Dengue virus surveillance for early warning, Singapore.
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ABSTRACT:
SUBMITTER: Lee KS
PROVIDER: S-EPMC2953985 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundWith its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention.ObjectivesWe sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak.MethodsWe developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore.ResultsOperationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore's dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response.ConclusionsStatistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue.CitationShi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369-1375;?http://dx.doi.org/10.1289/ehp.1509981.
| S-EPMC5010413 | biostudies-literature
Project description:Emerging and re-emerging infectious diseases and zoonoses indicate the importance of the One Health (OH) approach for early warning. At present, even when surveillance data are available, they are infrequently timeously shared between the health sectors. In the context of the MediLabSecure (MLS) Project, we investigated the collection of a set of surveillance indicators able to provide data for the implementation of integrated early warning systems in the 22 MLS countries of the Mediterranean, Black Sea and Sahel regions. We used an online questionnaire (covering vector, human, and animal sectors), focusing on seven relevant arboviruses, that was submitted to 110 officially appointed experts. Results showed that West Nile virus was perceived as the most relevant zoonotic pathogen, while Dengue virus was the most relevant non-zoonotic pathogen in the study area. Data collection of early warning indicators is in place at a different level for all the investigated pathogens and in almost all the MLS Countries. Further assessments on the reliability of the collection in place and on the feasibility of piloting an integrated early warning system for arbovirus could verify if integrated early warning really represents the Achilles' heel of OH.
| S-EPMC7022449 | biostudies-literature
Project description:BackgroundA dengue fever outbreak occured in French Guiana in 2006. The objectives were to study the value of a syndromic surveillance system set up within the armed forces, compared to the traditional clinical surveillance system during this outbreak, to highlight issues involved in comparing military and civilian surveillance systems and to discuss the interest of syndromic surveillance for public health response.MethodsMilitary syndromic surveillance allows the surveillance of suspected dengue fever cases among the 3,000 armed forces personnel. Within the same population, clinical surveillance uses several definition criteria for dengue fever cases, depending on the epidemiological situation. Civilian laboratory surveillance allows the surveillance of biologically confirmed cases, within the 200,000 inhabitants.ResultsIt was shown that syndromic surveillance detected the dengue fever outbreak several weeks before clinical surveillance, allowing quick and effective enhancement of vector control within the armed forces. Syndromic surveillance was also found to have detected the outbreak before civilian laboratory surveillance.ConclusionMilitary syndromic surveillance allowed an early warning for this outbreak to be issued, enabling a quicker public health response by the armed forces. Civilian surveillance system has since introduced syndromic surveillance as part of its surveillance strategy. This should enable quicker public health responses in the future.
| S-EPMC2459153 | biostudies-literature
Project description:Dengue is one of the most important emerging diseases of humans, with no preventative vaccines or antiviral cures available at present. Although one-third of the world's population live at risk of infection, little is known about the pattern and dynamics of dengue virus (DENV) within outbreak situations. By exploiting genomic data from an intensively studied major outbreak, we are able to describe the molecular epidemiology of DENV at a uniquely fine-scaled temporal and spatial resolution. Two DENV serotypes (DENV-1 and DENV-3), and multiple component genotypes, spread concurrently and with similar epidemiological and evolutionary profiles during the initial outbreak phase of a major dengue epidemic that took place in Singapore during 2005. Although DENV-1 and DENV-3 differed in viremia and clinical outcome, there was no evidence for adaptive evolution before, during, or after the outbreak, indicating that ecological or immunological rather than virological factors were the key determinants of epidemic dynamics.
| S-EPMC2668455 | biostudies-literature
Project description:An early warning system for dengue is meant to predict outbreaks and prevent dengue cases by aiding timely decision making and deployment of interventions. However, only a system which is accepted and utilised by the public would be sustainable in the long run. This study aimed to explore the perception and attitude of the Malaysian public towards a dengue early warning system. The sample consisted of 847 individuals who were 18 years and above and living/working in the Petaling District, an area adjacent to Kuala Lumpur, Malaysia. A questionnaire consisting of personal information and three sub-measures of; i) perception, ii) attitude towards dengue early warning and iii) response towards early warning; was distributed to participants. We found that most of the respondents know about dengue fever (97.1%) and its association with climate factors (90.6%). Most of them wanted to help reduce the number of dengue cases in their area (91.5%). A small percentage of the respondents admitted that they were not willing to be involved in public activities, and 64% of them admitted that they did not check dengue situations or hotspots around their area regularly. Despite the high awareness on the relationship between climate and dengue, about 45% of respondents do not know or are not sure how this can be used to predict dengue. Respondents would like to know more about how climate data can be used to predict a dengue outbreak (92.7%). Providing more information on how climate can influence dengue cases would increase public acceptability and improve response towards climate-based warning system. The most preferred way of communicating early warning was through the television (66.4%). This study shows that the public in Petaling District considers it necessary to have a dengue warning system to be necessary, but more education is required.
