Occurrence Of Dengue Research Articles

Imported Dengue Case Numbers and Local Climatic Patterns Are Associated with Dengue Virus Transmission in Florida, USA.

Simple SummaryDengue virus transmission from Aedes aegypti mosquitoes to humans is a growing issue in the state of Florida in the United States of America. The majority of cases have occurred in the southernmost counties, even though Ae. aegypti are present throughout much of Florida. To understand this geographic partitioning of dengue fever cases in Florida, we compiled ten years (2009–2019) of county-level data on human, environmental, and vector elements and identified risk factors for laboratory-confirmed cases of dengue virus incidence in the state. Counties with the highest average temperatures, highest minimum temperatures, and lowest maximum temperatures were significantly more likely to have local dengue virus transmission. Additionally, moderate rainfall and an increasing number of travel-related dengue cases were also significantly associated with local transmission. This is the first study of its kind to identify county-level risk factors for dengue incidence in Florida. This study provides a parsimonious model that may be useful for prediction of future dengue occurrence based on routinely collected, publicly available data sources. Our findings also highlight the importance of travel-related dengue fever cases to the state as well as environmental conditions that promote dengue virus transmission in Florida by Ae. aegypti.Aedes aegypti mosquitoes are the main vector of dengue viruses globally and are present throughout much of the state of Florida (FL) in the United States of America. However, local transmission of dengue viruses in FL has mainly occurred in the southernmost counties; specifically Monroe and Miami-Dade counties. To get a better understanding of the ecologic risk factors for dengue fever incidence throughout FL, we collected and analyzed numerous environmental factors that have previously been connected to local dengue cases in disease-endemic regions. We analyzed these factors for each county-year in FL, between 2009–2019, using negative binomial regression. Monthly minimum temperature of 17.5–20.8 °C, an average temperature of 26.1–26.7 °C, a maximum temperature of 33.6–34.7 °C, rainfall between 11.4–12.7 cm, and increasing numbers of imported dengue cases were associated with the highest risk of dengue incidence per county-year. To our knowledge, we have developed the first predictive model for dengue fever incidence in FL counties and our findings provide critical information about weather conditions that could increase the risk for dengue outbreaks as well as the important contribution of imported dengue cases to local establishment of the virus in Ae. aegypti populations.

The triple epidemics of arboviruses in Feira de Santana, Brazilian Northeast: Epidemiological characteristics and diffusion patterns

Arboviruses are diseases of worldwide importance in the field of communicable diseases. In Brazil, the reemergence of dengue and the emergence of chikungunya and Zika since 2014 have led to epidemic waves of great magnitude and rapid spread. However, their diffusion patterns vary and change over time. This study analyzes the spatial diffusion of the simultaneous circulation of three arboviruses transmitted by the same vector in a large urban space over two epidemic waves in consecutive years. An ecological study of spatial and temporal aggregates on the occurrence of dengue, chikungunya, and Zika, from 2014 to 2019, in Feira de Santana, Bahia State, was carried out using data of cases reported to the national surveillance system. Four different methods were used to analyze the spatial diffusion: Kernel Estimation with sequential maps, cumulative nearest-neighbor ratios (NNI) over time, spatial correlograms and local autocorrelation changes (LISA) over time. From 2014–2019, there were 21,723 confirmed cases of arboviruses. The highest incidences were among women (496.9, 220.2, and 91.0 cases/100,000 women for dengue, chikungunya and Zika respectively). By age group, the highest incidences were from ages 10–19 years old (609.3 dengue cases/100,000), from 60 and more (306.7 chikungunya cases/100,000), and from 0-9 years old (124.1 Zika cases/100,000 inhabitants). The temporal distribution demonstrated two epidemic waves of simultaneous circulation in 2014 and 2015. Kernel maps indicate that arboviruses spread to neighboring areas near the first hotspots, suggesting an expansion diffusion pattern. The NNI, spatial correlograms and LISA changes results suggest expansion patterns for the three arboviruses in all periods. The spatial diffusion pattern of dengue, Zika, and chikungunya in the 2014–2015 epidemics in Feira de Santana was expansion. These findings are useful to guide prevention measures and reduce occurrence in other areas.

