Population Movements Research Articles

BackgroundEstimating the effective reproduction number (R_t) is essential for monitoring and responding to infectious disease outbreaks. However, conventional methods such as the Wallinga–Teunis (WT) estimator assume intra-regional transmission and do not explicitly account for spatial connectivity. This underscores the need for approaches that explicitly incorporate spatial connectivity, such as inter-regional mobility, into transmission dynamics.MethodsWe propose a mobility-adjusted framework for regional R_t estimation that incorporates inter-regional population movement data to reallocate transmission pressure across regions. This approach allows us to better account for the contribution of external transmission. We apply our method to real-world COVID-19 data from South Korea, using a daily mobility matrix derived from telecommunication records. The resulting R_t estimates are compared with those obtained from the WT method. We also conduct sensitivity analysis on the size of the sliding window and comparative analysis based on the structure of the mobility matrix.ResultsIn high-mobility regions such as Seoul and Gyeonggi, both methods produced broadly similar trends, though the mobility-adjusted R_t was more responsive during the early epidemic phase. In contrast, many low-incidence regions—including Jeonbuk, Jeonnam, and Ulsan—showed inflated WT R_t estimates, which our framework mitigated by accounting for inter-regional transmission. Daegu, the epicenter of the initial outbreak, exhibited a sharp early peak in mobility-adjusted R_t consistent with its role as a major source of onward transmission, while neighboring or closely connected regions such as Gyeongbuk and Busan showed largely consistent values across methods. Sensitivity analysis indicated that a 7-day sliding window provided stable and epidemiologically plausible estimates, and additional comparisons across alternative mobility matrix structures confirmed that incorporating temporally resolved, empirical movement data yields more robust and interpretable results than static or simplified assumptions.ConclusionsThis study presents a mobility-adjusted framework for estimating R_t that incorporates inter-regional movement to better capture spatial transmission dynamics. The method mitigates inflated estimates in low-incidence areas and identifies mobility-driven risks in highly connected regions, offering context-aware insights beyond conventional approaches. While not universally superior, it complements existing methods and provides a practical tool for geographically targeted interventions, adaptable to diverse epidemiological settings where spatial connectivity shapes outbreaks.

In disaster research, individual-level mobile phone location data is considered highly valuable for assessing population mobility and disaster impacts. However, due to privacy regulations in China, only spatially aggregated mobile data with a resolution of 1 km × 1 km are available. These data do not contain explicit population individual population movement, which poses challenges for analyzing population movement patterns in disaster research. To using this grid-based mobile data to describe population movement, we applied an empirical orthogonal function (EOF) method to the post-disaster phase of the 2017 Jiuzhaigou earthquake. The first EOF mode (EOF1) primarily exhibits positive anomalies centered over the Jiuzhaigou Valley. The principal components for the EOF1 show a decreasing trend from midnight to 20:00, indicating a continuous outflow of population from the Jiuzhaigou Valley during this period. The second mode (EOF2) exhibits negative anomalies at the Jiuzhaigou Valley and along the road to the southwest of the Valley, while positive anomalies appear along two roads, i.e., one extending from the Jiuzhaigou Valley to Shuanghe, and the other from the Chuanzhusi Town government square to western Chuanzhusi. The primary components of EOF2 reveal that, from midnight to 10:00, population increased along these two roads while decreasing over the Jiuzhaigou Valley and the road leading southward to the Chuanzhusi Town government square. After 10:00, this population change pattern diminished between 10:00–15:00. Based on the EOF2 results, two evacuation routes were identified: Path 1 extended northwest from the Chuanzhusi Town government square; Path 2 led southeast from Jiuzhaigou Valley through Shuanghe Town. In comparison, the BBAC_I clustering method identifies clusters with similar temporal trends but fails to pinpoint the most affected areas or infer evacuation directions. In contrast, EOF analysis overcomes these limitations by revealing key impact zones and evacuation patterns, even in the absence of trajectory data.