| S-EPMC6394956 | biostudies-literature
Project description:Singapore experienced its first documented Zika virus outbreak in 2016. We identified clinical and laboratory parameters that increase the probability for Zika or dengue virus infection. Early during the illness, combinations of key parameters obtained through clinical assessment and hematologic tests can help distinguish between these infections.
| S-EPMC5708236 | biostudies-literature
Project description:BACKGROUND:Many studies have shown associations between rising temperatures, El Niño events and dengue incidence, but the effect of sustained periods of extreme high temperatures (i.e., heatwaves) on dengue outbreaks has not yet been investigated. This study aimed to compare the short-term temperature-dengue associations during different dengue outbreak periods, estimate the dengue cases attributable to temperature, and ascertain if there was an association between heatwaves and dengue outbreaks in Hanoi, Vietnam. METHODOLOGY/PRINCIPAL FINDINGS:Dengue outbreaks were assigned to one of three categories (small, medium and large) based on the 50th, 75th, and 90th percentiles of distribution of weekly dengue cases during 2008-2016. Using a generalised linear regression model with a negative binomial link that controlled for temporal trends, temperature variation, rainfall and population size over time, we examined and compared associations between weekly average temperature and weekly dengue incidence for different outbreak categories. The same model using weeks with or without heatwaves as binary variables was applied to examine the potential effects of extreme heatwaves, defined as seven or more days with temperatures above the 95th percentile of daily temperature distribution during the study period. This study included 55,801 dengue cases, with an average of 119 (range: 0 to 1454) cases per week. The exposure-response relationship between temperature and dengue risk was non-linear and differed with dengue category. After considering the delayed effects of temperature (one week lag), we estimated that 4.6%, 11.6%, and 21.9% of incident cases during small, medium, and large outbreaks were attributable to temperature. We found evidence of an association between heatwaves and dengue outbreaks, with longer delayed effects on large outbreaks (around 14 weeks later) than small and medium outbreaks (4 to 9 weeks later). Compared with non-heatwave years, dengue outbreaks (i.e., small, moderate and large outbreaks combined) in heatwave years had higher weekly number of dengue cases (p<0.05). Findings were robust under different sensitivity analyses. CONCLUSIONS:The short-term association between temperature and dengue risk varied by the level of outbreaks and temperature seems more likely affect large outbreaks. Moreover, heatwaves may delay the timing and increase the magnitude of dengue outbreaks.
| S-EPMC6994101 | biostudies-literature
Project description:BackgroundDespite dengue dynamics being driven by complex interactions between human hosts, mosquito vectors and viruses that are influenced by climate factors, an operational model that will enable health authorities to anticipate the outbreak risk in a dengue non-endemic area has not been developed. The objectives of this study were to evaluate the temporal relationship between meteorological variables, entomological surveillance indices and confirmed dengue cases; and to establish the threshold for entomological surveillance indices including three mosquito larval indices [Breteau (BI), Container (CI) and House indices (HI)] and one adult index (AI) as an early warning tool for dengue epidemic.Methodology/principal findingsEpidemiological, entomological and meteorological data were analyzed from 2005 to 2012 in Kaohsiung City, Taiwan. The successive waves of dengue outbreaks with different magnitudes were recorded in Kaohsiung City, and involved a dominant serotype during each epidemic. The annual indigenous dengue cases usually started from May to June and reached a peak in October to November. Vector data from 2005-2012 showed that the peak of the adult mosquito population was followed by a peak in the corresponding dengue activity with a lag period of 1-2 months. Therefore, we focused the analysis on the data from May to December and the high risk district, where the inspection of the immature and mature mosquitoes was carried out on a weekly basis and about 97.9% dengue cases occurred. The two-stage model was utilized here to estimate the risk and time-lag effect of annual dengue outbreaks in Taiwan. First, Poisson regression was used to select the optimal subset of variables and time-lags for predicting the number of dengue cases, and the final results of the multivariate analysis were selected based on the smallest AIC value. Next, each vector index models with selected variables were subjected to multiple logistic regression models to examine the accuracy of predicting the occurrence of dengue cases. The results suggested that Model-AI, BI, CI and HI predicted the occurrence of dengue cases with 83.8, 87.8, 88.3 and 88.4% accuracy, respectively. The predicting threshold based on individual Model-AI, BI, CI and HI was 0.97, 1.16, 1.79 and 0.997, respectively.Conclusion/significanceThere was little evidence of quantifiable association among vector indices, meteorological factors and dengue transmission that could reliably be used for outbreak prediction. Our study here provided the proof-of-concept of how to search for the optimal model and determine the threshold for dengue epidemics. Since those factors used for prediction varied, depending on the ecology and herd immunity level under different geological areas, different thresholds may be developed for different countries using a similar structure of the two-stage model.
| S-EPMC4569482 | biostudies-literature
