Predicting Dengue Outbreaks in Cambodia.
Ontology highlight
ABSTRACT:
SUBMITTER: Cousien A
PROVIDER: S-EPMC6874239 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundDengue fever epidemic dynamics are driven by complex interactions between hosts, vectors and viruses. Associations between climate and dengue have been studied around the world, but the results have shown that the impact of the climate can vary widely from one study site to another. In French Guiana, climate-based models are not available to assist in developing an early warning system. This study aims to evaluate the potential of using oceanic and atmospheric conditions to help predict dengue fever outbreaks in French Guiana.Methodology/principal findingsLagged correlations and composite analyses were performed to identify the climatic conditions that characterized a typical epidemic year and to define the best indices for predicting dengue fever outbreaks during the period 1991-2013. A logistic regression was then performed to build a forecast model. We demonstrate that a model based on summer Equatorial Pacific Ocean sea surface temperatures and Azores High sea-level pressure had predictive value and was able to predict 80% of the outbreaks while incorrectly predicting only 15% of the non-epidemic years. Predictions for 2014-2015 were consistent with the observed non-epidemic conditions, and an outbreak in early 2016 was predicted.Conclusions/significanceThese findings indicate that outbreak resurgence can be modeled using a simple combination of climate indicators. This might be useful for anticipating public health actions to mitigate the effects of major outbreaks, particularly in areas where resources are limited and medical infrastructures are generally insufficient.
| S-EPMC4851397 | biostudies-literature
Project description:Dengue is a national priority disease in Cambodia. The Cambodian National Dengue Surveillance System is based on passive surveillance of dengue-like inpatients reported by public hospitals and on a sentinel, pediatric hospital-based active surveillance system. This system works well to assess trends but the sensitivity of the early warning and time-lag to usefully inform hospitals can be improved. During The ECOnomic development, ECOsystem MOdifications, and emerging infectious diseases Risk Evaluation (ECOMORE) project's knowledge translation platforms, Cambodian hospital staff requested an early warning tool to prepare for major outbreaks. Our objective was therefore to find adapted tools to improve the early warning system and preparedness. Dengue data was provided by the National Dengue Control Program (NDCP) and are routinely obtained through passive surveillance. The data were analyzed at the provincial level for eight Cambodian provinces during 2008-2015. The R surveillance package was used for the analysis. We evaluated the effectiveness of Bayesian algorithms to detect outbreaks using count data series, comparing the current count to an expected distribution obtained from observations of past years. The analyses bore on 78,759 patients with dengue-like syndromes. The algorithm maximizing sensitivity and specificity for the detection of major dengue outbreaks was selected in each province. The overall sensitivity and specificity were 73% and 97%, respectively, for the detection of significant outbreaks during 2008-2015. Depending on the province, sensitivity and specificity ranged from 50% to 100% and 75% to 100%, respectively. The final algorithm meets clinicians' and decisionmakers' needs, is cost-free and is easy to implement at the provincial level.
| S-EPMC6366704 | biostudies-literature
Project description: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.
| S-EPMC7653379 | biostudies-literature
Project description:Global dengue incidence has increased dramatically over the past few decades from approximately 500 000 reported cases in 2000 to over 5 million in 2019. This trend has been attributed to population growth in endemic areas, rapid unplanned urbanization, increasing global connectivity, and climate change expanding the geographic range of the Aedes spp. mosquito, among other factors. Reporting dengue surveillance data is key to understanding the scale of the problem, identifying important changes in the landscape of disease, and developing policies for clinical management, vector control and vaccine rollout. However, surveillance practices are not standardized, and data may be difficult to interpret particularly in low- and middle-income countries with fragmented health-care systems. The latest national dengue surveillance data for Cambodia was published in 2010. Since its publication, the country experienced marked changes in health policies, population demographics, climate and urbanization. How these changes affected dengue control remains unknown. In this article, we summarize two decades of policy changes, published literature, country statistics, and dengue case data collected by the Cambodia National Dengue Control Programme to: (i) identify important changes in the disease landscape; and (ii) derive lessons to inform future surveillance and disease control strategies. We report that while dengue case morbidity and mortality rates in Cambodia fell between 2002 and 2020, dengue incidence doubled and age at infection increased. Future national surveillance, disease prevention and treatment, and vector control policies will have to account for these changes to optimize disease control.
