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Scientific Publications


Anopheles maculipennis Complex in The Netherlands: First Record of Anopheles daciae (Diptera: Culicidae)

A. Ibáñez-JusticiaNathalie SmitzRody BlomAnn VanderheydenFrans JacobsKenny Meganck, Sophie Gombeer, Thierry BackeljauConstantianus J. M. Koenraadt, J. S. Griep, Marc De Meyer, Arjan Stroo


Abstract Despite their past importance as vectors of indigenous malaria, the species composition and spatial distribution of the members of the Anopheles maculipennis complex have been studied to a limited extent in the Netherlands. Therefore, this investigation focuses on the distribution of the members of this complex in the Netherlands, including Anopheles daciae, which has recently been found in countries bordering the Netherlands. In the framework of a national mosquito surveillance between 2010 and 2021, a total of 541 specimens of An. maculipennis s.l. were analyzed from 161 locations covering the entire territory. In addition, 89 specimens were analyzed from overwintering sites during the winter of 2020/2021. All individual mosquitoes were identified to species-level using Sanger sequencing of the ribosomal internal transcribed spacer 2. To characterize the habitat of An. maculipennis s.l. in the Netherlands, land cover use data was extracted in a 1 km buffer area around each finding location. For populations collected in summers between 2010 and 2021, the most frequent species was An. messeae, present in 88.19% of the locations, followed by An. maculipennis s.s. (11.80%), An. atroparvus (3.72%) and An. daciae (3.72%). Anopheles daciae was found in the southern inland areas of the country. Furthermore, An. messeae and An. daciae occurred in sympatry at overwintering sites. This study provides relevant information on the occurrence of species of the Anopheles maculipennis complex in the Netherlands, contributing to a better estimation of the risk of mosquito-borne disease in the country.

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Analytical framework to evaluate and optimize the use of imperfect diagnostics to inform outbreak response: Application to the 2017 plague epidemic in Madagascar

Quirine ten Bosch, Voahangy Andrianaivoarimanana, Beza Ramasindrazana, Guillain Mikaty, Rado J. L. Rakotonanahary, Birgit Nikolay, Soloandry Rahajandraibe, Maxence Feher, Quentin Grassin, Juliette Paireau, Soanandrasana Rahelinirina, Rindra Randremanana, Feno Rakotoarimanana, Marie Melocco, Voahangy Rasolofo, Javier Pizarro-Cerdá, Anne-Sophie Le Guern, Eric Bertherat, Maherisoa Ratsitorahina, André Spiegel, Laurence Baril, Minoarisoa Rajerison, Simon Cauchemez 


During outbreaks, the lack of diagnostic “gold standard” can mask the true burden of infection in the population and hamper the allocation of resources required for control. Here, we present an analytical framework to evaluate and optimize the use of diagnostics when multiple yet imperfect diagnostic tests are available. We apply it to laboratory results of 2,136 samples, analyzed with 3 diagnostic tests (based on up to 7 diagnostic outcomes), collected during the 2017 pneumonic (PP) and bubonic plague (BP) outbreak in Madagascar, which was unprecedented both in the number of notified cases, clinical presentation, and spatial distribution. The extent of these outbreaks has however remained unclear due to nonoptimal assays. Using latent class methods, we estimate that 7% to 15% of notified cases were Yersinia pestis-infected. Overreporting was highest during the peak of the outbreak and lowest in the rural settings endemic to Y. pestis. Molecular biology methods offered the best compromise between sensitivity and specificity. The specificity of the rapid diagnostic test was relatively low (PP: 82%, BP: 85%), particularly for use in contexts with large quantities of misclassified cases. Comparison with data from a subsequent seasonal Y. pestis outbreak in 2018 reveal better test performance (BP: specificity 99%, sensitivity: 91%), indicating that factors related to the response to a large, explosive outbreak may well have affected test performance. We used our framework to optimize the case classification and derive consolidated epidemic trends. Our approach may help reduce uncertainties in other outbreaks where diagnostics are imperfect.