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While a broad consensus about the first successful migration modern humans out of Africa seems established, the peopling of Arabia remains somewhat enigmatic. Identifying the ancestral populations that contributed to the gene pool of the current populations inhabiting Arabia and the impact of their contributions remains a challenging task. We investigate the genetic makeup of the current Yemeni population using 46 whole genomes and 169 genotype arrays derived from Yemeni individuals from all geographic regions across Yemen and 351 genotype arrays derived from neighboring populations providing regional context. Principal Component Analysis shows stratification between Yemen districts but also with respect to nearby populations: Yemeni, other Arabian and Bedouin samples form a continuum towards the populations of the Levant, whereas East Africa and India appear strongly differentiated. This finding is further supported by higher Principal Components, admixture and haplogroup analyses, and F-statistics. Moreover, two-reference linkage disequilibrium decay estimates are most significant for Yemeni admixture from an ancient northern influx (up to 5220BP from Palestine) and East Africa (750BP). We show that the initial gene flow into the Yemeni populations of today came from the rest of Arabia and the Levant, and a less substantial and more recent genetic impact into coastal Yemen from East Africa, particularly., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethical approval: All subjects provided signed informed consent forms before participation in the study. The study was approved by the IRB (LAU.SOP.PZ1.2007.R4.6/November/2015) of the Lebanese American University., (© 2024. The Author(s).)

Background: Co-existence of efficient transportation networks and geographic imbalance of medical resources greatly facilitated inter-city migration of patients of infectious diseases in China., Methods: To characterize the migration patterns of major notifiable infectious diseases (NIDs) during 2016-2020 in China, we collected migratory cases, who had illness onset in one city but were diagnosed and reported in another, from the National Notifiable Infectious Disease Reporting System, and conducted a nationwide network analysis of migratory cases of major NIDs at the city (prefecture) level., Findings: In total, 2,674,892 migratory cases of NIDs were reported in China during 2016-2020. The top five diseases with the most migratory cases were hepatitis B, tuberculosis, hand, foot and mouth disease (HFMD), syphilis, and influenza, accounting for 79% of all migratory cases. The top five diseases with the highest proportions of migratory cases were all zoonotic or vector-borne (37.89%‒99.98%). The network analysis on 14 major diseases identified three distinct migration patterns, where provincial capitals acted as key node cities: short distance (e.g., pertussis), long distance (e.g., HIV/AIDS), and mixed (e.g., HFMD). Strong drivers for patient migration include population mobility and labor flow intensities between cities as well as the economic development level of the destination city., Interpretation: Collaborative prevention and control strategies should target cities experiencing frequent patient migration and cater to unique migration patterns of each disease. Addressing disparity in healthcare accessibility can also help alleviate case migration and thereby reduce cross-regional transmission., Funding: National Key Research and Development Program of China., Competing Interests: Authors declare that they have no competing interests., (© 2024 The Author(s).)

Background: Households (HH) have been traditionally described as the main environments where people are at risk of dengue (and other arbovirus) infection. Mounting entomological evidence has suggested a larger role of environments other than HH in transmission. Recently, an agent-based model (ABM) estimated that over half of infections occur in non-household (NH) environments like workplaces, markets, and recreational sites. However, the importance of human and vector mobility and the configurations of urban spaces in mediating the effects of NH on dengue transmission remains understudied., Methods: To improve our knowledge of the relevance of NH in transmission, we expanded an ABM calibrated from field data in Kenya to examine movement of people and vectors under different spatial configurations of buildings. In this model, we assessed the number of people traveling between HH and NH and their distance. Those were studied on three different urban configurations, on which the NH are spatially distributed either randomly (scattered), centered (in a single center), or clustered (in more than one cluster)., Results: Across simulations, the number of people moving is a major influential variable where higher levels of movement between HH and NH increases the number of cases. In addition, the number of cases is higher when NH are scattered. Intriguingly, the distance that people travel from HH to NH seems to have little effect on dengue burden; however, it affects the level of spatial clustering of cases., Conclusions: These results highlight the importance of NH as a major spreader of infections between HH and NH environments supporting the relevance of NH in transmission and its interaction with human movement in driving dengue dynamics., Competing Interests: Competing interests The authors declare that they have no competing interests.

Mental stress issues are emerging among residents of modern cities. Among environmental factors associated with stress mitigation, greenspace has consistently been shown to have significant stress-reducing properties. However, the temporality of greenspace exposure, particularly the cumulative threshold effect in urban environments, has been largely neglected in past studies. In addition, different algorithms and their related measurements of greenspace have led to inconsistent mental health outcomes. To address both gaps, we evaluated the dynamic greenspace exposure of 221 Hong Kong residents by integrating three distinct green space measurements: the Normalized Difference Vegetation Index (NDVI), the Green Space Area Ratio (GSAR), and the Eye-level Green View Index (GVI) based on individual real-time GPS data. We subsequently gauged individual momentary stress levels via Ecological Momentary Assessment (EMA) and modeled its association with dynamic green space exposure using mixed ordinal logistic regression across diverse cumulative time frames. The results reveal great disparities in greenspace-stress association between different greenspace measurements and different cumulative time frames: (1) Among the three measurements, GVI is the most robust and effective measurement in assessing the stress-reducing effect in urban environments. (2) Within specific time frames, cumulative exposure has a more pronounced stress-reducing influence than momentary exposure. (3) The stress-reducing effects of cumulative eye-level greenspace exposure exhibit two temporal phases: A continuous exposure spanning 12-36 min leads to a progressive enhancement in the stress-mitigating effect of eye-level greenspace, peaking initially and then diminishing after 36 min. Upon extended exposure reaching 2.3 h, the stress-alleviating impact of eye-level green space peaks once again before gradually waning. Our research underscores the need for multiple measurements of environmental exposure to address the algorithmic uncertainty in environmental health research and deeper insights into the temporality of the greenspace-mental relationship., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Research on advanced human capital migration has focused on the constructs: brain drain, brain gain and brain circulation (grouped as brain migration). The behavior of brain drainers and their migration flow is affected by perceived well-being. Our study comprehensively reviews research on the well-being of highly skilled migrants in search of the constructs and classes with which well-being in brain migration is studied. Research published in the WoSCC and Scopus databases was reviewed. Subsequently, a scoping review of the literature with the PRISMA - ScR guidelines, reporting information sources and search strategy, eligibility criteria (PICOS tool), mode of study selection and data extraction, and ways to assess quality, control bias, and synthesize results. Finally, nine papers were selected, with three types of populations analyzed and two epistemic-methodological approaches: 1) the effects of roots at origin, the ethical and moral dilemmas of the brain drain and the possibilities of return on the well-being of highly qualified emigrants, and 2) identification of the business, economic and political aspects of the destination country that condition individual well-being, adaptation and permanence. Future lines of research will focus on both categories and evaluate their effects on brain migration, and on the origin and destination countries., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

