Chikungunya virus (CHIKV) was first detected in Brazil in 2014, becoming a significant public health concern. Metropolitan areas were initially the port of entry, but the disease spread to less populated municipalities, challenging the focus of monitoring and control in large cities. This study presents a descriptive analysis of the spread of CHIKV in Brazil up to 2022, examining regional differences. Data on chikungunya cases in municipalities were obtained from the Brazilian Information System for Notifiable Diseases (SINAN) from 2014-2022. Population size data were obtained from the Brazilian Institute of Geography and Statistics (IBGE). The chikungunya introduction dates in the states were organized into timelines and maps. A decentralization index was proposed to estimate the ratio between the incidence in non-metropolitan and metropolitan municipalities. Our analysis revealed that the spread of chikungunya from metropolitan to non-metropolitan areas varied among states. As expected, most states initially showed higher metropolitan incidence rates, with a few exceptions, such as Amapá, located at the national border with French Guiana. The Northeast remained the epicenter during the study period, but significant regional differences in disease patterns were observed across Brazil. Several factors, including environmental suitability and demographic changes such as internal migration, may have facilitated the differential spread of chikungunya to less densely populated regions. In conclusion, the index proved useful for monitoring the dynamics of disease spread, highlighting areas that require specific surveillance and control measures extending beyond large urban centers.

Forecast models are a key decision-support tool for public health authorities in managing epidemics, feeding into early warning systems, scenario evaluations, and an empirical basis for resource allocation. In Brazil, improving dengue forecasting became a priority in response to the unprecedented increase in cases, which surpassed the total of the previous decade and expanded to new regions. The Infodengue-Mosqlimate consortium launched the Infodengue-Mosqlimate Dengue Challenge 2024 (IMDC24), or Dengue Forecast Sprint, bringing together six international teams provided with cases and climate covariates data to generate actionable forecasts for 2024 and 2025 seasons in five diverse Brazilian states, leveraging advanced machine learning and classical statistical models. This paper outlines the structure and findings of the IMDC24. The performance of the models varied between years and locations, and no single model consistently excelled, especially during 2024's unprecedentedly large season. This performance variability highlighted the need for ensemble approaches. The ensemble models developed are presented as the main results of this collaborative development. As intended, the ensemble models have been adopted by Brazilian public health authorities to help with planning and response to the forecasted 2025 dengue epidemics across the country.

Resumo: Este estudo investiga, por meio do uso de indicadores ambientais, uso da terra e técnicas de análise de agrupamento, características de municípios da Amazônia Legal que podem estar associadas à incidência de doenças vetoriais. Identificamos e descrevemos seis agrupamentos de municípios amazônicos com características ambientais e agrárias similares. Com base em um conjunto de indicadores epidemiológicos, exploramos a incidência de doenças tropicais vetoriais negligenciadas ao longo desses agrupamentos. Os resultados evidenciam uma grande heterogeneidade ambiental dos municípios amazônicos, com perfis diferenciados e bem definidos nos agrupamentos obtidos. Além disso, demonstram a relação de atividades econômicas e degradação ambiental com a propagação de doenças. Essa abordagem permite uma visão abrangente dos desafios ambientais e de saúde enfrentados pela região, contribuindo para a elaboração de estratégias de conservação mais eficazes e adaptadas às necessidades específicas de cada perfil de município.

The influence of climate on mosquito-borne diseases like dengue and chikungunya is well-established, but comprehensively tracking long-term spatial and temporal trends across large areas has been hindered by fragmented data and limited analysis tools. This study presents an unprecedented analysis, in terms of breadth, estimating the SIR transmission parameters from incidence data in all 5,570 municipalities in Brazil over 14 years (2010-2023) for both dengue and chikungunya. We describe the Episcanner computational pipeline, developed to estimate these parameters, producing a reusable dataset describing all dengue and chikungunya epidemics that have taken place in this period, in Brazil. The analysis reveals new insights into the climate-epidemic nexus: We identify distinct geographical and temporal patterns of arbovirus disease incidence across Brazil, highlighting how climatic factors like temperature and precipitation influence the timing and intensity of dengue and chikungunya epidemics. The innovative Episcanner tool empowers researchers and public health officials to explore these patterns in detail, facilitating targeted interventions and risk assessments. This research offers a new perspective on the long-term dynamics of climate-driven mosquito-borne diseases and their geographical specificities linked to the effects of global temperature fluctuations such as those captured by the ENSO index.

