Human Travel Research Articles

Assessing and Optimizing the Connectivity of the Outdoor Green Recreation Network in Zhengzhou from the Perspective of Green Travel

With the increasing demand for outdoor recreation and fitness, this study aims to assess the connectivity of the outdoor green recreation (OGR) network from the perspective of green travel and propose optimization framework. The Point of Interest (POI) and Area of Interest (AOI) datasets of OGR spots in Zhengzhou were utilized as the primary research materials. A combination of GIS spatial analysis and Graph index calculation is employed to quantify and diagnose the connectivity of the OGR network based on multi-source data (land cover, topography, and road network). The index system for cost surface establishment was improved and proposed, shifting its focus from previous biological migration and ecological network to human green travel and improving the connectivity of the OGR network. The technical optimization process of the OGR network is explored and presented. The results show that: (1) The scale, number, and distribution of OGR spots and the connectivity of the OGR network are significantly different in urban and rural areas. Numerous small-scale OGR spots and short-distance recreational paths are distributed in urban areas, while a limited number of large-scale OGR spots and long-distance recreational paths are situated in rural areas with better natural resources. (2) Compared with driving travel, the connectivity of the OGR network is poor when walking and cycling. Graph indexes of Dg, BC, and dPC can be used to reflect the connection capability, bridging role, and contribution of each spot to overall network connectivity. (3) The current OGR network is optimized through 30 new spots based on the perspective of green travel and land suitability analysis. The network connectivity will improve by 4%, and the number of recreational paths suitable for green travel increased by 41. (4) The methodologies for quantifying and optimizing OGR network connectivity from the perspective of green travel will offer valuable references for future research in this field.

Investigating day-to-day route choices based on multi-scenario laboratory experiments, Part II: Route-dependent attraction-based stochastic process model

Laboratory experiments are one of the important means used to investigate travel choice behavior under strategic uncertainty. Many experiment-based studies have shown that the Nash equilibrium can predict aggregated route choices, while the fluctuations, whose mechanisms are still unclear, continue to exist until the end. To understand the fluctuations, this paper proposes a route-dependent attraction-based stochastic process model, which shares exactly the same behavioral foundation introduced in Part I of the study (Qi et al., 2023), i.e., route-dependent inertia and route-dependent preference. The model predictions are carefully compared with the experimental observations obtained from the congestible parallel-route laboratory experiments containing 312 subjects and eight decision-making scenarios (Qi et al., 2023). The results show that the proposed stochastic process model can precisely reproduce the random oscillations both in terms of flow switching and route flow evolution. Subsequently, an approximated model is developed to enhance the efficiency in evaluating the equilibrium distribution, providing a practical tool to evaluate the impacts of transportation policies in both long- and short-term runs. To the best of our knowledge, this paper is the first attempt to model and explain experimental phenomena by introducing stochastic process theories, as well as a successful example of applying experimental economics methodology to improve our understanding of human travel choice behavior.

Human Spaceflight: Technological Advances, Health Implications, and the Path to Interplanetary Travel

Human spaceflight has advanced from brief orbital missions to sustained international cooperation on the International Space Station (ISS). As humanity looks toward more ambitious goals, such as lunar bases and Mars exploration, understanding the challenges and opportunities of human space travel is critical. This paper provides a comprehensive analysis of the current state of human spaceflight, focusing on technological advancements, health and safety concerns, and future mission planning. It reviews the physical, psychological, and environmental factors affecting astronauts, technological innovations supporting deep-space missions, and emerging ethical and logistical considerations. This research concludes with an evaluation of potential pathways for human settlement on Mars, the Moon, and beyond.

Pharmacological Innovations in Space: Challenges and Future Perspectives.