The neighborhood effect averaging problem (NEAP) is a fundamental statistical phenomenon in mobility-dependent environmental exposures. It suggests that individual environmental exposures tend toward the average exposure in the study area when considering human mobility. However, the universality of the NEAP across various environmental exposures and the mechanisms underlying its occurrence remain unclear. Here, using a large human mobility data set of more than 27 000 individuals in the Chicago Metropolitan Area, we provide robust evidence of the existence of the NEAP in a range of individual environmental exposures, including green spaces, air pollution, healthy food environments, transit accessibility, and crime rates. We also unveil the social and spatial disparities in the NEAP's influence on individual environmental exposure estimates. To further reveal the mechanisms behind the NEAP, we perform multiscenario analyses based on environmental variation and human mobility simulations. The results reveal that the NEAP is a statistical phenomenon of regression to the mean (RTM) under the constraints of spatial autocorrelation in environmental data. Increasing travel distances and out-of-home durations can intensify and promote the NEAP's impact, particularly for highly dynamic environmental factors like air pollution. These findings illuminate the complex interplay between human mobility and environmental factors, guiding more effective public health interventions.

Traditional surveillance relies on medical facilities, such as clinics and laboratories, as sentinels to monitor disease activities. Few studies have investigated the feasibility of using Point of Interests (POIs) as sentinels for disease surveillance. POIs, such as restaurants, retail stores, and churches, are places where people often interact with one another and thus play a critical role in transmission of infectious diseases like influenza and COVID-19. To fill this gap, we proposed a method to estimate people's potential crowdedness at POIs and explored its utility as an early indicator to signal local disease outbreaks. In a case study in Florida, USA, we utilized weekly foot traffic data at 0.3 million POIs to calculate their weekly crowdedness, and tested local correlations between the crowdedness of each POI and its surrounding COVID-19 incidences with different time lags. We identified 261 POIs as potential sentinels that could signal the risk one to three weeks ahead of disease outbreaks. Most of these sentinel POIs provided food/drink services, ambulatory healthcare and religious/civic services. They were characterized by a relatively large group of customers and a stable patronization over time. This research provides new insights into improving current disease surveillance systems by incorporating more diverse and widely distributed POIs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Human travelling behaviours are markedly regular, to a large extent predictable, and mostly driven by biological necessities and social constructs. Not surprisingly, such predictability is influenced by an array of factors ranging in scale from individual preferences and choices, through social groups and households, all the way to the global scale, such as mobility restrictions in response to external shocks such as pandemics. In this work, we explore how temporal, activity and location variations in individual-level mobility-referred to as predictability states -carry a large degree of information regarding the nature of mobility regularities at the population level. Our findings indicate the existence of contextual and activity signatures in predictability states, suggesting the potential for a more nuanced approach to estimating both short-term and higher-order mobility predictions. The existence of location contexts, in particular, serves as a parsimonious estimator for predictability patterns even in the case of low resolution and missing data., Competing Interests: We declare we have no competing interests., (© 2024 The Author(s).)

Neighborhood level social determinants of health are commonly measured using a patient's most recent residential location. Not accounting for residential history, and therefore missing accumulated stressors from prior social vulnerabilities, could increase misclassification bias. We tested the hypothesis that the electronic health record could capture the residential history of lung transplant patients -a vulnerable population. After applying the Social Vulnerability Index (SVI) to individual residential histories, the most recent SVI equaled the first SVI in only 15.4% (58/374) of patients. There is a need for databases with residential histories to inform place-based determinants of health and applications to patient care., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The global impact of the COVID-19 pandemic is widely recognized as a significant concern, with human flow playing a crucial role in its propagation. Consequently, recent research has focused on identifying and analyzing factors that can effectively regulate human flow. However, among the multiple factors that are expected to have an effect, few studies have investigated those that are particularly associated with human flow during the COVID-19 pandemic. In addition, few studies have investigated how regional characteristics and the number of vaccinations for these factors affect human flow. Furthermore, increasing the number of verified cases in countries and regions with insufficient reports is important to generalize conclusions. Therefore, in this study, a group-level analysis was conducted for Narashino City, Chiba Prefecture, Japan, using a human flow prediction model based on machine learning. High-importance groups were subdivided by regional characteristics and the number of vaccinations, and visual and correlation analyses were conducted at the factor level. The findings indicated that tree-based models, especially LightGBM, performed better in terms of prediction. In addition, the cumulative number of vaccinated individuals and the number of newly infected individuals are likely explanatory factors for changes in human flow. The analyses suggested a tendency to move with respect to the number of newly infected individuals in Japan or Tokyo, rather than the number of new infections in the area where they lived when vaccination had not started. With the implementation of vaccination, attention to the number of newly infected individuals in their residential areas may increase. However, after the spread of vaccination, the perception of infection risk may decrease. These findings can contribute to the proposal of new measures for efficiently controlling human flows and determining when to mitigate or reinforce specific measures.

Introduction: Community-level changes in population mobility can dramatically change the trajectory of any directly-transmitted infectious disease, by modifying where and between whom contact occurs. This was highlighted throughout the COVID-19 pandemic, where community response and nonpharmaceutical interventions changed the trajectory of SARS-CoV-2 spread, sometimes in unpredictable ways. Population-level changes in mobility also occur seasonally and during other significant events, such as hurricanes or earthquakes. To effectively predict the spread of future emerging directly-transmitted diseases, we should better understand how the spatial spread of infectious disease changes seasonally, and when communities are actively responding to local disease outbreaks and travel restrictions., Methods: Here, we use population mobility data from Virginia spanning Aug 2019-March 2023 to simulate the spread of a hypothetical directly-transmitted disease under the population mobility patterns from various months. By comparing the spread of disease based on where the outbreak begins and the mobility patterns used, we determine the highest-risk areas and periods, and elucidate how seasonal and pandemic-era mobility patterns could change the trajectory of disease transmission., Results and Discussion: Through this analysis, we determine that while urban areas were at highest risk pre-pandemic, the heterogeneous nature of community response induced by SARS-CoV-2 cases meant that when outbreaks were occurring across Virginia, rural areas became relatively higher risk. Further, the months of September and January led to counties with large student populations to become particularly at risk, as population flows in and out of these counties were greatly increased with students returning to school., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Cai, Spencer and Ruktanonchai.)

