scholarly journals Artificial Intelligence Predicts and Explains West Nile Virus Risks Across Europe: Extraordinary Outbreaks Determined by Climate and Local Factors

2020 ◽  
Author(s):  
Albert A Gayle
Keyword(s):  
West Nile Virus ◽  
Large Scale ◽  
Predictive Accuracy ◽  
Local Effect ◽  
Research Literature ◽  
West Nile ◽  
Priority Direction ◽  
Minimal Data ◽  
Novel Method ◽  
Using Data

Year-to-year emergence of West Nile virus has been sporadic and notoriously hard to predict. In Europe, 2018 saw a dramatic increase in the number of cases and locations affected. In this work, we demonstrate a novel method for predicting outbreaks and understanding what drives them. This method creates a simple model for each region that directly explains how each variable affects risk. Behind the scenes, each local explanation model is produced by a state-of-the-art AI engine. This engine unpacks and restructures output from an XGBoost machine learning ensemble. XGBoost, well-known for its predictive accuracy, has always been considered a "black box" system. Not any more. With only minimal data curation and no "tuning", our model predicted where the 2018 outbreak would occur with an AUC of 97%. This model was trained using data from 2010-2016 that reflected many domains of knowledge. Climate, sociodemographic, economic, and biodiversity data were all included. Our model furthermore explained the specific drivers of the 2018 outbreak for each affected region. These effect predictions were found to be consistent with the research literature in terms of priority, direction, magnitude, and size of effect. Aggregation and statistical analysis of local effects revealed strong cross-scale interactions. From this, we concluded that the 2018 outbreak was driven by large-scale climatic anomalies enhancing the local effect of mosquito vectors. We also identified substantial areas across Europe at risk for sudden outbreak, similar to that experienced in 2018. Taken as a whole, these findings highlight the role of climate in the emergence and transmission of West Nile virus. Furthermore, they demonstrate the crucial role that the emerging "eXplainable AI" (XAI) paradigm will have in predicting and controlling disease.

scholarly journals Introduction, Spread, and Establishment of West Nile Virus in the Americas

2019 ◽  
Vol 56 (6) ◽  
pp. 1448-1455 ◽  
Author(s):  
Laura D Kramer ◽  
Alexander T Ciota ◽  
A Marm Kilpatrick
Keyword(s):  
West Nile Virus ◽  
Large Scale ◽  
Spatial And Temporal Variation ◽  
West Nile ◽  
Spatial Spread ◽  
Wildlife Reservoirs ◽  
Viral Adaptation ◽  
Rapid Spread ◽  
Integrative Studies ◽  
Transmission Ecology

Abstract The introduction of West Nile virus (WNV) to North America in 1999 and its subsequent rapid spread across the Americas demonstrated the potential impact of arboviral introductions to new regions, and this was reinforced by the subsequent introductions of chikungunya and Zika viruses. Extensive studies of host–pathogen–vector–environment interactions over the past two decades have illuminated many aspects of the ecology and evolution of WNV and other arboviruses, including the potential for pathogen adaptation to hosts and vectors, the influence of climate, land use and host immunity on transmission ecology, and the difficulty in preventing the establishment of a zoonotic pathogen with abundant wildlife reservoirs. Here, we focus on outstanding questions concerning the introduction, spread, and establishment of WNV in the Americas, and what it can teach us about the future of arboviral introductions. Key gaps in our knowledge include the following: viral adaptation and coevolution of hosts, vectors and the virus; the mechanisms and species involved in the large-scale spatial spread of WNV; how weather modulates WNV transmission; the drivers of large-scale variation in enzootic transmission; the ecology of WNV transmission in Latin America; and the relative roles of each component of host–virus–vector interactions in spatial and temporal variation in WNV transmission. Integrative studies that examine multiple factors and mechanisms simultaneously are needed to advance our knowledge of mechanisms driving transmission.

