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Mousa A, Winskill P, Watson OJ, Ratmann O, Monod M, Ajelli M, Diallo A, Dodd PJ, Grijalva CG, Kiti MC, Krishnan A, Kumar R, Kumar S, Kwok KO, Lanata CF, le Polain de Waroux O, Leung K, Mahikul W, Melegaro A, Morrow CD, Mossong J, Neal EF, Nokes DJ, Pan-Ngum W, Potter GE, Russell FM, Saha S, Sugimoto JD, Wei WI, Wood RR, Wu J, Zhang J, Walker P, Whittaker C. Social contact patterns and implications for infectious disease transmission: a systematic review and meta-analysis of contact surveys. eLife 2021; 10:70294. [PMID: 34821551 PMCID: PMC8765757 DOI: 10.7554/elife.70294] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 11/24/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Transmission of respiratory pathogens such as SARS-CoV-2 depends on patterns of contact and mixing across populations. Understanding this is crucial to predict pathogen spread and the effectiveness of control efforts. Most analyses of contact patterns to date have focused on high-income settings. Methods: Here, we conduct a systematic review and individual-participant meta-analysis of surveys carried out in low- and middle-income countries and compare patterns of contact in these settings to surveys previously carried out in high-income countries. Using individual-level data from 28,503 participants and 413,069 contacts across 27 surveys, we explored how contact characteristics (number, location, duration, and whether physical) vary across income settings. Results: Contact rates declined with age in high- and upper-middle-income settings, but not in low-income settings, where adults aged 65+ made similar numbers of contacts as younger individuals and mixed with all age groups. Across all settings, increasing household size was a key determinant of contact frequency and characteristics, with low-income settings characterised by the largest, most intergenerational households. A higher proportion of contacts were made at home in low-income settings, and work/school contacts were more frequent in high-income strata. We also observed contrasting effects of gender across income strata on the frequency, duration, and type of contacts individuals made. Conclusions: These differences in contact patterns between settings have material consequences for both spread of respiratory pathogens and the effectiveness of different non-pharmaceutical interventions. Funding: This work is primarily being funded by joint Centre funding from the UK Medical Research Council and DFID (MR/R015600/1). Infectious diseases, particularly those caused by airborne pathogens like SARS-CoV-2, spread by social contact, and understanding how people mix is critical in controlling outbreaks. To explore these patterns, researchers typically carry out large contact surveys. Participants are asked for personal information (such as gender, age and occupation), as well as details of recent social contacts, usually those that happened in the last 24 hours. This information includes, the age and gender of the contact, where the interaction happened, how long it lasted, and whether it involved physical touch. These kinds of surveys help scientists to predict how infectious diseases might spread. But there is a problem: most of the data come from high-income countries, and there is evidence to suggest that social contact patterns differ between places. Therefore, data from these countries might not be useful for predicting how infections spread in lower-income regions. Here, Mousa et al. have collected and combined data from 27 contact surveys carried out before the COVID-19 pandemic to see how baseline social interactions vary between high- and lower-income settings. The comparison revealed that, in higher-income countries, the number of daily contacts people made decreased with age. But, in lower-income countries, younger and older individuals made similar numbers of contacts and mixed with all age groups. In higher-income countries, more contacts happened at work or school, while in low-income settings, more interactions happened at home and people were also more likely to live in larger, intergenerational households. Mousa et al. also found that gender affected how long contacts lasted and whether they involved physical contact, both of which are key risk factors for transmitting airborne pathogens. These findings can help researchers to predict how infectious diseases might spread in different settings. They can also be used to assess how effective non-medical restrictions, like shielding of the elderly and workplace closures, will be at reducing transmissions in different parts of the world.