In disaster research, individual-level mobile phone location data is considered highly valuable for assessing population mobility and disaster impacts. However, due to privacy regulations in China, only spatially aggregated mobile data with a resolution of 1 km × 1 km are available. These data do not contain explicit population individual population movement, which poses challenges for analyzing population movement patterns in disaster research. To using this grid-based mobile data to describe population movement, we applied an empirical orthogonal function (EOF) method to the post-disaster phase of the 2017 Jiuzhaigou earthquake. The first EOF mode (EOF1) primarily exhibits positive anomalies centered over the Jiuzhaigou Valley. The principal components for the EOF1 show a decreasing trend from midnight to 20:00, indicating a continuous outflow of population from the Jiuzhaigou Valley during this period. The second mode (EOF2) exhibits negative anomalies at the Jiuzhaigou Valley and along the road to the southwest of the Valley, while positive anomalies appear along two roads, i.e., one extending from the Jiuzhaigou Valley to Shuanghe, and the other from the Chuanzhusi Town government square to western Chuanzhusi. The primary components of EOF2 reveal that, from midnight to 10:00, population increased along these two roads while decreasing over the Jiuzhaigou Valley and the road leading southward to the Chuanzhusi Town government square. After 10:00, this population change pattern diminished between 10:00-15:00. Based on the EOF2 results, two evacuation routes were identified: Path 1 extended northwest from the Chuanzhusi Town government square; Path 2 led southeast from Jiuzhaigou Valley through Shuanghe Town. In comparison, the BBAC_I clustering method identifies clusters with similar temporal trends but fails to pinpoint the most affected areas or infer evacuation directions. In contrast, EOF analysis overcomes these limitations by revealing key impact zones and evacuation patterns, even in the absence of trajectory data.

The article discusses the issue of creating a fourth teacher’s institute in Western Siberia. The conditions in which the process of formation and then the activities of this educational institution took place were extremely difficult: two revolutions in 1917, the transfer of power from the Provisional Government first to the Soviet government created considerable difficulties in organizing the educational process. It should also be taken into account that in 1918 the Soviet government was developing a new, more effective model of the public education system. These experiments covered not only primary and secondary schools, but also higher schools. The opening of a new educational institution, essentially a secondary specialized one, required the presence of highly qualified teaching staff with work experience. The city of Novo-Nikolaevsk, which belonged to the group of largest cities in Western Siberia, was second in number of inhabitants only to Omsk and Tomsk. But the named cities already had their own higher educational institutions in which people with higher education worked. To a certain extent, Novo-Nikolaevsk was helped in resolving the personnel issue by the Civil War that began in the spring of 1918, which led to large-scale population movements within the country. Among the people evacuated from the European part of the country and from the Urals were people with higher education who became part of the teaching staff of the local teachers’ institute. In general, the events of the Civil War greatly complicated the activities of the institute and negatively affected the life and work of its staff. It should also be taken into account that during the reign of the Kolchak government, not only students, but also some teachers of the institute were mobilized into the ranks of the White Army. Despite the fact that the Novo-Nikolaev Teachers’ Institute existed for a very short period of time – about three years, it managed to prepare within its walls the first and only graduating class of its students by the summer of 1920. The article also lists the city educational institutions in which teachers continued their activities after the closure of the teacher’s institute.

