istanbul, Turkey
| | - Müjgan Tez
- Department of Statistics, Faculty of Arts and Sciences, Marmara University, Istanbul, Turkey
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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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Souch JM, Cossman JS, Hayward MD. Interstates of Infection: Preliminary Investigations of Human Mobility Patterns in the COVID-19 Pandemic. J Rural Health 2021; 37:266-271. [PMID: 33720459 PMCID: PMC8101290 DOI: 10.1111/jrh.12558] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Purpose The COVID‐19 pandemic has illuminated various heterogeneities between urban and rural environments in public health. The SARS‐CoV‐2 virus initially spread into the United States from international ports of entry and into urban population centers, like New York City. Over the course of the pandemic, cases emerged in more rural areas, implicating issues of transportation and mobility. Additionally, many rural areas developed into national hotspots of prevalence and transmission. Our aim was to investigate the preliminary impacts of road travel on the spread of COVID‐19. This investigation has implications for future public health mitigation efforts and travel restrictions in the United States. Methods County‐level COVID‐19 data were analyzed for spatiotemporal patterns in time‐to‐event distributions using animated choropleth maps. Data were obtained from The New York Times and the Bureau of the Census. The arrival event was estimated by examining the number of days between the first reported national case (January 21, 2020) and the date that each county attained a given prevalence rate. Of the 3108 coterminous US counties, 2887 were included in the analyses. Data reflect cases accumulated between January 21, 2020, and May 17, 2020. Findings Animations revealed that COVID‐19 was transmitted along the path of interstates. Quantitative results indicated rural–urban differences in the estimated arrival time of COVID‐19. Counties that are intersected by interstates had an earlier arrival than non‐intersected counties. The arrival time difference was the greatest in the most rural counties and implicates road travel as a factor of transmission into rural communities. Conclusion Human mobility via road travel introduced COVID‐19 into more rural communities. Interstate travel restrictions and road travel restrictions would have supported stronger mitigation efforts during the earlier stages of the COVID‐19 pandemic and reduced transmission via network contact.
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Affiliation(s)
- Jacob M Souch
- Department of Sociology and Anthropology, West Virginia University, Morgantown, West Virginia, USA.,The Data HEART (Health, Engagement, and Research Team) of West Virginia, Morgantown, West Virginia, USA
| | - Jeralynn S Cossman
- Department of Sociology and Anthropology, West Virginia University, Morgantown, West Virginia, USA.,The Data HEART (Health, Engagement, and Research Team) of West Virginia, Morgantown, West Virginia, USA
| | - Mark D Hayward
- Department of Sociology, The University of Texas at Austin, Austin, Texas, USA
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