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Affiliation(s)
- Andria Mousa
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Oliver John Watson
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Oliver Ratmann
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Mélodie Monod
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Marco Ajelli
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, United States
| | - Aldiouma Diallo
- VITROME, Institut de Recherche pour le Developpement, Dakar, Senegal
| | - Peter J Dodd
- School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
| | - Carlos G Grijalva
- Division of Pharmacoepidemiology, Department of Health Policy, Vanderbilt University Medical Center, Nashville, United States
| | | | - Anand Krishnan
- Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Rakesh Kumar
- Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Supriya Kumar
- Bill and Melinda Gates Foundation, Seattle, WA, United States
| | - Kin O Kwok
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
| | | | | | - Kathy Leung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Wiriya Mahikul
- Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Alessia Melegaro
- Dondena Centre for Research on Social Dynamics and Public Policy, Department of Social and Political Sciences, Bocconi University, Milano, Italy
| | - Carl D Morrow
- Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | | | - Eleanor Fg Neal
- Infection and Immunity, Murdoch Children's Research Institute, Victoria, Australia
| | - D James Nokes
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | | | - Gail E Potter
- National Institute for Allergies and Infectious Diseases, National Institutes of Health, Rockville, United States
| | - Fiona M Russell
- Infection and Immunity, Murdoch Children's Research Institute, Victoria, Australia
| | - Siddhartha Saha
- US Centers for Disease Control and Prevention, New Delhi, India
| | - Jonathan D Sugimoto
- Seattle Epidemiologic Research and Information Center, United States Department of Veterans Affairs, Seattle, United States
| | - Wan In Wei
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Robin R Wood
- Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Joseph Wu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Juanjuan Zhang
- School of Public Health, Fudan University, Shanghai, China
| | - Patrick Walker
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
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Del Fava E, Adema I, Kiti MC, Poletti P, Merler S, Nokes DJ, Manfredi P, Melegaro A. Individual's daily behaviour and intergenerational mixing in different social contexts of Kenya. Sci Rep 2021; 11:21589. [PMID: 34732732 PMCID: PMC8566563 DOI: 10.1038/s41598-021-00799-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 10/15/2021] [Indexed: 12/20/2022] Open
Abstract
We investigated contact patterns in diverse social contexts in Kenya and the daily behaviours that may play a pivotal role in infection transmission to the most vulnerable leveraging novel data from a 2-day survey on social contacts and time use (TU) from a sample of 1407 individuals (for a total of 2705 person days) from rural, urban formal, and informal settings. We used TU data to build six profiles of daily behaviour based on the main reported activities, i.e., Homestayers (71.1% of person days), Workers (9.3%), Schoolers (7.8%), or locations at increasing distance from home, i.e., Walkers (6.6%), Commuters (4.6%), Travelers (0.6%). In the rural setting, we observed higher daily contact numbers (11.56, SD 0.23) and percentages of intergenerational mixing with older adults (7.5% of contacts reported by those younger than 60 years vs. less than 4% in the urban settings). Overall, intergenerational mixing with older adults was higher for Walkers (7.3% of their reported contacts), Commuters (8.7%), and Homestayers (5.1%) than for Workers (1.5%) or Schoolers (3.6%). These results could be instrumental in defining effective interventions that acknowledge the heterogeneity in social contexts and daily routines, either in Kenya or other demographically and culturally similar sub-Saharan African settings.
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Affiliation(s)
- Emanuele Del Fava
- Carlo F. Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy
- Max Planck Institute for Demographic Research, Rostock, Germany
| | - Irene Adema
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Moses C Kiti
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | | | | | - D James Nokes
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- School of Life Sciences and Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UK
| | | | - Alessia Melegaro
- Carlo F. Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy.
- Department of Social and Political Sciences, Bocconi University, Milan, Italy.
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Grantz KH, Cummings DAT, Zimmer S, Vukotich Jr. C, Galloway D, Schweizer ML, Guclu H, Cousins J, Lingle C, Yearwood GMH, Li K, Calderone P, Noble E, Gao H, Rainey J, Uzicanin A, Read JM. Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys. Sci Rep 2021; 11:2319. [PMID: 33504823 PMCID: PMC7840989 DOI: 10.1038/s41598-021-81673-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 12/23/2020] [Indexed: 01/30/2023] Open
Abstract
Comparisons of the utility and accuracy of methods for measuring social interactions relevant to disease transmission are rare. To increase the evidence base supporting specific methods to measure social interaction, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area. Although the number and type of contacts recorded by each participant differed between the two methods, we found good correspondence between the two methods in aggregate measures of age-specific interactions. Fewer, but longer, contacts were reported in surveys, relative to the generally short proximal interactions captured by wearable sensors. When adjusted for expectations of proportionate mixing, though, the two methods produced highly similar, assortative age-mixing matrices. These aggregate mixing matrices, when used in simulation, resulted in similar estimates of risk of infection by age. While proximity sensors and survey methods may not be interchangeable for capturing individual contacts, they can generate highly correlated data on age-specific mixing patterns relevant to the dynamics of respiratory virus transmission.