Since the first human experience in space, the interest in space research and medicine to explore universe is growing day by day. The extreme space conditions mainly radiation and microgravity effects on human physiology, antimicrobial susceptibility, and efficacy, safety, and stability of drugs. Therefore, the aim of this review is to address the impact of extreme space conditions, mainly microgravity and radiation, on human physiology and highlights the need for future approaches by evaluating the effectiveness of strategies to prevent or mitigate health problems. Published papers and NASA technical documents were searched in Pubmed and Google Scholar databases using the keywords ''antimicrobial susceptibility or drug resistance or drug stability or innovations or pharmacokinetic or pharmacodynamics'' and ''radiation or microgravity or space environments or space medicine or space pharmacy'' to prepare this review. In this review, the challenges regarding physiological effects and drug-related problems are examined through the evaluation of extreme conditions in space. Medications used in spaceflight are summarized, and the role of pharmacists specializing in space medicine is briefly explained. Last but not least, to overcome the aforementioned issues, novel approaches have been addressed, such as personalised treatments, development of space-resistant formulations and various microbial applications. Further research in the space medicine is required to facilitate the safe and healthy travel of humans to the Moon, Mars and other extraterrestrial destinations. One bear in mind that space research will contribute not only to the exploration of the universe, but also to the advancement of health and technological discoveries on Earth.

Identification of Resistance to <i>Puccinia striiformisf</i>. sp. <i>tritici</i> (<i>Pst</i>) in Some Commercial and Elite Bread Wheat Lines

Stripe rust is caused by <i>Puccinia striiformisf</i>. sp. <i>tritici</i> (<i>Pst</i>) threatening wheat production in Ethiopia. The emerged virulent stripe rust races at one point of the world spread to the rest of wheat-producing countries by the wind as well as human travels and damaged popular resistant wheat cultivars thereby posing food insecurity. However, wheat cultivars succumb to new Pst race (s) soon after their release from research centers. This study aimed to determine stripe rust resistance in some Ethiopian commercial and elite bread wheat lines. In 2017, a total of 37 commercial and elite bread wheat lines were exposed to stripe rust in under the disease hot spot areas (Kulumsa and Meraro) Arsi zone Oromia regional state. In the second year (2018), 22 promising lines consisting of 16 commercial bread wheat and 6 elite’s lines were evaluated both at seedling and adult plant growth stages. The seedling test was conducted in the greenhouse at Kulumsa research center using three Pst races. In field evaluations, terminal severity (TRS), coefficient of infection (CI), area under disease progress curve (AUDPC), disease progress rate (DPR), and spike infection (SI) were considered. Of the 37 commercial and elite bread wheat lines, 19 (51.4%) exhibited lower or equal disease reaction compared to the resistant check (Enjoy) across locations and seasons. Eleven bread wheat lines showed both adult plant and seedling resistance. The 37 commercial wheat lines that showed field resistance was further exposed to three Pst races at the seedling stage and 11 exhibited seedling resistance to all races. This study has identified seedling and adult plant resistance in some commercial and elite bread wheat lines to the prevailing Pst races.

Emergence of the B.1.214.2 SARS-CoV-2 lineage with an Omicron-like spike insertion and a unique upper airway immune signature

We investigate the emergence, mutation profile, and dissemination of SARS-CoV-2 lineage B.1.214.2, first identified in Belgium in January 2021. This variant, featuring a 3-amino acid insertion in the spike protein similar to the Omicron variant, was speculated to enhance transmissibility or immune evasion. Initially detected in international travelers, it substantially transmitted in Central Africa, Belgium, Switzerland, and France, peaking in April 2021. Our travel-aware phylogeographic analysis, incorporating travel history, estimated the origin to the Republic of the Congo, with primary European entry through France and Belgium, and multiple smaller introductions during the epidemic. We correlate its spread with human travel patterns and air passenger data. Further, upon reviewing national reports of SARS-CoV-2 outbreaks in Belgian nursing homes, we found this strain caused moderately severe outcomes (8.7% case fatality ratio). A distinct nasopharyngeal immune response was observed in elderly patients, characterized by 80% unique signatures, higher B- and T-cell activation, increased type I IFN signaling, and reduced NK, Th17, and complement system activation, compared to similar outbreaks. This unique immune response may explain the variant's epidemiological behavior and underscores the need for nasal vaccine strategies against emerging variants.

Radiation exposure of astronauts following an intense solar particle event: analysis and comparison of doses in male and female voxel phantoms.

