Measuring the contribution of human mobility to malaria persistence.

BACKGROUND: To achieve malaria elimination, it is important to determine the role of human mobility in parasite transmission maintenance. The Alto Juruá basin (Brazil) exhibits one of the largest vivax and falciparum malaria prevalence in the Amazon. The goal of this study was to estimate the contribution of human commutes to malaria persistence in this region, using data from an origin-destination survey. METHODS: Data from an origin-destination survey were used to describe the intensity and motivation for commutations between rural and urban areas in two Alto Juruá basin (Brazil) municipalities, Mâncio Lima and Rodrigues Alves. The relative time-person spent in each locality per household was estimated. A logistic model was developed to estimate the effect of commuting on the probability of contracting malaria for a certain residence zone inhabitant commuting to another zone. RESULTS: The main results suggest that the assessed population is not very mobile. A total of [Formula: see text] households reported spending over [Formula: see text] of their annual person-hour in areas within the same residence zone. Study and work were the most prevalent commuting motivations, calculated at [Formula: see text] and [Formula: see text] respectively. Spending person-hours in urban Rodrigues Alves conferred relative protection to urban Mâncio Lima residents. The opposite effect was observed for those spending time in rural areas of both municipalities. CONCLUSION: Residence area is a stronger determinant for contracting malaria than commuting zones in the Alto Juruá region. As these municipalities are a hotspot for Plasmodium transmission, understanding the main local human fluxes is essential for planning control strategies, since the probability of contracting malaria is dependent on the transmission intensity of both the origin and the displacement area. The natural conditions for the circulation of certain pathogens, such as Plasmodium spp., combined with the Amazon human mobility pattern indicate the need for disease control perspective changes. Therefore, intersectoral public policies should become the basis for health mitigation actions.

A modelling approach for correcting reporting delays in disease surveillance data.

One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil.

Socioeconomic and demographic characterization of an endemic malaria region in Brazil by multiple correspondence analysis.

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.

Correction to: Socioeconomic and demographic characterization of an endemic malaria region in Brazil by multiple correspondence analysis.

After publication of the article [1], it has been brought to our attention that the y-axis of Fig. 6 has been labeled incorrectly. It should read "linear predictor". This has now been corrected in the original article.

Prenatal care and childbirth assistance in Amazonian women before and after the Pacific Highway Construction (2003-2011): a cross-sectional study.

BACKGROUND: Attention to prenatal care and child delivery is important for the health of women and children, but in the Amazon these indicators tend to be historically unfavorable, in part by geographical and political isolation. In 2003 both Brazilian and Peru governments have finished paving an international road connecting remotes areas in the Brazilian Amazon to the Pacific coast in Peru. METHODS: The situation of prenatal care and child delivery with mothers of children under 5 years old living in the urban area of Assis Brasil, Acre was assessed in two cross-sectional studies performed in 2003 and 2011, corresponding to the period before and after the Pacific highway construction. RESULTS: In 2003, most mothers were of black/Afro-American ethnicity, or "pardos" (the offspring of a Caucasian with a African descendant) (77.69 %), had more than 4 years of schooling (73.40 %) and had a mean age of 22.18 years. In 2011, the number of as a migration of indigenous women increased from 0 to 14.40 % of the respondents, because of migration from communities along the rivers to urban areas, with no other significant changes in maternal characteristics. No significant improvement in childbirth assistance was noticed between 1997 and 2011; only the percentage of in-hospital vaginal deliveries performed by doctors increased from 17.89 to 66.26 % (p <0.001) during this period. Access to prenatal care was associated with white ethnicity in 2003, and higher socioeconomic level and white ethnicity in 2011, while the higher number of prenatal visits was associated with higher maternal education and higher socioeconomic levels in 2011. Vaginal child delivery at a hospital facility was associated with maternal age in 2003, and year of birth, being of white ethnicity and higher level of education in 2011. CONCLUSIONS: The indicators of prenatal care and child delivery were below the national average, showing that geographical isolation still affects women's health care in the Amazon, despite the construction of the highway and governmental health protocols adopted during this period.

Unveiling time in dose-response models to infer host susceptibility to pathogens.

The biological effects of interventions to control infectious diseases typically depend on the intensity of pathogen challenge. As much as the levels of natural pathogen circulation vary over time and geographical location, the development of invariant efficacy measures is of major importance, even if only indirectly inferrable. Here a method is introduced to assess host susceptibility to pathogens, and applied to a detailed dataset generated by challenging groups of insect hosts (Drosophila melanogaster) with a range of pathogen (Drosophila C Virus) doses and recording survival over time. The experiment was replicated for flies carrying the Wolbachia symbiont, which is known to reduce host susceptibility to viral infections. The entire dataset is fitted by a novel quantitative framework that significantly extends classical methods for microbial risk assessment and provides accurate distributions of symbiont-induced protection. More generally, our data-driven modeling procedure provides novel insights for study design and analyses to assess interventions.