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Affiliation(s)
- Kyra H. Grantz
- grid.15276.370000 0004 1936 8091Department of Biology, University of Florida, Gainesville, FL 32611 USA ,grid.15276.370000 0004 1936 8091Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611 USA ,grid.21107.350000 0001 2171 9311Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205 USA
| | - Derek A. T. Cummings
- grid.15276.370000 0004 1936 8091Department of Biology, University of Florida, Gainesville, FL 32611 USA ,grid.15276.370000 0004 1936 8091Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611 USA ,grid.21107.350000 0001 2171 9311Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205 USA
| | - Shanta Zimmer
- grid.21925.3d0000 0004 1936 9000Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 USA ,grid.241116.10000000107903411Department of Medicine, University of Colorado School of Medicine, Denver, CO 80045 USA
| | - Charles Vukotich Jr.
- grid.21925.3d0000 0004 1936 9000Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 USA
| | - David Galloway
- grid.21925.3d0000 0004 1936 9000Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213 USA
| | - Mary Lou Schweizer
- grid.21925.3d0000 0004 1936 9000Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 USA
| | - Hasan Guclu
- grid.21925.3d0000 0004 1936 9000Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213 USA ,grid.411776.20000 0004 0454 921XPresent Address: Department of Biostatistics and Medical Informatics, School of Medicine, Istanbul Medeniyet University, Istanbul, Turkey
| | - Jennifer Cousins
- grid.21925.3d0000 0004 1936 9000Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213 USA ,grid.21925.3d0000 0004 1936 9000Present Address: Department of Psychology, University of Pittsburgh, Pittsburgh, PA USA
| | - Carrie Lingle
- grid.21925.3d0000 0004 1936 9000Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213 USA ,Present Address: Toledo Lucas County Health Department, Toledo, OH USA
| | - Gabby M. H. Yearwood
- grid.21925.3d0000 0004 1936 9000Department of Anthropology, University of Pittsburgh, Pittsburgh, PA 15213 USA
| | - Kan Li
- grid.21925.3d0000 0004 1936 9000Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213 USA ,Present Address: Merck Pharmaceuticals, Philadelphia, PA USA
| | - Patti Calderone
- grid.21925.3d0000 0004 1936 9000Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 USA
| | - Eva Noble
- grid.21107.350000 0001 2171 9311Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205 USA
| | - Hongjiang Gao
- grid.416738.f0000 0001 2163 0069Division of Global Migration and Quarantine, US Centers for Disease Control and Prevention, Atlanta, GA 30033 USA
| | - Jeanette Rainey
- grid.416738.f0000 0001 2163 0069Division of Global Migration and Quarantine, US Centers for Disease Control and Prevention, Atlanta, GA 30033 USA ,grid.416738.f0000 0001 2163 0069Present Address: Division of Global Health Protection, US Centers for Disease Control and Prevention, Atlanta, GA USA
| | - Amra Uzicanin
- grid.416738.f0000 0001 2163 0069Division of Global Migration and Quarantine, US Centers for Disease Control and Prevention, Atlanta, GA 30033 USA
| | - Jonathan M. Read
- grid.9835.70000 0000 8190 6402Centre for Health Informatics Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster, LA1 4YW UK ,grid.10025.360000 0004 1936 8470Institute of Infection and Global Health, University of Liverpool, Liverpool, L69 7BE UK
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Hoang T, Coletti P, Melegaro A, Wallinga J, Grijalva CG, Edmunds JW, Beutels P, Hens N. A Systematic Review of Social Contact Surveys to Inform Transmission Models of Close-contact Infections. Epidemiology 2019; 30:723-36. [PMID: 31274572 DOI: 10.1097/EDE.0000000000001047] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
BACKGROUND Researchers increasingly use social contact data to inform models for infectious disease spread with the aim of guiding effective policies about disease prevention and control. In this article, we undertake a systematic review of the study design, statistical analyses, and outcomes of the many social contact surveys that have been published. METHODS We systematically searched PubMed and Web of Science for articles regarding social contact surveys. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines as closely as possible. RESULTS In total, we identified 64 social contact surveys, with more than 80% of the surveys conducted in high-income countries. Study settings included general population (58%), schools or universities (37%), and health care/conference/research institutes (5%). The largest number of studies did not focus on a specific age group (38%), whereas others focused on adults (32%) or children (19%). Retrospective (45%) and prospective (41%) designs were used most often with 6% using both for comparison purposes. The definition of a contact varied among surveys, e.g., a nonphysical contact may require conversation, close proximity, or both. We identified age, time schedule (e.g., weekday/weekend), and household size as relevant determinants of contact patterns across a large number of studies. CONCLUSIONS We found that the overall features of the contact patterns were remarkably robust across several countries, and irrespective of the study details. By considering the most common approach in each aspect of design (e.g., sampling schemes, data collection, definition of contact), we could identify recommendations for future contact data surveys that may be used to facilitate comparison between studies.
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Grantz KH, Cummings DAT, Zimmer S, Vukotich C, Galloway D, Schweizer ML, Guclu H, Cousins J, Lingle C, Yearwood GMH, Li K, Calderone PA, Noble E, Gao H, Rainey J, Uzicanin A, Read JM. Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys. medRxiv 2020:2020.07.12.20151696. [PMID: 32699859 PMCID: PMC7373148 DOI: 10.1101/2020.07.12.20151696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Comparisons of the utility and accuracy of methods for measuring social interactions relevant to disease transmission are rare. To increase the evidence base supporting specific methods to measure social interaction, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area. Although the number and type of contacts recorded by each participant differed between the two methods, we found good correspondence between the two methods in aggregate measures of age-specific interactions. Fewer, but longer, contacts were reported in surveys, relative to the generally short proximal interactions captured by wearable sensors. When adjusted for expectations of proportionate mixing, though, the two methods produced highly similar, assortative age-mixing matrices. These aggregate mixing matrices, when used in simulation, resulted in similar estimates of risk of infection by age. While proximity sensors and survey methods may not be interchangeable for capturing individual contacts, they can generate highly correlated data on age-specific mixing patterns relevant to the dynamics of respiratory virus transmission.
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Neal EFG, Flasche S, Nguyen CD, Ratu FT, Dunne EM, Koyamaibole L, Reyburn R, Rafai E, Kama M, Ortika BD, Boelsen LK, Kado J, Tikoduadua L, Devi R, Tuivaga E, Satzke C, Mulholland EK, Edmunds WJ, Russell FM. Associations between ethnicity, social contact, and pneumococcal carriage three years post-PCV10 in Fiji. Vaccine 2019; 38:202-211. [PMID: 31668367 PMCID: PMC6964150 DOI: 10.1016/j.vaccine.2019.10.030] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 09/10/2019] [Accepted: 10/10/2019] [Indexed: 11/25/2022]
Abstract
BACKGROUND Pneumococcal carriage is a prerequisite for pneumococcal disease. Little is known about whether social contact frequency and intensity are associated with pneumococcal carriage. In Fiji, indigenous iTaukei have higher prevalence of pneumococcal carriage compared with Fijians of Indian Descent (FID). We hypothesised that contact differences may contribute to ethnic differences in pneumococcal carriage prevalence and density. METHODS In 2015, young infants (5-8 weeks), toddlers (12-23 months), children (2-6 years), and caregivers from Suva and surrounding areas, participated in a cross-sectional survey (n = 2014), three years post pneumococcal conjugate vaccine introduction. Demographic and contact data, and nasopharyngeal swabs were collected. Pneumococci were detected, and quantified using quantitative real-time PCR, with molecular serotyping by microarray. Associations between ethnicity, contact, and pneumococcal carriage and density were estimated using multivariable generalised estimating equation regression models. RESULTS iTaukei participants had larger household sizes, higher pneumococcal carriage rates, more contacts, and more frequent contacts of longer duration, compared with FID. The odds of vaccine-type carriage increased by 28% (95% CI 8-53%) P < 0.01 in association with physical contact with 7-14 year old children. iTaukei ethnicity was associated with vaccine-type carriage (aOR) 1.73; 95% CI 1.06-2.82, P = 0.03) and non-vaccine type carriage (aOR 5.98; 95% CI 4.47-8.00, P < 0.01). Ethnicity and contact were not associated with pneumococcal density. CONCLUSIONS iTaukei had greater frequency and intensity of contact compared with FID. Physical contact was associated with pneumococcal carriage. Observed differences in pneumococcal nasopharyngeal carriage prevalence between iTaukei and FID were not explained by differences in social contact patterns by ethnicity.
