scholarly journals Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data

2019 ◽  
Author(s):  
Ari Whiteman ◽  
Michael R. Desjardins ◽  
Gilberto A. Eskildsen ◽  
Jose R. Loaiza
Keyword(s):  
Dengue Fever ◽  
Risk Estimation ◽  
Dengue Infection ◽  
Space Time ◽  
Surveillance Data ◽  
Dengue Case ◽  
Vector Species ◽  
Vector Surveillance ◽  
Disease Case ◽  
Presence And Absence

AbstractLong term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data, which may result in inaccurate risk projection when several vector species are present, and little is known about their likely role in local transmission. Here, we integrate 13 years of dengue case surveillance and associatedAedesoccurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama. Our space-time modelling approach detected the presence of five clusters, which varied by duration, relative risk, and spatial extent after incorporating vector species as covariates. Dengue prevalence (n = 49,910) was predicted by the presence of residentAedes aegyptialone, while all other covariates exhibited insignificant statistical relationships with it, including the presence and absence of invasiveAedes albopictus. Furthermore, theAe. aegyptimodel contained the highest number of districts with more dengue cases than would be expected given baseline population levels. This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation, improving efforts to understand outbreak dynamics at national scales.Author SummaryDengue cases have increased in tropical regions worldwide owing to climate change, urbanization, and globalization facilitating the spread ofAedesmosquito vectors. National surveillance programs monitor trends in dengue fever and inform the public about epidemiological scenarios where outbreak preventive actions are most needed. Yet, most estimations of dengue risk so far derive only from disease case data, ignoringAedesoccurrence as a key aspect of dengue transmission dynamic. Here we illustrate how incorporating vector presence and absence as a model covariate can considerably alter the characteristics of space-time cluster estimations of dengue cases. We further show thatAe. aegyptihas likely been a greater driver of dengue infection in high risk districts of Panama thanAe. albopictus, and provide a discussion of possible public health implications of both spatial and non-spatial model outcomes.

scholarly journals Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data

2019 ◽  
Vol 13 (9) ◽  
pp. e0007266 ◽  
Author(s):  
Ari Whiteman ◽  
Michael R. Desjardins ◽  
Gilberto A. Eskildsen ◽  
Jose R. Loaiza
Keyword(s):  
Dengue Fever ◽  
Space Time ◽  
Surveillance Data ◽  
Vector Surveillance

scholarly journals Space-time conditional autoregressive modeling to estimate neighborhood-level risks for dengue fever in Cali, Colombia

2020 ◽  
Author(s):  
M.R. Desjardins ◽  
M.D. Eastin ◽  
R. Paul ◽  
I. Casas ◽  
E.M. Delmelle
Keyword(s):  
Risk Factors ◽  
Dengue Fever ◽  
Local Level ◽  
Active Role ◽  
Space Time ◽  
Vector Surveillance ◽  
Vector Borne Diseases ◽  
Monte Carlo Techniques ◽  
Conditional Autoregressive ◽  
The City

AbstractVector-borne diseases (VBDs) affect more than 1 billion people a year worldwide, cause over 1 million deaths, and cost hundreds of billions of dollars in societal costs. Mosquitoes are the most common vectors, responsible for transmitting a variety of arboviruses. Dengue fever (DENF) has been responsible for nearly 400 million infections annually. Dengue fever is primarily transmitted by female Aedes aegypti and Aedes albopictus mosquitoes. Since both Aedes species are peri-domestic and container-breeding mosquitoes, dengue surveillance should begin at the local level - where a variety of local factors may increase the risk of transmission. Dengue has been endemic in Colombia for decades and is notably hyperendemic in the city of Cali. For this study, we use weekly cases of DENF in Cali, Colombia from 2015-2016; and develop space-time conditional autoregressive models to quantify how DENF risk is influenced by socioeconomic, environmental, and accessibility risk factors, and lagged weather variables. Our models identify high-risk neighborhoods for DENF throughout Cali. Statistical inference is drawn under Bayesian paradigm using Markov Chain Monte Carlo techniques. The results provide detailed insight about the spatial heterogeneity of DENF risk and the associated risk factors (such as weather, proximity to Aedes habitats, and socioeconomic classification) at a fine-level, informing public health officials to motivate at-risk neighborhoods to take an active role in vector surveillance and control, and improving educational and surveillance resources throughout the city of Cali.