Socioeconomic inequities and hepatitis A virus infection in Western Brazilian Amazonian children: spatial distribution and associated factors.

BACKGROUND: Hepatitis A is still a neglected health problem in the world. The most affected areas are the ones with disadvantaged socioeconomic conditions. In Brazil, seroprevalence studies showed that 64.7 % of the general population has antibodies against HAV (hepatitis A virus), and the Amazon region has the highest seroprevalence in the country. METHODS: In the present study the seroprevalence of total HAV antibodies in children between 1 and 5 years old residing in the urban area of Assis Brasil, Acre was measured and spatial distribution of several socioeconomic inequities was evaluated. RESULTS: In the year of 2011, seroprevalence rate was 16.66 %. Factors associated with having a positive serology identified by multivariate analysis were being of indigenous ethnicity [adjusted Odds Ratio (aOR) = 3.27, CI 1.45-7.28], usage of water from the public system (aOR = 8.18, CI 1.07-62.53), living in a house not located in a street (aOR = 3.48, CI 1.54-7.87), and child age over 4 years old (aOR = 2.43, CI 1.23-4.79). The distribution of seropositive children was clustered in the eastern part of the city, where several socioeconomic inequities (lack of flushed toilets, lack of piped water inside the household and susceptibility of the household to flooding during rain, low maternal education, having wood or ground floor at home, and not owning a house, lack of piped water at home, and type of drinking water) also clustered. CONCLUSIONS: The findings highlight that sanitation and water treatment still need improvement in the Brazilian Amazon, and that socioeconomic development is warranted in order to decrease this and other infectious diseases.

Surveillance of dengue vectors using spatio-temporal Bayesian modeling.

BACKGROUND: At present, dengue control focuses on reducing the density of the primary vector for the disease, Aedes aegypti, which is the only vulnerable link in the chain of transmission. The use of new approaches for dengue entomological surveillance is extremely important, since present methods are inefficient. With this in mind, the present study seeks to analyze the spatio-temporal dynamics of A. aegypti infestation with oviposition traps, using efficient computational methods. These methods will allow for the implementation of the proposed model and methodology into surveillance and monitoring systems. METHODS: The study area includes a region in the municipality of Rio de Janeiro, characterized by high population density, precarious domicile construction, and a general lack of infrastructure around it. Two hundred and forty traps were distributed in eight different sentinel areas, in order to continually monitor immature Aedes aegypti and Aedes albopictus mosquitoes. Collections were done weekly between November 2010 and August 2012. The relationship between egg number and climate and environmental variables was considered and evaluated through Bayesian zero-inflated spatio-temporal models. Parametric inference was performed using the Integrated Nested Laplace Approximation (INLA) method. RESULTS: Infestation indexes indicated that ovipositing occurred during the entirety of the study period. The distance between each trap and the nearest boundary of the study area, minimum temperature and accumulated rainfall were all significantly related to the number of eggs present in the traps. Adjusting for the interaction between temperature and rainfall led to a more informative surveillance model, as such thresholds offer empirical information about the favorable climatic conditions for vector reproduction. Data were characterized by moderate time (0.29 - 0.43) and spatial (21.23 - 34.19 m) dependencies. The models also identified spatial patterns consistent with human population density in all sentinel areas. The results suggest the need for weekly surveillance in the study area, using traps allocated between 18 and 24 m, in order to understand the dengue vector dynamics. CONCLUSIONS: Aedes aegypti, due to it short generation time and strong response to climate triggers, tend to show an eruptive dynamics that is difficult to predict and understand through just temporal or spatial models. The proposed methodology allowed for the rapid and efficient implementation of spatio-temporal models that considered zero-inflation and the interaction between climate variables and patterns in oviposition, in such a way that the final model parameters contribute to the identification of priority areas for entomological surveillance.

Nutritional status of children under 5 years of age in the Brazilian Western Amazon before and after the Interoceanic highway paving: a population-based study.