Background: The COVID-19 pandemic has profoundly impacted all aspects of human life for over 3 years. Understanding the evolution of public risk perception during these periods is crucial. Few studies explore the mechanisms for reducing disease transmission due to risk perception. Thus, we hypothesize that changes in human mobility play a mediating role between risk perception and the progression of the pandemic., Objective: The study aims to explore how various forms of human mobility, including essential, nonessential, and job-related behaviors, mediate the temporal relationships between risk perception and pandemic dynamics., Methods: We used distributed-lag linear structural equation models to compare the mediating impact of human mobility across different virus variant periods. These models examined the temporal dynamics and time-lagged effects among risk perception, changes in mobility, and virus transmission in Taiwan, focusing on two distinct periods: (1) April-August 2021 (pre-Omicron era) and (2) February-September 2022 (Omicron era)., Results: In the pre-Omicron era, our findings showed that an increase in public risk perception correlated with significant reductions in COVID-19 cases across various types of mobility within specific time frames. Specifically, we observed a decrease of 5.59 (95% CI -4.35 to -6.83) COVID-19 cases per million individuals after 7 weeks in nonessential mobility, while essential mobility demonstrated a reduction of 10.73 (95% CI -9.6030 to -11.8615) cases after 8 weeks. Additionally, job-related mobility resulted in a decrease of 3.96 (95% CI -3.5039 to -4.4254) cases after 11 weeks. However, during the Omicron era, these effects notably diminished. A reduction of 0.85 (95% CI -1.0046 to -0.6953) cases through nonessential mobility after 10 weeks and a decrease of 0.69 (95% CI -0.7827 to -0.6054) cases through essential mobility after 12 weeks were observed., Conclusions: This study confirms that changes in mobility serve as a mediating factor between heightened risk perception and pandemic mitigation in both pre-Omicron and Omicron periods. This suggests that elevating risk perception is notably effective in impeding virus progression, especially when vaccines are unavailable or their coverage remains limited. Our findings provide significant value for health authorities in devising policies to address the global threats posed by emerging infectious diseases., (© Min-Chien Chang, Tzai-Hung Wen. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org).)

The escalating frequency, duration, and intensity of extreme heat events have posed a significant threat to human society in recent decades. Understanding the dynamic patterns of human mobility under extreme heat will contribute to accurately assessing the risk of extreme heat exposure. This study leverages an emerging geospatial data source, anonymous cell phone location data, to investigate how people in different communities adapt travel behaviors responding to extreme heat events. Taking the Greater Houston Metropolitan Area as an example, we develop two indices, the Mobility Disruption Index (MDI) and the Activity Time Shift Index (ATSI), to quantify diurnal mobility changes and activity time shift patterns at the city and intra-urban scales. The results reveal that human mobility decreases significantly in the daytime of extreme heat events in Houston while the proportion of activity after 8 p.m. is increased, accompanied with a delay in travel time in the evening. Moreover, these mobility-decreasing and activity-delaying effects exhibited substantial spatial heterogeneity across census block groups. Causality analysis using the Geographical Convergent Cross Mapping (GCCM) model combined with correlation analyses indicates that people in areas with a high proportion of minorities and poverty are less able to adopt heat adaptation strategies to avoid the risk of heat exposure. These findings highlight the fact that besides the physical aspect of environmental justice on heat exposure, the inequity lies in the population's capacity and knowledge to adapt to extreme heat. This research is the first of the kind that quantifies multi-level mobility for extreme heat responses, and sheds light on a new facade to plan and implement heat mitigations and adaptation strategies beyond the traditional approaches., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Limited access to food stores is often linked to higher health risks and lower community resilience. Socially vulnerable populations experience persistent disparities in equitable food store access. However, little research has been done to examine how people's access to food stores is affected by natural disasters. Previous studies mainly focus on examining potential access using the travel distance to the nearest food store, which often falls short of capturing the actual access of people. Therefore, to fill this gap, this paper incorporates human mobility patterns into the measure of actual access, leveraging large-scale mobile phone data. Specifically, we propose a novel enhanced two-step floating catchment area method with travel preferences (E2SFCA-TP) to measure accessibility, which extends the traditional E2SFCA model by integrating actual human mobility behaviors. We then analyze people's actual access to grocery and convenience stores across both space and time under the devastating winter storm Uri in Harris County, Texas. Our results highlight the value of using human mobility patterns to better reflect people's actual access behaviors. The proposed E2SFCA-TP measure is more capable of capturing mobility variations in people's access, compared with the traditional E2SFCA measure. This paper provides insights into food store access across space and time, which could aid decision making in resource allocation to enhance accessibility and mitigate the risk of food insecurity in underserved areas., (© 2024 Society for Risk Analysis.)

Human mobility is fundamental to a range of applications including epidemic control, urban planning, and traffic engineering. While laws governing individual movement trajectories and population flows across locations have been extensively studied, the predictability of population-level mobility during the COVID-19 pandemic driven by specific activities such as work, shopping, and recreation remains elusive. Here we analyze mobility data for six place categories at the US county level from 2020 February 15 to 2021 November 23 and measure how the predictability of these mobility metrics changed during the COVID-19 pandemic. We quantify the time-varying predictability in each place category using an information-theoretic metric, permutation entropy. We find disparate predictability patterns across place categories over the course of the pandemic, suggesting differential behavioral changes in human activities perturbed by disease outbreaks. Notably, predictability change in foot traffic to residential locations is mostly in the opposite direction to other mobility categories. Specifically, visits to residences had the highest predictability during stay-at-home orders in March 2020, while visits to other location types had low predictability during this period. This pattern flipped after the lifting of restrictions during summer 2020. We identify four key factors, including weather conditions, population size, COVID-19 case growth, and government policies, and estimate their nonlinear effects on mobility predictability. Our findings provide insights on how people change their behaviors during public health emergencies and may inform improved interventions in future epidemics., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

Natural disasters bring indelible negative impacts to human beings, and people usually adopt some post hoc strategies to alleviate such impacts. However, the same strategies may have different effects in different countries (or regions), which is rarely paid attention by the academic community. In the context of COVID-19, we examine the effect of distance restriction policies (DRP) on reducing human mobility and thus inhibiting the spread of the virus. By establishing a multi-period difference-in-differences model to analyse the unique panel dataset constructed by 44 countries, we show that DRP does significantly reduce mobility, but the effectiveness varies from country to country. We built a moderating effect model to explain the differences from the cultural perspective and found that DRP can be more effective in reducing human mobility in countries with a lower indulgence index. The results remain robust when different sensitivity analyses are performed. Our conclusions call for governments to adapt their policies to the impact of disasters rather than copy each other.

Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals' movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: In recent years, a range of novel smartphone-derived data streams about human mobility have become available on a near-real-time basis. These data have been used, for example, to perform traffic forecasting and epidemic modeling. During the COVID-19 pandemic in particular, human travel behavior has been considered a key component of epidemiological modeling to provide more reliable estimates about the volumes of the pandemic's importation and transmission routes, or to identify hot spots. However, nearly universally in the literature, the representativeness of these data, how they relate to the underlying real-world human mobility, has been overlooked. This disconnect between data and reality is especially relevant in the case of socially disadvantaged minorities., Objective: The objective of this study is to illustrate the nonrepresentativeness of data on human mobility and the impact of this nonrepresentativeness on modeling dynamics of the epidemic. This study systematically evaluates how real-world travel flows differ from census-based estimations, especially in the case of socially disadvantaged minorities, such as older adults and women, and further measures biases introduced by this difference in epidemiological studies., Methods: To understand the demographic composition of population movements, a nationwide mobility data set from 318 million mobile phone users in China from January 1 to February 29, 2020, was curated. Specifically, we quantified the disparity in the population composition between actual migrations and resident composition according to census data, and shows how this nonrepresentativeness impacts epidemiological modeling by constructing an age-structured SEIR (Susceptible-Exposed-Infected- Recovered) model of COVID-19 transmission., Results: We found a significant difference in the demographic composition between those who travel and the overall population. In the population flows, 59% (n=20,067,526) of travelers are young and 36% (n=12,210,565) of them are middle-aged (P<.001), which is completely different from the overall adult population composition of China (where 36% of individuals are young and 40% of them are middle-aged). This difference would introduce a striking bias in epidemiological studies: the estimation of maximum daily infections differs nearly 3 times, and the peak time has a large gap of 46 days., Conclusions: The difference between actual migrations and resident composition strongly impacts outcomes of epidemiological forecasts, which typically assume that flows represent underlying demographics. Our findings imply that it is necessary to measure and quantify the inherent biases related to nonrepresentativeness for accurate epidemiological surveillance and forecasting., (©Chuchu Liu, Petter Holme, Sune Lehmann, Wenchuan Yang, Xin Lu. Originally published in JMIR Formative Research (https://formative.jmir.org), 28.06.2024.)

Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

Background: The international flight network creates multiple routes by which pathogens can quickly spread across the globe. In the early stages of infectious disease outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common and can help inform disease control efforts. A challenge faced in this modelling is that the latest aviation statistics (referred to as contemporary data) are typically not immediately available. Therefore, flight patterns from a previous year are often used (referred to as historical data). We explored the suitability of historical data for predicting the spatial spread of emerging epidemics., Methods: We analysed monthly flight passenger data from the International Air Transport Association to assess how baseline air travel patterns were affected by outbreaks of Middle East respiratory syndrome (MERS), Zika and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) over the past decade. We then used a stochastic discrete time susceptible-exposed-infected-recovered (SEIR) metapopulation model to simulate the global spread of different pathogens, comparing how epidemic dynamics differed in simulations based on historical and contemporary data., Results: We observed local, short-term disruptions to air travel from South Korea and Brazil for the MERS and Zika outbreaks we studied, whereas global and longer-term flight disruptions occurred during the SARS-CoV-2 pandemic. For outbreak events that were accompanied by local, small and short-term changes in air travel, epidemic models using historical flight data gave similar projections of the timing and locations of disease spread as when using contemporary flight data. However, historical data were less reliable to model the spread of an atypical outbreak such as SARS-CoV-2, in which there were durable and extensive levels of global travel disruption., Conclusion: The use of historical flight data as a proxy in epidemic models is an acceptable practice, except in rare, large epidemics that lead to substantial disruptions to international travel., (© International Society of Travel Medicine 2024. Published by Oxford University Press.)

Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Background: The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate., Methods: We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number., Results: Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q 1 )-third quartile (Q 3 ) = 94-108%) of typical usage to 10% (Q 1 -Q 3 = 6-15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp( β ) = 0.877; 95% confidence interval (CI): [0.859-0.896]) and a decrease in the effective reproduction number ( β = -0.058; 95% CI: [-0.068 to -0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models., Discussion: Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges., Competing Interests: The authors declare that they have no competing interests., (© 2024 Soucy et al.)

Natural, geographical barriers have historically limited the spread of communicable diseases. This is no longer the case in today's interconnected world, paired with its unprecedented environmental and climate change, emphasising the intersection of evolutionary biology, epidemiology and geography (i.e. biogeography). A total of 14 articles of the special issue entitled "Geography and health: role of human translocation and access to care" document enhanced disease transmission of diseases, such as malaria, leishmaniasis, schistosomiasis, COVID-19 (Severe acute respiratory syndrome corona 2) and Oropouche fever in spite of spatiotemporal surveillance. High-resolution satellite images can be used to understand spatial distributions of transmission risks and disease spread and to highlight the major avenue increasing the incidence and geographic range of zoonoses represented by spill-over transmission of coronaviruses from bats to pigs or civets. Climate change and globalization have increased the spread and establishment of invasive mosquitoes in non-tropical areas leading to emerging outbreaks of infections warranting improved physical, chemical and biological vector control strategies. The translocation of pathogens and their vectors is closely connected with human mobility, migration and the global transport of goods. Other contributing factors are deforestation with urbanization encroaching into wildlife zones. The destruction of natural ecosystems, coupled with low income and socioeconomic status, increase transmission probability of neglected tropical and zoonotic diseases. The articles in this special issue document emerging or re-emerging diseases and surveillance of fever symptoms. Health equity is intricately connected to accessibility to health care and the targeting of healthcare resources, necessitating a spatial approach. Public health comprises successful disease management integrating spatial surveillance systems, including access to sanitation facilities. Antimicrobial resistance caused, e.g. by increased use of antibiotics in health, agriculture and aquaculture, or acquisition of resistance genes, can be spread by horizontal gene transfer. This editorial reviews the key findings of this 14-article special issue, identifies important gaps relevant to our interconnected world and makes a number of specific recommendations to mitigate the transmission risks of infectious diseases in the post-COVID-19 pandemic era., (© 2024. The Author(s).)

Given the worldwide increase of forcibly displaced populations, particularly internally displaced persons (IDPs), it's crucial to have an up-to-date and precise tracking framework for population movements. Here, we study how the spatial and temporal pattern of a large-scale internal population movement can be monitored using human mobility datasets by exploring the case of IDPs in Ukraine at the beginning of the Russian invasion of 2022. Specifically, this study examines the sizes and travel distances of internal displacements based on GPS human mobility data, using the combinations of mobility pattern estimation methods such as truncated power law fitting and visualizing the results for humanitarian operations. Our analysis reveals that, although the city of Kyiv started to lose its population around 5 weeks before the invasion, a significant drop happened in the second week of the invasion (4.3 times larger than the size of the population lost in 5 weeks before the invasion), and the population coming to the city increased again from the third week of the invasion, indicating that displaced people started to back to their homes. Meanwhile, adjacent southern areas of Kyiv and the areas close to the western borders experienced many migrants from the first week of the invasion and from the second to third weeks of the invasion, respectively. In addition, people from relatively higher-wealth areas tended to relocate their home locations far away from their original locations compared to those from other areas. For example, 19 % of people who originally lived in higher wealth areas in the North region, including the city of Kyiv, moved their home location more than 500 km, while only 9 % of those who originally lived in lower wealth areas in the North region moved their home location more than 500 km.., (© 2024. The World Bank 2024.)