Identifying High-risk Areas of Dengue by Meteorological factors in Thailand

Dengue is a public health problem in Thailand. This study aimed to investigate the spatial distribution pattern and meteorological factors associated with dengue in the central region over the past 10 years. Dengue incidence and meteorological data were collected from 2007 to 2016. Geographical information system modeling was done to generate sub-district level risk map of dengue incidences, with five risk levels. The findings revealed that mean temperature, rainfall, relative humidity, and wind speed are important meteorological factors that influence component of dengue disease occurrence. The identified sub-district level risk estimates can be very useful in planning and executing dengue control in Thailand.

Forecasting Multivariate time-series data using LSTM Neural Network in Mysore district, Karnataka

Advanced and precise forecasting of infectious diseases plays a critical role in planning and providing resources effectively.Time series forecasting for non-linear issues are accessible using deep learning techniques. The association betweenclimatic parameters and dengue occurrences was investigated in this work, and a forecasting model was constructedusing a deep learning approach called long short-term memory (LSTM). Univariate and multivariate LSTM time seriesforecast models were developed using meteorological and dengue incidence data from January 2006 to December 2019.For univariate data, the Pycharm/Google Colab platform was implemented, as the deep learning framework Keras,which is one of the models in the machine learning library based on Tensorflow. The Pandas Python package with builtinsupport for time series data was used for multivariate data. The final model was chosen using the mae loss and theAdam optimizer. Once the model had been fixed, predictions were made using the model. The research showed thatthe meteorological factors such as maximum temperature at lag 3, minimum temperature at lag 3, maximum vapourpressure (lag 0,1 and 2), minimum vapour pressure at lag 1, and vapour pressure daily mean at lag 0,1,4 are all significantpredictors of dengue along with RMSE value of 1.121 . The results indicated that LSTM network has higher predictionaccuracy than any other traditional forecasting methods. Timely management of seasonal diseases such as dengue alongwith meteorological parameters can predict epidemics in the future.

Development and Use of a Reproducible Framework for Spatiotemporal Climatic Risk Assessment and its Association with Decadal Trend of Dengue in India.

Introduction:The study aimed to develop a reproducible, open-source, and scalable framework for extracting climate data from satellite imagery, understanding dengue's decadal trend in India, and estimating the relationship between dengue occurrence and climatic factors.Materials and Methods:A framework was developed in the Open Source Software, and it was empirically tested using reported annual dengue occurrence data in India during 2010–2019. Census 2011 and population projections were used to calculate incidence rates. Zonal statistics were performed to extract climate parameters. Correlation coefficients were calculated to estimate the relationship of dengue with the annual average of daily mean and minimum temperature and rainy days.Results:Total 818,973 dengue cases were reported from India, with median annual incidence of 6.57 per lakh population; it was high in 2019 and 2017 (11.80 and 11.55 per lakh) and the Southern region (8.18 per lakh). The highest median annual dengue incidence was observed in Punjab (24.49 per lakh). Daily climatic data were extracted from 1164 coordinate locations across the country for the decadal period (4,249,734 observations). The annual average of daily temperature and rainy days positively correlated with dengue in India (r = 0.31 and 0.06, at P < 0.01 and 0.30, respectively).Conclusion:The study provides a reproducible algorithm for bulk climatic data extraction from research-level satellite imagery. Infectious disease models can be used to understand disease epidemiology and strengthen disease surveillance in the country.

Ecoepidemiology of dengue in Brazil: from the virus to the environment

Dengue is an infectious disease caused by the dengue virus. In Brazil, the main vector is the mosquito Aedes aegypti (L.); however, Aedes albopictus (Skuse) can also transmit this pathogen. According to the WHO, more than 125 countries are endemic for dengue, and an estimated 50-100 million infections occur annually. In Brazil, the Northeast region is one with high incidence rates and records of successive epidemics. Dengue has been circulating in Brazil for over 30 years, due to the fact that there are areas that offer favorable environmental conditions, as well as municipalities with low socioeconomic conditions and frequent water crises. In addition, Brazil is a tourist hub with an intense flow of visitors, keeping the doors open for the entry and circulation of vector-borne diseases. The role of epidemiology is to analyze the distribution of diseases and their determinants in space and time and to unveil the social inequalities that influence the health-disease process. Thus, a review study that describes the occurrence of Dengue may provide a perspective of future areas of greater risk for dengue epidemics.

