ORCID Profile
0000-0002-0666-9755
Current Organisations
Universidade de Lisboa Centro de Estudos Geográficos: Lisboa, PT
,
Zoologisches Forschungsinstitut und Museum Alexander Koenig
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Publisher: Cold Spring Harbor Laboratory
Date: 18-10-2019
DOI: 10.1101/19009555
Abstract: The recent emergence and established presence of Aedes aegypti in the Autonomous Region of Madeira, Portugal, was responsible for the first autochthonous outbreak of dengue in Europe. The island has not reported any dengue cases since the outbreak in 2012. However, there is a high risk that an introduction of the virus would result in another autochthonous outbreak given the presence of the vector and permissive environmental conditions. Understanding the dynamics of a potential epidemic is critical for targeted local control strategies. Here, we adopt a deterministic model for the transmission of dengue in Aedes aegypti mosquitoes. The model integrates empirical and mechanistic parameters for virus transmission, under seasonally varying temperatures for Funchal, Madeira Island. We examine the epidemic dynamics as triggered by the arrival date of an infectious in idual the influence of seasonal temperature mean and variation on the epidemic dynamics and performed a sensitivity analysis on the following quantities of interest: the epidemic peak size, time to peak and the final epidemic size. Our results demonstrate the potential for summer to early winter transmission of dengue, with the arrival date significantly affecting the distribution of the timing and peak size of the epidemic. Mid-summer to early autumn arrivals are more likely to produce larger epidemics within a short peak time. Epidemics within this favorable period had an average of 18% of the susceptible population infected at the peak, at an average peak time of 70 days. We also demonstrated that seasonal temperature variation dramatically affects the epidemic dynamics, with warmer starting temperatures producing peaks more quickly after an introduction and larger epidemics. Overall, our quantities of interest were most sensitive to variance in the date of arrival, seasonal temperature, biting rate, transmission rates, and the mosquito population the magnitude of sensitivity differs across quantities. Our model could serve as a useful guide in the development of effective local control and mitigation strategies for dengue fever in Madeira Island. The presence of Aedes aegypti mosquitoes in Madeira Island had recently caused the first local outbreak of dengue in Europe. The island is at risk of another local transmission if triggered by the introduction of the dengue virus by an infected person. Using a mathematical model for the transmission of dengue, we examine the dynamics of a potential epidemic triggered by the arrival of an infected person on the island. We also examine the impact of seasonal temperature variation on the epidemic dynamics. Our results show the potential for summer to early winter transmission of dengue on the island, and that the arrival date of an infectious person affects the distribution of the timing and peak size of the epidemic. Arrival dates during mid-summer to early autumn were more likely to produce larger epidemic peak size within a short time. We also show that seasonal temperature variation dramatically affects the epidemic dynamics. With warmer starting temperatures, epidemics peak more rapidly and produce a larger epidemic size. Our model could be useful to estimate the risk of an epidemic outbreak and as a guide for local control and mitigation strategies for dengue on the island.
Publisher: Proceedings of the National Academy of Sciences
Date: 05-02-2018
Abstract: Our ability to predict the identity of future invasive alien species is largely based upon knowledge of prior invasion history. Emerging alien species—those never before encountered as aliens—therefore pose a significant challenge to biosecurity interventions worldwide. Using a global database of the first regional records of alien species covering the years 1500–2005 we detected a surprisingly high proportion of species in recent records that have never been recorded as alien before. The high proportion of these emerging alien species mainly resulted from the increased accessibility of new source species pools in the native range. Risk assessment approaches that rely less on invasion history will need to be prioritized.