Aim: This study aims to analyze the spatio-temporal dynamics of urban expansion in the lower plain sector between the Shkumbin and Seman Rivers, Albania, from 1991 to 2020, with a focus on identifying the impacts of this expansion on agricultural land and regional planning. The study area lies in the Myzeqe Plain, part of the Western Lowland of Albania. The definition of urban areas through the use of satellite images brings further development of this analysis, where land use and land cover are taken into account. Methods: The study employed multi-temporal Landsat satellite imagery from 1991, 1995, 2000, 2005, 2013, and 2020, analyzed through GIS-based land use/land cover classification and change detection techniques to quantify spatial urban expansion. Results: The results present the spatial change in km2, but also by presenting it in percentages to summarize the findings more practically. These results are enriched with the findings from the combination of urban areas with the LULC map of 1991, where this study begins. This approach analyzes the expansion of urban areas by reflecting what type of land cover these lands occupied in the first year of the scientific analysis. Before the 1990s, Albania was part of the former communist countries and as a result of this political situation, population movement was controlled. Before this period, the organization of urban systems was planned and almost throughout this time, expansions were few. After this period, people began to have more rights over changing their place of residence, accompanied by the chaotic development of urban areas and new buildings. This study found that urban areas increased from about 6.5 km² in 1991, in the Myzeqe Plain, which corresponds to the study area in 2020, to 61.1 km². These territories are divided into large cities and new urbanized villages. Conclusion: This increased urban stress has mostly fallen on agricultural lands, with 52.4% of new urban areas being built on agricultural lands. Recommendation: These findings support the need for sustainable urban planning strategies to mitigate agricultural land loss and ensure balanced regional development. The study recommends integrating GIS-based monitoring tools into urban planning to manage expansion pressures and preserve critical agricultural areas.

ABSTRACT This article discusses the non-hegemonic view of water held by black communities in the Ribeira Valley, Brazil, in contrast to the hegemonic perspective of modern water. We present the mobilization of this people for the right to water, which has resisted the implementation of hydroelectric plants, through the Movement of People Threatened by Dams in the Ribeira Valley. We also discuss the local narratives about the ‘Negros d’Água’, mythical beings who point to a circular perception of time, to a fluid relationship between the material and spiritual worlds and to the slavery memory. Both cases differ from modern water, which considers water as something de-territorialized and homogenous. We analyse the two faces of water in the Ribeira Valley – water as a common and as a symbolic and sacred narrative – to compare it with the modern paradigm. Based on secondary data and from a qualitative approach, this discussion is a partial result of ‘PHOENIX – Human Mobility, Global Challenges and Resilience in an Age of Social Stress’, a project that is being developed by an international platform, that aims to understand how population movements are affected by global change processes.

This article explores the role of the Vatican and Catholic aid agencies in resettling European displaced victims of war in the aftermath of the Second World War, contributing to the field of refugee history and humanitarian studies. Focusing on the entanglements between Catholic humanitarians and UN agencies such as UNRRA and IRO, it shows how controlling mobility and managing population movements became a central part of the Vatican’s agenda and paved the way to greater involvement of the Catholic Church in the post-war reconstruction. In doing so, the essay intersects primary sources (predominantly from the Vatican and US Catholic archives) and different historiographical debates, with the aim of nuancing our understanding of post-war ‘refugeedom’ and the emergence of the ‘Cold War West’.

This paper examines the migration processes of the Antique period through the lens of burial findings, comparing archaeological evidence from Central Asia, the Near East, and Europe. Using interpretative and bioarchaeological methods, the research highlights the relationship between funerary practices, material culture, and biological traces of population movement. The study emphasizes Uzbekistan’s role as a cultural bridge between East and West, revealing how migration contributed to the transformation of social, religious, and ethnic structures in antiquity. The results indicate that burial data serve as one of the most reliable sources for reconstructing ancient migration routes and intercultural interactions.

Diet composition and trophic ecology of two Antarctic storm-petrel species.

In the current era of mobile Internet, personalized location services have significantly enhanced user experience. However, they have concomitantly given rise to substantial challenges related to the protection of location privacy. The study aims to develop a location privacy protection model for static and dynamic data publishing scenarios by deeply analyzing the differential privacy mechanism and its application on mobile aggregated data features. The study designs a protection model using diverse perturbation mechanisms for static data and constructs a set of protection models for dynamic data by combining prediction, adaptive adjustment, and data grouping and merging. The experiments were validated using the Chengdu cab trajectory dataset (real data) and the simulated synthetic population movement dataset (simulated generated data). The experimental results showed that the static model successfully reduced the trajectory recovery rate to 18% and achieved the lowest mean absolute error of 0.8 and the lowest mean relative error of 0.45 on the simulated dataset. The dynamic model achieved a minimum mean absolute error of 0.047 and a minimum mean relative error of 20 with a fixed privacy budget. The above results demonstrate the potential of the two models in improving the efficacy of location privacy protection and point out the direction for the future development of privacy protection techniques in personalized location services.

