Dengue in Brazil in 2017: what happened?

Lopes, Thaísa Regina Rocha; Silva, Caroline Simões; Pastor, André Filipe; Júnior, José Valter Joaquim Silva

doi:10.1590/s1678-9946201860043

Cited by 27 publications

(28 citation statements)

References 15 publications

(15 reference statements)

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“…We found that these susceptibility cycles were critical to the performance of models, particularly those which identified weather patterns suitable for a dengue outbreak in a year with potentially low population susceptibility to infection. For instance, this approach correctly identified 3 additional non-epidemics in 2017 compared to weather patterns alone, supporting the discourse on the unusually low dengue activity seen in Brazil in 2017 34 . Still, our models missed half (6/12) of non-epidemics in 2014, which was predicted by experts to be a low transmission year due to immunity provided by a large 2013 outbreak with no changes in circulating DENV serotypes 35,36 .…”

Section: Discussionsupporting

confidence: 55%

Combining weather patterns and cycles of population susceptibility to forecast dengue fever epidemic years in Brazil: a dynamic, ensemble learning approach

et al. 2019

Preprint

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Transmission of dengue fever depends on a complex interplay of human, climate, and mosquito dynamics, which often change in time and space. It is well known that disease dynamics are highly influenced by a population's susceptibility to infection and microclimates, small-area climatic conditions which create environments favorable for the breeding and survival of the mosquito vector. Here, we present a novel machine learning dengue forecasting approach, which, dynamically in time and adaptively in space, identifies local patterns in weather and population susceptibility to make epidemic predictions at the city-level in Brazil, months ahead of the occurrence of disease outbreaks. Weather-based predictions are improved when information on population susceptibility is incorporated, indicating that immunity is an important predictor neglected by most dengue forecast models. Given the generalizability of our methodology, it may prove valuable for public-health decision making aimed at mitigating the effects of seasonal dengue outbreaks in locations globally.

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Section: Discussionsupporting

confidence: 55%

Combining weather patterns and cycles of population susceptibility to forecast dengue fever epidemic years in Brazil: a dynamic, ensemble learning approach

et al. 2019

Preprint

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“…Although preventive measures sponsored by the public health authorities have been suggested as the cause of the decline in dengue incidence (Santiago, 2016), it is not clear why such supposedly measures did not protect against the huge Zika virus outbreak that occurred during the same year and whose etiologic agent is transmitted by the same vector, likewise why the peak in the Zika virus epidemic took place in 2016 despite the highly positive coefficient value in wavelet transformation. In any case, the causes of this dengue decline are still not fully understood (Lopes et al, 2018). The temporal coincidence between the two viruses might have cause some sort of competition that would have caused only one of the two to peak at a time.…”

Section: Temporal Analysismentioning

confidence: 99%

“…For instance, we do not know if had the Zika virus arrived earlier its peak might had been even stronger due to even more favorable El Niño conditions and it might have coincided temporally with that of dengue or conversely, if one of the two would have just inhibited the other. In any case, the causes of this dengue decline are still not fully understood (Lopes et al, 2018).…”

Section: Temporal Analysismentioning

confidence: 99%

Spatial‐Temporal Assessment of Environmental Factors Related to Dengue Outbreaks in São Paulo, Brazil

Ogashawara

Moreno-Madriñán

2019

GeoHealth

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Dengue fever, a disease caused by a vector‐borne flavivirus, is endemic to tropical countries, but its occurrence has been reported worldwide. This study aimed to understand important factors contributing to the spatial and temporal patterns of dengue occurrence in São Paulo, the largest municipality of Brazil. The temporal assessment of dengue occurrence covered the 2011–2016 time period and was based on climatological data, such as the El Niño indices and time series statistical tools such as the continuous wavelet transformation. The spatial assessment used Landsat 8 data for years 2014–2016 to estimate land surface temperature and normalized indices for vegetation, urban areas, and leaf water. Results from a cross correlation for the temporal analysis found a relationship between the sea surface temperature anomalies index and the number of reported dengue cases in São Paulo (r = 0.5) with a lag of +29 (weeks) between the climatic event and the response on the dengue incidence. This relationship, initially nonlinear, became linear after correcting for the lag period. For the spatial assessment, the linear stepwise regression model detected a low relationship between dengue incidence and minimum surface temperature (r = 0.357) and no relationship with other environmental parameters. The poor relationship might be due to confounding effects of socioeconomic factors as these seem to influence the spatial dynamics of dengue incidence. More testing is needed to validate these methods in other locations. Nevertheless, we presented possible tools to be used for the improvement of dengue control programs.

