Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study
Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Epidemics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1755436523000026 |
| _version_ | 1797894701243695104 |
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| author | Jack Wardle Sangeeta Bhatia Moritz U.G. Kraemer Pierre Nouvellet Anne Cori |
| author_facet | Jack Wardle Sangeeta Bhatia Moritz U.G. Kraemer Pierre Nouvellet Anne Cori |
| author_sort | Jack Wardle |
| collection | DOAJ |
| description | Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases.We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest.Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings. |
| first_indexed | 2024-04-10T07:14:37Z |
| format | Article |
| id | doaj.art-f265395e9cf5461295c703d2a52ff73e |
| institution | Directory Open Access Journal |
| issn | 1755-4365 |
| language | English |
| last_indexed | 2024-04-10T07:14:37Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Epidemics |
| spelling | doaj.art-f265395e9cf5461295c703d2a52ff73e2023-02-26T04:26:59ZengElsevierEpidemics1755-43652023-03-0142100666Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation studyJack Wardle0Sangeeta Bhatia1Moritz U.G. Kraemer2Pierre Nouvellet3Anne Cori4MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UKMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UKDepartment of Biology, University of Oxford, Oxford, UKMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK; School of Life Sciences, University of Sussex, Brighton, UKMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK; Corresponding author.Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases.We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest.Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.http://www.sciencedirect.com/science/article/pii/S1755436523000026Infectious disease spreadMathematical modellingHuman mobility dataGravity modelRadiation model |
| spellingShingle | Jack Wardle Sangeeta Bhatia Moritz U.G. Kraemer Pierre Nouvellet Anne Cori Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study Epidemics Infectious disease spread Mathematical modelling Human mobility data Gravity model Radiation model |
| title | Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study |
| title_full | Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study |
| title_fullStr | Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study |
| title_full_unstemmed | Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study |
| title_short | Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study |
| title_sort | gaps in mobility data and implications for modelling epidemic spread a scoping review and simulation study |
| topic | Infectious disease spread Mathematical modelling Human mobility data Gravity model Radiation model |
| url | http://www.sciencedirect.com/science/article/pii/S1755436523000026 |
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