BACKGROUND: The aim of this study was to analyse the prevalence of undernutrition, overweight and associated factors, before and after the implementation of the Interoceanic Highway. METHODS: A population-based cross-sectional study on children under 5 years of age was conducted in the municipality of Assis Brasil, AC, Brazil, in 2003 and 2010. Prevalence of undernutrition was observed by using height-for-age Z-scores (HAZ) and adopting a cut-off point equal to or lower than a -2 Z-score. Overweight prevalence was defined by a cut-off point equal to or greater than a +2 Z-score of the WHZ index. Z-scores were calculated relative to WHO 2006 reference data. Semi-structured questionnaires were applied to the children's guardians, investigating family socio-economic and demographic characteristics, morbidities, access to services and child care. Associated factors were identified by hierarchical multiple logistic regression analysis. RESULTS: The prevalence of low HAZ (undernutrition) was 7.0% in 2003 and 12.2% in 2010. The prevalence of high WHZ (overweight) was 1.0% and 6.6% for 2003 and 2010, respectively. It was not possible to adjust the multiple model for the year 2003. The factors associated with low HAZ in 2010 were: wealth index, the situation of living with biological parents, maternal height and presence of open sewage, whereas the factors associated with a high WHZ in the same year were: child's age, mother's time of residence in the location, mother's body mass index. CONCLUSIONS: Overweight increase within this undernutrition scenario reveals that the process of nutritional transition began in this Amazonian city only in the last decade, and therefore, it is delayed when compared to overweight in other parts of Brazil. Such nutritional transition in Assis Brasil may have been facilitated by the construction of the Interoceanic Highway.

Trajetorias: a dataset of environmental, epidemiological, and economic indicators for the Brazilian Amazon.

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.

Systematic review of Integrated Disease Surveillance and Response (IDSR) implementation in the African region.

BACKGROUND: 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. OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS AND IMPLICATIONS OF FINDINGS: 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.

Epidemiology, Biodiversity, and Technological Trajectories in the Brazilian Amazon: From Malaria to COVID-19.

The Amazon biome is under severe threat due to increasing deforestation rates and loss of biodiversity and ecosystem services while sustaining a high burden of neglected tropical diseases. Approximately two thirds of this biome are located within Brazilian territory. There, socio-economic and environmental landscape transformations are linked to the regional agrarian economy dynamics, which has developed into six techno-productive trajectories (TTs). These TTs are the product of the historical interaction between Peasant and Farmer and Rancher practices, technologies and rationalities. This article investigates the distribution of the dominant Brazilian Amazon TTs and their association with environmental degradation and vulnerability to neglected tropical diseases. The goal is to provide a framework for the joint debate of the local economic, environmental and health dimensions. We calculated the dominant TT for each municipality in 2017. Peasant trajectories (TT1, TT2, and TT3) are dominant in ca. fifty percent of the Amazon territory, mostly concentrated in areas covered by continuous forest where malaria is an important morbidity and mortality cause. Cattle raising trajectories are associated with higher deforestation rates. Meanwhile, Farmer and Rancher economies are becoming dominant trajectories, comprising large scale cattle and grain production. These trajectories are associated with rapid biodiversity loss and a high prevalence of neglected tropical diseases, such as leishmaniasis, Aedes-borne diseases and Chagas disease. Overall, these results defy simplistic views that the dominant development trajectory for the Amazon will optimize economic, health and environmental indicators. This approach lays the groundwork for a more integrated narrative consistent with the economic history of the Brazilian Amazon.

Dynamic modeling of vaccinating behavior as a function of individual beliefs.

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.

Predicting re-emergence times of dengue epidemics at low reproductive numbers: DENV1 in Rio de Janeiro, 1986-1990.

Predicting arbovirus re-emergence remains challenging in regions with limited off-season transmission and intermittent epidemics. Current mathematical models treat the depletion and replenishment of susceptible (non-immune) hosts as the principal drivers of re-emergence, based on established understanding of highly transmissible childhood diseases with frequent epidemics. We extend an analytical approach to determine the number of 'skip' years preceding re-emergence for diseases with continuous seasonal transmission, population growth and under-reporting. Re-emergence times are shown to be highly sensitive to small changes in low R0 (secondary cases produced from a primary infection in a fully susceptible population). We then fit a stochastic Susceptible-Infected-Recovered (SIR) model to observed case data for the emergence of dengue serotype DENV1 in Rio de Janeiro. This aggregated city-level model substantially over-estimates observed re-emergence times either in terms of skips or outbreak probability under forward simulation. The inability of susceptible depletion and replenishment to explain re-emergence under 'well-mixed' conditions at a city-wide scale demonstrates a key limitation of SIR aggregated models, including those applied to other arboviruses. The predictive uncertainty and high skip sensitivity to epidemiological parameters suggest a need to investigate the relevant spatial scales of susceptible depletion and the scaling of microscale transmission dynamics to formulate simpler models that apply at coarse resolutions.