According to NASA's plans, a human travel to the Moon is planned by the end of 2025 with the Artemis II mission, and humans should land on the Moon again in 2026. Exposure to space radiation is one of the main risks for the crew members; while for these short missions the doses from galactic cosmic rays would be relatively low, the possible occurrence of an intense solar particle event (SPE) represents a major concern, especially considering that in 2025 the Sun activity will be at its peak. Quantifying the amount and the effects of such exposure is therefore crucial, to identify shielding conditions that allow respecting the dose limits established by the various space agencies. By exploiting an interface between the BIANCA biophysical model and the FLUKA Monte Carlo radiation transport code, in this work we implemented a male and a female voxel phantom and we calculated absorbed doses and Gy-Eq doses in the various tissues/organs, as well as effective doses, following exposure to the August 1972 SPE, the most intense event of the modern era. The calculations were performed respect the organ dose limits for 30 d missions. A detailed comparison between male and female doses was then carried out, also considering that the Artemis II crew will include a woman. The results showed that female doses tend to be higher than male doses, especially with light shielding. This should be taken into account in mission design, also considering that, in a typical lunar mission, up to 15% of time may be spent in extra-vehicular activities, and thus with light shielding. More generally, this work outlines the importance of performing separate calculations for male and female astronauts when dealing with radiation doses and effects.

Space exploration in China

Abstract In the past 20 years, China has implemented Manned Space Engineering, Lunar and Mars exploration missions, becoming an active participant in human space travel and exploration. So far China has established its Space Station in 2022, continuing to carry out large-scale space science research for the next 10-15 years. China has successfully achieved the soft landing on the lunar, far side of the Moon and on the Mars surface, obtained 1.73 kg of lunar sample, and will also implement the lunar Antarctic sample return and Mars sample return mission later. The exploration missions for the asteroid and Jupiter system are being prepared. Overall the future science exportations and collaborations between human and robot, as well as the utilisation of extraterrestrial resources will become the new development focus. Chinese scientists eagerly look forward to close international collaboration.

Tantangan dan Solusi Terkini dalam Penanganan Penyakit Menular

Infectious disease management continues to face various dynamic challenges along with environmental changes, human travel patterns, and the emergence of new pathogens. This study aims to analyze and identify current challenges and the solution in infectious disease management through a literature review method. The review covered scientific literature from various relevant sources, including journals, books, and reports from international health organizations. The results of this study show that the main challenges include antimicrobial resistance, and lack of health infrastructure in some regions. In addition, global issues such as climate change and urbanization exacerbate the situation by increasing the risk of disease spread. The study also found that international collaboration, innovations in medical technology, and increased public awareness and education are key to overcoming these challenges. In conclusion, addressing infectious diseases requires a multidisciplinary and cross-sectoral approach to improve the global response to evolving health threats.

TKSTAGNet: A Top-K Spatio-Temporal Attention Gating Network for air pollution prediction

Accurate air pollution prediction is of great significance for human health, travel, and environmental management. As an emerging artificial intelligence technology, Transformer has already shown tremendous potential in air pollution prediction. Unfortunately, existing Transformer-based methods are challenging to accurately predict air pollution due to two limitations: (1) they neglect irrelevant spatial and temporal neighbors (e.g., neighboring sensors and timesteps) when modeling spatio-temporal correlations, and (2) they lack an adaptive mechanism to mine different effects caused by various external factors (e.g., meteorological conditions). Jointly taking these limitations into account, we propose a novel Transformer-based architecture for air pollution prediction, named Top-K Spatio-Temporal Attention Gating Network (TKSTAGNet). In this architecture, to effectively extract spatio-temporal correlations, we first design a TopKpooling Spatial Attention (TopK-SA) and a TopKpooling Temporal Attention (TopK-TA). In TopK-SA, the TopKpooling mechanism is introduced into spatial attention to filter out irrelevant spatial neighbors, accurately capturing spatial dependencies. In TopK-TA, the TopKpooling mechanism is integrated with temporal attention to identify and exclude irrelevant temporal neighbors, precisely extracting temporal dependencies. To adaptively fuse various external factors, a gating fusion module is then proposed. We conduct experiments on three real-world air pollution datasets from urban regions in Beijing, Shanghai, and Chongqing, respectively. Experimental results demonstrate that TKSTAGNet outperforms state-of-the-art baselines in prediction precision and fitting the variation trends of air pollution.