Predicting West Nile Virus (WNV) occurrences in North Dakota using data mining techniques

2016 ◽  
Author(s):  
Mitch Campion ◽  
Calvin Bina ◽  
Martin Pozniak ◽  
Todd Hanson ◽  
Jeff Vaughan ◽  
...  
Keyword(s):  
Data Mining ◽  
West Nile Virus ◽  
North Dakota ◽  
West Nile ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals Predicting the spatio-temporal spread of West Nile virus in Europe

2021 ◽  
Vol 15 (1) ◽  
pp. e0009022
Author(s):  
José-María García-Carrasco ◽  
Antonio-Román Muñoz ◽  
Jesús Olivero ◽  
Marina Segura ◽  
Raimundo Real
Keyword(s):  
West Nile Virus ◽  
Large Scale ◽  
River Basins ◽  
Maximum Temperature ◽  
Environmental Predictors ◽  
Migration Routes ◽  
West Nile Fever ◽  
Environmental Model ◽  
West Nile ◽  
First Case

West Nile virus is a widely spread arthropod-born virus, which has mosquitoes as vectors and birds as reservoirs. Humans, as dead-end hosts of the virus, may suffer West Nile Fever (WNF), which sometimes leads to death. In Europe, the first large-scale epidemic of WNF occurred in 1996 in Romania. Since then, human cases have increased in the continent, where the highest number of cases occurred in 2018. Using the location of WNF cases in 2017 and favorability models, we developed two risk models, one environmental and the other spatio-environmental, and tested their capacity to predict in 2018: 1) the location of WNF; 2) the intensity of the outbreaks (i.e. the number of confirmed human cases); and 3) the imminence of the cases (i.e. the Julian week in which the first case occurred). We found that climatic variables (the maximum temperature of the warmest month and the annual temperature range), human-related variables (rain-fed agriculture, the density of poultry and horses), and topo-hydrographic variables (the presence of rivers and altitude) were the best environmental predictors of WNF outbreaks in Europe. The spatio-environmental model was the most useful in predicting the location of WNF outbreaks, which suggests that a spatial structure, probably related to bird migration routes, has a role in the geographical pattern of WNF in Europe. Both the intensity of cases and their imminence were best predicted using the environmental model, suggesting that these features of the disease are linked to the environmental characteristics of the areas. We highlight the relevance of river basins in the propagation dynamics of the disease, as outbreaks started in the lower parts of the river basins, from where WNF spread towards the upper parts. Therefore, river basins should be considered as operational geographic units for the public health management of the disease.

High prevalence of West Nile virus in equines from the two provinces of Pakistan

2014 ◽  
Vol 143 (9) ◽  
pp. 1931-1935 ◽  
Author(s):  
A. ZOHAIB ◽  
M. SAQIB ◽  
C. BECK ◽  
M. H. HUSSAIN ◽  
S. LOWENSKI ◽  
...  
Keyword(s):  
West Nile Virus ◽  
Japanese Encephalitis Virus ◽  
Large Scale ◽  
Encephalitis Virus ◽  
Serum Samples ◽  
West Nile ◽  
Elisa Kit ◽  
Tick Borne Encephalitis ◽  
High Prevalence ◽  
Tick Borne Encephalitis Virus

SUMMARYThis study describes the first large-scale serosurvey on West Nile virus (WNV) conducted in the equine population in Pakistan. Sera were collected from 449 equids from two provinces of Pakistan during 2012–2013. Equine serum samples were screened using a commercial ELISA kit detecting antibodies against WNV and related flaviviruses. ELISA-positive samples were further investigated using virus-specific microneutralization tests (MNTs) to identify infections with Japanese encephalitis virus (JEV), WNV and tick-borne encephalitis virus (TBEV). Anti-WNV antibodies were detected in 292 samples by ELISA (seroprevalence 65·0%) and WNV infections were confirmed in 249 animals by MNT. However, there was no animal found infected by JEV or TBEV. The detection of WNV-seropositive equines in Pakistan strongly suggests a widespread circulation of WNV in Pakistan.

The Geographic Dispersal of Visitors

2016 ◽  
Vol 56 (1) ◽  
pp. 108-121 ◽  
Author(s):  
Tay T.R. Koo ◽  
Pong-Lung Lau ◽  
Larry Dwyer
Keyword(s):  
Power Law ◽  
Research Literature ◽  
Specific Power ◽  
Power Exponent ◽  
Power Law Distribution ◽  
Novel Method ◽  
The Rich ◽  
Using Data ◽  
International Visitors ◽  
Geographic Dispersal

This article aims to examine the conjecture that geographic dispersal of visitors follows the power law using data on international visitors’ spatial distribution in Australia. Our finding suggests that as tourism market matures, the pattern of tourist dispersal tends to converge toward a specific power law distribution. The article provides estimates of this unique power exponent for each country and tracks its temporal evolution using a novel method. One of the key implications for sustainable destination management is that for continued tourism growth, large destinations need a large number of small peripheral destinations. Our findings also shed light on the rich research literature that is fundamental in developing a power law–based theory to guide our understanding of the mechanics underpinning the spatial evolution of tourism.