Monitoring urban structure and development requires high-quality data at high spatio-temporal resolution. While traditional censuses have provided foundational insights into demographic and socio-economic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analysing alternative big-data sources, such as human mobility data. However, these often noisy and unstructured big data pose new challenges. Here, we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which are produced as a by-product of mobile communication, we show that meaningful features can be extracted, revealing, for example, the emergence and absorption of subcentres. This method allows the analysis of urban dynamics at a high-spatial resolution (here 500 m) and near real-time frequency, and high computational efficiency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.

This article introduces an analytical framework that interprets individual measures of entropy-based mobility derived from mobile phone data. We explore and analyze two widely recognized entropy metrics: random entropy and uncorrelated Shannon entropy. These metrics are estimated through collective variables of human mobility, including movement trends and population density. By employing a collisional model, we establish statistical relationships between entropy measures and mobility variables. Furthermore, our research addresses three primary objectives: firstly, validating the model; secondly, exploring correlations between aggregated mobility and entropy measures in comparison to five economic indicators; and finally, demonstrating the utility of entropy measures. Specifically, we provide an effective population density estimate that offers a more realistic understanding of social interactions. This estimation takes into account both movement regularities and intensity, utilizing real-time data analysis conducted during the peak period of the COVID-19 pandemic.

Understanding the impact of heavy precipitation on human mobility is critical for finer-scale urban flood risk assessment and achieving sustainable development goals #11 to build resilient and safe cities. Using ∼2.6 million mobile phone signal data collected during the summer of 2018 in Jiangsu, China, this study proposes a novel framework to assess human mobility changes during rainfall events at a high spatial granularity (500 m grid cell). The fine-scale mobility map identifies spatial hotspots with abnormal clustering or reduced human activities. When aggregating to the prefecture-city level, results show that human mobility changes range between -3.6 and 8.9%, revealing varied intracity movement across cities. Piecewise structural equation modeling analysis further suggests that city size, transport system, and crowding level directly affect mobility responses, whereas economic conditions influence mobility through multiple indirect pathways. When overlaying a historical urban flood map, we find such human mobility changes help 23 cities reduce 2.6% flood risks covering 0.45 million people but increase a mean of 1.64% flood risks in 12 cities covering 0.21 million people. The findings help deepen our understanding of the mobility pattern of urban dwellers after heavy precipitation events and foster urban adaptation by supporting more efficient small-scale hazard management.

Understanding and predicting the emergence and evolution of cultural tastes manifested in consumption patterns is of central interest to social scientists, analysts of culture, and purveyors of content. Prior research suggests that taste preferences relate to personality traits, values, shifts in mood, and immigration destination. Understanding everyday patterns of listening and the function music plays in life has remained elusive, however, despite speculation that musical nostalgia may compensate for local disruption. Using more than one hundred million streams of four million songs by tens of thousands of international listeners from a global music service, we show that breaches in personal routine are systematically associated with personal musical exploration. As people visited new cities and countries, their preferences diversified, converging toward their travel destinations. As people experienced the very different disruptions associated with COVID-19 lockdowns, their preferences diversified further. Personal explorations did not tend to veer toward the global listening average, but away from it, toward distinctive regional musical content. Exposure to novel music explored during periods of routine disruption showed a persistent influence on listeners' future consumption patterns. Across all of these settings, musical preference reflected rather than compensated for life's surprises, leaving a lasting legacy on tastes. We explore the relationship between these findings and global patterns of behavior and cultural consumption., Competing Interests: Competing interests statement:The authors declare no competing interest.

Epidemiological models allow for quantifying the dynamic characteristics of large-scale outbreaks. However, capturing detailed and accurate epidemiological information often requires consideration of multiple kinetic mechanisms and parameters. Due to the uncertainty of pandemic evolution, such as pathogen variation, host immune response and changes in mitigation strategies, the parameter evaluation and state prediction of complex epidemiological models are challenging. Here, we develop a data-driven epidemic model with a generalized SEIR mechanistic structure that includes new compartments, human mobility and vaccination protection. To address the issue of model complexity, we embed the epidemiological model dynamics into physics-informed neural networks (PINN), taking the observed series of time instances as direct input of the network to simultaneously infer unknown parameters and unobserved dynamics of the underlying model. Using actual data during the COVID-19 outbreak in Australia, Israel, and Switzerland, our model framework demonstrates satisfactory performance in multi-step ahead predictions compared to several benchmark models. Moreover, our model infers time-varying parameters such as transmission rates, hospitalization ratios, and effective reproduction numbers, as well as calculates the latent period and asymptomatic infection count, which are typically unreported in public data. Finally, we employ the proposed data-driven model to analyze the impact of different mitigation strategies on COVID-19., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The speed of spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic highlights the importance of understanding how infections are transmitted in a highly connected world. Prior to vaccination, changes in human mobility patterns were used as non-pharmaceutical interventions to eliminate or suppress viral transmission. The rapid spread of respiratory viruses, various intervention approaches, and the global dissemination of SARS-CoV-2 underscore the necessity for epidemiological models that incorporate mobility to comprehend the spread of the virus. Here, we introduce a metapopulation susceptible-exposed-infectious-recovered model parametrized with human movement data from 340 cities in China. Our model replicates the early-case trajectory in the COVID-19 pandemic. We then use machine learning algorithms to determine which network properties best predict spread between cities and find travel time to be most important, followed by the human movement-weighted personalized PageRank. However, we show that travel time is most influential locally, after which the high connectivity between cities reduces the impact of travel time between individual cities on transmission speed. Additionally, we demonstrate that only significantly reduced movement substantially impacts infection spread times throughout the network.

Following the global emergence of the SARS-CoV-2 Alpha variant of concern (VOC) in 2020, the Delta variant triggered another wave in 2021. The AY.69 lineage, a Delta VOC, was particularly prevalent in Republic of Korea (South Korea) from May 2021 to January 2022, despite the synchronized implementation of vaccination programmes and non-pharmaceutical interventions (NPIs) such as social distancing. In this study, we used phylogeographic analysis combined with a generalized linear model (GLM) to examine the impact of human movement and vaccination on viral transmission. Our findings indicated that transmission primarily originated in South Korea's metropolitan areas, and a positive correlation was observed between total human mobility (tracked by GPS on mobile phones and estimated through credit card consumption) and viral spread. The phylodynamic analysis further revealed that non-vaccinated individuals were the primary transmitters of the virus during the study period, even though vaccination programmes had commenced three months prior to the AY.69 outbreak. Our study emphasizes the need to focus on controlling SARS-CoV-2 transmission in metropolitan regions and among unvaccinated populations. Furthermore, the positive correlation between mobility data and viral dissemination could contribute to the development of more accurate predictive models for local spread of pandemics.