Spatial- Temporal Spread of Dengue in Delhi: An Empirical Analysis

Background & objectives: The epidemiology of dengue in Delhi is changing over the years and is now making it an endemic disease. It is not only rising but is slowly becoming more severe and spreading much faster than before. Within this perspective, this paper aims to map the distribution and intensity of dengue spread in Delhi on a fine spatial scale for the last decade to build an early warning system for the future. Methods: For this paper, the study followed a methodology that combined dengue data with suitable statistical approaches to find their spatial and temporal spread of dengue over Delhi. For this, primary data on dengue in Delhi was collected from 2009 to 2020 from the South Delhi Municipal Corporation (SDMC). GIS and compiled data were used to map the spatial distribution of Dengue occurrences at the Zonal and ward level. Dengue intensity indices were developed to identify the dengue hotspots and enable localised actions to manage dengue. A time-series analysis enabled understanding of dengue trends in SDMC and carrying out vulnerability assessments to develop a more targeted approach to manage dengue at the local level. Interviews with the select SDMC official using open-ended questions were used to identify the key challenges and issues in the management of dengue in Delhi. Results: Dengue is hyper endemic in the region and shows its presence throughout the year. Dengue is hyper endemic in the city and shows its presence throughout the year, with peaks observed from September to November. The trends in dengue indicate that the city has very rapidly come into the grip of the dengue and the post-monsoon periods observe high incidence rates, with cooler months also experiencing dengue cases. The dengue vector is fast spreading into the areas where it was not present before, which indicates that the dengue vector is adapting to the local environment and soon becomes a perennial feature in the city. We also observe clusters where the dengue is endemic and calls for a targeted measure. Interpretation & conclusion: Dengue in Delhi is perennial now, with peaks observed during the months from September- November. The trend in dengue indicates that the city has very rapidly come into the grip of the dengue, and the post-monsoon periods observe high incidence rates. The dengue vector is fast spreading into the areas where it was not present before. This indicates that dengue is adapting to the local environment and soon will become a perennial feature in the city and calls for a targeted measure.

The impact of climate suitability, urbanisation, and connectivity on the expansion of dengue in 21st century Brazil

Dengue is hyperendemic in Brazil, with outbreaks affecting all regions. Previous studies identified geographical barriers to dengue transmission in Brazil, beyond which certain areas, such as South Brazil and the Amazon rainforest, were relatively protected from outbreaks. Recent data shows these barriers are being eroded. In this study, we explore the drivers of this expansion and identify the current limits to the dengue transmission zone. We used a spatio-temporal additive model to explore the associations between dengue outbreaks and temperature suitability, urbanisation, and connectivity to the Brazilian urban network. The model was applied to a binary outbreak indicator, assuming the official threshold value of 300 cases per 100,000 residents, for Brazil's municipalities between 2001 and 2020. We found a nonlinear relationship between higher levels of connectivity to the Brazilian urban network and the odds of an outbreak, with lower odds in metropoles compared to regional capitals. The number of months per year with suitable temperature conditions for Aedes mosquitoes was positively associated with the dengue outbreak occurrence. Temperature suitability explained most interannual and spatial variation in South Brazil, confirming this geographical barrier is influenced by lower seasonal temperatures. Municipalities that had experienced an outbreak previously had double the odds of subsequent outbreaks. We identified geographical barriers to dengue transmission in South Brazil, western Amazon, and along the northern coast of Brazil. Although a southern barrier still exists, it has shifted south, and the Amazon no longer has a clear boundary. Few areas of Brazil remain protected from dengue outbreaks. Communities living on the edge of previous barriers are particularly susceptible to future outbreaks as they lack immunity. Control strategies should target regions at risk of future outbreaks as well as those currently within the dengue transmission zone.

Analysis of Immune Markers and Hematological Features in Plasmodium/Dengue Virus Co-infected Patients in Ouagadougou, Burkina Faso

Malaria and dengue are two important vector‑borne infections that are common in tropical countries. In these areas, simultaneous occurrence of malaria and dengue in an individual cannot be excluded, although published reports of co‑infections are scarce till date. The Aims of this study were to identify dengue and malaria co-infection cases as well as to ascertain the severity of dengue and malaria in co‑infection based on some hematological parameters. A cross-sectional study was conducted from June to November 2019 and 385 subjects with an undifferentiated febrile illness patients were recruited at outpatient clinics and blood sample were collected. All samples were tested for dengue NS1 antigen and for dengue IgM/IgG. Malaria diagnosis was based on the identification of Plasmodium parasites on a thin and thick blood films microscopy. Among the 385 patients included in the study, 23.9% were tested positive for malaria, 32.5% were positive for dengue, and 7.01% showed concurrent infection of both malaria and dengue. Malaria and dengue co-infected patients presented lower leukocytes level (5.024±0.559) than malaria monoinfected patients (7.304± 0.388; p=0.002). A significant reduction in platelet counts was observed in co-infected patients (164.0±10.91 vs 229.1±14.32; p=0.005). Conversely, hemoglobin level was statistically lower in malaria monoinfected patients than dengue monoinfected patients (p=0.014). Thus this study demonstrates that the diagnosis of dengue and malaria should be carried out routinely for better management of fever.