Publisher: Cold Spring Harbor Laboratory
Date: 18-10-2019
DOI: 10.1101/19009589
Abstract: The spread of dengue through global human mobility is a major public health concern. A key challenge is understanding the transmission pathways and mediating factors that characterized the patterns of dengue importation into non-endemic areas. Utilizing a network connectivity-based approach, we analyze the importation patterns of dengue fever into European countries. Seven connectivity indices were developed to characterize the role of the air passenger traffic, seasonality, incidence rate, geographical proximity, epidemic vulnerability, and wealth of a source country, in facilitating the transport and importation of dengue fever. We used generalized linear mixed models (GLMMs) to examine the relationship between dengue importation and the connectivity indices while accounting for the air transport network structure. We also incorporated network autocorrelation within a GLMM framework to investigate the propensity of a European country to receive an imported case, by virtue of its position within the air transport network. The connectivity indices and dynamical processes of the air transport network were strong predictors of dengue importation in Europe. With more than 70% of the variation in dengue importation patterns explained. We found that transportation potential was higher for source countries with seasonal dengue activity, high passenger traffic, high incidence rates, lower economic status, and geographical proximity to a destination country in Europe. We also found that position of a European country within the air transport network was a strong predictor of the country’s propensity to receive an imported case. Our findings provide evidence that the importation patterns of dengue into Europe can be largely explained by appropriately characterizing the heterogeneities of the source, and topology of the air transport network. This contributes to the foundational framework for building integrated predictive models for bio-surveillance of dengue importation.
Publisher: Wiley
Date: 11-09-2023
Publisher: Proceedings of the National Academy of Sciences
Date: 29-08-2018
Abstract: Islands are hotspots of alien species invasions, and their distinct bio ersity is particularly vulnerable to invading species. While isolation has shaped natural colonization of islands for millions of years, globalization in trade and transport has led to a breakdown of biogeographical barriers and subsequent colonization of islands by alien species. Using a large dataset of 257 subtropical and tropical islands, we show that alien richness increases with increasing isolation of islands. This pattern is consistent for plants, ants, mammals, and reptiles, and it cannot simply be explained by island economics and trade alone. Geographical isolation does not protect islands from alien species, and island species richness may reach a new dynamic equilibrium at some point, likely at the expense of many endemic species.
Publisher: Springer Science and Business Media LLC
Date: 12-06-2017
Publisher: Elsevier BV
Date: 06-2021
Publisher: Public Library of Science (PLoS)
Date: 05-10-2020
Publisher: Cold Spring Harbor Laboratory
Date: 29-11-2019
DOI: 10.1101/19013383
Abstract: The geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation. Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model’s predictions on a global and local scale. Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country’s dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions. We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.
Publisher: Frontiers Media SA
Date: 04-09-2020
Publisher: Springer Science and Business Media LLC
Date: 28-07-2022
DOI: 10.1038/S41598-022-15079-9
Abstract: Biological invasions by hibian and reptile species (i.e. herpetofauna) are numerous and widespread, having caused severe impacts on ecosystems, the economy and human health. However, there remains no synthesised assessment of the economic costs of these invasions. Therefore, using the most comprehensive database on the economic costs of invasive alien species worldwide (InvaCost), we analyse the costs caused by invasive alien herpetofauna according to taxonomic, geographic, sectoral and temporal dimensions, as well as the types of these costs. The cost of invasive herpetofauna totaled at 17.0 billion US$ between 1986 and 2020, ided split into 6.3 billion US$ for hibians, 10.4 billion US$ for reptiles and 334 million US$ for mixed classes. However, these costs were associated predominantly with only two species (brown tree snake Boiga irregularis and American bullfrog Lithobates catesbeianus ), with 10.3 and 6.0 billion US$ in costs, respectively. Costs for the remaining 19 reported species were relatively minor ( 0.6 billion US$), and they were entirely unavailable for over 94% of known invasive herpetofauna worldwide. Also, costs were positively correlated with research effort, suggesting research biases towards well-known taxa. So far, costs have been dominated by predictions and extrapolations (79%), and thus empirical observations for impact were relatively scarce. The activity sector most affected by hibians was authorities-stakeholders through management ( 99%), while for reptiles, impacts were reported mostly through damages to mixed sectors (65%). Geographically, Oceania and Pacific Islands recorded 63% of total costs, followed by Europe (35%) and North America (2%). Cost reports have generally increased over time but peaked between 2011 and 2015 for hibians and 2006 to 2010 for reptiles. A greater effort in studying the costs of invasive herpetofauna is necessary for a more complete understanding of invasion impacts of these species. We emphasise the need for greater control and prevention policies concerning the spread of current and future invasive herpetofauna.