Assessing the spread of COVID-19 in Brazil: Mobility, morbidity and social vulnerability.

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.

COVID-19 and hospitalizations for SARI in Brazil: a comparison up to the 12th epidemiological week of 2020. / COVID-19 e hospitalizações por SRAG no Brasil: uma comparação até a 12ª semana epidemiológica de 2020.

Surveillance of the severe acute respiratory illness (SARI) in Brazil aims to characterize the circulation of the Influenza A and B viruses in hospitalized cases and deaths, having been expanded in 2012 to include other respiratory viruses. COVID-19 was detected in Brazil for the time in the 9th epidemiological week of 2020, and the test for the SARS-CoV-2 virus was included in the surveillance protocol starting in the 12th epidemiological week. This study's objective was to investigate the pattern of hospitalizations for SARI in Brazil since the entry of SARS-CoV-2, comparing the temporal and age profiles and laboratory results to the years 2010 through 2019. In 2020, hospitalizations for SARI, compiled from the date of the first confirmed case of COVID-19 up to the 12th week, exceeded the numbers observed during the same period in each of the previous 10 years. The age bracket over 60 years was the most heavily affected, at higher than historical levels. There was a considerable increase in negative laboratory tests, suggesting circulation of a different virus from those already present in the panel. We concluded that the increase in hospitalizations for SARI, the lack of specific information on the etiological agent, and the predominance of cases among the elderly during the same period in which there was an increase in the number of new cases of COVID-19 are all consistent with the hypothesis that severe cases of COVID-19 are already being detected by SARI surveillance, placing an overload on the health system. The inclusion of testing for SARS-CoV-2 in the SARI surveillance protocol and the test's effective nationwide deployment are extremely important for monitoring the evolution of severe COVID-19 cases in Brazil.

A vigilância de síndrome respiratória aguda grave (SRAG) no Brasil visa a caracterizar a circulação dos vírus Influenza A e B em casos hospitalizados e óbitos, tendo sido ampliada em 2012 para incluir outros vírus respiratórios. A COVID-19 foi detectada no Brasil pela primeira vez na 9ª semana epidemiológica de 2020 e o teste para o vírus SARS-CoV-2 foi incluído no protocolo de vigilância a partir da 12ª semana epidemiológica. O objetivo deste estudo foi investigar o padrão de hospitalizações por SRAG no país após a entrada do SARS-CoV-2, comparando o perfil temporal, etário e de resultados laboratoriais com os anos de 2010 a 2019. Em 2020, a hospitalização por SRAG, contabilizada desde a data do primeiro caso de COVID-19 confirmado até a 12ª semana, superou o observado, no mesmo período, em cada um dos 10 anos anteriores. A faixa etária acima de 60 anos foi a mais acometida, em nível acima do histórico. Houve um aumento considerável de testes laboratoriais negativos, sugerindo a circulação de um vírus diferente dos presentes no painel. Concluímos que o aumento das hospitalizações por SRAG, a falta de informação específica sobre o agente etiológico e a predominância de casos entre idosos, no mesmo período de tempo em que cresce o número de casos novos de COVID-19, é coerente com a hipótese de que os casos graves da doença já estejam sendo detectados pela vigilância de SRAG com sobrecarga para o sistema de saúde. A inclusão da testagem para SARS-CoV-2 no protocolo de vigilância de SRAG e sua efetiva implementação são de grande importância para acompanhar a evolução dos casos graves da doença no país.

La vigilancia del síndrome respiratorio agudo grave (SRAG) en Brasil tiene como objetivo caracterizar la circulación de los virus de la Influenza A y B en casos y muertes hospitalizadas, y se expandió en 2012 para incluir otros virus respiratorios. La COVID-19 se detectó en Brasil por la primera vez en la 9ª semana epidemiológica de 2020, y el examen test para el virus SARS-CoV-2 se incluyó en el protocolo de vigilancia a partir de la 12ª semana epidemiológica. El objetivo de este estudio fue investigar el patrón de hospitalizaciones por SRAG en Brasil desde la entrada de SARS-CoV-2, comparando el perfil temporal y de edad y los resultados de laboratorio entre los años 2010 a 2019. En 2020, las hospitalizaciones por SRAG, compiladas a partir de la fecha del primer caso confirmado de COVID-19 hasta la 12ª semana, excedió los números observados durante el mismo período en cada uno de los 10 años anteriores. El grupo de edad mayor de 60 años fue el más afectado, a niveles superiores a los históricos. Hubo un aumento considerable en las pruebas de laboratorio negativas, lo que sugiere la circulación de un virus diferente de los que ya están presentes en el panel. Se concluye que el aumento de las hospitalizaciones por SRAG, la falta de información específica sobre el agente etiológico y el predominio de casos entre los ancianos en el mismo período en que hubo un aumento de casos nuevos de COVID-19 se entiende que con esta hipótesis de que los casos graves de COVID-19 ya estén siendo monitorados por la vigilancia de SRAG, lo que genera una sobrecarga en el sistema de salud. La inclusión de los exámenes para SARS-CoV-2 en el protocolo de vigilancia de SRAG y la eficacia de implementación son de grande importancia para monitorear la evolución de los casos graves de COVID-19 en Brasil.