IntroductionIn the evolving landscape of disaster response, integrating advanced digital technologies is critical to enhancing the efficiency and effectiveness of public health systems. Traditional anomaly detection methods often fall short due to their inability to handle the dynamic, heterogeneous, and real-time nature of disaster-related data. These methods typically rely on static models that struggle with integrating continuous data streams from diverse sources like hospitals, emergency services, social media, and environmental sensors. As a result, they often fail to capture sudden shifts in disease patterns, environmental conditions, or population movements, leading to delayed risk identification and suboptimal decisions. The increasing frequency and complexity of natural disasters and pandemics underscore the need for flexible, adaptive systems capable of learning from evolving data. Recent advances in machine learning, artificial intelligence, and big data analytics offer promising tools to address these limitations by enabling real-time, high-dimensional data analysis. In recent years, the integration of advanced digital technologies has become essential for improving public health disaster response.MethodsThis study proposes a deep learning-based framework for anomaly detection and early risk identification during digital disaster response scenarios, leveraging data from hospitals, emergency services, social media, and environmental sensors. The objective of the study is to enhance real-time decision-making and situational awareness in public health crises.Results and discussionExperimental results across multiple datasets (EM-DAT, FEMA, UNOSAT, Earthquake) demonstrate that our proposed model improves anomaly detection performance by 23% in precision and reduces false alarms by 31% compared to baseline models. The method combines LSTM and transformer-based architectures to effectively analyze spatiotemporal data, offering both high accuracy and interpretability for public health experts.

The study of problems related to the demography of Western Armenia is highly topical, because it covers the real goals of the anti-Armenian policy of the Ottoman authorities. The main trends of population movements, which were recorded in the provinces of Van and Bitlis at the end of XIX and beginning of XX centuries are discussed within the framework of the article. Movements, both within the country and to adjacent or more distant areas, were common to the Armenians of almost all provinces of Western Armenia, but Van and Bitlis were characterized by certain commonalities, which were especially related to more insecure and poor condition of the population of these provinces, as well as referred to the main directions of movements: the main destinations of emigration from Van and Bitlis were to Eastern Armenia, other parts of the Russian Empire and Persia. The article also touches on the reasons of emigration and also the labor migration, such as - insecurity, persecution, violence, massacres, forced Islamization, and direct consequences: decrease of the number of the Armenian population, depopulation of Armenian settlements, etc. The study was carried out using a historical-comparative approach, supported by historical-demographic and content analysis methods.

