Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil.

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010-2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the "normalized burn ratio," experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of "adaptive models" rather than "one-size-fits-all" models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.

Inferring high-resolution human mixing patterns for disease modeling.

Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.

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.

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.

Model-based Respondent-driven sampling analysis for HIV prevalence in brazilian MSM.

Respondent Driven Sampling study (RDS) is a population sampling method developed to study hard-to-reach populations. A sample is obtained by chain-referral recruitment in a network of contacts within the population of interest. Such self-selected samples are not representative of the target population and require weighing observations to reduce estimation bias. Recently, the Network Model-Assisted (NMA) method was described to compute the required weights. The NMA method relies on modeling the underlying contact network in the population where the RDS was conducted, in agreement with directly observable characteristics of the sample such as the number of contacts, but also with more difficult-to-measure characteristics such as homophily or differential characteristics according to the response variable. Here we investigated the use of the NMA method to estimate HIV prevalence from RDS data when information on homophily is limited. We show that an iterative procedure based on the NMA approach allows unbiased estimations even in the case of strong population homophily and differential activity and limits bias in case of preferential recruitment. We applied the methods to determine HIV prevalence in men having sex with men in Brazilian cities and confirmed a high prevalence of HIV in these populations from 3.8% to 22.1%.

COVID-19 e hospitalizações por SRAG no Brasil: uma comparação até a 12ª semana epidemiológica de 2020 / COVID-19 y las hospitalizaciones por el SRAG en Brasil: una comparación hasta la 12ª semana epidemiológica de 2020 / COVID-19 and hospitalizations for SARI in Brazil: a comparison up to the 12th epidemiological week of 2020

Resumo: 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.

Resumen: 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.

Abstract: 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 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.

Spatiotemporal spread of the 2014 outbreak of Ebola virus disease in Liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis.

BACKGROUND: The 2014 epidemic of Ebola virus disease in parts of west Africa defines an unprecedented health threat. We developed a model of Ebola virus transmission that integrates detailed geographical and demographic data from Liberia to overcome the limitations of non-spatial approaches in projecting the disease dynamics and assessing non-pharmaceutical control interventions. METHODS: We modelled the movements of individuals, including patients not infected with Ebola virus, seeking assistance in health-care facilities, the movements of individuals taking care of patients infected with Ebola virus not admitted to hospital, and the attendance of funerals. Individuals were grouped into randomly assigned households (size based on Demographic Health Survey data) that were geographically placed to match population density estimates on a grid of 3157 cells covering the country. The spatial agent-based model was calibrated with a Markov chain Monte Carlo approach. The model was used to estimate Ebola virus transmission parameters and investigate the effectiveness of interventions such as availability of Ebola treatment units, safe burials procedures, and household protection kits. FINDINGS: Up to Aug 16, 2014, we estimated that 38·3% of infections (95% CI 17·4-76·4) were acquired in hospitals, 30·7% (14·1-46·4) in households, and 8·6% (3·2-11·8) while participating in funerals. We noted that the movement and mixing, in hospitals at the early stage of the epidemic, of patients infected with Ebola virus and those not infected was a sufficient driver of the reported pattern of spatial spread. The subsequent decrease of incidence at country and county level is attributable to the increasing availability of Ebola treatment units (which in turn contributed to drastically decreased hospital transmission), safe burials, and distribution of household protection kits. INTERPRETATION: The model allows assessment of intervention options and the understanding of their role in the decrease in incidence reported since Sept 7, 2014. High-quality data (eg, to estimate household secondary attack rate, contact patterns within hospitals, and effects of ongoing interventions) are needed to reduce uncertainty in model estimates. FUNDING: US Defense Threat Reduction Agency, US National Institutes of Health.

Assessing the international spreading risk associated with the 2014 west african ebola outbreak.

BACKGROUND: The 2014 West African Ebola Outbreak is so far the largest and deadliest recorded in history. The affected countries, Sierra Leone, Guinea, Liberia, and Nigeria, have been struggling to contain and to mitigate the outbreak. The ongoing rise in confirmed and suspected cases, 2615 as of 20 August 2014, is considered to increase the risk of international dissemination, especially because the epidemic is now affecting cities with major commercial airports. METHOD: We use the Global Epidemic and Mobility Model to generate stochastic, individual based simulations of epidemic spread worldwide, yielding, among other measures, the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. The mobility model integrates daily airline passenger traffic worldwide and the disease model includes the community, hospital, and burial transmission dynamic. We use a multimodel inference approach calibrated on data from 6 July to the date of 9 August 2014. The estimates obtained were used to generate a 3-month ensemble forecast that provides quantitative estimates of the local transmission of Ebola virus disease in West Africa and the probability of international spread if the containment measures are not successful at curtailing the outbreak. RESULTS: We model the short-term growth rate of the disease in the affected West African countries and estimate the basic reproductive number to be in the range 1.5 - 2.0 (interval at the 1/10 relative likelihood). We simulated the international spreading of the outbreak and provide the estimate for the probability of Ebola virus disease case importation in countries across the world. RESULTS indicate that the short-term (3 and 6 weeks) probability of international spread outside the African region is small, but not negligible. The extension of the outbreak is more likely occurring in African countries, increasing the risk of international dissemination on a longer time scale.