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Brazil detected community transmission of COVID-19 on March 13, 2020. In this study we identified which areas in the country were the most vulnerable for COVID-19, both in terms of the risk of arrival of cases, the risk of sustained transmission and their social vulnerability. Probabilistic models were used to calculate the probability of COVID-19 spread from São Paulo and Rio de Janeiro, the initial hotspots, using mobility data from the pre-epidemic period, while multivariate cluster analysis of socio-economic indices was done to identify areas with similar social vulnerability. The results consist of a series of maps of effective distance, outbreak probability, hospital capacity and social vulnerability. They show areas in the North and Northeast with high risk of COVID-19 outbreak that are also highly socially vulnerable. Later, these areas would be found the most severely affected. The maps produced were sent to health authorities to aid in their efforts to prioritize actions such as resource allocation to mitigate the effects of the pandemic. In the discussion, we address how predictions compared to the observed dynamics of the disease.

The WHO African region frequently experiences outbreaks and epidemics of infectious diseases often exacerbated by weak health systems and infrastructure, late detection, and ineffective outbreak response. To address this, the WHO Regional Office for Africa developed and began implementing the Integrated Disease Surveillance and Response strategy in 1998. This systematic review aims to document the identified successes and challenges surrounding the implementation of IDSR in the region available in published literature to highlight areas for prioritization, further research, and to inform further strengthening of IDSR implementation. A systematic review of peer-reviewed literature published in English and French from 1 July 2012 to 13 November 2019 was conducted using PubMed and Web of Science. Included articles focused on the WHO African region and discussed the use of IDSR strategies and implementation, assessment of IDSR strategies, or surveillance of diseases covered in the IDSR framework. Data were analyzed descriptively using Microsoft Excel and Tableau Desktop 2019. The number of peer-reviewed articles discussing IDSR remained low, with 47 included articles focused on 17 countries and regional level systems. Most commonly discussed topics were data reporting (n = 39) and challenges with IDSR implementation (n = 38). Barriers to effective implementation were identified across all IDSR core and support functions assessed in this review: priority disease detection; data reporting, management, and analysis; information dissemination; laboratory functionality; and staff training. Successful implementation was noted where existing surveillance systems and infrastructure were utilized and streamlined with efforts to increase access to healthcare. These findings highlighted areas where IDSR is performing well and where implementation remains weak. While challenges related to IDSR implementation since the first edition of the technical guidelines were released are not novel, adequately addressing them requires sustained investments in stronger national public health capabilities, infrastructure, and surveillance processes.

Strategies to minimize dengue transmission commonly rely on vector control, which aims to maintain Ae. aegypti density below a theoretical threshold. Mosquito abundance is traditionally estimated from mark-release-recapture (MRR) experiments, which lack proper analysis regarding accurate vector spatial distribution and population density. Recently proposed strategies to control vector-borne diseases involve replacing the susceptible wild population by genetically modified individuals' refractory to the infection by the pathogen. Accurate measurements of mosquito abundance in time and space are required to optimize the success of such interventions. In this paper, we present a hierarchical probabilistic model for the estimation of population abundance and spatial distribution from typical mosquito MRR experiments, with direct application to the planning of these new control strategies. We perform a Bayesian analysis using the model and data from two MRR experiments performed in a neighborhood of Rio de Janeiro, Brazil, during both low- and high-dengue transmission seasons. The hierarchical model indicates that mosquito spatial distribution is clustered during the winter (0.99 mosquitoes/premise 95% CI: 0.80-1.23) and more homogeneous during the high abundance period (5.2 mosquitoes/premise 95% CI: 4.3-5.9). The hierarchical model also performed better than the commonly used Fisher-Ford's method, when using simulated data. The proposed model provides a formal treatment of the sources of uncertainty associated with the estimation of mosquito abundance imposed by the sampling design. Our approach is useful in strategies such as population suppression or the displacement of wild vector populations by refractory Wolbachia-infected mosquitoes, since the invasion dynamics have been shown to follow threshold conditions dictated by mosquito abundance. The presence of spatially distributed abundance hotspots is also formally addressed under this modeling framework and its knowledge deemed crucial to predict the fate of transmission control strategies based on the replacement of vector populations.

The Trajetorias dataset is a harmonized set of environmental, epidemiological, and poverty indicators for all municipalities of the Brazilian Legal Amazon (BLA). This dataset is the result of a scientific synthesis research initiative conducted by scientists from several natural and social sciences fields, consolidating multidisciplinary indicators into a coherent dataset for integrated and interdisciplinary studies of the Brazilian Amazon. The dataset allows the investigation of the association between the Amazonian agrarian systems and their impacts on environmental and epidemiological changes, furthermore enhancing the possibilities for understanding, in a more integrated and consistent way, the scenarios that affect the Amazonian biome and its inhabitants.