ABSTRACT Pseudomonas aeruginosa sequence type (ST)-1047 is emerging globally as a carbapenemase-rich lineage, yet its evolutionary history and population structure are not known. Here, we performed a comprehensive genomic and epidemiological investigation of 141 ST-1047 isolates from 15 countries, integrating short- and long-read sequencing data with Bayesian phylogenetics and mobile genetic element analyses. Two clonal subpopulations were identified. Subclone 1, defined by bla VIM-11 carriage and loss of exoU , is proposed to have been imported to the United States following the medical evacuation of wounded service members from Afghanistan in 2005 and later seeded a nosocomial outbreak in Washington state. Subclone 2, carrying bla IMP-1 , is undergoing rapid clonal expansion due to nosocomial outbreaks in Ukraine hospitals where infection control is impaired by the war with Russia. Genomic islands resembling P. aeruginosa genomic island-97B mediated bla IMP-1 duplication and integration at multiple chromosomal sites, including between iron-regulated small RNAs PrrF1 and PrrF2. Outside these subclones, independent acquisitions of bla NDM-1 and/or bla DIM-1 occurred via diverse resistance islands. While plasmids were detected in some ST-1047 isolates, chromosomal integration of carbapenemase genes has promoted stability and driven the population structure. This global study reveals that, since its emergence in the late 19th century, the ST-1047 lineage showed an exceptional ability to acquire diverse carbapenemases, and that geopolitical conflicts influenced its global spread on at least two occasions. These findings underscore the need for sustained global surveillance and high-resolution genomic analyses to prevent further spread of this high-risk pathogen. IMPORTANCE Carbapenemase-producing Pseudomonas aeruginosa is a major cause of healthcare-associated infections worldwide and is associated with high mortality due to limited treatment options. In this study, we characterize the emergence and international spread of a previously underrecognized lineage of P. aeruginosa that has independently acquired and stabilized multiple resistance genes, including those encoding VIM, IMP, NDM, and Dutch imipenemase carbapenemases. Using genomic sequencing and evolutionary analyses, we show how this lineage emerged in the late 19th century and has since adapted by integrating resistance genes directly into its chromosome, promoting long-term stability and outbreak potential. Strikingly, we link its global expansion to population movements, soldier evacuations, and healthcare disruptions during armed conflicts in Afghanistan and Ukraine. This work reveals how political instability can drive the spread of multidrug-resistant bacteria and underscores the value of high-resolution surveillance to detect and contain emerging threats before they become dominant in clinical settings.

Countries can deliberately create, manipulate and exploit cross-border population movements to induce concessions from a target. Such ‘coercive engineered migrations’ are more likely to be successful when targeting domestically unstable states. I argue that environmental stress can add to this instability and ‘swamp’ a target’s ability to cope with cross-border population movements. Ultimately, the chances of migration-driven coercion to be successful should increase when target countries are both domestically unstable and suffer from environmental shocks. This claim is tested using quantitative data on the outcomes of coercive engineered migrations since the 1950s, which I combine with information on environmental extremes, as measured by the number of environmental disasters. Controlling for several other influences that may affect the outcome and employing sample-selection estimators that account for the non-random assignment of coercive engineered migration, the results support the argument as I show that the likelihood of successful migration-related coercion increases when domestically unstable target countries also face environmental disasters. This finding contributes to our understanding of migration as a foreign-policy instrument, it sheds new light on the role of environmental stress in international bargaining, and there are direct implications for conflict as a driver of cross-border population movements.

ABSTRACTTo explore the impact of spatial heterogeneity, population movement inevitability, and asymptomatic infection prevalence on vector‐borne disease transmission, we developed a partially degenerate reaction–diffusion model to characterize pathogen spread between vectors and hosts. Using some subtle estimates and induction methods, the well‐posedness of solutions of this model are established, including the global existence of solutions, ultimate boundedness of solutions, and existence of global compact attractors. Further, by applying the next‐generation operator, we define the basic reproduction number and derive its explicit expression. And then, the critical threshold based on are obtained: for , the disease‐free steady state () is globally asymptotically stable; for , the disease becomes uniformly persistent. Notably, we also establish that the positive steady state () is globally asymptotically stable under conditions of homogeneous environments and when all infections progress through the asymptomatic stage. Furthermore, the asymptotic profiles of the are derived as the diffusion rates approach zero or infinity. The theoretical findings are validated through numerical simulations, which reveal that the mobility of asymptomatic hosts plays a critical role in shaping the spatio‐temporal distribution of infected individuals. Further analysis indicates that the ratio of asymptomatic to symptomatic hosts can induce both risk accumulation and risk‐shifting phenomena. Using the partial rank correlation coefficient (PRCC) method, we identify key parameters that significantly affect the basic reproduction number. Based on these insights, we propose implementing integrated control strategies, including targeted insecticide application, measures to reduce host–vector contact, and improved early screening and management of asymptomatic infections.