The Impact of Meteorological Factors on Communicable Disease Incidence and Its Projection: A Systematic Review.

Background: Climate change poses a real challenge and has contributed to causing the emergence and re-emergence of many communicable diseases of public health importance. Here, we reviewed scientific studies on the relationship between meteorological factors and the occurrence of dengue, malaria, cholera, and leptospirosis, and synthesized the key findings on communicable disease projection in the event of global warming. Method: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow checklist. Four databases (Web of Science, Ovid MEDLINE, Scopus, EBSCOhost) were searched for articles published from 2005 to 2020. The eligible articles were evaluated using a modified scale of a checklist designed for assessing the quality of ecological studies. Results: A total of 38 studies were included in the review. Precipitation and temperature were most frequently associated with the selected climate-sensitive communicable diseases. A climate change scenario simulation projected that dengue, malaria, and cholera incidence would increase based on regional climate responses. Conclusion: Precipitation and temperature are important meteorological factors that influence the incidence of climate-sensitive communicable diseases. Future studies need to consider more determinants affecting precipitation and temperature fluctuations for better simulation and prediction of the incidence of climate-sensitive communicable diseases.

Effect of social isolation in dengue cases in the state of Sao Paulo, Brazil: An analysis during the COVID-19 pandemic

BackgroundStudies have shown that human mobility is an important factor in dengue epidemiology. Changes in mobility resulting from COVID-19 pandemic set up a real-life situation to test this hypothesis. Our objective was to evaluate the effect of reduced mobility due to this pandemic in the occurrence of dengue in the state of São Paulo, Brazil. MethodIt is an ecological study of time series, developed between January and August 2020. We use the number of confirmed dengue cases and residential mobility, on a daily basis, from secondary information sources. Mobility was represented by the daily percentage variation of residential population isolation, obtained from the Google database. We modeled the relationship between dengue occurrence and social distancing by negative binomial regression, adjusted for seasonality. We represent the social distancing dichotomously (isolation versus no isolation) and consider lag for isolation from the dates of occurrence of dengue. ResultsThe risk of dengue decreased around 9.1% (95% CI: 14.2 to 3.7) in the presence of isolation, considering a delay of 20 days between the degree of isolation and the dengue first symptoms. ConclusionsWe have shown that mobility can play an important role in the epidemiology of dengue and should be considered in surveillance and control activities.

Clinical and epidemiologic characteristics associated with dengue fever in 2011-2016 in Bang Phae district, Ratchaburi province, Thailand.

BackgroundDengue is a major public health problem in Thailand, but data are often focused on certain dengue-endemic areas. Methods: To better understand dengue epidemiology and clinical characteristics in Thailand, a fever surveillance study was conducted among patients aged 1–55 years, who presented with non-localized febrile illness at Bang Phae Community Hospital in Ratchaburi province, Thailand from October 2011 to September 2016.ResultsAmong 951 febrile episodes, 130 were dengue-confirmed. Individuals aged 10–14 years were mostly affected, followed by those 15–19 years-of-age, with about 15% of dengue-confirmed cases from adults 25 years and older. There were annual peaks of dengue occurrence between June-November. Most prevalent serotype in circulation was DENV-2 in 2012, DENV-3 in 2014, and DENV-4 & -3 in 2015. Among dengue cases, 65% were accurately detected using the dengue NS1 RDT. Detection rate was similar between secondary and primary dengue cases where 66% of secondary vs. 60% of primary dengue cases had positive results on the NS1 RDT. Among dengue cases, 66% were clinically diagnosed with suspected dengue or DHF, prior to lab confirmation. Dengue was positively associated with rash, headache, hematemesis and alterations to consciousness, when compared to non-dengue. Dengue patients were 10.6 times more likely to be hospitalized, compared to non-dengue cases. Among dengue cases, 95 were secondary and 35 were primary infections. There were 8 suspected DHF cases and all were identified to be secondary dengue. Secondary dengue cases were 3.5 times more likely to be hospitalized compared to primary dengue cases. Although the majority of our dengue-positive patients were secondary dengue cases, with few patients showing manifestations of DHF, our dengue cases were mostly mild disease. Even among children < 10 years-of-age, 61% had secondary infection and the rate of secondary infection increased with age.ConclusionWhile the majority of dengue-confirmed cases were children, almost three-quarters of dengue-confirmed cases in this study were secondary dengue. Our study results consistent with previous data from the country confirm the hyperendemic transmission of DENV in Thailand, even in the non-epidemic years. With various interventions becoming available for dengue prevention and control, including dengue vaccines, decision-making on future implementation strategies should be based on such burden of disease data.

