Mapping global variation in human mobility

Kraemer, Moritz U. G.; Sadilek, Adam; Zhang, Qian; Marchal, Nahema; Tuli, Gaurav; Cohn, Emily; Hswen, Yulin; Perkins, T. Alex; Smith, David L.; Reiner, Robert C.; Brownstein, John S.

doi:10.1038/s41562-020-0875-0

Cited by 96 publications

(83 citation statements)

References 60 publications

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“…We found that the majority of trips tended to occur along shorter distances within counties, while longer distance trips between counties and across countries occurred less frequently within the dataset (Figure S1), in line with other studies (1,14,30). We further found that between-county and between-country trips tended to occur between spatially proximate counties and countries, with the majority of domestic mobility comprised of movements between Nairobi and Kiambu counties (Central province), Kajiado county (Rift Valley province), and Machakos county (Eastern province), comprising nearly 75% of the overall movement into Nairobi (Figure S4).…”

Section: Discussionsupporting

confidence: 91%

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Practical geospatial and sociodemographic predictors of human mobility

Ruktanonchai

Lai

Utazi

et al. 2021

Preprint

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Background: Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe seasonal subnational mobility in Kenya, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings. Methods: To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates important to predicting human movement patterns, while accounting for spatial and temporal autocorrelations. Results: Mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, respectively, across both years. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly predicted mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. Conclusions: These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates and predictors of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

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Section: Discussionsupporting

confidence: 91%

“…Within-county trips (blue) across shorter distances tended to comprise the majority of travel across months, followed by domestic travel (red) and international travel (green). Similar datasets have been described further in (7,11,30).…”

Section: Human Mobility Datamentioning

confidence: 93%

Practical geospatial and sociodemographic predictors of human mobility

Ruktanonchai

Lai

Utazi

et al. 2021

Preprint

Self Cite

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“…In principle, parameters characterizing the transitions between states in any SIR-like model are related to quantities amenable to empirical estimations. For instance,

quantifies the transmissibility of the virus;

should relate to the fraction of confined population and vary with different nonpharmaceutical measures put in place;

quantifies the adherence of population to confinement rules, and, thus, can be estimated through data on mobile phone location ( 29 ); and so on. However, it is important to emphasize that a direct empirical estimation of the parameters need not be the right value to input the model with.…”

Section: Discussionmentioning

confidence: 99%

The turning point and end of an expanding epidemic cannot be precisely forecast

Castro

Ares

Cuesta

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

144

120

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Epidemic spread is characterized by exponentially growing dynamics, which are intrinsically unpredictable. The time at which the growth in the number of infected individuals halts and starts decreasing cannot be calculated with certainty before the turning point is actually attained; neither can the end of the epidemic after the turning point. A susceptible–infected–removed (SIR) model with confinement (SCIR) illustrates how lockdown measures inhibit infection spread only above a threshold that we calculate. The existence of that threshold has major effects in predictability: A Bayesian fit to the COVID-19 pandemic in Spain shows that a slowdown in the number of newly infected individuals during the expansion phase allows one to infer neither the precise position of the maximum nor whether the measures taken will bring the propagation to the inhibition regime. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that grows extremely fast. The impossibility to predict in the midterm is not due to wrong or incomplete data, since it persists in error-free, synthetically produced datasets and does not necessarily improve by using larger datasets. Our study warns against precise forecasts of the evolution of epidemics based on mean-field, effective, or phenomenological models and supports that only probabilities of different outcomes can be confidently given.

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“…Given the extent of global travel patterns [1][2][3], newly emerging diseases can rapidly spread globally. In general, respiratory pathogens spread faster [4] than vector-borne viruses [5,6] or those that require very close contact such as Ebola [7] or Lassa [8].…”

Section: Introductionmentioning

confidence: 99%

Two complementary model-based methods for calculating the risk of international spreading of a novel virus from the outbreak epicentre. The case of COVID-19

et al. 2020

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We present two complementary model-based methods for calculating the risk of international spread of the novel coronavirus SARS-CoV-2 from the outbreak epicentre. One model aims to calculate the number of cases that would be exported from an endemic country to disease-free regions by travellers. The second model calculates the probability that an infected traveller will generate at least one secondary autochthonous case in the visited country. Although this paper focuses on the data from China, our methods can be adapted to calculate the risk of importation and subsequent outbreaks. We found an average R0 = 5.31 (ranging from 4.08 to 7.91) and a risk of spreading of 0.75 latent individuals per 1000 travellers. In addition, one infective traveller would be able to generate at least one secondary autochthonous case in the visited country with a probability of 23%.

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Mapping global variation in human mobility

Cited by 96 publications

References 60 publications

Practical geospatial and sociodemographic predictors of human mobility

Practical geospatial and sociodemographic predictors of human mobility

The turning point and end of an expanding epidemic cannot be precisely forecast

Two complementary model-based methods for calculating the risk of international spreading of a novel virus from the outbreak epicentre. The case of COVID-19

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