This study addresses the co-occurrence of malaria and Chagas disease in municipalities in the Amazon, a region characterized by geographic and climatic diversity and by socioeconomic and environmental transformations. This study aimed to identify the factors related to the co-occurrence of malaria and Chagas disease in the Brazilian Amazon from 2015 to 2019. The analysis explored 19 environmental indicators and two socioeconomic indicators related to habitat loss, land use and cover, climate anomalies, and the multidimensional poverty index. Modeling was performed by Conditional Inference Trees, adjusting models with and without contextual variables, to map areas of probable co-occurrence of the diseases. The incidence of malaria is predominant in the western Amazon, while Chagas disease is more frequent in areas of Pará and parts of Amazonas and Acre. Municipalities with high coverage of native vegetation showed higher incidences of malaria, but not necessarily of Chagas disease. Municipalities with native vegetation cover and pasture areas showed heterogeneous incidence of diseases, with some presenting a high incidence of both diseases. The predictive analysis showed an increase in the number of municipalities with a high expected incidence of malaria (moderate) and disease Chagas (high) from 1 to 7, when compared to observed data. The study showed areas with a risk of moderate and high incidence of both diseases, covering a larger region than that observed in the period. Alternatives of shared surveillance and the integration of programs for the identification of cases and treatment can be a measure to optimize resources and help eradicate these diseases in the region.

Resumo: Este estudo aborda a coocorrência de malária e doença de Chagas nos municípios da Amazônia, região caracterizada por diversidade geográfica e climática e por transformações socioeconômicas e ambientais. O objetivo do estudo foi identificar os fatores relacionados à coocorrência de malária e doença de Chagas na Amazônia Brasileira no período entre 2015 e 2019. A análise explorou 19 indicadores ambientais e dois socioeconômicos relacionados à perda de habitat, ao uso e cobertura da terra, às anomalias climáticas e ao índice de pobreza multidimensional. A modelagem foi realizada por Árvores de Regressão com Inferência Condicional, ajustando modelos com e sem variáveis contextuais, para mapear áreas de provável coocorrência das doenças. A incidência de malária predomina na Amazônia ocidental, enquanto a doença de Chagas apresenta maior concentração em áreas do Pará e partes do Amazonas e Acre. Municípios com alta cobertura de vegetação nativa mostraram maiores incidências de malária, mas não necessariamente de doença de Chagas. Municípios com cobertura vegetal nativa e áreas de pastagem apresentaram uma heterogeneidade na incidência das doenças, com alguns apresentando alta incidência de ambas. A análise preditiva passou de 1 para 7 o número de municípios com alta incidência esperada de malária (média) e doença de Chagas (alta) em comparação com os dados observados. As análises indicam áreas com risco de média e alta incidência de ambas as doenças, cobrindo uma região maior do que a observada no período. Alternativas de vigilância compartilhada e a integração de programas para identificação de casos e tratamento podem ser um caminho para otimização de recursos e busca pela erradicação desses agravos na região.

Climate change-related weather and extreme events are increasing in intensity and frequency, affecting infectious disease transmission globally. Dengue, a climate-sensitive vector-borne disease, to which over half the world’s population is at risk of infection, has expanded its geographical range over recent decades. The 2023/24 season marked the largest ever dengue outbreak year in the Americas, coinciding with the hottest year on record in the Americas. Here, we use statistical models to investigate the Brazil 2023/24 dengue season and attribute how anthropogenic climate change impacted it. We analyze >20 years of dengue data across >5000 municipalities and find that observed temperature anomalies in municipalities of southern Brazil pushed those locations into optimal thermal conditions for dengue transmission. In contrast, in northern Brazil, 2023/24 temperatures became too high for effective transmission, resulting in lower dengue incidence compared to a counterfactual scenario without anthropogenic climate change. We test the generalizability of our model to high altitude areas in Mexico, where dengue has been expanding. Our work empirically demonstrates how a climate-change-related temperature anomaly led to the range expansion and growth of dengue across variable ecological and socio-economic settings, with implications for preparedness, adaptation, mitigation, and resilience planning.

Dengue is a vector-borne disease and a major public health concern in Brazil. Its continuing and rising burden has led the Brazilian Ministry of Health to request for modelling efforts to aid in the preparedness and response to the disease. In this context, we propose a Bayesian forecasting model based on historical data to predict the number of cases 52 weeks ahead for the 118 health districts of Brazil. We leverage the predictions to build probabilistic epidemics bands to be used for dengue monitoring. We define four disjoint probabilistic bands (≤50% (50%, 75%] (75%, 90%], and > 90%), based on the percentiles of the predicted cases distribution and interpreted according to the historical number of cases and past occurrence probability (below the median, typical; moderately high, fairly typical; fairly high, atypical; exceptionally high, very atypical). We performed out-of-sample validation for 2022-2023 and 2023-2024 and forecasted 2024-2025. In the 2022-2023 and 2023-2024 seasons, the epidemic bands followed the observed cases' curve shape, with a sharp increase after January and a decline after the peak around April. In 2022-2023, the observed number of cases (1,436,034) was slightly above the estimated 75% percentile (1,405,191), being classified as "fairly high, atypical". Most health districts in South Brazil showed exceptionally high numbers of cases during this season. The situation worsened in 2023-2024 and the observed number of cases (6,454,020) was way above the 90% percentile (2,221,557), characterising an "exceptionally high, very atypical" season. For the 2024-2025 season, we estimated a median number of cases of 1,526,523 (maximum value for the "below the median, typical" probabilistic epidemic band. The maximum estimated values for the upper bands were 2,213,282 (moderately high, fairly typical) and 3,803,898 (fairly high, atypical) with the upper limits of the probabilistic epidemic bands of 1,452,359. Probabilistic epidemic bands serve as a valuable monitoring tool by enabling prospective comparisons between observed case curves and historical epidemic patterns, facilitating the assessment of ongoing outbreaks about past occurrences.