Background: Using human mobility as a proxy for social interaction, previous studies revealed bidirectional associations between COVID-19 incidence and human mobility. For example, while an increase in COVID-19 cases may affect mobility to decrease due to lockdowns or fear, conversely, an increase in mobility can potentially amplify social interactions, thereby contributing to an upsurge in COVID-19 cases. Nevertheless, these bidirectional relationships exhibit variations in their nature, evolve over time, and lack generalizability across different geographical contexts. Consequently, a systematic approach is required to detect functional, spatial, and temporal variations within the intricate relationship between disease incidence and mobility., Methods: We introduce a spatial time series workflow to investigate the bidirectional associations between human mobility and disease incidence, examining how these associations differ across geographic space and throughout different waves of a pandemic. By utilizing daily COVID-19 cases and mobility flows at the county level during three pandemic waves in the US, we conduct bidirectional Granger causality tests for each county and wave. Furthermore, we employ dynamic time warping to quantify the similarity between the trends of disease incidence and mobility, enabling us to map the spatial distribution of trends that are either similar or dissimilar., Results: Our analysis reveals significant bidirectional associations between COVID-19 incidence and mobility, and we develop a typology to explain the variations in these associations across waves and counties. Overall, COVID-19 incidence exerts a greater influence on mobility than vice versa, but the correlation between the two variables exhibits a stronger connection during the initial wave and weakens over time. Additionally, the relationship between COVID-19 incidence and mobility undergoes changes in direction and significance for certain counties across different waves. These shifts can be attributed to alterations in disease control measures and the presence of evolving confounding factors that differ both spatially and temporally., Conclusions: This study provides insights into the spatial and temporal dynamics of the relationship between COVID-19 incidence and human mobility across different waves. Understanding these variations is crucial for informing the development of more targeted and effective healthcare policies and interventions, particularly at the city or county level where such policies must be implemented. Although we study the association between mobility and COVID-19 incidence, our workflow can be applied to investigate the associations between the time series trends of various infectious diseases and relevant contributing factors, which play a role in disease transmission., (© 2023. The Author(s).)

Introduction: Human mobility is a critical aspect of existence and survival, but may compromise care engagement among people living with HIV (PLHIV). We examined the association between various forms of human mobility with retention in HIV care and antiretroviral treatment (ART) interruptions., Methods: In a cohort of adult PLHIV in Kenya and Uganda, we collected surveys in 2016 about past 6-month travel and lifetime migration histories, including reasons and locations, and engagement in HIV care defined as (1) discontinuation of care, and (2) history of a treatment interruption among those who remained in care. We estimated associations between mobility and these care engagement outcomes via logistic regression, adjusted for sex, prior mobility, age, region, marital status, household wealth, and education., Results: Among 1081 participants, 56 (5%) reported having discontinued care; among those in care, 104 (10%) reported treatment interruption. Past-year migration was associated with a higher risk of discontinuation of care (adjusted odds ratio [aOR] 1.98, 95% CI 1.08-3.63). In sex-stratified models, the association was somewhat attenuated in women, but remained robust among men. Past-year migration was associated with reduced odds of having a treatment interruption among men (aOR 0.51, 95% CI 0.34-0.77) but not among women (aOR 2.67, 95% CI 0.78, 9.16). Travel in the past 6 months was not associated with discontinuation of care or treatment interruptions., Conclusions: We observed both negative and protective effects of recent migration on care engagement and ART use that were most pronounced among men in this cohort. Migration can break ties to ongoing care, but for men, who have more agency in the decision to migrate, may foster new care and treatment strategies. Strategies that enable health facilities to support individuals throughout the process of transferring care could alleviate the risk of care disengagement.

This data descriptor presents two main datasets and a set of auxiliary files. The mobility dataset presents a long-term study of human mobility in the Rio de Janeiro Metropolitan Area (RJMA) performed in the entire year of 2014 based on mobile phone data. The socioeconomic dataset presents selected socioeconomic variables of the Brazilian 2010 census. A set of auxiliary files is included to present georeferenced information and geographic features (shapefiles) and data used to validate the mobility estimates. The human mobility estimation was carried out using a methodology that allows direct integration with census data, based on an approximation of the geographic boundaries of census units by an aggregation of Voronoi polygons of the mobile phone antennas. The study area is the Brazilian local area 21, which includes the entire RJMA and four other municipalities. The mobility dataset is divided into two files: one is an estimation of the origin-destination (OD) matrix per day, and the other is a visitors' dataset where the number of visitors of each location is estimated in four shifts each day. The socioeconomic dataset presents information of 55 variables for each location, which have been used in different studies and present the longest human mobility dataset available for public use., (© 2023 The Authors.)

Understanding human mobility patterns is vital for the coordinated development of cities in urban agglomerations. Existing mobility models can capture single-scale travel behavior within or between cities, but the unified modeling of multi-scale human mobility in urban agglomerations is still analytically and computationally intractable. In this study, by simulating people's mental representations of physical space, we decompose and model the human travel choice process as a cascaded multi-class classification problem. Our multi-scale unified model, built upon cascaded deep neural networks, can predict human mobility in world-class urban agglomerations with thousands of regions. By incorporating individual memory features and population attractiveness features extracted by a graph generative adversarial network, our model can simultaneously predict multi-scale individual and population mobility patterns within urban agglomerations. Our model serves as an exemplar framework for reproducing universal-scale laws of human mobility across various spatial scales, providing vital decision support for urban settings of urban agglomerations., Competing Interests: The authors declare no competing interests., (© 2023 The Author(s).)

The coronavirus disease 2019 (COVID-19) pandemic and the measures taken by authorities to control its spread have altered human behavior and mobility patterns in an unprecedented way. However, it remains unclear whether the population response to a COVID-19 outbreak varies within a city or among demographic groups. Here, we utilized passively recorded cellular signaling data at a spatial resolution of 1 km × 1 km for over 5 million users and epidemiological surveillance data collected during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BA.2 outbreak from February to June 2022 in Shanghai, China, to investigate the heterogeneous response of different segments of the population at the within-city level and examine its relationship with the actual risk of infection. Changes in behavior were spatially heterogenous within the city and population groups and associated with both the infection incidence and adopted interventions. We also found that males and individuals aged 30 to 59 y old traveled more frequently, traveled longer distances, and their communities were more connected; the same groups were also associated with the highest SARS-CoV-2 incidence. Our results highlight the heterogeneous behavioral change of the Shanghai population to the SARS-CoV-2 Omicron BA.2 outbreak and the effect of heterogenous behavior on the spread of COVID-19, both spatially and demographically. These findings could be instrumental for the design of targeted interventions for the control and mitigation of future outbreaks of COVID-19, and, more broadly, of respiratory pathogens.

