Spatial patterns of dengue cases in Brazil

A scaling investigation of urban form features in Latin America cities

This paper examines scaling behaviors of urban landscape and street design metrics with respect to city population in Latin America. We used data from the SALURBAL project, which has compiled and harmonized data on health, social, and built environment for 371 Latin American cities above 100,000 inhabitants. These metrics included total urbanized area, effective mesh size, area in km2 and number of streets. We obtained scaling relations by regressing log(metric) on log (city population). The results show an overall sub-linear scaling behavior of most variables, indicating a relatively lower value of each variable in larger cities. We also explored the potential influence of colonization on the current built environment, by analyzing cities colonized by Portuguese (Brazilian cities) or Spaniards (Other cities in Latin America) separately. We found that the scaling behaviors are similar for both sets of cities.

Google Trends correlation and sensitivity for outbreaks of dengue and yellow fever in the state of São Paulo

Objective

To assess Google Trends accuracy for epidemiological surveillance of dengue and yellow fever, and to compare the incidence of these diseases with the popularity of its terms in the state of São Paulo.

Methods

Retrospective cohort. Google Trends survey results were compared to the actual incidence of diseases, obtained from Centro de Vigilância Epidemiológica “Prof. Alexandre Vranjac”, in São Paulo, Brazil, in periods between 2017 and 2019. The correlation was calculated by Pearson’s coefficient and cross-correlation function. The accuracy was analyzed by sensitivity and specificity values.

Results

There was a statistically significant correlation between the variables studied for both diseases, Pearson coefficient of 0.91 for dengue and 0.86 for yellow fever. Correlation with up to 4 weeks of anticipation for time series was identified. Sensitivity was 87% and 90%, and specificity 69% and 78% for dengue and yellow fever, respectively.

Conclusion

The incidence of dengue and yellow fever in the State of São Paulo showed a strong correlation with the popularity of its terms measured by Google Trends in weekly periods. Google Trends tool provided early warning, with high sensitivity, for the detection of outbreaks of these diseases.

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.

Predicting dengue outbreaks at neighbourhood level using human mobility in urban areas

Dengue is a vector-borne disease transmitted by the Aedes genus mosquito. It causes financial burdens on public health systems and considerable morbidity and mortality. Tropical regions in the Americas and Asia are the areas most affected by the virus. Fortaleza is a city with approximately 2.6 million inhabitants in northeastern Brazil that, during the recent decades, has been suffering from endemic dengue transmission, interspersed with larger epidemics. The objective of this paper is to study the impact of human mobility in urban areas on the spread of the dengue virus, and to test whether human mobility data can be used to improve predictions of dengue virus transmission at the neighbourhood level. We present two distinct forecasting systems for dengue transmission in Fortaleza: the first using artificial neural network methods and the second developed using a mechanistic model of disease transmission. We then present enhanced versions of the two forecasting systems that incorporate bus transportation data cataloguing movement among 119 neighbourhoods in Fortaleza. Each forecasting system was used to perform retrospective forecasts for historical dengue outbreaks from 2007 to 2015. Results show that both artificial neural networks and mechanistic models can accurately forecast dengue cases, and that the inclusion of human mobility data substantially improves the performance of both forecasting systems. While the mechanistic models perform better in capturing seasons with large-scale outbreaks, the neural networks more accurately forecast outbreak peak timing, peak intensity and annual dengue time series. These results have two practical implications: they support the creation of public policies from the use of the models created here to combat the disease and help to understand the impact of urban mobility on the epidemic in large cities.