scholarly journals Autochthonous dengue fever in Croatia, August–September 2010

2011 ◽  
Vol 16 (9) ◽  
Author(s):  
I Gjenero-Margan ◽  
B Aleraj ◽  
D Krajcar ◽  
V Lesnikar ◽  
A Klobučar ◽  
...  
Keyword(s):  
Dengue Virus ◽  
Dengue Fever ◽  
Health Professionals ◽  
Mosquito Control ◽  
Dengue Infection ◽  
Control Measures ◽  
Dengue Case ◽  
The Public

After information about a dengue case in Germany acquired in Croatia, health professionals and the public in Croatia were alerted to assess the situation and to enhance mosquito control, resulting in the diagnosis of a second case of autochthonous dengue fever in the same area and the detection of 15 persons with evidence of recent dengue infection. Mosquito control measures were introduced. The circumstances of dengue virus introduction to Croatia remain unresolved.

scholarly journals Knowledge Level of Community Participant on Dengue Fever Symptoms and Early Treatment in Bandung City

2019 ◽  
pp. 75-82
Author(s):  
Lia Faridah ◽  
Nisa Fauziah ◽  
Savira Ekawardhani
Keyword(s):  
Dengue Fever ◽  
Dengue Infection ◽  
High Index ◽  
Dengue Case ◽  
Care Community ◽  
Community Participant ◽  
Index Study ◽  
Level Of Knowledge ◽  
High Level ◽  
Study Participants

In tropical countries, dengue fever is often confused with other common tropical infections. There are no specific therapeutic treatment for dengue infections, and the key of successful dengue case management are a timely and judicious supportive care. Community knowledge about dengue fever and treatment at home, particularly for children is crucial to reduce the burden of dengue infection.  Unfortunately, studies on community's knowledge of dengue fever are still very limited. The aims of this study are to measure people's knowledge about dengue fever and to determine the main predictors of a high index on dengue knowledge, in Bandung City. Data collection was carried out by interviewing respondents from each household. Multivariate analysis with logistic regression was used to determine the odds-ratio demographic factors that reached a high index. Study participants generally showed medium-to-high level of knowledge regarding Dengue fever symptoms and its first aid. This group accounts for more than 70% of all respondents This study also showed that the likeliness of having high-score of knowledge was correlated with being a female, having at least an undergraduate level of education, and being an entrepreneur

scholarly journals Hubungan Perilaku Pemberantasan Sarang Nyamuk dan Kejadian Demam Berdarah Dengue di Kota Bandar Lampung

2007 ◽  
Vol 2 (2) ◽  
pp. 86
Author(s):  
Amrul Hasan ◽  
Dian Ayubi
Keyword(s):  
Aedes Aegypti ◽  
Incidence Rate ◽  
Dengue Fever ◽  
Dengue Infection ◽  
Community Health Center ◽  
Public Health Problem ◽  
Hemorrhagic Fever ◽  
Dengue Case ◽  
Important Public Health Problem ◽  
Center Staff