Factors associated with dengue fever patients attending primary health clinics in Kota Kinabalu

Background: Dengue fever infection has become a major public health concern in Malaysia with frequent epidemics occurring in urban areas. In Sabah, Dengue fever is among the 5 most reported communicable diseases and the district of Kota Kinabalu is among the top 3 districts with the highest number of dengue cases with 563 cases recorded in 2016.&#x0D; Aims and Methodolgy: This study aimed to determine the factors contributing or associated with dengue fever occurrence in Kota Kinabalu. A cross- sectional study was conducted among the primary health care clinic attendees in Kota Kinabalu. Data was collected via a set of structured questionnaires focusing on socio-demographic characteristic of participants, environmental characteristic of households, history of previous exposure to dengue fever, knowledge towards dengue fever and its vector, awareness on dengue fever and mosquito control and dengue fever prevention practices.&#x0D; Results and Recommendations: Among the 350 individual’s that participated in the interview, 53 were dengue fever cases. Subsequently, the prevalence of Dengue fever cases among the study population was calculated with prevalence in percentage concluded as 15.1%. Further analysis revealed that factors such as aged group by years, number of household occupants, staying at dengue outbreak areas, self – reported history of recent mosquito bites, indoor and outdoor storage of open water containers, installed house window screens, knowledge on dengue fever and its vector, practices of wearing protective clothing outdoor and usage of mosquito repellent ointment or creams showed significant association with dengue fever outcomes. However, further studies would need to be conducted to determine if these factors possess a higher threat or risk for dengue fever.&#x0D; Bangladesh Journal of Medical Science Vol.20(4) 2021 p.878-886

Multilevel analysis of social, climatic and entomological factors that influenced dengue occurrence in three municipalities in Colombia

According to the World Health Organization, dengue is a neglected tropical disease. Latin America, specifically Colombia is in alert regarding this arbovirosis as there was a spike in the number of reported dengue cases at the beginning of 2019. Although there has been a worldwide decrease in the number of reported dengue cases, Colombia has shown a growing trend over the past few years. This study performed a Poisson multilevel analysis with mixed effects on STATA® version 16 and R to assess sociodemographic, climatic, and entomological factors that may influence the occurrence of dengue in three municipalities for the period 2010–2015. Information on dengue cases and their sociodemographic variables was collected from the National Public Health Surveillance System (SIVIGILA) records. For climatic variables (temperature, relative humidity, and precipitation), we used the information registered by the weather stations located in the study area, which are managed by the Instituto de Hidrologia, Meteorologia y Estudios Ambientales (IDEAM) or the Corporación Autónoma Regional (CAR). The entomological variables (house index, container index, and Breteau index) were provided by the Health office of the Cundinamarca department. SIVIGILA reported 1921 dengue cases and 56 severe dengue cases in the three municipalities; of them, three died. One out of four cases occurred in rural areas. The age category most affected was adulthood, and there were no statistical differences in the number of cases between sexes. The Poisson multilevel analysis with the best fit model explained the presentation of cases were temperature, relative humidity, precipitation, childhood, live in urban area and the contributory healthcare system. The temperature had the biggest influence on the presentation of dengue cases in this region between 2010 and 2015.

Implications of the COVID-19 Lockdown on Dengue Transmission in Malaysia.

