23 research outputs found
Interdependence between confirmed and discarded cases of dengue, chikungunya and Zika viruses in Brazil: A multivariate time-series analysis.
The co-circulation of different arboviruses in the same time and space poses a significant threat to public health given their rapid geographic dispersion and serious health, social, and economic impact. Therefore, it is crucial to have high quality of case registration to estimate the real impact of each arboviruses in the population. In this work, a Vector Autoregressive (VAR) model was developed to investigate the interrelationships between discarded and confirmed cases of dengue, chikungunya, and Zika in Brazil. We used data from the Brazilian National Notifiable Diseases Information System (SINAN) from 2010 to 2017. There were three peaks in the series of dengue notification in this period occurring in 2013, 2015 and in 2016. The series of reported cases of both Zika and chikungunya reached their peak in late 2015 and early 2016. The VAR model shows that the Zika series have a significant impact on the dengue series and vice versa, suggesting that several discarded and confirmed cases of dengue could actually have been cases of Zika. The model also suggests that the series of confirmed and discarded chikungunya cases are almost independent of the cases of Zika, however, affecting the series of dengue. In conclusion, co-circulation of arboviruses with similar symptoms could have lead to misdiagnosed diseases in the surveillance system. We argue that the routinely use of mathematical and statistical models in association with traditional symptom-surveillance could help to decrease such errors and to provide early indication of possible future outbreaks. These findings address the challenges regarding notification biases and shed new light on how to handle reported cases based only in clinical-epidemiological criteria when multiples arboviruses co-circulate in the same population
Network analysis of spreading of dengue, Zika and chikungunya in the state of Bahia based on notified, confirmed and discarded cases
Despite successful results of using complex networks to model and characterize the spread of dengue cases, works to date have mainly used data from primarily reported cases, without further consideration whether they were later confirmed or not. On the other hand, a study of the interdependence of confirmed and discarded cases of arboviruses have emphasized that the co-circulation of three arboviruses—dengue, Zika and chikungunya—may have led to false diagnoses due to several similarities in the early symptoms of the three diseases on acute phase. This implies that case notifications of one disease could be confirmed cases of others, and that discarded cases must be taken into account to avoid misinterpretations of the phenomenon. In this work we investigated the consequences of including information from discarded and confirmed cases in the analysis of arbovirus networks. This is done by firstly evaluating the possible changes in the networks after removing the discarded cases from the database of each arbovirus, and secondly by verifying the cross-relationship of the indices of the networks of confirmed and discarded cases of arboviruses. As will be detailed later on, our results reveal changes in the network indices when compared to when only confirmed cases are considered. The magnitudes of the changes are directly proportional to the amount of discarded cases. The results also reveal a strong correlation between the average degree of the networks of discarded cases of dengue and confirmed cases of Zika, but only a moderate correlation between that for networks of discarded cases of dengue and confirmed cases of chikungunya. This finding is compatible with the fact that dengue and Zika diseases are caused by closely related flaviviruses, what is not the case of the chikungunya caused by a togavirus
Complex network analysis of arboviruses in the same geographic domain: Differences and similarities.
Arbovirus can cause diseases with a broad spectrum from mild to severe and long-lasting symptoms, affecting humans worldwide and therefore considered a public health problem with global and diverse socio-economic impacts. Understanding how they spread within and across different regions is necessary to devise strategies to control and prevent new outbreaks. Complex network approaches have widespread use to get important insights on several phenomena, as the spread of these viruses within a given region. This work uses the motif-synchronization methodology to build time varying complex networks based on data of registered infections caused by Zika, chikungunya, and dengue virus from 2014 to 2020, in 417 cities of the state of Bahia, Brazil. The resulting network sets capture new information on the spread of the diseases that are related to the time delay in the synchronization of the time series among different municipalities. Thus the work adds new and important network-based insights to previous results based on dengue dataset in the period 2001-2016. The most frequent synchronization delay time between time series in different cities, which control the insertion of edges in the networks, ranges 7 to 14 days, a period that is compatible with the time of the individual-mosquito-individual transmission cycle of these diseases. As the used data covers the initial periods of the first Zika and chikungunya outbreaks, our analyses reveal an increasing monotonic dependence between distance among cities and the time delay for synchronization between the corresponding time series. The same behavior was not observed for dengue, first reported in the region back in 1986, either in the previously 2001-2016 based results or in the current work. These results show that, as the number of outbreaks accumulates, different strategies must be adopted to combat the dissemination of arbovirus infections
Zonal Classification by Pattern Recognition Methods: An Example from Asmari Formation (Mansuri Oil Field, South of Iran)
Tengiz Field (Republic of Kazakhstan) Unit 1 Platform Static Model: Using a Hybrid Depositional — Diagenetic Approach