Kasus demam berdarah dengue di Kota Bandar Lampung terus mengalami peningkatan. Pada tahun 2001 incidence rate sebesar 13,56 per 100.000 penduduk, meningkat menjadi 109,8/100.000 penduduk pada tahun 2006 dan akhir Februari 2007 Kota Bandar Lampung dinyatakan Kejadian Luar Biasa (KLB) Demam berdarah dengue lokal. Penelitian ini bertujuan mengetahui hubungan kebiasaan melakukan pemberantasan sarang nyamuk (PSN) dengan kejadian demam berdarah dengue di Kota Bandar Lampung. Penelitian ini menggunakan desain kasus kontrol dengan jumlah sampel sebanyak 406 individu terdiri dari 203 kasus dan 203 kontrol. Kasus adalah individu yang menderita DBD yang pernah dirawat di rumah sakit dan dilaporkan ke Dinas Kesehatan Kota Bandar Lampung dari tanggal 1 Maret 2007 sampai 15 Mei 2007, sedangkan kontrol dipilih dari tetangga kasus yang bertempat tinggal dalam radius 100 meter dari tempat tinggal kasus. Penelitian ini menemukan bahwa ada hubungan kebiasaan melakukan PSN dengan kejadian demam berdarah dengue, individu yang tidak melakukan PSN berisiko 5,85 kali terkena DBD dibandingkan dengan individu yang melakukan PSN setelah variabel riwayat tetangga yang pernah sakit DBD, keberadaan benda yang dapat penampung air di sekitar rumah dan kebiasaan melakukan pencegahan gigitan nyamuk dikendalikan. Petugas puskesmas agar melaksanakan kegiatan Penyelidikan Epidemiologi dalam menanggulangi demam berdarah lebih memfokuskan kepada penggerakan masyarakat.Kata kunci : DBD, Aedes aegypti, Pencegahan gigitan nyamukAbstractDengue hemorrhagic fever poses as the most important public health problem in Bandar Lampung today. Increasing number of cases has been occurred from 2001 to 2006, when in 2001 incidence rate was 13.56/100.000 and became 109.8/100.00 at 2006 and at the end of February 2007 it was stated that Bandar Lampung experienced local outbreak dengue hemorrhagic fever. A case-control study was conducted to explore the correlation of suspected risk factors with dengue infection in Bandar Lampung from 20 April to 30 May 2007. 230 cases and 230 controls were included for statistical analysis. After further adjusting of confounders, there are strong correlation between habitual elimination of mosquito breeding sites and use of personal protective (e.g. the use repellent, mosquito coil and use insecticide hand sprayer) with dengue case. Individual has one PSN estimated to be 2,22 (95% CI : 1,32-3,72) times as great for individual has 3 PSN and individual did not PSN estimates to be 5,85 (95% CI : 2,86 - 11,99) times as great has dengue fever for individual has 3 PSN after controlled by history neighborhood DHF, water container around house, use of mosquito prevention agent. Community health center staff should conduct epidemiology investigation to eradicate dengue fever by focusing on community empowerment.Keywords: Dengue, Aedes aegypti, Personal protection

scholarly journals 1405. Clinical and laboratory features of fatal dengue fever in children: a case-control study

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S710-S711
Author(s):  
Dolores E Freire ◽  
Jeniffer D Olaya ◽  
Michael Hawkes
Keyword(s):  
Dengue Fever ◽  
Case Control Study ◽  
Dengue Infection ◽  
Healthcare Providers ◽  
Case Control ◽  
Warning Signs ◽  
Total Leukocyte Count ◽  
Retrospective Case ◽  
Time To Death ◽  
Control Study

