Subclinical hepatitis E virus genotype 1 infection:The concept of“dynamic human reservoir”

Hepatitis E virus(HEV)is hyperendemic in South Asia and Africa accounting for half of total Global HEV burden.There are eight genotypes of HEV.Among them,the four common ones known to infect humans,genotypes 1 and 2 are prevalent in the developing world and genotypes 3 and 4 are causing challenge in the industrialized world.Asymptomatic HEV viremia in the general population,especially among blood donors,has been reported in the literature worldwide.The clinical implications related to this asymptomatic viremia are unclear and need further exploration.Detection of viremia due to HEV genotype 1 infection,apparently among healthy blood donors is also reported without much knowledge about its infection rate.Similarly,while HEV genotype 3 is known to be transmitted via blood transfusion in humans and has been subjected to screening in many European nations,instances of transmission have also been documented albeit without significant clinical consequences.Epidemiology of HEV genotype 1 in endemic areas often show waxing and waning pattern.Occasional sporadic occurrence of HEV infection interrupted by outbreaks have been frequently seen.In absence of known animal reservoir,where HEV exists in between outbreak is a mystery that needs further exploration.However,occurrence of asymptomatic HEV viremia due to HEV genotype 1 during epidemiologically quiescent period may explain that this phenomenon may act as a dynamic reservoir.Since HEV genotype 1 infection cannot cause chronicity,subclinical transient infection and transmission of virus might be the reason it sustains in interepidemic period.This might be the similar phenomenon with SARS COVID-19 corona virus infection which is circulating worldwide in distinct phases with peaks and plateaus despite vaccination against it.In view of existing evidence,we propose the concept of“Dynamic Human Reservoir.”Quiescent subclinical infection of HEV without any clinical consequences and subsequent transmission may contribute to the existence of the virus in a community.The potential for transmitting HEV infection by asymptomatic HEV infected individuals by fecal shedding of virus has not been reported in literature.This missing link may be a key to Pandora's box in understanding epidemiology of HEV infection in genotype 1 predominant region.

Comparison of the spatio-temporal dynamics of vegetation between the Changbai Mountains of eastern Eurasia and the Appalachian Mountains of eastern North America

The Changbai Mountains and the Appalachian Mountains have similar spatial contexts.The elevation,latitude,and moisture gradients of both mountain ranges offer regional insight for investigating the vegetation dynamics in eastern Eurasia and eastern North America.We determined and compared the spatial patterns and temporal trends in the normalized difference vegetation index(NDVI)in the Changbai Mountains and the Appalachian Mountains using time series data from the Global Inventory Modeling and Mapping Studies 3^(rd) generation dataset from 1982 to 2013.The spatial pattern of NDVI in the Changbai Mountains exhibited fragmentation,whereas NDVI in the Appalachian Mountains decreased from south to north.The vegetation dynamics in the Changbai Mountains had an insignificant trend at the regional scale,whereas the dynamics in the Appalachian Mountains had a significant increasing trend.NDVI increased in 55% of the area of the Changbai Mountains and in 95% of the area of the Appalachian Mountains.The peak NDVI occurred one month later in the Changbai Mountains than in the Appalachian Mountains.The results revealed a significant increase in NDVI in autumn in both mountain ranges.The climatic trend in the Changbai Mountains included warming and decreased precipitation,and whereas that in the Appalachian Mountains included significant warming and increased precipitation.Positive and negative correlations existed between NDVI and temperature and precipitation,respectively,in both mountain ranges.Particularly,the spring temperature and NDVI exhibited a significant positive correlation in both mountain ranges.The results of this study suggest that human actives caused the differences in the spatial patterns of NDVI and that various characteristics of climate change and intensity of human actives dominated the differences in the NDVI trends between the Changbai Mountains and the Appalachian Mountains.Additionally,the vegetation dynamics of both mountain ranges were not identical to those in previous broader-scale studies.

