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Gibbs H, Musah A, Seidu O, Ampofo W, Asiedu-Bekoe F, Gray J, Adewole WA, Cheshire J, Marks M, Eggo RM. Call detail record aggregation methodology impacts infectious disease models informed by human mobility. PLoS Comput Biol 2023; 19:e1011368. [PMID: 37561812 PMCID: PMC10443843 DOI: 10.1371/journal.pcbi.1011368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 08/22/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023] Open
Abstract
This paper demonstrates how two different methods used to calculate population-level mobility from Call Detail Records (CDR) produce varying predictions of the spread of epidemics informed by these data. Our findings are based on one CDR dataset describing inter-district movement in Ghana in 2021, produced using two different aggregation methodologies. One methodology, "all pairs," is designed to retain long distance network connections while the other, "sequential" methodology is designed to accurately reflect the volume of travel between locations. We show how the choice of methodology feeds through models of human mobility to the predictions of a metapopulation SEIR model of disease transmission. We also show that this impact varies depending on the location of pathogen introduction and the transmissibility of infections. For central locations or highly transmissible diseases, we do not observe significant differences between aggregation methodologies on the predicted spread of disease. For less transmissible diseases or those introduced into remote locations, we find that the choice of aggregation methodology influences the speed of spatial spread as well as the size of the peak number of infections in individual districts. Our findings can help researchers and users of epidemiological models to understand how methodological choices at the level of model inputs may influence the results of models of infectious disease transmission, as well as the circumstances in which these choices do not alter model predictions.
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Affiliation(s)
- Hamish Gibbs
- Department of Geography, University College London, London, United Kingdom
| | - Anwar Musah
- Department of Geography, University College London, London, United Kingdom
| | | | - William Ampofo
- Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Accra, Ghana
| | | | | | | | - James Cheshire
- Department of Geography, University College London, London, United Kingdom
| | - Michael Marks
- Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Hospital for Tropical Diseases, University College London Hospital, London, United Kingdom
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Rosalind M. Eggo
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
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Rennie S, Atuire C, Mtande T, Jaoko W, Litewka S, Juengst E, Moodley K. Public health research using cell phone derived mobility data in sub-Saharan Africa: Ethical issues. S AFR J SCI 2023; 119:14777. [PMID: 39328369 PMCID: PMC11426410 DOI: 10.17159/sajs.2023/14777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 05/12/2023] [Indexed: 09/28/2024] Open
Abstract
The movements of humans have a significant impact on population health. While studies of such movements are as old as public health itself, the COVID-19 pandemic has raised the profile of mobility research using digital technologies to track transmission routes and calculate the effects of health policies, such as lockdowns. In sub-Saharan Africa, the high prevalence of cell phone and smartphone use is a source of potentially valuable mobility data for public health purposes. Researchers can access call data records, passively collected in real time from millions of clients by cell phone companies, and associate these records with other data sets to generate insights, make predictions or draw possible policy implications. The use of mobility data from this source could have a range of significant benefits for society, from better control of infectious diseases, improved city planning, more efficient transportation systems and the optimisation of health resources. We discuss key ethical issues raised by public health studies using mobility data from cell phones in sub-Saharan Africa and identify six key ethical challenge areas: autonomy, including consent and individual or group privacy; bias and representativeness; community awareness, engagement and trust; function creep and accountability; stakeholder relationships and power dynamics; and the translation of mobility analyses into health policy. We emphasise the ethical importance of narrowing knowledge gaps between researchers, policymakers and the general public. Given that individuals do not really provide valid consent for the research use of phone data tracking their movements, community understanding and input will be crucial to the maintenance of public trust.
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Affiliation(s)
- Stuart Rennie
- Department of Social Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- UNC Center for Bioethics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Caesar Atuire
- Department of Philosophy and Classics, University of Ghana, Accra, Ghana
| | - Tiwonge Mtande
- Centre for Medical Ethics and Law, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Walter Jaoko
- KAVI-Institute of Clinical Research, University of Nairobi, Nairobi, Kenya
| | - Sergio Litewka
- Institute for Bioethics and Health Policy, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Eric Juengst
- UNC Center for Bioethics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Keymanthri Moodley
- Centre for Medical Ethics and Law, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
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Future behaviours decision-making regarding travel avoidance during COVID-19 outbreaks. Sci Rep 2022; 12:19780. [PMID: 36396687 PMCID: PMC9671889 DOI: 10.1038/s41598-022-24323-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
Abstract
Human behavioural changes are poorly understood, and this limitation has been a serious obstacle to epidemic forecasting. It is generally understood that people change their respective behaviours to reduce the risk of infection in response to the status of an epidemic or government interventions. We must first identify the factors that lead to such decision-making to predict these changes. However, due to an absence of a method to observe decision-making for future behaviour, understanding the behavioural responses to disease is limited. Here, we show that accommodation reservation data could reveal the decision-making process that underpins behavioural changes, travel avoidance, for reducing the risk of COVID-19 infections. We found that the motivation to avoid travel with respect to only short-term future behaviours dynamically varied and was associated with the outbreak status and/or the interventions of the government. Our developed method can quantitatively measure and predict a large-scale population's behaviour to determine the future risk of COVID-19 infections. These findings enable us to better understand behavioural changes in response to disease spread, and thus, contribute to the development of reliable long-term forecasting of disease spread.
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Abstract
Abstract
In this editorial, Guest Editors Richard Benjamins (Telefónica), Jeanine Vos (GSMA), and Stefaan Verhulst (Data & Policy Editor-in-Chief) draw insights from a set of peer-reviewed, open access articles in a Data & Policy special collection dedicated to the use of Telco Big Data Analytics for COVID-19.
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