Publisher: Elsevier BV
Date: 09-2019
Publisher: Springer Science and Business Media LLC
Date: 10-05-2021
DOI: 10.1038/S41598-021-89096-5
Abstract: The Asian tiger mosquito ( Aedes albopictus ), a vector of dengue, Zika and other diseases, was introduced in Europe in the 1970s, where it is still widening its range. Spurred by public health concerns, several studies have delivered predictions of the current and future distribution of the species for this region, often with differing results. We provide the first joint analysis of these predictions, to identify consensus hotspots of high and low suitability, as well as areas with high uncertainty. The analysis focused on current and future climate conditions and was carried out for the whole of Europe and for 65 major urban areas. High consensus on current suitability was found for the northwest of the Iberian Peninsula, southern France, Italy and the coastline between the western Balkans and Greece. Most models also agree on a substantial future expansion of suitable areas into northern and eastern Europe. About 83% of urban areas are expected to become suitable in the future, in contrast with ~ 49% nowadays. Our findings show that previous research is congruent in identifying wide suitable areas for Aedes albopictus across Europe and in the need to effectively account for climate change in managing and preventing its future spread.
Publisher: Springer Science and Business Media LLC
Date: 16-06-2020
DOI: 10.1038/S41598-020-66650-1
Abstract: The geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation. Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model’s predictions on a global and local scale. Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country’s dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions. We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.
Publisher: Wiley
Date: 08-09-2017
DOI: 10.1111/DDI.12617
Publisher: Public Library of Science (PLoS)
Date: 12-03-2020
Publisher: Pensoft Publishers
Date: 26-01-2022
DOI: 10.3897/ARPHAPREPRINTS.E81207
Abstract: Observations are key to understand the drivers of bio ersity loss, and the impacts on ecosystem services and ultimately on people. Many EU policies and initiatives demand unbiased, integrated and regularly updated bio ersity and ecosystem service data. However, efforts to monitor bio ersity are spatially and temporally fragmented, taxonomically biased, and lack integration in Europe. EuropaBON aims to bridge this gap by designing an EU-wide framework for monitoring bio ersity and ecosystem services. EuropaBON harnesses the power of modelling essential variables to integrate different reporting streams, data sources, and monitoring schemes. These essential variables provide consistent knowledge about multiple dimensions of bio ersity change across space and time. They can then be analyzed and synthesized to support decision-making at different spatial scales, from the sub-national to the European scale, through the production of indicators and scenarios. To develop essential bio ersity and ecosystem variables workflows that are policy relevant, EuropaBON is built around stakeholder engagement and knowledge exchange (WP2). EuropaBON will work with stakeholders to identify user and policy needs for bio ersity monitoring and investigate the feasibility of setting up a center to coordinate monitoring activities across Europe (WP2). Together with stakeholders, EuropaBON will assess current monitoring efforts to identify gaps, data and workflow bottlenecks, and analyse cost-effectiveness of different schemes (WP3). This will be used to co-design improved monitoring schemes using novel technologies to become more representative temporally, spatially and taxonomically, delivering multiple benefits to users and society (WP4). Finally, EuropaBON will demonstrate in a set of showcases how workflows tailored to the Birds Directive, Habitats Directive, Water Framework Directive, Climate and Restoration Policy, and the Bioeconomy Strategy, can be implemented (WP5).
Publisher: Springer Science and Business Media LLC
Date: 09-01-2020
Publisher: Springer Science and Business Media LLC
Date: 29-08-2018
Publisher: Elsevier BV
Date: 09-2017
Publisher: Elsevier BV
Date: 2021
Location: Portugal
Location: Portugal
Location: Portugal
Location: Germany
Location: Portugal
No related grants have been discovered for César Capinha.