Standard environmental hazard exposure assessment methods have been primarily based on residential places, neglecting individuals' hazard exposures due to activities outside home neighborhood and underestimating peoples' overall hazard exposures. To address this limitation, this study proposes a novel mobility-based index for the hazard exposure evaluation. Using large-scale human mobility data, we quantify the extent of population dwell time in high environmental hazard places in 239 US counties for three environmental hazards. We explore how human mobility extends the reach of environmental hazards and leads to the emergence of latent exposure for populations living outside high-hazard areas. Notably, neglect of mobility can lead to over 10% underestimation of hazard exposures. The interplay of spatial clustering in high-hazard regions and human movement trends creates "environmental hazard traps." Poor and ethnic minority residents disproportionately face multiple types of environmental hazards. This data-driven evidence supports the severity of these injustices. We also studied latent exposure arising from visits outside residents' home areas, revealing millions of the population having 5 to 10% of daily activities occur in high-exposure zones. Despite living in perceived safe areas, human mobility could expose millions of residents to different hazards. These findings provide crucial insights for targeted policies to mitigate these severe environmental injustices.

COVID-19 has posed formidable challenges as a significant global health crisis. Its complexity stems from factors like viral contagiousness, population density, social behaviors, governmental regulations, and environmental conditions, with interpersonal interactions and large-scale activities being particularly pivotal. To unravel these complexities, we used a modified SEIR epidemiological model to simulate various outbreak scenarios during the holiday season, incorporating both inter-regional and intra-regional human mobility effects into the parameterization scheme. In addition, evaluation metrics were used to evaluate the accuracy of the model simulation by comparing the congruence between simulated results and recorded confirmed cases. The findings suggested that intra-city mobility led to an average surge of 57.35% in confirmed cases of China, while inter-city mobility contributed to an average increase of 15.18%. In the simulation for Tianjin, China, a one-week delay in human mobility attenuated the peak number of cases by 34.47% and postponed the peak time by 6 days. The simulation for the United States revealed that human mobility played a more pronounced part in the outbreak, with a notable disparity in peak cases when mobility was considered. This study highlights that while inter-regional mobility acted as a trigger for the epidemic spread, the diffusion effect of intra-regional mobility was primarily responsible for the outbreak. We have a better understanding on how human mobility and infectious disease epidemics interact, and provide empirical evidence that could contribute to disease prevention and control measures., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Recent advances in technology can be leveraged to enhance public health research and practice. This study aimed to assess the effects of mobility and policy changes on COVID-19 case growth and the effects of policy changes on mobility using data from Google Mobility Reports, information on public health policy, and COVID-19 testing results. Multiple bivariate regression analyses were conducted to address the study objectives. Policies designed to limit mobility led to decreases in mobility in public areas. These policies also decreased COVID-19 case growth. Conversely, policies that did not restrict mobility led to increases in mobility in public areas and led to increases in COVID-19 case growth. Mobility increases in public areas corresponded to increases in COVID-19 case growth, while concentration of mobility in residential areas corresponded to decreases in COVID-19 case growth. Overall, restrictive policies were effective in decreasing COVID-19 incidence in the Dominican Republic, while permissive policies led to increases in COVID-19 incidence., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Inc.)

COVID-19 vaccination is being used to control SARS-CoV-2 transmission globally, and many countries have relaxed some non-pharmaceutical interventions. It is unclear whether relaxing human mobility restrictions is proper and increases transmission risk. To fill this knowledge gap, we aim to examine if human mobility impairs the role of vaccination on COVID-19 transmission. We apply dynamic panel data models with three lag levels (i.e. 0 day, 7 days, and 14 days) to investigate the relationship between vaccination and COVID-19 transmission and the moderating effects of different kinds of human mobility. Our results show a negative relationship between the percentage of the vaccinated population and daily new COVID-19 cases after controlling for human mobility. We also observe that when people are only vaccinated with one dose, increased human mobility in retail and recreation, grocery and pharmacy, parks, transit stations, and workplaces could impair the role of vaccination on COVID-19 transmission, although their moderating effects appear at different lag levels. However, there are no moderating effects of human mobility in transit stations and workplaces at all lag levels when people are fully vaccinated. This finding does not mean that governments may relax restrictions in these two areas since mobility in transit stations and workplaces could affect COVID-19 transmission by themselves directly. Therefore, we recommend that governments still insist on implementing human mobility control measures from the perspective of transmission risk.

Mobile phone data have been widely used to model the spread of COVID-19; however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. Using multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on COVID-19 spread. Our approach allows us to determine the best metric for predicting disease incidence in a particular location, at different spatial scales. Additionally, we identify geographic and demographic factors, such as users' coverage and commuting patterns, that explain the (non)observed relationship between mobility and epidemic patterns. Our work provides epidemiologists and public health officials with a general-not limited to COVID-19-framework to evaluate the usefulness of human mobility data in responding to epidemics., (© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.)

