Dataset Information

Individual and Spatial Risk of Dengue Virus Infection in Puerto Maldonado, Peru.

ABSTRACT: Dengue virus (DENV) affects more than 100 countries worldwide. Dengue virus infection has been increasing in the southern Peruvian Amazon city of Puerto Maldonado since 2000. We designed this study to describe the prevalence of past DENV infection and to evaluate risk factors. In 2012, we conducted a cross-sectional serosurvey and administered a knowledge, attitudes, and practices (KAP) questionnaire to members of randomly selected households. Sera were screened for antibodies to DENV by enzyme-linked immunosorbent assay and confirmed by plaque reduction neutralization test. We created indices for KAP (KAPi). We used SaTScan (Martin Kulldorff with Information Management Services Inc., Boston, MA) to detect clustering and created a multivariate model introducing the distance of households to potential vector and infection sources. A total of 505 participants from 307 households provided a blood sample and completed a questionnaire. Fifty-four percent of participants (95% confidence interval [CI]: 49.6; 58.5) had neutralizing antibodies to DENV. Higher values of KAPi were positively associated with having DENV antibodies in the multivariate analysis (odds ratio [OR_II]: 1.6, 95% CI: 0.6, 2.4; OR_III: 2.7, 95% CI: 1.3, 5.5; and OR_IV: 2.4, 95% CI: 1.2, 5.0). Older groups had lower chances of having been exposed to DENV than younger people (OR_20-30: 0.5, 95% CI: 0.2, 0.8; OR_31-45: 0.5, 95% CI: 0.3, 0.9; and OR_>45: 0.6, 95% CI: 0.3, 1.3). Multivariate data analysis from the 270 households with location information showed male gender to have lower risk of past DENV infection (OR: 0.6, 95% CI: 0.4, 0.9). We conclude that risk of DENV infection in Puerto Maldonado is related to gender, age of the population, and location.

SUBMITTER: Salmon-Mulanovich G

PROVIDER: S-EPMC6283522 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Individual and Spatial Risk of Dengue Virus Infection in Puerto Maldonado, Peru.

Salmón-Mulanovich Gabriela G Blazes David L DL Guezala V M Claudia MC Rios Zonia Z Espinoza Angelica A Guevara Carolina C Lescano Andrés G AG Montgomery Joel M JM Bausch Daniel G DG Pan William K WK

The American journal of tropical medicine and hygiene 20181201 6

Dengue virus (DENV) affects more than 100 countries worldwide. Dengue virus infection has been increasing in the southern Peruvian Amazon city of Puerto Maldonado since 2000. We designed this study to describe the prevalence of past DENV infection and to evaluate risk factors. In 2012, we conducted a cross-sectional serosurvey and administered a knowledge, attitudes, and practices (KAP) questionnaire to members of randomly selected households. Sera were screened for antibodies to DENV by enzyme- ...[more]

PMID: 30298808

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Project description:BackgroundPredictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models.MethodsWe developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC).ResultsFor near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61.ConclusionsOur results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models.

Project description:BackgroundKnowledge of spatial patterns of dengue virus (DENV) infection is important for understanding transmission dynamics and guiding effective disease prevention strategies. Because movement of infected humans and mosquito vectors plays a role in the spread and persistence of virus, spatial dimensions of transmission can range from small household foci to large community clusters. Current understanding is limited because past analyses emphasized clinically apparent illness and did not account for the potentially large proportion of inapparent infections. In this study we analyzed both clinically apparent and overall infections to determine the extent of clustering among human DENV infections.Methodology/principal findingsWe conducted spatial analyses at global and local scales, using acute case and seroconversion data from a prospective longitudinal cohort in Iquitos, Peru, from 1999-2003. Our study began during a period of interepidemic DENV-1 and DENV-2 transmission and transitioned to epidemic DENV-3 transmission. Infection status was determined by seroconversion based on plaque neutralization testing of sequential blood samples taken at approximately six-month intervals, with date of infection assigned as the middate between paired samples. Each year was divided into three distinct seasonal periods of DENV transmission. Spatial heterogeneity was detected in baseline seroprevalence for DENV-1 and DENV-2. Cumulative DENV-3 seroprevalence calculated by trimester from 2001-2003 was spatially similar to preexisting DENV-1 and DENV-2 seroprevalence. Global clustering (case-control Ripley's K statistic) appeared at radii of ?200-800 m. Local analyses (Kuldorf spatial scan statistic) identified eight DENV-1 and 15 DENV-3 clusters from 1999-2003. The number of seroconversions per cluster ranged from 3-34 with radii from zero (a single household) to 750 m; 65% of clusters had radii >100 m. No clustering was detected among clinically apparent infections.Conclusions/significanceSeroprevalence of previously circulating DENV serotypes can be a predictor of transmission risk for a different invading serotype and, thus, identify targets for strategically placed surveillance and intervention. Seroprevalence of a specific serotype is also important, but does not preclude other contributing factors, such as mosquito density, in determining where transmission of that virus will occur. Regardless of the epidemiological context or virus serotype, human movement appears to be an important factor in defining the spatial dimensions of DENV transmission and, thus, should be considered in the design and evaluation of surveillance and intervention strategies.

Project description:BackgroundThe World Health Organization estimates that the global number of dengue infections range between 80-100 million per year, with some studies estimating approximately three times higher numbers. Furthermore, the geographic range of dengue virus transmission is extending with the disease now occurring more frequently in areas such as southern Europe. Ae. aegypti, one of the most prominent dengue vectors, is endemic to the far north-east of Australia and the city of Cairns frequently experiences dengue outbreaks which sometimes lead to large epidemics.MethodA spatially-explicit, individual-based mathematical model that accounts for the spread of dengue infection as a result of human movement and mosquito dispersion is presented. The model closely couples the four key sub-models necessary for representing the overall dynamics of the physical system, namely those describing mosquito population dynamics, human movement, virus transmission and vector control. Important features are the use of high quality outbreak data and mosquito trapping data for calibration and validation and a strategy to derive local mosquito abundance based on vegetation coverage and census data.ResultsThe model has been calibrated using detailed 2003 dengue outbreak data from Cairns, together with census and mosquito trapping data, and is shown to realistically reproduce a further dengue outbreak. The simulation results replicating the 2008/2009 Cairns epidemic support several hypotheses (formulated previously) aimed at explaining the large-scale epidemic which occurred in 2008/2009; specifically, while warmer weather and increased human movement had only a small effect on the spread of the virus, a shorter virus strain-specific extrinsic incubation time can explain the observed explosive outbreak of 2008/2009.ConclusionThe proof-of-concept simulation model described in this study has potential as a tool for understanding factors contributing to dengue spread as well as planning and optimizing dengue control, including reducing the Ae. aegypti vector population and for estimating the effectiveness and cost-effectiveness of future vaccination programmes. This model could also be applied to other vector borne viral diseases such as chikungunya, also spread by Ae. aegypti and, by re-parameterisation of the vector sub-model, to dengue and chikungunya viruses spread by Aedes albopictus.

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Individual and Spatial Risk of Dengue Virus Infection in Puerto Maldonado, Peru.

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Individual and Spatial Risk of Dengue Virus Infection in Puerto Maldonado, Peru.

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