We aim to investigate the effect of large-scale human movement restrictions during the COVID-19 lockdown on both the dengue transmission and vector occurrences. This study compared the weekly dengue incidences during the period of lockdown to the previous years (2015 to 2019) and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model that expected no movement restrictions. We found that the trend of dengue incidence during the first two weeks (stage 1) of lockdown decreased significantly with the incidences lower than the lower confidence level (LCL) of SARIMA. By comparing the magnitude of the gradient of decrease, the trend is 319% steeper than the trend observed in previous years and 650% steeper than the simulated model, indicating that the control of population movement did reduce dengue transmission. However, starting from stage 2 of lockdown, the dengue incidences demonstrated an elevation and earlier rebound by four weeks and grew with an exponential pattern. We revealed that Aedes albopictus is the predominant species and demonstrated a strong correlation with the locally reported dengue incidences, and therefore we proposed the possible diffusive effect of the vector that led to a higher acceleration of incidence rate.

Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression Analysis.

Background: Determination of the key factors affecting dengue occurrence is of significant importance for the successful response to its outbreak. Yunnan and Guangdong Provinces in China are hotspots of dengue outbreak during recent years. However, few studies focused on the drive of multi-dimensional factors on dengue occurrence failing to consider the possible multicollinearity of the studied factors, which may bias the results.Methods: In this study, multiple linear regression analysis was utilized to explore the effect of multicollinearity among dengue occurrences and related natural and social factors. A principal component regression (PCR) analysis was utilized to determine the key dengue-driven factors in Guangzhou city of Guangdong Province and Xishuangbanna prefecture of Yunnan Province, respectively.Results: The effect of multicollinearity existed in both Guangzhou city and Xishuangbanna prefecture, respectively. PCR model revealed that the top three contributing factors to dengue occurrence in Guangzhou were Breteau Index (BI) (positive correlation), the number of imported dengue cases lagged by 1 month (positive correlation), and monthly average of maximum temperature lagged by 1 month (negative correlation). In contrast, the top three factors contributing to dengue occurrence in Xishuangbanna included monthly average of minimum temperature lagged by 1 month (positive correlation), monthly average of maximum temperature (positive correlation), monthly average of relative humidity (positive correlation), respectively.Conclusion: Meteorological factors presented stronger impacts on dengue occurrence in Xishuangbanna, Yunnan, while BI and the number of imported cases lagged by 1 month played important roles on dengue transmission in Guangzhou, Guangdong. Our findings could help to facilitate the formulation of tailored dengue response mechanism in representative areas of China in the future.

Predictors of severe dengue amongst children as per the revised WHO classification.

World Health Organization (WHO) revised its guidelines for classification and management of dengue in 2009. This revised system was found out to have good sensitivity and negative predictive value but poor specificity as well as positive predictive value. This retrospective study was carried out in a tertiary care hospital of Delhi, India to assess factors predicting the occurrence of severe dengue in children as per the revised classification. A total of 647 suspected dengue cases were admitted in the hospital in the year 2015. Detailed clinical and epidemiological data of 170 patients who were confirmed as dengue either by NS1 antigen test or by serology (Ig M positive) were recorded and statistically analyzed. The number of laboratory-confirmed cases was 170 and included thirty (17.65%) dengue fever (DF), 106 (62.35%) dengue with warning signs (DWS) and 34 (20.0%) severe dengue (SD) patients. Regression analysis revealed that presence of vomiting, altered sensorium, shock, peri-orbital edema, hepatomegaly, splenomegaly, severe anemia, thrombocytopenia, elevated urea and creatinine, decreased total protein and globulin were significantly associated with occurrence of severe disease. The addition of clinical features (peri-orbital edema and splenomegaly); and laboratory findings (elevated urea and creatinine, decreased serum protein and globulin) might help improve the sensitivity and specificity of the revised WHO dengue classification in predicting severe dengue.

Climate factors and dengue fever occurrence in Makassar during period of 2011–2017

ObjectiveDengue fever is a global burden because of high cases number. Climate factors became determinant of the mosquito's growth. This study aimed to analyze the relationship between climate factors (humidity, temperature, wind speed, rainfall) and dengue cases in Makassar during 2011–2017. MethodsIt was quantitative study located in Makassar. Data were analyzed by General Estimating Equation (GEE). Gee was used to showing the model of variables. This study used secondary data from Health District Office of Makassar to get Dengue Cases Data and Meteorological, Climatological, and Geophysical Agency of Makassar for monthly climate data. ResultsThe result showed significant correlation between climate variables that have been researched which were temperature, humidity, rainfall, and wind speed to dengue fever cases. ConclusionsAs conclusion, the humidity had strongest correlation to dengue fever cases. It also showed positive correlation, while others showed negative correlation.