Abstract Background Dengue fever (DF) is a mosquito-borne illness that causes significant morbidity and mortality in tropical climates. This study compared the clinical features of fatal DF cases to severe non-fatal, and non-severe controls in Ecuador. Methods Retrospective case-control study of children (1 month to 15 years) hospitalized with serologically-confirmed DF in Guayaquil, Ecuador from 2013 to 2017. Cases of severe, fatal (SF) DF were compared to two control groups: (1) severe DF survivors (SS); and (2) patients with dengue with warning signs (DWS), matched 3:1 to cases for age, sex, and admission date. Observational trial profile Results 1051 patients were admitted with suspected DF and 552 were IgM-positive. Patients were classified as SF (n=11), SS (n=30), or DWS (n=511) (Figure1). Among SF cases, median age was 9.6 years (IQR 5.5-11), 7 (64%) were male, and median time to death was 1.5 days (IQR 0.8-4.0). (Table 1) SF cases had a median of 3 (Range 0-5) encounters with healthcare providers prior to presentation, compared to 2 (Range 0-5, p=0.02) for SS and 2 (Range 0-3, p=0.02) for DWS. Physical findings more common in SF cases than controls included: higher weight, tachycardia, tachypnea, delayed capillary refill, and hepatomegaly (p< 0.05 for all comparisons). Neurological manifestations were more prevalent in the SF group: 9/11 (82%) patients compared to 15/30 (50%, p=0.09) in SS and 7/33 (21%, p< 0.01) in DWS. Total leukocyte count (7.8x103/µL versus 4.5x103/µL, p=0.03) and absolute neutrophil count (5.1x103/µL versus 2.1x103/µL, p=0.03) were higher in SF cases than DWS controls. Fewer SF patients received intravenous dextrose than SS controls (27% versus 70%, p=0.03) (Table 2). Admission characteristics of children with dengue fever Management and outcome Conclusion Delayed recognition by healthcare workers, higher weight, vital sign abnormalities, hepatomegaly, neurological symptoms, leukocytosis, neutrophilia, and lack of dextrose in intravenous solutions were associated with mortality in children with DF. These findings have implications for optimizing the diagnosis and management of severe pediatric dengue infection. Disclosures All Authors: No reported disclosures

scholarly journals Multi-Tissue Transcriptomic-Informed In Silico Investigation of Drugs for the Treatment of Dengue Fever Disease

Viruses ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1540
Author(s):  
Beatriz Sierra ◽  
Ana Cristina Magalhães ◽  
Daniel Soares ◽  
Bruno Cavadas ◽  
Ana B. Perez ◽  
...  
Keyword(s):  
Receptor Antagonist ◽  
Dengue Fever ◽  
In Silico ◽  
Serotonin Receptor ◽  
Neglected Tropical Diseases ◽  
Dengue Infection ◽  
Severe Dengue ◽  
Functional Evaluation ◽  
Atpase Inhibitor ◽  
Serotonin Receptor Antagonist

Transcriptomics, proteomics and pathogen-host interactomics data are being explored for the in silico–informed selection of drugs, prior to their functional evaluation. The effectiveness of this kind of strategy has been put to the test in the current COVID-19 pandemic, and it has been paying off, leading to a few drugs being rapidly repurposed as treatment against SARS-CoV-2 infection. Several neglected tropical diseases, for which treatment remains unavailable, would benefit from informed in silico investigations of drugs, as performed in this work for Dengue fever disease. We analyzed transcriptomic data in the key tissues of liver, spleen and blood profiles and verified that despite transcriptomic differences due to tissue specialization, the common mechanisms of action, “Adrenergic receptor antagonist”, “ATPase inhibitor”, “NF-kB pathway inhibitor” and “Serotonin receptor antagonist”, were identified as druggable (e.g., oxprenolol, digoxin, auranofin and palonosetron, respectively) to oppose the effects of severe Dengue infection in these tissues. These are good candidates for future functional evaluation and clinical trials.

scholarly journals Infectious Disease Relational Data Analysis Using String Grammar Non-Euclidean Relational Fuzzy C-Means

2021 ◽  
Vol 18 (15) ◽  
pp. 8153
Author(s):  
Apiwat Budwong ◽  
Sansanee Auephanwiriyakul ◽  
Nipon Theera-Umpon
Keyword(s):  
High Risk ◽  
Dengue Fever ◽  
Policy Development ◽  
Dengue Infection ◽  
Prevention Policy ◽  
Fuzzy C Means ◽  
North East ◽  
B Virus ◽  
North And South ◽  
Better Than