Probability of dengue transmission and propagation in a non-endemic temperate area: conceptual model and decision risk levels for early alert, prevention and control.

BACKGROUND: Dengue viruses have spread rapidly across tropical regions of the world in recent decades. Today, dengue transmission is observed in the Americas, Southeast Asia, Western Pacific, Africa and in non-endemic areas of the USA and Europe. Dengue is responsible for 16% of travel-related febrile illnesses. Although most prevalent in tropical areas, risk maps indicate that subtropical regions are suitable for transmission. Dengue-control programs in these regions should focus on minimizing virus importation, community engagement, improved vector surveillance and control. RESULTS: We developed a conceptual model for the probability of local introduction and propagation of dengue, comprising disease vulnerability and receptivity, in a temperate area, considering risk factors and social media indicators. Using a rich data set from a temperate area in the south of Brazil (where there is active surveillance of mosquitoes, viruses and human cases), we used a conceptual model as a framework to build two probabilistic models to estimate the probability of initiation and propagation of local dengue transmission. The final models estimated with good accuracy the probabilities of local transmission and propagation, with three and four weeks in advance, respectively. Vulnerability indicators (number of imported cases and dengue virus circulation in mosquitoes) and a receptivity indicator (vector abundance) could be optimally integrated with tweets and temperature data to estimate probability of early local dengue transmission. CONCLUSIONS: We demonstrated how vulnerability and receptivity indicators can be integrated into probabilistic models to estimate initiation and propagation of dengue transmission. The models successfully estimate disease risk in different scenarios and periods of the year. We propose a decision model with three different risk levels to assist in the planning of prevention and control measures in temperate regions at risk of dengue introduction.

Estimating the effective reproduction number of dengue considering temperature-dependent generation intervals.

The effective reproduction number, Rt, is a measure of transmission that can be calculated from standard incidence data to timely detect the beginning of epidemics. It has being increasingly used for surveillance of directly transmitted diseases. However, current methods for Rt estimation do not apply for vector borne diseases, whose transmission cycle depends on temperature. Here we propose a method that provides dengue's Rt estimates in the presence of temperature-mediated seasonality and apply this method to simulated and real data from two cities in Brazil where dengue is endemic. The method shows good precision in the simulated data. When applied to the real data, it shows differences in the transmission profile of the two cities and identifies periods of higher transmission.

Vaccinating in disease-free regions: a vaccine model with application to yellow fever.

Concerns regarding natural or induced emergence of infectious diseases have raised a debate on the pros and cons of pre-emptive vaccination of populations under uncertain risk. In the absence of immediate risk, ethical issues arise because even smaller risks associated with the vaccine are greater than the immediate disease risk (which is zero). The model proposed here seeks to formalize the vaccination decision process looking from the perspective of the susceptible individual, and results are shown in the context of the emergence of urban yellow fever in Brazil. The model decomposes the individual's choice about vaccinating or not into uncertain components. The choice is modelled as a function of (i) the risk of a vaccine adverse event, (ii) the risk of an outbreak and (iii) the probability of receiving the vaccine or escaping serious disease given an outbreak. Additionally, we explore how this decision varies as a function of mass vaccination strategies of varying efficiency. If disease is considered possible but unlikely (risk of outbreak less than 0.1), delay vaccination is a good strategy if a reasonably efficient campaign is expected. The advantage of waiting increases as the rate of transmission is reduced (low R0) suggesting that vector control programmes and emergency vaccination preparedness work together to favour this strategy. The opposing strategy, vaccinating pre-emptively, is favoured if the probability of yellow fever urbanization is high or if expected R0 is high and emergency action is expected to be slow. In summary, our model highlights the nonlinear dependence of an individual's best strategy on the preparedness of a response to a yellow fever outbreak or other emergent infectious disease.