Emerging vector-borne diseases (VBDs) are a major public health concern worldwide. Climate change, environmental degradation and globalisation have led to an expansion in the range of many vectors and an erosion of transmission barriers, increasing human exposure to new pathogens and the risk for emerging VBD outbreaks. Europe is potentially underprepared for the increasing threat of VBDs, due to attention and funding being diverted to other public health priorities. Proactive, rather than reactive, prevention and control approaches can greatly reduce the socio-economic toll of VBDs. Endemic countries globally have decades of experience in controlling VBDs, and Europe has much to learn from this knowledge. Here, we advocate for the expansion of transdisciplinary knowledge-sharing partnerships, to co-create proactive measures against VBDs. We present the experiences and expertise of our diverse international team and explore how an array of interventions can be applied and adapted to the European context.

This study investigates the characteristics of municipalities in the Legal Amazon that may be associated with the incidence of vector-borne diseases using environmental and land use indicators and cluster analysis. We identified and described six groups of Amazonian municipalities with similar environmental and agrarian characteristics. Based on a set of epidemiological indicators, we explore the incidence of neglected vector-borne tropical diseases in these groups. Results show a great environmental heterogeneity in the Amazonian municipalities, with well-defined profiles in the obtained groups. Moreover, they show the relation of economic activities and environmental degradation with the spread of diseases. This approach can comprehensively show the environmental and health challenges of regions, contributing to the development of more effective conservation strategies to be adapted to the specific needs of each municipality profile.

A country with continental dimensions like Brazil, characterized by heterogeneity of climates, biomes, natural resources, population density, socioeconomic conditions, and regional challenges, also exhibits significant spatial variation in dengue outbreaks. This study aimed to characterize Brazilian territory based on epidemiological and climate data to determine the optimal time to guide preventive and control strategies. To achieve this, the Moving Epidemics Method (MEM) was employed to analyze dengue historical patterns using 14-year disease data (2010-2023) aggregated by the 120 Brazilian Health Macro-Regions (HMR). Statistical outputs from MEM included the mean outbreak onset, duration, and variation of these measurements, pre- and post-epidemic thresholds, and the high-intensity level of cases. Environmental data used includes mean annual precipitation, temperature, and altitude, as well as the Köppen Climate Classification of each area. A multivariate cluster analysis using the k-means algorithm was applied to MEM outputs and climate data. Four clusters/regions were identified, with the mean temperature, mean precipitation, mean outbreak onset, high-intensity level of cases, and mean altitude explaining 80% of the centroid variation among the clusters. Region 1 (North-Northwest) encompasses areas with the highest temperatures, precipitation, and early outbreak onset, in February. Region 2a (Northeast) has the lowest precipitation and a later onset, in March. Region 3 (Southeast) presents higher altitude, and early outbreak onset in February; while Region 4 (South) has a lower temperature, with onset in March. To better adjust the results, the unique Roraima state HMR state was manually classified as Region 2b (Roraima) because of its outbreak onset in July and the highest precipitation volume. The results suggested preventive and control measures should be implemented first in Regions North-Northwest and Southeast, followed by Regions Northeast, South, and Roraima, highlighting the importance of regional vector control measures based on historical and climatic patterns. Integrating these findings with monitoring systems and fostering cross-sector collaboration can enhance surveillance and mitigate future outbreaks. The proposed methodology also holds potential for application in controlling other mosquito-transmitted viral diseases, expanding its public health impact.

Dengue fever remains a major public health concern, requiring continuous efforts to mitigate its impact. This study investigates the influence of key temperature-dependent parameters on dengue transmission dynamics in Foz do Iguaçu, a tri-border municipality in southern Brazil, using a mathematical model based on a system of ordinary differential equations. The fitted model aligns well with observed data. To track changes in dengue transmission over time and detect epidemic onset, we calculated the effective reproduction number. Additionally, we explored the potential effects of climate variability on dengue dynamics. Our findings highlight the importance of vector population dynamics, climate, and incidence, offering insights into dengue transmission in Foz do Iguaçu. This research provides a foundation for optimizing intervention strategies in other cities, improving outbreak prediction, and supporting public health efforts in dengue control.