Statistical analysis in infectious diseases is becoming more important, especially in prevention policy development. To achieve that, the epidemiology, a study of the relationship between the occurrence and who/when/where, is needed. In this paper, we develop the string grammar non-Euclidean relational fuzzy C-means (sgNERF-CM) algorithm to determine a relationship inside the data from the age, career, and month viewpoint for all provinces in Thailand for the dengue fever, influenza, and Hepatitis B virus (HBV) infection. The Dunn’s index is used to select the best models because of its ability to identify the compact and well-separated clusters. We compare the results of the sgNERF-CM algorithm with the string grammar relational hard C-means (sgRHCM) algorithm. In addition, their numerical counterparts, i.e., relational hard C-means (RHCM) and non-Euclidean relational fuzzy C-means (NERF-CM) algorithms are also applied in the comparison. We found that the sgNERF-CM algorithm is far better than the numerical counterparts and better than the sgRHCM algorithm in most cases. From the results, we found that the month-based dataset does not help in relationship-finding since the diseases tend to happen all year round. People from different age ranges in different regions in Thailand have different numbers of dengue fever infections. The occupations that have a higher chance to have dengue fever are student and teacher groups from the central, north-east, north, and south regions. Additionally, students in all regions, except the central region, have a high risk of dengue infection. For the influenza dataset, we found that a group of people with the age of more than 1 year to 64 years old has higher number of influenza infections in every province. Most occupations in all regions have a higher risk of infecting the influenza. For the HBV dataset, people in all regions with an age between 10 to 65 years old have a high risk in infecting the disease. In addition, only farmer and general contractor groups in all regions have high chance of infecting HBV as well.

scholarly journals Selection and integration of Earth Observation-based data for an operational disease forecasting system

2020 ◽  
Author(s):  
Eleanor A Ainscoe ◽  
Barbara Hofmann ◽  
Felipe Colon ◽  
Iacopo Ferrario ◽  
Quillon Harpham ◽  
...  
Keyword(s):  
Land Cover ◽  
Dengue Fever ◽  
Water Availability ◽  
Land Surface ◽  
Warning System ◽  
Earth Observation ◽  
Disease Forecasting ◽  
Current Increase ◽  
Disease Case ◽  
Wide Range

<p>The current increase in the volume and quality of Earth Observation (EO) data being collected by satellites offers the potential to contribute to applications across a wide range of scientific domains. It is well established that there are correlations between characteristics that can be derived from EO satellite data, such as land surface temperature or land cover, and the incidence of some diseases. Thanks to the reliable frequent acquisition and rapid distribution of EO data it is now possible for this field to progress from using EO in retrospective analyses of historical disease case counts to using it in operational forecasting systems.</p><p>However, bringing together EO-based and non-EO-based datasets, as is required for disease forecasting and many other fields, requires carefully designed data selection, formatting and integration processes. Similarly, it requires careful communication between collaborators to ensure that the priorities of that design process match the requirements of the application.</p><p>Here we will present work from the D-MOSS (Dengue forecasting MOdel Satellite-based System) project. D-MOSS is a dengue fever early warning system for South and South East Asia that will allow public health authorities to identify areas at high risk of disease epidemics before an outbreak occurs in order to target resources to reduce spreading of epidemics and improve disease control. The D-MOSS system uses EO, meteorological and seasonal weather forecast data, combined with disease statistics and static layers such as land cover, as the inputs into a dengue fever model and a water availability model. Water availability directly impacts dengue epidemics due to the provision of mosquito breeding sites. The datasets are regularly updated with the latest data and run through the models to produce a new monthly forecast. For this we have designed a system to reliably feed standardised data to the models. The project has involved a close collaboration between remote sensing scientists, geospatial scientists, hydrologists and disease modelling experts. We will discuss our approach to the selection of data sources, data source quality assessment, and design of a processing and ingestion system to produce analysis-ready data for input to the disease and water availability models.</p>

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