The mosquito Aedes aegypti, vector of dengue, chikungunya and yellow fever viruses, is an important target of vector control programs in tropical countries. Most mosquito surveillance programs are still based on the traditional household larval surveys, despite the availability of new trapping devices. We report the results of a multicentric entomological survey using four types of traps, besides the larval survey, to compare the entomological indices generated by these different surveillance tools in terms of their sensitivity to detect mosquito density variation. The study was conducted in five mid-sized cities, representing variations of tropical climate regimens. Surveillance schemes using traps for adults (BG-Sentinel, Adultrap and MosquiTRAP) or eggs (ovitraps) were applied monthly to three 1 km(2) areas per city. Simultaneously, larval surveys were performed. Trap positivity and density indices in each area were calculated and regressed against meteorological variables to characterize the seasonal pattern of mosquito infestation in all cities, as measured by each of the four traps. The House Index was consistently low in most cities, with median always 0. Traps rarely produced null indices, pointing to their greater sensitivity in detecting the presence of Ae. aegypti in comparison to the larval survey. Trap positivity indices tend to plateau at high mosquito densities. Despite this, both indices, positivity and density, agreed on the seasonality of mosquito abundance in all cities. Mosquito seasonality associated preferentially with temperature than with precipitation even in areas where temperature variation is small. All investigated traps performed better than the House Index in measuring the seasonal variation in mosquito abundance and should be considered as complements or alternatives to larval surveys. Choice between traps should further consider differences of cost and ease-of-use.

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.

Individual perception of vaccine safety is an important factor in determining a person's adherence to a vaccination program and its consequences for disease control. This perception, or belief, about the safety of a given vaccine is not a static parameter but a variable subject to environmental influence. To complicate matters, perception of risk (or safety) does not correspond to actual risk. In this paper we propose a way to include the dynamics of such beliefs into a realistic epidemiological model, yielding a more complete depiction of the mechanisms underlying the unraveling of vaccination campaigns. The methodology proposed is based on Bayesian inference and can be extended to model more complex belief systems associated with decision models. We found the method is able to produce behaviors which approximate what has been observed in real vaccine and disease scare situations. The framework presented comprises a set of useful tools for an adequate quantitative representation of a common yet complex public-health issue. These tools include representation of beliefs as Bayesian probabilities, usage of logarithmic pooling to combine probability distributions representing opinions, and usage of natural conjugate priors to efficiently compute the Bayesian posterior. This approach allowed a comprehensive treatment of the uncertainty regarding vaccination behavior in a realistic epidemiological model.

Visceral leishmaniasis belongs to the list of neglected tropical diseases and is considered a public health problem worldwide. Spatial correlation between the occurrence of the disease in humans and high rates of canine infection suggests that in the presence of the vector, canine visceral leishmaniasis is the key factor for triggering transmission to humans. Despite the control strategies implemented, such as the sacrifice of infected dogs being put down, the incidence of American visceral leishmaniasis remains high in many Latin American countries. Mathematical models were developed to describe the transmission dynamics of canine leishmaniasis and its control by culling. Using these models, imperfect control scenarios were implemented to verify the possible factors which alter the effectiveness of controlling this disease in practice. A long-term continuous program targeting both asymptomatic and symptomatic dogs should be effective in controlling canine leishmaniasis in areas of low to moderate transmission (R0 up to 1.4). However, the indiscriminate sacrifice of asymptomatic dogs with positive diagnosis may jeopardize the effectiveness of the control program, if tests with low specificity are used, increasing the chance of generating outrage in the population, and leading to lower adherence to the program. Therefore, culling must be planned accurately and implemented responsibly and never as a mechanical measure in large scale. In areas with higher transmission, culling alone is not an effective control strategy.

Vector-borne diseases, such as dengue and chikungunya, along with air-borne diseases like influenza and COVID-19, are prone to epidemics, which increases the demand for real-time outbreak monitoring. Developing such systems requires harmonized datasets for calibration and validation. In this study, we created a dataset containing official disease notifications for dengue, chikungunya, and severe acute respiratory infections (SARI) across Brazilian states for over a decade. The dataset integrates Google Trends search data for each disease and all associated symptoms, organized by the corresponding epidemiological week. By providing this integrated resource, we aim to encourage the use of alternative online data to explore associations between digital search behavior and official disease incidence, thereby supporting the development of nowcasting models for early outbreak detection to inform timely public health responses and decision-making.

Background In the process of geographical retraction of malaria, some important endemicity pockets remain. Here, we report results from a study developed to obtain detailed community data from an important malaria hotspot in Latin America (Alto Juruá, Acre, Brazil), to investigate the association of malaria with socioeconomic, demographic and living conditions. Methods A household survey was conducted in 40 localities (n = 520) of Mâncio Lima and Rodrigues Alves municipalities, Acre state. Information on previous malaria, schooling, age, gender, income, occupation, household structure, habits and behaviors related to malaria exposure was collected. Multiple correspondence analysis (MCA) was applied to characterize similarities between households and identify gradients. The association of these gradients with malaria was assessed using regression. Results The first three dimensions of MCA accounted for almost 50% of the variability between households. The first dimension defined an urban/rurality gradient, where urbanization was associated with the presence of roads, basic services as garbage collection, water treatment, power grid energy, and less contact with the forest. There is a significant association between this axis and the probability of malaria at the household level, OR = 1.92 (1.23–3.02). The second dimension described a gradient from rural settlements in agricultural areas to those in forested areas. Access via dirt road or river, access to electricity power-grid services and aquaculture were important variables. Malaria was at lower risk at the forested area, OR = 0.55 (1.23–1.12). The third axis detected intraurban differences and did not correlate with malaria. Conclusions Living conditions in the study area are strongly geographically structured. Although malaria is found throughout all the landscapes, household traits can explain part of the variation found in the odds of having malaria. It is expected these results stimulate further discussions on modelling approaches targeting a more systemic and multi-level view of malaria dynamics.