West Nile virus emergence and large-scale declines of North American bird populations

Nature ◽  
2007 ◽  
Vol 447 (7145) ◽  
pp. 710-713 ◽  
Author(s):  
Shannon L. LaDeau ◽  
A. Marm Kilpatrick ◽  
Peter P. Marra
Keyword(s):  
West Nile Virus ◽  
North American ◽  
Large Scale ◽  
West Nile ◽  
Bird Populations ◽  
North American Bird ◽  
Virus Emergence

scholarly journals A Diagnostic Framework for the Empirical Evaluation of Learning Maps

2022 ◽  
Vol 6 ◽  
Author(s):  
W. Jake Thompson ◽  
Brooke Nash
Keyword(s):  
Large Scale ◽  
Empirical Evaluation ◽  
Essential Elements ◽  
Research Literature ◽  
Assessment System ◽  
Content Standards ◽  
Dynamic Learning ◽  
Diagnostic Classification Models ◽  
Diagnostic Framework ◽  
Using Data

Learning progressions and learning map structures are increasingly being used as the basis for the design of large-scale assessments. Of critical importance to these designs is the validity of the map structure used to build the assessments. Most commonly, evidence for the validity of a map structure comes from procedural evidence gathered during the learning map creation process (e.g., research literature, external reviews). However, it is also important to provide support for the validity of the map structure with empirical evidence by using data gathered from the assessment. In this paper, we propose a framework for the empirical validation of learning maps and progressions using diagnostic classification models. Three methods are proposed within this framework that provide different levels of model assumptions and types of inferences. The framework is then applied to the Dynamic Learning Maps® alternate assessment system to illustrate the utility and limitations of each method. Results show that each of the proposed methods have some limitations, but they are able to provide complementary information for the evaluation of the proposed structure of content standards (Essential Elements) in the Dynamic Learning Maps assessment.

scholarly journals Perspectives on Applying Artificial Intelligence to Model Outbreak Risk: Contextualizing the Impact of Climate Change on Vector-borne Disease

2020 ◽  
Author(s):  
Alberto Alexander Gayle
Keyword(s):  
Public Health ◽  
Climate Change ◽  
West Nile Virus ◽  
Large Scale ◽  
Climatic Factors ◽  
Disease Spread ◽  
Behavioral Variability ◽  
Climatic Effects ◽  
West Nile ◽  
The Impact

As recent history has shown, changing climate not only threatens to increase the spread of known disease, but also the emergence of new and dangerous phenotypes. This occurred most recently with West Nile virus: a virus previously known for mild febrile illness rapidly emerged to become a major cause of mortality and long-term disability throughout the world. As we move forward, into increasingly uncertain times, public health research must begin to incorporate a broader understanding of the determinants of disease emergence – what, how, why, and when. The increasing mainstream availability of high-quality open data and high-powered analytical methods presents promising new opportunities. Up to now, quantitative models of disease outbreak risk have been largely based on just a few key drivers, namely climate and large-scale climatic effects. Such limited assessments, however, often overlook key interacting processes and downstream determinants more likely to drive local manifestation of disease. Such pivotal determinants may include local host abundance, human behavioral variability, and population susceptibility dynamics. The results of such analyses can therefore be misleading in cases where necessary downstream requirements are not fulfilled. It is therefore important to develop models that include climate and higher-level climatic effects alongside the downstream non-climatic factors that ultimately determine individual disease manifestation. Today, few models attempt to comprehensively address such dynamics: up until very recently, the technology simply hasn’t been available. Herein, we present an updated overview of current perspectives on the varying drivers and levels of interactions that drive disease spread. We review the predominant analytical paradigms, discuss their strengths and weaknesses, and highlight promising new analytical solutions. Our focus is on the prediction of arboviruses, particularly West Nile virus, as these diseases represent the pinnacle of epidemiological complexity – solution to which would serve as an effective “gatekeeper”. We present the current state-of-the-art with respect to known drivers of arbovirus outbreak risk and severity, differentially highlighting the impact of climate and non-climatic drivers. The reality of multiple classes of drivers interacting at different geospatial and temporal scales requires advanced new methodologies. We therefore close out by presenting and discussing some promising new applications of AI. Given the reality of accelerating disease risks due to climate change, public health and other related fields must begin the process of updating their research programs to incorporate these much needed, new capabilities.

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