Effects of simulated space conditions on CD4+ T cells: a multi modal analysis.

The immune system is an intricate network of cellular components that safeguards against pathogens and aberrant cells, with CD4+ T cells playing a central role in this process. Human space travel presents unique health challenges, such as heavy ion ionizing radiation, microgravity, and psychological stress, which can collectively impede immune function. The aim of this research was to examine the consequences of simulated space stressors on CD4+ T cell activation, cytokine production, and gene expression. CD4+ T cells were obtained from healthy individuals and subjected to Fe ion particle radiation, Photon irradiation, simulated microgravity, and hydrocortisone, either individually or in different combinations. Cytokine levels for Th1 and Th2 cells were determined using multiplex Luminex assays, and RNA sequencing was used to investigate gene expression patterns and identify essential genes and pathways impacted by these stressors. Simulated microgravity exposure resulted in an apparent Th1 to Th2 shift, evidenced on the level of cytokine secretion as well as altered gene expression. RNA sequencing analysis showed that several gene pathways were altered, particularly in response to Fe ions irradiation and simulated microgravity exposures. Individually, each space stressor caused differential gene expression, while the combination of stressors revealed complex interactions. The research findings underscore the substantial influence of the space exposome on immune function, particularly in the regulation of T cell responses. Future work should focus expanding the limited knowledge in this field. Comprehending these modifications will be essential for devising effective strategies to safeguard the health of astronauts during extended space missions. The effects of simulated space stressors on CD4+ T cell function are substantial, implying that space travel poses a potential threat to immune health. Additional research is necessary to investigate the intricate relationship between space stressors and to develop effective countermeasures to mitigate these consequences.

Dynamic predictability and activity-location contexts in human mobility.

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.

Automatic road network selection method considering functional semantic features of roads with graph convolutional networks

Road network selection plays a key role in map generalization for creating multi-scale road network maps. Existing methods usually determine road importance based on road geometric and topological features, few evaluate road importance from the perspective of road utilization based on human travel data, ignoring the functional values of roads, which leads to a mismatch between the generated results and people’s needs. This paper develops two functional semantic features (i.e. travel path selection probability and regional attractiveness) to measure the functional importance of roads and proposes an automatic road network selection method based on graph convolutional networks (GCN), which models road network selection as a binary classification. Firstly, we create a dual graph representing the source road network and extract road features including six graphical and two functional semantic features. Then, we develop an extended GCN model with connectivity loss for generating multi-scale road networks and propose a refinement strategy based on the road continuity principle to ensure road topology. Experiments demonstrate the proposed model with functional features improves the quality of selection results, particularly for large and medium scale maps. The proposed method outperforms state-of-the-art methods and provides a meaningful attempt for artificial intelligence models empowering cartography.

Navigate through the haze: Wildfire smoke exposure and Metrorail ridership

Adverse weather events significantly impact the operations of urban transportation systems and change human travel behaviors. Over the decades, wildfires have emerged as a pressing concern due to their increased frequency and intensity, yet the relationship between wildfire smoke and public transportation usage remains largely unexplored. Leveraging high-resolution daily wildfire-driven PM2.5 concentration estimates and station-level Metrorail ridership data in the Washington Metropolitan Area spanning 2012–2019, we examine the effects of wildfire smoke exposure on Metrorail usage. We find that wildfire smoke exposure results in a 0.8% increase in Metrorail ridership on weekdays and a more pronounced 3.7% rise on weekends. Additionally, we show a stronger response in Metrorail ridership to wildfire smoke during off-peak hours compared to peak hours, with the most substantial increase observed during the winter. Our heterogeneity analysis further suggests that a lack of vehicle ownership and higher reliance on walking and public transportation are key factors leading to increased Metrorail ridership. Collectively, these results highlight the need for proactive service adjustments and effective communication strategies to accommodate the potential shifts in human travel behaviors and Metrorail ridership on days exposed to wildfire smoke.