Abstract Flooding is one of the most devastating natural hazards affecting human society. Accurate exposure assessment is critical for effective risk management. Traditional approaches based on static overlays of population and flood hazard data overlook both cross‐city mobility dynamics in urban agglomerations and reservoir‐induced hazard shifts. This work calculates dynamic flood population exposure in coupled human‐water systems by integrating hydrological modeling with human mobility data. The ERA5‐Land and Today's Earth data sets were used as forcing data to drive the Global River Hydrodynamics Model (CaMa‐Flood) for fluvial flood simulation. Human spatiotemporal distribution maps were derived from mobile positioning data through spatiotemporal reorganization and Kernel Density Estimation (KDE) interpolation. It is found that incorporating reservoir operations reduces the hazard level for 8%–11.8% of the exposed population under flood scenarios with return periods of 5–100 years. Diurnal population fluctuations alter flood exposure, with nighttime exposure surpassing daytime levels by 681–777 thousand people for 5–100 years return period events. The proposed method provides a novel framework to assess dynamic flood population exposure by coupling water conservation regulation with cross‐city population movement, supporting collaborative flood management in urban agglomerations.

Historical and archaeological records indicate that the Maritime and Land Silk Roads played a pivotal role in facilitating Trans-Eurasian migrations and cultural exchanges. However, the extent to which population movements or the spread of ideas shape Chinese Hui populations remains debated. We present the largest genomic resource to date, including 2,280 Hui individuals sequenced or genotyped from 30 diverse regions, to examine the genetic origins, population structure, and biological adaptations of this underrepresented group in global human genome research. We identified a detailed population structure characterized by five distinct genetic lineages of the Hui, influenced by geography and varying gene flow. The admixture history and demographic events suggest that the northwestern and northern Hui lineages emerged from demic diffusion during the Tang and Yuan Dynasties via the Land Silk Road. In contrast, the southern and island Hui lineages reflect cultural diffusion along the Maritime Silk Road, while the mixed southern–northern lineage likely developed through a combination of demic and cultural diffusion. Our findings support a hybrid model for Hui formation, indicating that both demographic processes and sociocultural transmissions contributed to their population history. We identified east–west highly differentiated variants and pre- and post-admixture adaptations in Hui genomes, demonstrating that admixture-driven adaptive or neutral variants impacted susceptibility to cardiovascular diseases and immune- and diet-related traits. These adaptive signatures include post-admixture signals of SLC24A5 and ECHDC1 in the Hui, as well as pre-admixture signals of the HLA region, BCL2A1, and KCNH8 in the East Asian source. Overall, our study suggests that Han-related genetic components helped the Hui population rapidly adapt to new local environments. Additionally, the frequency spectrum of clinically essential variants differed significantly between Hui and Han individuals, emphasizing the importance of including underrepresented populations in genomic research to promote health equity.

An early East Asian lineage with unexpectedly low Denisovan ancestry.

Exploring structural changes in the Swedish cattle population and between-holding movements.

Abstract Climate conditions, in complex interrelations with other socioenvironmental, economic, political and cultural factors, significantly play a key role in shaping population density and movement. However, the impacts of climate change on global human migration remain poorly understood. This study assesses the sensitivity of international migration to climate change across 160 countries and regions, focusing on temperature-related climate indices. The results show that migration patterns are strongly influenced by a country’s baseline climate and economic conditions. Under global warming, countries in mid- to high-latitudes with lower baseline temperatures are projected to experience substantial increases in migration inflows, while low-latitude countries are likely to see rising migration outflows. This climate sensitivity is especially pronounced in higher Gross Domestic Product (GDP) countries, whereas lower-GDP countries show little dependence on baseline climate. A simple predictive model based on projected changes in climate and GDP suggests that populations in low-GDP, low-latitude countries will face worsening conditions and increasing migration pressure by the end of the 21st century. The results underscore the growing influence of economic factors on migration and call for urgent integration of climate justice into migration policy to support vulnerable populations in a warming world.