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Introduction The use of drones in environment and health research is a relatively new phenomenon. A principal research activity drones are used for is environmental monitoring, which can raise concerns in local communities. Existing ethical guidance for researchers is often not specific to drone technology and practices vary between research settings. Therefore, this scoping review aims to gather the evidence available on ethical considerations surrounding drone use as perceived by local communities, ethical considerations reported on by researchers implementing drone research, and published ethical guidance related to drone deployment. Methods and analysis This scoping review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI) guidelines. The literature search will be conducted using academic databases and grey literature sources. After pilot testing the inclusion criteria and data extraction tool, two researchers will double-screen and then chart available evidence independently. A content analysis will be carried out to identify patterns of categories or terms used to describe ethical considerations related to drone usage for environmental monitoring in the literature using the R Package RQDA. Discrepancies in any phase of the project will be solved through consensus between the two reviewers. If consensus cannot be reached, a third arbitrator will be consulted. Ethics and dissemination Ethical approval is not required; only secondary data will be used. This protocol is registered on the Open Science Framework (osf.io/a78et). The results will be disseminated through publication in a scientific journal and will be used to inform drone field campaigns in the Wellcome Trust funded HARMONIZE project. HARMONIZE aims to develop cost-effective and reproducible digital infrastructure for stakeholders in climate change hotspots in Latin America & the Caribbean a, RL is the principal investigator of the Wellcome Trust (https://wellcome.org/) funded HARMONIZE project. The project award reference is 224694/Z/21/Z. The funders had no role in the study design., Peer Reviewed, "Article signat per 12 autors/es: Remy Hoek Spaans, Bruna Drumond, Kim Robin van Daalen, Ana Claudia Rorato Vitor, Alison Derbyshire, Adriano Da Silva, Raquel Martins Lana, Mauricio Santos Vega ,Gabriel Carrasco-Escobar, Maria Isabel Sobral Escada, Claudia Codeço, Rachel Lowe", Postprint (published version)

Dengue is rapidly expanding its transmission area across Brazil and much of South America. In this study, data-mining techniques were used to identify climatic and demographic indicators that could explain the recent (2014–2020) and simultaneous trends of expansion and exacerbation of the incidence in some regions of Brazil. The previous circulation of the virus (dengue incidence rates between 2007 and 2013), urbanization, and the occurrence of temperature anomalies for a prolonged period were the main factors that led to increased incidence of dengue in the central region of Brazil. Regions with high altitudes, which previously acted as a barrier for dengue transmission, became areas of high incidence rates. The algorithm that was developed during this study can be utilized to assess future climate scenarios and plan preventive actions., This work was supported by the HARMONIZE project funded by the Wellcome Trust (224694/Z/21/Z) and the E4Warning project funded by EU’s Horizon Europe research and innovation programme (grant agreement 101086640). The Brazilian Climate and Health Observatory is financed by the Brazilian National Council for Scientific and Technological Development (CNPq, 444665/2023-4). RL was supported by a Royal Society Dorothy Hodgkin Fellowship. RML acknowledges the Beatriu de Pinós program (2021 BP 00197) from the Secretariat of Universities and Research of the Research and Universities Department of the Generalitat de Catalunya., Peer Reviewed, Postprint (published version)

Objective: To analyze the spatio-temporal dynamics of COVID-19 in the Rio de Janeiro state within the nine health regions, between March 2020 and December 2022. Methods: The Poisson model with random effects was used to smooth and estimate the incidence of COVID-19 hospitalizations reported in the Influenza Epidemiological Surveillance Information System (SIVEP-Gripe) to verify the synchronicity of the epidemic in the state. Results: The COVID-19 epidemic in the state is characterized by the presence of seven peaks during the analyzed period corresponding to seven found. An asynchrony in hospitalizations was identified, varying according to the different virus variants in the nine health regions of the state. The incidence peaks of hospitalizations ranged from 1 to 12 cases per 100,000 inhabitants during the pandemic. Conclusion: This spatio-temporal analysis is applicable to other scenarios, enabling monitoring and decision-making for the control of epidemic diseases in different areas., LFA was supported by the Coordination for the Improvement of Higher Education Personnel (Capes), funding code 001. LSB had productivity scholarships from CNPq (310530/2021-0), and JCNE, from Faperj (E-26/201.277/2021). LSB, RML and LFA were partially funded by Inova Fiocruz/Fundação Oswaldo Cruz (COVID-19 Knowledge generation)., Peer Reviewed, Postprint (published version)

Dengue is a vector-borne disease present in most tropical countries, infecting an average of 50 to 100 million people per year. Socioeconomic, demographic, and environmental factors directly influence the transmission cycle of the dengue virus (DENV). In Brazil, these factors vary between regions producing different profiles of dengue transmission and challenging the epidemiological surveillance of the disease. In this article, we aimed at classifying the profiles of dengue transmission in 1,823 Brazilian municipalities, covering different climates, from 2010 to 2019. Time series data of dengue cases were obtained from six states: Ceará and Maranhão in the semiarid Northeast, Minas Gerais in the countryside, Espírito Santo and Rio de Janeiro in the tropical Atlantic coast, and Paraná in the subtropical region. To describe the time series, we proposed a set of epi-features of the magnitude and duration of the dengue epidemic cycles, totaling 13 indicators. Using these epi-features as inputs, a multivariate cluster algorithm was employed to classify the municipalities according to their dengue transmission profile. Municipalities were classified into four distinct dengue transmission profiles: persistent transmission (7.8%), epidemic (21.3%), episodic/epidemic (43.2%), and episodic transmission (27.6%). Different profiles were associated with the municipality’s population size and climate. Municipalities with higher incidence and larger populations tended to be classified as persistent transmission, suggesting the existence of critical community size. This association, however, varies depending on the state, indicating the importance of other factors. The proposed classification is useful for developing more specific and precise surveillance protocols for regions with different dengue transmission profiles, as well as more precise public policies for dengue prevention., This study was partially supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES - Finance Code 001. The funders had no role in the study design, data collection and analysis, publication decision or manuscript preparation., Peer Reviewed, Postprint (published version)