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Affiliation(s)
- Eleanor F G Neal
- Infection and Immunity, Murdoch Children's Research Institute, Parkville, Victoria, Australia; Centre for International Child Health, Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia.
| | - Stefan Flasche
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Cattram D Nguyen
- Infection and Immunity, Murdoch Children's Research Institute, Parkville, Victoria, Australia; Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia
| | - F Tupou Ratu
- Ministry of Health and Medical Services, Suva, Fiji
| | - Eileen M Dunne
- Infection and Immunity, Murdoch Children's Research Institute, Parkville, Victoria, Australia; Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia
| | | | - Rita Reyburn
- Infection and Immunity, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Eric Rafai
- Ministry of Health and Medical Services, Suva, Fiji
| | - Mike Kama
- Ministry of Health and Medical Services, Suva, Fiji
| | - Belinda D Ortika
- Infection and Immunity, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Laura K Boelsen
- Infection and Immunity, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Joseph Kado
- College of Medicine Nursing and Health Sciences, Fiji National University, Suva, Fiji; Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia
| | | | - Rachel Devi
- Ministry of Health and Medical Services, Suva, Fiji
| | | | - Catherine Satzke
- Infection and Immunity, Murdoch Children's Research Institute, Parkville, Victoria, Australia; Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia; Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - E Kim Mulholland
- Infection and Immunity, Murdoch Children's Research Institute, Parkville, Victoria, Australia; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - W John Edmunds
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Fiona M Russell
- Infection and Immunity, Murdoch Children's Research Institute, Parkville, Victoria, Australia; Centre for International Child Health, Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia
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Xu Z, Lau CL, Zhou X, Fuimaono S, Soares Magalhães RJ, Graves PM. The extensive networks of frequent population mobility in the Samoan Islands and their implications for infectious disease transmission. Sci Rep 2018; 8:10136. [PMID: 29973612 DOI: 10.1038/s41598-018-28081-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 06/15/2018] [Indexed: 11/09/2022] Open
Abstract
Population mobility has been demonstrated to contribute to the persistent transmission and global diffusion of epidemics. In the Pacific Islands, population mobility is particularly important for emerging infectious diseases, disease elimination programs, and diseases spread by close contact. The extent of population mobility between American Samoa villages, Samoa districts and other countries was investigated based on travel data collected during community surveys in American Samoa in 2010 and 2014. Within American Samoa, workers commuted daily across the whole of the main island of Tutuila, with work hubs drawing from villages across the island. Of the 670 adult workers surveyed, 37% had traveled overseas in the past year, with 68% of trips to Samoa. Of children aged 8–13 years (n = 337), 57% had traveled overseas, with 55% of trips to Samoa. An extensive network of connections between American Samoa villages and Samoa districts was demonstrated, with most trips lasting one week to one month. Our study showed that populations in the Samoan islands are highly mobile, and quantified the extent and destinations of their travels. Our findings offer insight into the impact of population mobility on the transmission of infectious diseases and data to refine existing models of disease transmission in the Pacific islands.
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