| S-EPMC10452936 | biostudies-literature
Project description:In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems.
| S-EPMC5095208 | biostudies-literature
Project description:To determine potential risk for bird-to-human transmission during influenza A virus (H5N1) outbreaks among backyard poultry in rural Cambodia, we collected environmental specimens. Viral RNA was detected in 27 (35%) of 77 specimens of mud, pond water, water plants, and soil swabs. Our results underscore the need for regular disinfection of poultry areas.
| S-EPMC2600401 | biostudies-literature
Project description:A simple mathematical model without seasonality indicated that the apparently chaotic dengue epidemics in Singapore have characteristics similar to epidemics resulting from chance. Randomness as a sufficient condition for patterns of dengue epidemics in equatorial regions calls into question existing explanations for dengue outbreaks there.
| S-EPMC4550167 | biostudies-literature
Project description:Dengue is emerging as one of the most abundant vector-borne disease globally. Although the majority of infections are asymptomatic or result in only a brief systemic viral illness, a small proportion of patients develop potentially fatal complications. These severe manifestations, including a unique plasma leakage syndrome, a coagulopathy sometimes accompanied by bleeding, and organ impairment, occur relatively late in the disease course, presenting a window of opportunity to identify the group of patients likely to progress to these complications. However, as yet, differentiating this group from the thousands of milder cases seen each day during outbreaks remains challenging, and simple and inexpensive strategies are urgently needed in order to improve case management and to facilitate appropriate use of limited resources. This review will cover the current understanding of the risk factors associated with poor outcome in dengue. We focus particularly on the clinical features of the disease and on conventional investigations that are usually accessible in mid-level healthcare facilities in endemic areas, and then discuss a variety of viral, immunological and vascular biomarkers that have the potential to improve risk prediction. We conclude with a description of several novel methods of assessing vascular function and intravascular volume status non-invasively.
| S-EPMC4154521 | biostudies-literature
Project description:Dengue outbreaks have increased in size and frequency in Australia, and transfusion-transmitted dengue poses a risk to transfusion safety. Using whole blood samples collected during the large 2008-2009 dengue epidemic, we estimated the risk for a dengue-infectious blood donation as ≈1 in 7,146 (range 2,218-50,021).
| S-EPMC3647514 | biostudies-literature
Project description:Mosquito-borne virus infections, such as dengue and chikungunya, are continuously expanding their geographical range. The dengue virus, which is known to be a common cause of febrile illness in tropical areas of the Old World, is now widespread in the Americas. In most affected areas, all the four dengue virus serotypes have circulated. Recently, small clusters of dengue have been identified also in Southern Europe during the hot season. The chikungunya virus, initially restricted to Central Africa, where is a common cause of sporadic cases or small outbreaks, and Asia, where it is used to cause large epidemics, has recently invaded new territories. After ravaging Indian Ocean Islands and the Indian subcontinent, CHIKV caused an outbreak in north-eastern Italy. Recently, chikungunya has reached the Caribbean, causing for the first time a large epidemic on the American continent. Although Aedes aegypti is the main vector of both viruses, Aedes albopictus, the Asian 'Tiger' mosquito, is now playing an increasingly important role, contributing to their spread in temperate climate areas. Hereby, we focus the attention on outbreaks of dengue and chikungunya occurring in previously disease-free areas and discuss factors associated with the long-distance spread of the vector-borne infections, such as mutations increasing viral fitness, climate change, urbanization, and globalization of humans and vectors.
| S-EPMC4394667 | biostudies-other