Defining ecological regions in Italy based on a multivariate clustering approach: A first step towards a targeted vector borne disease surveillance

Ippoliti, Carla; Candeloro, Luca; Gilbert, Marius; Goffredo, Maria; Mancini, Giuseppe; Curci, Gabriele; Falasca, Serena; Tora, Susanna; Lorenzo, Alessio Di; Quaglia, Michela; Conte, Annamaria

doi:10.1371/journal.pone.0219072

Cited by 31 publications

(18 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Whether monitoring had to be limited to some parts of the country provinces representing the highest and the lowest seroprevalence should be included. However, if feasible, randomized cluster sampling of ecoregions with homogenous land use, husbandry practices, climatic and topographic features, would be the most recommended in studies on arthropod vectors and arthropod‐borne viruses (Ippoliti et al., 2019). Testing youngstock at an appropriate age following the decline of passive immunity and at the end of the vector‐active season gives the most reliable information about active virus circulation in that respective year.…”

Section: Discussionmentioning

confidence: 99%

Schmallenberg virus in Poland endemic or re‐emerging? A six‐year serosurvey

Kęsik-Maliszewska

Collins

Rola

et al. 2020

Transbound Emerg Dis

View full text Add to dashboard Cite

Schmallenberg virus (SBV) emerged in Europe in 2011 at the border region between Germany, the Netherlands and Belgium (Hoffmann et al., 2012). The disease was initially identified due to a rapid increase in calls from farmers reporting diarrhoea, fever and milk drop in cattle. Several months later, congenital malformations, such as arthrogryposis-hydranencephaly syndrome (AHS), were identified in the offspring of ewes and cattle (Hoffmann et al., 2012;Veldhuis et al., 2019). The novel virus was detected by metagenomic analyses and subsequently identified as belonging to the Peribunyaviridae family in the Orthobunyavirus genus (Hoffmann et al., 2012). The European Food and Safety Authority (EFSA) was requested to collect and manage data on SBV suspected/confirmed cases (Afonso et al., 2014). The case definition was specified and approved by member states (EFSA, 2012). The zoonotic status of this pathogen was established as unlikely (Reusken et al., 2012). Culicoides biting midges (Diptera: Ceratopogonidae) were confirmed as arthropod

show abstract

Section: Discussionmentioning

confidence: 99%

Schmallenberg virus in Poland endemic or re‐emerging? A six‐year serosurvey

Kęsik-Maliszewska

Collins

Rola

et al. 2020

Transbound Emerg Dis

View full text Add to dashboard Cite

show abstract

“…The time-dependent variables have been chosen for their proven association to VBDs in general and to WND transmission in particular [17,19,35,[68][69][70][71]. Their global coverage guarantees the possibility to apply the approach to any other part of the globe, enabling the development of predictive systems on regional and national scales.…”

Section: Discussionmentioning

confidence: 99%

Predicting WNV Circulation in Italy Using Earth Observation Data and Extreme Gradient Boosting Model

et al. 2020

Self Cite

View full text Add to dashboard Cite

West Nile Disease (WND) is one of the most spread zoonosis in Italy and Europe caused by a vector-borne virus. Its transmission cycle is well understood, with birds acting as the primary hosts and mosquito vectors transmitting the virus to other birds, while humans and horses are occasional dead-end hosts. Identifying suitable environmental conditions across large areas containing multiple species of potential hosts and vectors can be difficult. The recent and massive availability of Earth Observation data and the continuous development of innovative Machine Learning methods can contribute to automatically identify patterns in big datasets and to make highly accurate identification of areas at risk. In this paper, we investigated the West Nile Virus (WNV) circulation in relation to Land Surface Temperature, Normalized Difference Vegetation Index and Surface Soil Moisture collected during the 160 days before the infection took place, with the aim of evaluating the predictive capacity of lagged remotely sensed variables in the identification of areas at risk for WNV circulation. WNV detection in mosquitoes, birds and horses in 2017, 2018 and 2019, has been collected from the National Information System for Animal Disease Notification. An Extreme Gradient Boosting model was trained with data from 2017 and 2018 and tested for the 2019 epidemic, predicting the spatio-temporal WNV circulation two weeks in advance with an overall accuracy of 0.84. This work lays the basis for a future early warning system that could alert public authorities when climatic and environmental conditions become favourable to the onset and spread of WNV.

show abstract

“…Climate, ecotype, and location (latitude/longitude) were used in a multivariate distance analysis (Venables and Ripley, 2002;Ripley, 2007;Cox and Cox, 2008) to define the state space of the US (all 50 states & Puerto Rico) and divide it into ecological clusters using information that is important for capturing continental patterns in CH4. The purpose of this analysis is to identify the interrelatedness of all ecological components-biotic, abiotic, terrestrial, and aquatic within a dynamic landscape (Ippoliti et al, 2019). We included location (latitude/longitude) to incorporate the interaction between climate, ecotypes, and most importantly, seasonality.…”

Section: Climate and Dominant Land Cover Typesmentioning

confidence: 99%

Gaps in Network Infrastructure limit our understanding of biogenic methane emissions in the United States

Malone

Oh²,

Arndt

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. Understanding the sources and sinks of CH4 is critical to both predicting and mitigating future climate change. There are large uncertainties in the global budget of atmospheric CH4, but natural emissions are estimated to be of a similar magnitude to total anthropogenic emissions. The largest sources of uncertainty in scaling bottom-up CH4 estimates stem from limited ground-based measurements and the misalignment between drivers of CH4 fluxes and current land use classifications. To understand the CH4 flux potential of natural ecosystems and agricultural lands in the United States (US) of America, a multi-scale CH4 observation network focused on CH4 flux rates, processes, and scaling methods is required. This can be achieved with a network of ground-based observations that are distributed based on climatic regions and landcover. To determine the gaps in physical infrastructure for developing this network, we need to understand the representativeness of current measurements. We focus here on eddy covariance (EC) flux towers because they are essential for a bottom-up framework that bridges the gap between point-based chamber measurements and airborne or satellite platforms, informing the remote sensing and modelling communities and policy decisions, all the way to IPCC reports. Using multidimensional scaling and a cluster analysis, the US was divided into 10 clusters that were distributed across temperature and wetness gradients. We evaluated the distance to the medoid condition within each cluster for research sites with EC tower infrastructure to identify the gaps in existing infrastructure that limit our ability to constrain the contribution of US biogenic CH4 emissions to the global budget. These gaps occurred across all EC flux tower networks and independently managed sites as well as in some environmental clusters. Through our analysis using climate, land cover, and location variables, we have identified priority areas to target for research infrastructure to provide a more complete understanding of the CH4 flux potential of ecosystem types across the US.

show abstract

Defining ecological regions in Italy based on a multivariate clustering approach: A first step towards a targeted vector borne disease surveillance

Cited by 31 publications

References 56 publications

Schmallenberg virus in Poland endemic or re‐emerging? A six‐year serosurvey

Schmallenberg virus in Poland endemic or re‐emerging? A six‐year serosurvey

Predicting WNV Circulation in Italy Using Earth Observation Data and Extreme Gradient Boosting Model

Gaps in Network Infrastructure limit our understanding of biogenic methane emissions in the United States

Contact Info

Product

Resources

About