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Remote Sensing And Image Interpretation

Over the last years, an intensive worldwide effort is speeding up the developments in the establishment of a global surveillance network for combating pandemics of emergent and re-emergent infectious diseases. Scientists from different fields extending from medicine and molecular biology to computer science and applied mathematics have teamed up for rapid assessment of potentially urgent situations. Toward this aim mathematical modeling plays an important role in efforts that focus on predicting, assessing, and controlling potential outbreaks. To better understand and model the contagious dynamics the impact of numerous variables ranging from the micro host–pathogen level to host-to-host interactions, as well as prevailing ecological, social, economic, and demographic factors across the globe have to be analyzed and thoroughly studied. Here, we present and discuss the main approaches that are used for the surveillance and modeling of infectious disease dynamics. We present the basic concepts underpinning ...

https://www.tandfonline.com/doi/pdf/10.4161/viru.24041

Mathematical modeling of infectious disease dynamics

Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos

Unusual clusters of disease must be detected rapidly for effective public health interventions to be introduced. Over the past decade there has been a surge in interest in statistical methods for the early detection of infectious disease outbreaks.This growth in interest has given rise to much new methodological work, ranging across the spectrum of statistical methods.The paper presents a comprehensive review of the statistical approaches that have been proposed. Applications to both laboratory and syndromic surveillance data are provided to illustrate the various methods

/pdf/statistical-methods-for-the-prospective-detection-of-fr2mkym3hu.pdf

Statistical methods for the prospective detection of infectious disease outbreaks: a review

Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among vari...

/pdf/an-optimal-parameters-based-geographical-detector-model-1qws35du5a.pdf

An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data

The integration of Building Information Modeling (BIM) and Geographic Information System (GIS) has been identified as a promising but challenging topic to transform information towards the generation of knowledge and intelligence. Achievement of integrating these two concepts and enabling technologies will have a significant impact on solving problems in the civil, building and infrastructure sectors. However, since GIS and BIM were originally developed for different purposes, numerous challenges are being encountered for the integration. To better understand these two different domains, this paper reviews the development and dissimilarities of GIS and BIM, the existing integration methods, and investigates their potential in various applications. This study shows that the integration methods are developed for various reasons and aim to solve different problems. The parameters influencing the choice can be summarized and named as “EEEF” criteria: effectiveness, extensibility, effort, and flexibility. Compared with other methods, semantic web technologies provide a promising and generalized integration solution. However, the biggest challenges of this method are the large efforts required at early stage and the isolated development of ontologies within one particular domain. The isolation problem also applies to other methods. Therefore, openness is the key of the success of BIM and GIS integration.

https://www.mdpi.com/2220-9964/6/2/53/pdf

A state-of-the-art review on the integration of Building Information Modeling (BIM) and Geographic Information System (GIS)

The benefits brought by the integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) are being proved by more and more research. The integration of the two systems is difficult for many reasons. Among them, data incompatibility is the most significant, as BIM and GIS data are created, managed, analyzed, stored, and visualized in different ways in terms of coordinate systems, scope of interest, and data structures. The objective of this paper is to review the relevant research papers to (1) identify the most relevant data models used in BIM/GIS integration and understand their advantages and disadvantages; (2) consider the possibility of other data models that are available for data level integration; and (3) provide direction on the future of BIM/GIS data integration.

A Critical Review of the Integration of Geographic Information System and Building Information Modelling at the Data Level

https://isiarticles.com/bundles/Article/pre/pdf/16346.pdf

Market segments based on the dominant movement patterns of tourists

Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data. A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum. Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms. Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.

/pdf/disease-surveillance-using-a-hidden-markov-model-4ty20ih0jz.pdf

Disease surveillance using a hidden Markov model

The automated monitoring of routinely collected disease surveillance data has the potential to ensure that important changes in disease incidence are promptly recognised. However, few studies have established whether the signals produced by automated monitoring methods correspond with events considered by epidemiologists to be of public health importance. This study investigates the correspondence between retrospective epidemiological evaluation of notifications of Ross River virus (RRv) disease in Western Australia, and the signals produced by two cumulative sum (cusum)-based automated monitoring methods. RRv disease case notification data between 1991 and 2004 were assessed retrospectively by two experienced epidemiologists, and the timing of identified outbreaks was compared with signals generated from two different types of cusum-based automated monitoring algorithms; the three Early Aberration Reporting System (EARS) cusum algorithms (C1, C2 and C3), and a negative binomial cusum. We found the negative binomial cusum to have a significantly greater area under the receiver operator characteristic curve when compared with the EARS algorithms, suggesting that the negative binomial cusum has a greater level of agreement with epidemiological opinion than the EARS algorithms with respect to the existence of outbreaks of RRv disease, particularly at low false alarm rates. However, the performance of individual EARS and negative binomial cusum algorithms were not significantly different when timeliness was also incorporated into the area under the curve analyses. Our retrospective analysis of historical data suggests that, compared with the EARS algorithms, the negative binomial cusum provides greater sensitivity for the detection of outbreaks of RRv disease at low false alarm levels, and decreased timeliness early in the outbreak period. Prospective studies are required to investigate the potential usefulness of these algorithms in practice.

/pdf/applying-cusum-based-methods-for-the-detection-of-outbreaks-4oiym1pk7f.pdf

Graeme Wright

Papers

A state-of-the-art review on the integration of Building Information Modeling (BIM) and Geographic Information System (GIS)

A Critical Review of the Integration of Geographic Information System and Building Information Modelling at the Data Level

Market segments based on the dominant movement patterns of tourists

Disease surveillance using a hidden Markov model

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia