Wild Rabbit Exposure to <i>Leishmania infantum</i>, <i>Toxoplasma gondii</i>, <i>Anaplasma phagocytophilum</i> and <i>Babesia caballi</i> Evidenced by Serum and Aqueous Humor Antibody Detection

Wild rabbits (Oryctolagus cuniculus) can be important sentinel species for the presence of zoonotic pathogens. Therefore, we collected blood samples from wild rabbits harvested by hunters during the hunting season 2019–2020 on the island of Lemnos, to determine exposure of wild rabbits to the zoonotic pathogens Leishmania infantum, Toxoplasma gondii, Anaplasma phagocytophilum and Babesia caballi, as well as aqueous humor to assess its diagnostic performance in terms of sensitivity, specificity, positive and negative likelihood ratios. Antibodies against these pathogens were detected by Indirect Immunofluorescence Antibody (IFA) assay. Out of the 72 wild rabbits included in the study, 4.2%, 5.5%, 18% and 9.7% were seropositive to L. infantum, T. gondii, A. phagocytophilum and B. caballi, respectively. Although less frequently, antibodies were also detected in aqueous humor of wild rabbits. The antibody detection in aqueous humor presented 100% specificity but decreased sensitivity compared to serum suggesting that aqueous humor could be successfully used in epidemiological studies to confirm exposure at the population level but has little diagnostic value at the individual level. This is the first report on the seropositivity of wild rabbits to A. phagocytophilum and B. caballi and the detection of antibodies against A. phagocytopylum, L. infantum, T. gondii and B. caballi in the aqueous humor

Mosquito Alert Dataset

The Mosquito Alert dataset includes occurrence records of adult mosquitoes. The records were collected through Mosquito Alert, a citizen science system for investigating and managing disease-carrying mosquitoes. Each record presented in the database is linked to a photograph submitted by a citizen scientist and validated by entomological experts to determine if it provides evidence of the presence of any of five targeted mosquito vectors of top concern in Europe (i.e. Aedes albopictus, Aedes aegypti, Aedes japonicus, Aedes koreicus, Culex pipiens). The temporal coverage of the database is from 2014 through 2022 and the spatial coverage is worldwide. Most of the records from 2014 to 2020 are from Spain, reflecting the fact that the project was funded by Spanish national and regional funding agencies. Since autumn 2020 the data has expanded to include substantial records from other countries in Europe, particularly the Netherlands, Italy, and Hungary, thanks to a human volunteering network of entomologists coordinated by the AIM-COST Action and to technological developments through the VEO project to increase scalability. Among many possible applications, Mosquito Alert dataset facilitates the development of citizen-based early warning systems for mosquito-borne disease risk. This dataset can be further re-used for modelling vector exposure risk or training machine-learning detection and classification routines on the linked images, to help experts in data validation and build up automated alert systems

Mosquito Alert Dataset

The Mosquito Alert dataset includes occurrence records of adult mosquitoes. The records were collected through Mosquito Alert, a citizen science system for investigating and managing disease-carrying mosquitoes. Each record presented in the database is linked to a photograph submitted by a citizen scientist and validated by entomological experts to determine if it provides evidence of the presence of any of five targeted mosquito vectors of top concern in Europe (i.e. Aedes albopictus, Aedes aegypti, Aedes japonicus, Aedes koreicus, Culex pipiens). The temporal coverage of the database is from 2014 through 2022 and the spatial coverage is worldwide. Most of the records from 2014 to 2020 are from Spain, reflecting the fact that the project was funded by Spanish national and regional funding agencies. Since autumn 2020 the data has expanded to include substantial records from other countries in Europe, particularly the Netherlands, Italy, and Hungary, thanks to a human volunteering network of entomologists coordinated by the AIM-COST Action and to technological developments through the VEO project to increase scalability. Among many possible applications, Mosquito Alert dataset facilitates the development of citizen-based early warning systems for mosquito-borne disease risk. This dataset can be further re-used for modelling vector exposure risk or training machine-learning detection and classification routines on the linked images, to help experts in data validation and build up automated alert systems

Mosquito Alert - Leveraging Citizen Science to Create a GBIF Mosquito Occurrence Dataset

The Mosquito Alert dataset includes occurrence records of adult mosquitoes collected worldwide in 2014–2020 through Mosquito Alert, a citizen science system for investigating and managing disease-carrying mosquitoes. Records are linked to citizen science-submitted photographs and validated by entomologists to determine the presence of five targeted European mosquito vectors: Aedes albopictus, Ae. aegypti, Ae. japonicus, Ae. koreicus, and Culex pipiens. Most records are from Spain, reflecting Spanish national and regional funding, but since autumn 2020 substantial records from other European countries are included, thanks to volunteer entomologists coordinated by the AIM-COST Action, and to technological developments to increase scalability. Among other applications, the Mosquito Alert dataset will help develop citizen science-based early warning systems for mosquito-borne disease risk. It can also be reused for modelling vector exposure risk, or to train machine-learning detection and classification routines on the linked images, to assist with data validation and establishing automated alert systems

Mosquito alert: leveraging citizen science to create a GBIF mosquito occurrence dataset

Este artículo contiene 13 páginas, 2 figuras.The Mosquito Alert dataset includes occurrence records of adult mosquitoes collected worldwide in 2014–2020 through Mosquito Alert, a citizen science system for investigating and managing disease-carrying mosquitoes. Records are linked to citizen science-submitted photographs and validated by entomologists to determine the presence of five targeted European mosquito vectors: Aedes albopictus, Ae. aegypti, Ae. japonicus, Ae. koreicus, and Culex pipiens. Most records are from Spain, reflecting Spanish national and regional funding, but since autumn 2020 substantial records from other European countries are included, thanks to volunteer entomologists coordinated by the AIM-COST Action, and to technological developments to increase scalability. Among other applications, the Mosquito Alert dataset will help develop citizen science-based early warning systems for mosquito-borne disease risk. It can also be reused for modelling vector exposure risk, or to train machine-learning detection and classification routines on the linked images, to assist with data validation and establishing automated alert systems.This work was supported by: • 2021–2022 Fair Computational Epidemiology (FACE); Plataforma Temática Interdisciplinar PTI+ Salud Global, Consejo Superior de Investigaciones Científicas (CSIC); Grant No.: N/A. • 2020–2025 Human-Mosquito Interaction Project: Host-vector networks, mobility and the socio-ecological context of mosquito-borne disease; European Research Council (ERC); Grant No.: 853271. • 2020–2021 Strengthening Barcelona’s Defenses Against Disease-Vector Mosquitoes: Automatically Calibrated Citizen-Based Surveillance, Barcelona Ciència; Ajuntament de Barcelona, Institut de Cultura; Grant No.: BCNPC/00041. • 2020–2024 VEO: Versatile Emerging infectious disease Observatory, H2020 SC1-BHC-13-2019; European Commission (EC); Grant No.: 874735. • 2020–2025 Preparing for vector-borne virus outbreaks in a changing world: a One Health Approach; Dutch National Research Agenda (NWA); Grant No.: NWA/00686468. • 2019–2021 Big Mosquito Bytes: Community-Driven Big Data Intelligence to Fight Mosquito-Borne Disease; Fundació “La Caixa”, Health Research 2018 “la Caixa” Banking Foundation; Grant No.: HR19-00336. • 2018–2022 Aedes Invasive Mosquitoes (AIM), COST ACTION OC-2017-1-22105; European Cooperation in Science and Technology (COST); Grant No.: CA17108. • 2018 Mosquito Alert: programa para investigar y controlar mosquitos vectores de enfermedades como el Dengue, el Chikungunya y el Zika; Fundació “La Caixa”; Grant No.: N/A. • 2017–2019 Plataforma Integral per al Control de l’Arbovirosis a Catalunya (PICAT); Departament de Salut, Programa PERIS 2016–2020, Generalitat de Catalunya; Grant No.: 00466. • 2016–2018 Ciència ciutadana per a la millora de la gestió i els models predictius de dispersió i distribució real de mosquit tigre a la Província de Girona; Diputació de Salut de Girona (DIPSALUT); Grant No.: N/A. • 2016 Nuevas herramientas de participación en ciencia ciudadana: laboratorios de validación y cocreación para AtrapaelTigre.com; Fundación Española para la Ciencia y la Tecnología (FECYT); Grant No.: FCT-15-9515. • 2016–2017 Mosquito Alert: programa para investigar y controlar mosquitos vectores de enfermedades como el Dengue, el Chikungunya y el Zika; Fundació “La Caixa”; Grant No.: N/A. • 2016–2017 Ciència ciutadana per a la millora de la gestió i els models predictius de dispersió i distribució real de mosquit tigre a la Província de Girona; Diputació de Salut de Girona (DIPSALUT); Grant No.: N/A. • 2015–2016 Citizens-based early warning systems for invasive species and disease vectors: The case of the Asian Tiger mosquito; Fundació “La Caixa” and Centre de Recerca Ecològica i Aplicacions Forestals (CREAF); Grant No.: N/A. • 2014–2016 Invasión del mosquito tigre en España: Salud pública y cambio global; Ministerio de Economía y Competitividad, Plan Estatal I+D+I; Grant No.: CGL2013-43139-R. • 2014 Diseño e implementación de un sistema ciudadano de alerta y seguimiento del mosquito tigre: ciencia en sociedad (Atrapa el Tigre 2.0); Fundación Española para la Ciencia y la Tecnología (FECYT); Grant No.: FCT-13-701955.Peer reviewe