A Novel Collective User Web Behavior Simulation Method

A collective user web behavior simulation is an import means for generating a large-scale user network behavior in a network testbed or cyber range.Existing studies almost focus on individual web behavior analysis and prediction,which cannot simulate human dynamics that widely exist in large-scale users’behaviors.To address these issues,we propose a novel collective user web behavior simulation method,in which an algorithm for constructing a connected virtual social network is proposed,and then a collective user web behavior simulation algorithm is designed on the virtual social network.In the simulation method,a new epidemic information dissemination algorithm based on the SIR model is proposed to drive the user web behavior with Breadth—First Search algorithm on the connected virtual social network.We specially build an experiment environment with 12 servers by using Docker container technology and then perform a wide range of experiments with different user scales to evaluate the method.The experimental results demonstrate that not only the degrees of the social network but also the time intervals of the collective users’web behavior can be well fitted to a power-law distribution and show that our simulation method can well simulate a collective user web behavior.

Mathematical and computational approaches to epidemic modeling： a comprehensive review

Mathematical and computational approaches are important tools for understanding epidemic spread patterns and evaluating policies of disease control. In recent years, epidemiology has become increasingly integrated with mathematics, sociology, management science, complexity science, and computer science. The cross of multiple disciplines has caused rapid development of mathematical and computational approaches to epidemic modeling. In this article, we carry out a comprehensive review of epidemic models to provide an insight into the literature of epidemic modeling and simulation.We introduce major epidemic models in three directions, including mathematical models, complex network models, and agent-based models. We discuss the principles, applications, advantages, and limitations of these models. Meanwhile, we also propose some future research directions in epidemic modeling.

Spatiotemporal event detection: a review

The advancements of sensing technologies,including remote sensing,in situ sensing,social sensing,and health sensing,have tremendously improved our capability to observe and record natural and social phenomena,such as natural disasters,presidential elections,and infectious diseases.The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment,urban settings,health and disease propagation,business decisions,and crisis and crime.Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena.This paper reviews the literature for different sensing capabilities,spatiotemporal event extraction methods,and categories of applications for the detected events.The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow(from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events)as an agenda for future event detection research.Guidance is presented on the current challenges to this research agenda,and future directions are discussed for conducting spatiotemporal event detection in the era of big data,advanced sensing,and artificial intelligence.

Analyzing the effect of earthquakes on OpenStreetMap contribution patterns and tweeting activities

Natural disasters,such as wildfires,earthquakes,landslides,or floods,lead to an increase in topical information shared on social media and in increased mapping activities in volunteered geographic information(VGI)platforms.Using earthquakes in Nepal and Central Italy as case studies,this research analyzes the effects of natural disasters on short-term(weeks)and longer-term(half year)changes in OpenStreetMap(OSM)mapping behavior and tweet activities in the affected regions.An increase of activities in OSM during the events can be partially attributed to those focused OSM mapping campaigns,for example,through the Humanitarian OSM Team(HOT).Using source tags in OSM change-sets,it was found that only a small portion of external mappers actually travels to the affected regions,whereas the majority of external mappers relies on desktop mapping instead.Furthermore,the study analyzes the spatio-temporal sequence of posted tweets together with keyword filters to identify a subset of users who most likely traveled to the affected regions for support and rescue operations.It also explores where,geographically,earthquake information spreads within social networks.

Tracking Co-evolution of Behavior and Relationships with Mobile Phones

The co-evolution of social relationships and individual behavior in time and space has important implications, but is poorly understood because of the difficulty of closely tracking the everyday life of a com- plete community. We offer evidence that relationships and behavior co-evolve in a student dormitory, based on monthly surveys and location tracking through resident cellular phones over a period of nine months. We demonstrate that a Markov jump process could capture the co-evolution in terms of the rates at which resi- dents visit places and friends. Our co-evolution model will be useful in bridging sensor networks data and organizational dynamics theories, simulating different ways to shape behavior and relationships, and turning mobile phone data into data products.