Edge Computing-Enabled Secure Forecasting Nationwide Industry PM2.5 with LLM in the Heterogeneous Network

The heterogeneous network formed by the deployment and interconnection of various network devices (e.g., sensors) has attracted widespread attention. PM2.5 forecasting on the entire industrial region throughout mainland China is an important application of heterogeneous networks, which has great significance to factory management and human health travel. In recent times, Large Language Models (LLMs) have exhibited notability in terms of time series prediction. However, existing LLMs tend to forecast nationwide industry PM2.5, which encounters two issues. First, most LLM-based models use centralized training, which requires uploading large amounts of data from sensors to a central cloud. This entire transmission process can lead to security risks of data leakage. Second, LLMs fail to extract spatiotemporal correlations in the nationwide sensor network (heterogeneous network). To tackle these issues, we present a novel framework entitled Spatio-Temporal Large Language Model with Edge Computing Servers (STLLM-ECS) to securely predict nationwide industry PM2.5 in China. In particular, We initially partition the entire sensor network, located in the national industrial region, into several subgraphs. Each subgraph is allocated an edge computing server (ECS) for training and inference, avoiding the security risks caused by data transmission. Additionally, a novel LLM-based approach named Spatio-Temporal Large Language Model (STLLM) is developed to extract spatiotemporal correlations and infer prediction sequences. Experimental results prove the effectiveness of our proposed model.

Uncovering human behavioral heterogeneity in urban mobility under the impacts of disruptive weather events

Understanding the response of human mobility to disruptive weather events is beneficial for the development of urban risk mitigation and emergency response policies, thus enhancing urban resilience. Most human mobility studies relying on aggregate flow data inevitably neglect the heterogeneity of disaggregate travel patterns with distinctive spatiotemporal characteristics, causing the uncertainty problem for identifying meaningful travel behaviors. Moreover, there is a lack of robust methodological approaches to extracting stable and genuine travel patterns under normal or disruptive situations. To address these issues, this study proposes a data-driven approach to spatiotemporal flow decomposition based on non-negative matrix factorization. With sparseness factored in the decomposition, stable disaggregate travel patterns can be extracted from origin-destination mobility flows. By combining temporal, spatial, and urban functional perspectives, heterogeneous travel behaviors can be analyzed and inferred. With a case study of the Zhengzhou ‘7.20’ heavy rainfall in 2021, the most extreme rainfall ever recorded in China, this study validated the effectiveness of the proposed approach and managed to identify representative and interesting travel patterns and behaviors, facilitating a better understanding of human travel behaviors under external impacts. In practice, this study can provide valuable insights for coping strategies in the face of increasingly frequent disruptive events.

Nonrepresentativeness of Human Mobility Data and its Impact on Modeling Dynamics of the COVID-19 Pandemic: Systematic Evaluation.

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. 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. 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. 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. 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.