Spatial and temporal distribution of dengue in Brazil, 1990 - 2017

Dengue is a viral disease caused by an arbovirus of the genus Flavivirus transmitted in Brazil by the mosquito Aedes aegypti (Linnaeus, 1762). Simultaneous circulation of the four viral serotypes (DENV1, 2, 3 and 4) has been occurring since 2010 and determines a scenario of hyperendemicity of the disease in the country. This study aimed to describe the epidemiological situation of dengue in Brazil in the last three decades. This is a descriptive, observational study that used data of dengue notifications of the National Surveillance System from 1990 to 2017, available in the Epidemiological Bulletins and publications of the Ministry of Health. Dengue incidence increased in all Brazilian regions and the interepidemic periods are distinct in the different regions. The greatest epidemics was recorded in 2015 (1,688,688 cases), with an incidence of 826.0 cases per 100,000 inhabitants, which illustrates the occurrence of dengue in the last decade with increasingly higher epidemic peaks and shortening of the interepidemic periods. The incidence and mortality indices point to the need to improve the organization of response to dengue epidemics. This study provides information on the epidemiology of dengue in the country and can be used in the formulation of public health policies to reduce the impacts of viral transmission.

Spatio-temporal dynamics of dengue in Brazil: Seasonal travelling waves and determinants of regional synchrony

Dengue continues to be the most important vector-borne viral disease globally and in Brazil, where more than 1.4 million cases and over 500 deaths were reported in 2016. Mosquito control programmes and other interventions have not stopped the alarming trend of increasingly large epidemics in the past few years. Here, we analyzed monthly dengue cases reported in Brazil between 2001 and 2016 to better characterise the key drivers of dengue epidemics. Spatio-temporal analysis revealed recurring travelling waves of disease occurrence. Using wavelet methods, we characterised the average seasonal pattern of dengue in Brazil, which starts in the western states of Acre and Rondônia, then travels eastward to the coast before reaching the northeast of the country. Only two states in the north of Brazil (Roraima and Amapá) did not follow the countrywide pattern and had inconsistent timing of dengue epidemics throughout the study period. We also explored epidemic synchrony and timing of annual dengue cycles in Brazilian regions. Using gravity style models combined with climate factors, we showed that both human mobility and vector ecology contribute to spatial patterns of dengue occurrence. This study offers a characterization of the spatial dynamics of dengue in Brazil and its drivers, which could inform intervention strategies against dengue and other arboviruses.

Limiting global-mean temperature increase to 1.5-2 °C could reduce the incidence and spatial spread of dengue fever in Latin America

This study is a multigeneral circulation model, multiscenario modeling exercise developed to quantify the dengue-related health benefits of limiting global warming to 1.5–2.0 °C above preindustrial levels in Latin America and the Caribbean. We estimate the impact of future climate change and population growth on the additional number of dengue cases and provide insights about the regions and periods most likely affected by changes in the length of the transmission season. Here, we show that future climate change may amplify dengue transmission and that significant impacts could be avoided by constraining global warming to 1.5 °C above preindustrial levels. Our work could be a starting point for future risk assessments incorporating other important drivers of disease such as urbanization and international traveling.

The Paris Climate Agreement aims to hold global-mean temperature well below 2 °C and to pursue efforts to limit it to 1.5 °C above preindustrial levels. While it is recognized that there are benefits for human health in limiting global warming to 1.5 °C, the magnitude with which those societal benefits will be accrued remains unquantified. Crucial to public health preparedness and response is the understanding and quantification of such impacts at different levels of warming. Using dengue in Latin America as a study case, a climate-driven dengue generalized additive mixed model was developed to predict global warming impacts using five different global circulation models, all scaled to represent multiple global-mean temperature assumptions. We show that policies to limit global warming to 2 °C could reduce dengue cases by about 2.8 (0.8–7.4) million cases per year by the end of the century compared with a no-policy scenario that warms by 3.7 °C. Limiting warming further to 1.5 °C produces an additional drop in cases of about 0.5 (0.2–1.1) million per year. Furthermore, we found that by limiting global warming we can limit the expansion of the disease toward areas where incidence is currently low. We anticipate our study to be a starting point for more comprehensive studies incorporating socioeconomic scenarios and how they may further impact dengue incidence. Our results demonstrate that although future climate change may amplify dengue transmission in the region, impacts may be avoided by constraining the level of warming.