Traditional rock typing combined with an inferred depositional linkage for variogram-based simulation is a standard approach in carbonates. In the Tengiz Unit 1 platform, reservoir properties of carbonates as defined by Petrophysical Rock Types (PRTs) are the product of primary depositional facies and diagenetic modification that have separate spatial trends and interactions. Careful, multidisciplinary and targeted analysis is required to unravel such trends from the usually complex hard data sets, but is critical as an understanding of the trends forms the basis for reservoir modeling of the Tengiz Unit 1 platform. Depositional cycles in the Unit 1 Tengiz platform (Late Visean to Bashkirian) are made up of a succession of generally shoaling lithofacies overlying a sharp base with evidence for subaerial exposure and/or flooding. Systematic study of the diagenetic products of several sequences across the platform using petrography, stable isotopes and CL revealed that the diagenetic modification includes early meteoric dissolution and subsequent cementation, late burial dissolution and late burial bitumen cementation.. PRTs are designed to include spatial attributes of the combined stratigraphic, facies and diagenetic framework form the basis for the Multiple Point Statistics and Facies Distribution Modeling (MPS/FDM) simulation of the SIM08T Unit 1 static platform reservoir model. Of the six PRTs, one is linked to volcanic ash (PRT 1), one associated with bitumen (PRT 2) and four with increasing porosity (PRTs 3-6) where PRT 3 tight and PRTs 4-6 represent increasing reservoir quality with PRT 6 the highest quality. PRT maps and a vertical proportion curve were used to generate the facies probability cube and convolved with training images, specifying the spatial interrelationship, to generate a PRT realization. The revised sequence stratigraphic framework and integration of novel concepts in modeling the diagenetic overprint addressed the need for a refined understanding of the platform in preparation for the FGP miscible gas injection project. In addition, the extensive use of MPS/FDM modeling approaches in Unit 1 has resulted in a more realistic integration of both depositional and diagenetic trends in the Unit 1 platform
Spatial analysis of Dengue through the reproduction numbers relating to socioeconomic features: Case studies on two Brazilian urban centers
The study of the propagation of infectious diseases in urban centers finds a close connection with their population's social characteristics and behavior. This work performs a spatial analysis of dengue cases in urban centers based on the basic reproduction numbers, R0, and incidence by planning areas (PAs), as well as their correlations with the Human Development Index (HDI) and the number of trips. We analyzed dengue epidemics in 2002 at two Brazilian urban centers, Belo Horizonte (BH) and Rio de Janeiro (RJ), using PAs as spatial units. Our results reveal heterogeneous spatial scenarios for both cities, with very weak correlations between R0 and both the number of trips and the HDI; in BH, the values of R0 show a less spatial heterogeneous pattern than in RJ. For BH, there are moderate correlations between incidence and both the number of trips and the HDI; meanwhile, they weakly correlate for RJ. Finally, the absence of strong correlations between the considered measures indicates that the transmission process should be treated considering the city as a whole
Tengiz Field (Republic of Kazakhstan) Unit 1 Platform Static Model: Using a Hybrid Depositional — Diagenetic Approach
Traditional rock typing combined with an inferred depositional linkage for variogram-based simulation is a standard approach in carbonates. In the Tengiz Unit 1 platform, reservoir properties of carbonates as defined by Petrophysical Rock Types (PRTs) are the product of primary depositional facies and diagenetic modification that have separate spatial trends and interactions. Careful, multidisciplinary and targeted analysis is required to unravel such trends from the usually complex hard data sets, but is critical as an understanding of the trends forms the basis for reservoir modeling of the Tengiz Unit 1 platform. Depositional cycles in the Unit 1 Tengiz platform (Late Visean to Bashkirian) are made up of a succession of generally shoaling lithofacies overlying a sharp base with evidence for subaerial exposure and/or flooding. Systematic study of the diagenetic products of several sequences across the platform using petrography, stable isotopes and CL revealed that the diagenetic modification includes early meteoric dissolution and subsequent cementation, late burial dissolution and late burial bitumen cementation.. PRTs are designed to include spatial attributes of the combined stratigraphic, facies and diagenetic framework form the basis for the Multiple Point Statistics and Facies Distribution Modeling (MPS/FDM) simulation of the SIM08T Unit 1 static platform reservoir model. Of the six PRTs, one is linked to volcanic ash (PRT 1), one associated with bitumen (PRT 2) and four with increasing porosity (PRTs 3-6) where PRT 3 tight and PRTs 4-6 represent increasing reservoir quality with PRT 6 the highest quality. PRT maps and a vertical proportion curve were used to generate the facies probability cube and convolved with training images, specifying the spatial interrelationship, to generate a PRT realization. The revised sequence stratigraphic framework and integration of novel concepts in modeling the diagenetic overprint addressed the need for a refined understanding of the platform in preparation for the FGP miscible gas injection project. In addition, the extensive use of MPS/FDM modeling approaches in Unit 1 has resulted in a more realistic integration of both depositional and diagenetic trends in the Unit 1 platform
Risk Factors for Death from Pandemic (H1N1) 2009, Southern Brazil
To identify risk factors for death from pandemic (H1N1) 2009, we obtained data for 157 hospitalized patients with confirmed cases of this disease. Multivariate analysis showed that diabetes and class III obesity were associated with death. These findings helped define priority vaccination groups in Brazil