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“…losses, and led the authorities to formulate strategies to attempt to increase vector control [1]. In 2015 and 2016, an annual average of 1,586,155 probable cases of dengue were recorded in Brazil; although a reduction to 252,054 cases was observed in 2017 [2], which could be the result of the cross-herd immunity to Zika virus [3]. The differential diagnosis of these diseases is difficult because of the similarity of symptoms, cross-reactivity, co-circulation and overlap of infections by different arbovirus species and strains [4,5] Arboviral transmission has multiple causes and contributory contextual factors, impacted by the influence of social, environmental and cultural determinants in the course of the natural history of disease [4].…”

mentioning

confidence: 99%

“…Epidemiological studies have clearly associated the expansion of these diseases with haphazard and unplanned urban development processes, mainly caused by the lack of infrastructure and basic sanitation in areas that have been occupied without prior planning. In northeast Brazil, the population has been disproportionately affected by arboviruses, with 94% of all cases of congenital Zika syndrome being reported in this region [2,6,7]. Poor access to garbage collection and an intermittent water supply provide conditions for vector breeding, which in turn makes vector control more difficult in this region [3].…”

mentioning

confidence: 99%

Spatial modelling of the infestation indices of Aedes aegypti: an innovative strategy for vector control actions in developing countries

et al. 2020

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Background: Larval indices such as the house index (HI), Breteau index (BI) and container index (CI) are widely used to interpret arbovirus vector density in surveillance programmes. However, the use of such data as an alarm signal is rarely considered consciously when planning programmes. The present study aims to investigate the spatial distribution pattern of the infestation of Aedes aegypti, considering the data available in the Ae. aegypti Infestation Index Rapid Survey (LIRAa) for the city of Campina Grande, Paraíba State in Brazil. Methods:The global and local Moranʼs indices were used in spatial analysis to measure the effects of spatial dependencies between neighbourhoods, using secondary data related to HI and BI gathered from surveillance service.Results: Our analysis shows that there is a predominance of high rates of mosquito infestation, placing Campina Grande at a near-constant risk of arbovirus outbreaks and epidemics. A highly significant Moranʼs index value (P < 0.001) was observed, indicating a positive spatial dependency between the neighbourhoods in Campina Grande. Using the Moran mapping and LISA mapping, the autocorrelation patterns of Ae. aegypti infestation rates among neighbourhoods have revealed hotpots that should be considered a priority to preventive actions of the entomological surveillance services. Predominance of high infestation rates and clearer relationships of these between neighbourhoods were observed between the months of May and July, the period with the highest rainfall in the city. Conclusions:This analysis is an innovative strategy capable of providing detailed information on infestation locations to the relevant public health authorities, which will enable a more efficient allocation of resources, particularly for arbovirus prevention.

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Dengue in Brazil in 2017: what happened?

Cited by 27 publications

References 15 publications

Combining weather patterns and cycles of population susceptibility to forecast dengue fever epidemic years in Brazil: a dynamic, ensemble learning approach

Combining weather patterns and cycles of population susceptibility to forecast dengue fever epidemic years in Brazil: a dynamic, ensemble learning approach

Spatial‐Temporal Assessment of Environmental Factors Related to Dengue Outbreaks in São Paulo, Brazil

Spatial modelling of the infestation indices of Aedes aegypti: an innovative strategy for vector control actions in developing countries

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