The B.1.1.529 (Omicron) variant surge has raised concerns about the effectiveness of vaccines and the impact of imprudent reopening. Leveraging over two years of county-level COVID-19 data in the US, this study aims to investigate relationships among vaccination, human mobility, and COVID-19 health outcomes (assessed via case rate and case-fatality rate), controlling for socioeconomic, demographic, racial/ethnic, and partisan factors. A set of cross-sectional models was first fitted to empirically compare disparities in COVID-19 health outcomes before and during the Omicron surge. Then, time-varying mediation analyses were employed to delineate how the effects of vaccine and mobility on COVID-19 health outcomes vary over time. Results showed that vaccine effectiveness against case rate lost significance during the Omicron surge, while its effectiveness against case-fatality rate remained significant throughout the pandemic. We also documented salient structural inequalities in COVID-19-related outcomes, with disadvantaged populations consistently bearing a larger brunt of case and death tolls, regardless of high vaccination rates. Last, findings revealed that mobility presented a significantly positive relationship with case rates during each wave of variant outbreak. Mobility substantially mediated the direct effect from vaccination to case rate, leading to a 10.276 % (95 % CI: 6.257, 14.294) decrease in vaccine effectiveness on average. Altogether, our study implies that sole reliance on vaccination to halt COVID-19 needs to be re-examined. Well-resourced and coordinated efforts to enhance vaccine effectiveness, mitigate health disparity and selectively loosen non-pharmaceutical interventions are essential to bringing the pandemic to an end., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The COVID-19 pandemic has become a global public health crisis, causing significant morbidity and mortality worldwide. As an early response, different lockdowns were imposed in the UK (and the world) to limit the spread of the disease. Although effective, these measures profoundly impacted mobility patterns across cities, significantly reducing the number of people commuting to work or travelling for leisure. As different governments introduced massive vaccination programs to tackle the pandemic, cities have significantly but slowly increased human mobility, enabling the resumption of travel, work, and social activities. Nevertheless, how much can this return to normal mobility patterns be attributed to vaccines? In this study, we answer this question using a statistical approach, analysing two different open urban mobility datasets to quantify the effect vaccination rollouts have had on increased human activities., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Given that the effectiveness of COVID-19 vaccines and other therapies is greatly limited by the continuously emerging variants, non-pharmaceutical interventions have been adopted as primary control strategies in the global fight against the COVID-19 pandemic. However, implementing strict interventions over extended periods of time is inevitably hurting the economy. Many countries are faced with the dilemma of how to take appropriate policy actions for socio-economic recovery while curbing the further spread of COVID-19. With an aim to solve this multi-objective decision-making problem, we investigate the underlying temporal dynamics and associations between policies, mobility patterns, and virus transmission through vector autoregressive models and the Toda-Yamamoto Granger causality test. Our findings reveal the presence of temporal lagged effects and Granger causality relationships among various transmission and human mobility variables. We further assess the effectiveness of existing COVID-19 control measures and explore potential optimal strategies that strike a balance between public health and socio-economic recovery for individual states in the U.S. by employing the Pareto optimality and genetic algorithms. The results highlight the joint power of the state of emergency declaration, wearing face masks, and the closure of bars, and emphasize the necessity of pursuing tailor-made strategies for different states and phases of epidemiological transmission. Our framework enables policymakers to create more refined designs of COVID-19 strategies and can be extended to other countries regarding best practices in pandemic response., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhou, Zhang, Santi and Ratti.)

Studying the impacts of factors that may vary spatially and temporally as infectious disease progresses is critical for the prediction and intervention of COVID-19. This study aimed to quantitatively assess the spatiotemporal impacts of socio-demographic and mobility-related factors to predict the spread of COVID-19. We designed two different schemes that enhanced temporal and spatial features respectively, and both with the geographically and temporally weighted regression (GTWR) model adopted to consider the heterogeneity and non-stationarity problems, to reveal the spatiotemporal associations between the factors and the spread of COVID-19 pandemic. Results indicate that our two schemes are effective in facilitating the accuracy of predicting the spread of COVID-19. In particular, the temporally enhanced scheme quantifies the impacts of the factors on the temporal spreading trend of the epidemic at the city level. Simultaneously, the spatially enhanced scheme figures out how the spatial variances of the factors determine the spatial distribution of the COVID-19 cases among districts, particularly between the urban area and the surrounding suburbs. Findings provide potential policy implications in terms of dynamic and adaptive anti-epidemic., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Elsevier Ltd. All rights reserved.)

Social distancing policies and other restrictive measures have demonstrated efficacy in curbing the spread of the COVID-19 pandemic. However, these interventions have concurrently led to short- and long-term alterations in social connectedness. Comprehending the transformation in intracity social interactions is imperative for facilitating post-pandemic recovery and development. In this research, we employ social network analysis (SNA) to delve into the nuances of urban resilience. Specifically, we constructed intricate networks utilizing human mobility data to represent the impact of social interactions on the structural attributes of social networks while assessing urban resilience by examining the stability features of social connectedness. Our findings disclose a diverse array of responses to social distancing policies regarding social connectedness and varied social reactions across U.S. Metropolitan Statistical Areas (MSAs). Social networks generally exhibited a shift from dense to sparse configurations during restrictive orders, followed by a transition from sparse to dense arrangements upon relaxation of said orders. Furthermore, we analyzed the alterations in social connectedness as demonstrated by network centrality, which can presumably be attributed to the rigidity of policies and the inherent qualities of the examined MSAs. Our findings contribute valuable scientific insights to support informed decision-making for post-pandemic recovery and development initiatives., Competing Interests: The authors claim no conflict of interest., (© 2023 Elsevier Ltd. All rights reserved.)

Background: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread over the world and caused tremendous impacts on global health. Understanding the mechanism responsible for the spread of this pathogen and the impact of specific factors, such as human mobility, will help authorities to tailor interventions for future SARS-CoV-2 waves or newly emerging airborne infections. In this study, we aim to analyze the spatio-temporal transmission of SARS-CoV-2 in Belgium at municipality level between January and December 2021 and explore the effect of different levels of human travel on disease incidence through the use of counterfactual scenarios., Methods: We applied the endemic-epidemic modelling framework, in which the disease incidence decomposes into endemic, autoregressive and neighbourhood components. The spatial dependencies among areas are adjusted based on actual connectivity through mobile network data. We also took into account other important factors such as international mobility, vaccination coverage, population size and the stringency of restriction measures., Results: The results demonstrate the aggravating effect of international travel on the incidence, and simulated counterfactual scenarios further stress the alleviating impact of a reduction in national and international travel on epidemic growth. It is also clear that local transmission contributed the most during 2021, and municipalities with a larger population tended to attract a higher number of cases from neighboring areas., Conclusions: Although transmission between municipalities was observed, local transmission was dominant. We highlight the positive association between the mobility data and the infection spread over time. Our study provides insight to assist health authorities in decision-making, particularly when the disease is airborne and therefore likely influenced by human movement., (© 2023. The Author(s).)

Most studies modeling population mobility and the spread of infectious diseases, particularly those using meta-population multi-patch models, tend to focus on the theoretical properties and numerical simulation of such models. As such, there is relatively scant literature focused on numerical fit, inference, and uncertainty quantification of epidemic models with population mobility. In this research, we use three estimation techniques to solve an inverse problem and quantify its uncertainty for a human-mobility-based multi-patch epidemic model using mobile phone sensing data and confirmed COVID-19-positive cases in Hermosillo, Mexico. First, we utilize a Brownian bridge model using mobile phone GPS data to estimate the residence and mobility parameters of the epidemic model. In the second step, we estimate the optimal model epidemiological parameters by deterministically inverting the model using a Darwinian-inspired evolutionary algorithm (EA)-that is, a genetic algorithm (GA). The third part of the analysis involves performing inference and uncertainty quantification in the epidemic model using two Bayesian Monte Carlo sampling methods: t-walk and Hamiltonian Monte Carlo (HMC). The results demonstrate that the estimated model parameters and incidence adequately fit the observed daily COVID-19 incidence in Hermosillo. Moreover, the estimated parameters from the HMC method yield large credible intervals, improving their coverage for the observed and predicted daily incidences. Furthermore, we observe that the use of a multi-patch model with mobility yields improved predictions when compared to a single-patch model.