Application of Artificial Intelligence in Solar System Exploration and Beyond

Carl Sagan’s reflections emphasize the boundless potential awaiting humanity in space, regardless of our past achievements or failures. This optimistic view resonates with current space agencies, private space startups, and enthusiasts, all driven by the ambition to make humans a multiplanetary species. Key examples include Elon Musk’s goal to terraform Mars, despite skepticism from some scientists. Space exploration faces numerous challenges, such as dealing with space debris, unpredictable climates on other planets, and the complexities of missions like NASA’s attempts to land on Mars. Space missions confront obstacles in navigation, life detection, and sample testing. Astronauts also encounter physical and mental limitations due to the harsh conditions of space. Artificial Intelligence (AI) has emerged as a crucial tool in addressing these challenges. AI is categorized into supervised learning, unsupervised learning, and semi-supervised learning, with reinforcement learning being a distinct method used for autonomous applications. Neural networks, including convolutional and recurrent neural networks, mimic human behavior for tasks like image inspection and sequential data handling. Deep learning aids in complex pattern recognition, and natural language processing facilitates speech recognition and development. Expert systems enhance decision-making processes. This review explores various AI applications in space missions. AI technologies have been integral to recent missions such as the James Webb Space Telescope, which uses AI for object and galaxy inspection, and the ARTEMIS mission, which employs AI in designing new spacesuits. SpaceX leverages AI for reusable launch vehicles, while the European Space Agency (ESA) uses AI for autonomous spacecraft to cut mission costs. India plans to launch the AI-based Vyommitra robot in space and has utilized AI in the Chandrayaan mission for landing and navigation. Mars exploration benefits significantly from AI, given the planet’s unpredictable weather and cratered surface. AI technologies, like the OASIS system, aid in mission planning and rover navigation. AI also supports space agriculture research, as demonstrated by the UAE’s habitat design efforts. Beyond Mars, AI influences the exploration of asteroids and deep space. AI techniques, such as convolutional neural networks, assist in space dynamics calculations to predict asteroid trajectories and potential Earth impacts. AI has been employed in Voyager missions to seek extraterrestrial life and in the Hubble telescope for imaging exoplanets. AI enhances satellite communication and automates tasks on space stations, saving time and costs. Weather satellites, powered by AI, provide crucial alerts for natural disasters. Despite the challenges, AI offers promising solutions for advancing space exploration. The historical tendency to deem certain challenges insurmountable is gradually shifting, as past impossibilities, like human flight and space travel, have become historical milestones. This reinforces the importance of maintaining hope and persistence in the quest to explore space, as today's dreams may become tomorrow's history.

Global transboundary transmission path and risk of Mpox revealed with Least Cost Path model

ObjectivesThe recent surge of Mpox outbreaks in multiple countries has garnered global attention. As of July 12, 2023, there have been 88,288 reported cases of Mpox worldwide. Although genetic variation was not found to be the cause of the epidemic outbreak, the reasons for its rapid spread remain unclear. MethodsUsing the niche method, this study identified high-risk regions for Mpox and determined that human factors are the primary contributors to global risks. To further investigate, a travel network resistance surface was created based on various modes of transportation and was combined with sea, airline, highway, and railway routes to construct the least cost path for human travel networks in different risk areas. ResultsThe results indicated that high-risk regions for Mpox are mainly concentrated in Europe and the United States, with large risk ranges and high-risk values. The least cost path revealed three primary transmission paths rely on developed transportation networks, including internal transmission in North America, Europe-Africa, and Europe-Asia-Africa. These findings suggest that human activities, facilitated by developed travel networks, remain the main contributing factor to the spread. ConclusionsIn summary, based on the Mpox epidemic report, this study conducted risk prediction and driving factor analysis on Mpox. The research results indicate that human use of transportation for long-distance activities is a key factor leading to the rapid spread of the virus. Subsequently, we focused on studying the global transmission pathways of Mpox and revealed several transmission pathways with high global population migration rates by constructing the LCPs between different high-risk areas. This study also emphasizes the importance of applying early monitoring data of Mpox to model risk prediction in controlling emerging infectious diseases, providing a new perspective for controlling Mpox and similar diseases.

Spatial Interaction Analysis of Infectious Disease Import and Export between Regions

Human travel plays a crucial role in the spread of infectious disease between regions. Travel of infected individuals from one region to another can transport a virus to places that were previously unaffected or may accelerate the spread of disease in places where the disease is not yet well established. We develop and apply models and metrics to analyze the role of inter-regional travel relative to the spread of disease, drawing from data on COVID-19 in the United States. To better understand how transportation affects disease transmission, we established a multi-regional time-varying compartmental disease model with spatial interaction. The compartmental model was integrated with statistical estimates of travel between regions. From the integrated model, we derived a transmission import index to assess the risk of COVID-19 transmission between states. Based on the index, we determined states with high risk for disease spreading to other states at the scale of months, and we analyzed how the index changed over time during 2020. Our model provides a tool for policymakers to evaluate the influence of travel between regions on disease transmission in support of strategies for epidemic control.