Seroprevalence and associated risk factors of Dengue fever in Kassala state, eastern Sudan.

Dengue is a rapidly growing public health threat in Kassala state, eastern Sudan. The objective of this study was to determine the seroprevalence, entomological transmission indices, and socioeconomic risk factors associated with dengue in this region. A cross-sectional community-based study was conducted in four dengue-endemic sites; Khatmia, West Gash, Thoriba, and Shokriya between March 2016 to March 2017. Enzyme-linked immunosorbent assay (ELISA) of immunoglobulin G (IgG) was used to determine the prevalence of dengue virus among the study participants. An entomological survey was conducted using pyrethrum spray catch and dipping for the collection of adults and aquatic stages of Aedes aegypti, respectively. Ribonucleic acid was extracted from the buffy coat of participants as well as from adult female Ae. aegypti to assess the possible circulation of dengue virus using Reverse Transcription Polymerase Chain Reaction (RT-PCR). Multiple logistic regression model was used to estimate the association between potential risk factors and dengue seropositivity. A total of 409 persons were recruited to the study: 45.5% were in the 20–39 years’ age category; 57.9% were living in houses with 6–10 persons; and 29.1% had at most secondary school education. In the majority (65.8%) of the households, the socioeconomic status was low (P<0.001). Long-lasting insecticide-treated bed nets were used in 56.5% of the households. Over three-quarters (77.8%) claimed not to have experienced febrile illness in the last three months. Routine entomological survey across Kassala state identified a total of 3,304 larvae and 390 pupae Ae. aegypti, respectively. The overall house index was 32.8% and Breteau Index was 35.96% (146/406). The overall pupal demographic index was 13.31%, and the pupal children index was 97.26%. Antibodies against IgG were detected from 66 (42.04%) out of a total of 157 sera. Twenty-two positive sera (75.9%) were collected from Khatmia. A total of 329 adults Ae. aegypti were identified but only one (0.3%) was positive for DENV in Khatmia. Finally, four independent risk factors were identified to derive dengue circulation in Kassala: elder age (> 60 years) (OR 6.31, CI 1.09–36.36); type of bathroom (OR 3.52, CI 1.35–9.20); using water-based air conditioner (OR 6.90, CI 1.78–26.85) and previous infection of any household member with dengue (OR 28.73, CI 3.31–249.63). Our findings suggest that Kassala state is facing an increasing occurrence of dengue and emphasizes the need for developing appropriate interventions to address the identified risk factors, and place control programs into actions. Establishment of routine dengue epidemiological and entomological surveillance, and climate warning systems will contribute to early warning and timely detection and response to emerging outbreaks.

An Association between Rainy Days with Clinical Dengue Fever in Dhaka, Bangladesh: Findings from a Hospital Based Study.

Background: Dengue, a febrile illness, is caused by a Flavivirus transmitted by Aedes aegypti and Aedes albopictus mosquitoes. Climate influences the ecology of the vectors. We aimed to identify the influence of climatic variability on the occurrence of clinical dengue requiring hospitalization in Zone-5, a high incidence area of Dhaka City Corporation (DCC), Bangladesh. Methods and Findings: We retrospectively identified clinical dengue cases hospitalized from Zone-5 of DCC between 2005 and 2009. We extracted records of the four major catchment hospitals of the study area. The Bangladesh Meteorological Department (BMD) provided data on temperature, rainfall, and humidity of DCC for the study period. We used autoregressive integrated moving average (ARIMA) models for the number of monthly dengue hospitalizations. We also modeled all the climatic variables using Poisson regression. During our study period, dengue occurred throughout the year in Zone-5 of DCC. The median number of hospitalized dengue cases was 9 per month. Dengue incidence increased sharply from June, and reached its peak in August. One additional rainy day per month increased dengue cases in the succeeding month by 6% (RR = 1.06, 95% CI: 1.04–1.09). Conclusions: Dengue is transmitted throughout the year in Zone-5 of DCC, with seasonal variation in incidence. The number of rainy days per month is significantly associated with dengue incidence in the subsequent month. Our study suggests the initiation of campaigns in DCC for controlling dengue and other Aedes mosquito borne diseases, including Chikunguniya from the month of May each year. BMD rainfall data may be used to determine campaign timing.