Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors

BACKGROUND: During the last decades, dengue viruses have spread throughout the Americas region, with an increase in the number of severe forms of dengue. The surveillance system in Guadeloupe (French West Indies) is currently operational for the detection of early outbreaks of dengue. The goal of the study was to improve this surveillance system by assessing a modelling tool to predict the occurrence of dengue epidemics few months ahead and thus to help an efficient dengue control. METHODS: The Box-Jenkins approach allowed us to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA) model of dengue incidence from 2000 to 2006 using clinical suspected cases. Then, this model was used for calculating dengue incidence for the year 2007 compared with observed data, using three different approaches: 1 year-ahead, 3 months-ahead and 1 month-ahead. Finally, we assessed the impact of meteorological variables (rainfall, temperature and relative humidity) on the prediction of dengue incidence and outbreaks, incorporating them in the model fitting the best. RESULTS: The 3 months-ahead approach was the most appropriate for an effective and operational public health response, and the most accurate (Root Mean Square Error, RMSE = 0.85). Relative humidity at lag-7 weeks, minimum temperature at lag-5 weeks and average temperature at lag-11 weeks were variables the most positively correlated to dengue incidence in Guadeloupe, meanwhile rainfall was not. The predictive power of SARIMA models was enhanced by the inclusion of climatic variables as external regressors to forecast the year 2007. Temperature significantly affected the model for better dengue incidence forecasting (p-value = 0.03 for minimum temperature lag-5, p-value = 0.02 for average temperature lag-11) but not humidity. Minimum temperature at lag-5 weeks was the best climatic variable for predicting dengue outbreaks (RMSE = 0.72). CONCLUSION: Temperature improves dengue outbreaks forecasts better than humidity and rainfall. SARIMA models using climatic data as independent variables could be easily incorporated into an early (3 months-ahead) and reliably monitoring system of dengue outbreaks. This approach which is practicable for a surveillance system has public health implications in helping the prediction of dengue epidemic and therefore the timely appropriate and efficient implementation of prevention activities

Relationship between Transmission Intensity and Incidence of Dengue Hemorrhagic Fever in Thailand

An infection with dengue virus may lead to dengue hemorrhagic fever (DHF), a dangerous illness. There is no approved vaccine for this most prevalent mosquito-borne virus, which infects tens of millions (or more) people annually. Therefore, health authorities have been putting an emphasis on reduction of vector mosquitoes, genus Aedes. However, a new mathematical hypothesis predicted, quite paradoxically, that reducing Aedes mosquitoes in highly endemic countries may “increase” the incidence of DHF. To test this hypothesis based upon actual data, we compared DHF incidence collected from each of 1,000 districts in Thailand to data of Aedes abundance, which was obtained by surveying one million households. This analysis showed that reducing Aedes abundance from the highest level in Thailand to a moderate level would increase the incidence by more than 40%. In addition, we developed computer simulation software based upon the above hypothesis. The simulation predicted that epidemiological studies should be continued for a very long duration, preferably over a decade, to clearly detect such a paradoxical relationship between Aedes abundance and incidence of DHF. Such long-term studies are necessary, especially because tremendous efforts and resources have been (and perhaps will be) spent on combating Aedes

Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics

Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The unavailability of specific drugs and ready-to-use vaccines to prevent most of these epidemics makes the situation worse. These force public health officials and policymakers to rely on early warning systems generated by reliable and accurate forecasts of epidemics. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyse a wide variety of epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it EWNet model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with several statistical, machine learning, and deep learning models. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods

A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico

© 2013 IEEE. The mosquito-borne dengue fever is a major public health problem in tropical countries, where it is strongly conditioned by climate factors such as temperature. In this paper, we formulate a holistic machine learning strategy to analyze the temporal dynamics of temperature and dengue data and use this knowledge to produce accurate predictions of dengue, based on temperature on an annual scale. The temporal dynamics are extracted from historical data by utilizing a novel multi-stage combination of auto-encoding, window-based data representation and trend-based temporal clustering. The prediction is performed with a trend association-based nearest neighbour predictor. The effectiveness of the proposed strategy is evaluated in a case study that comprises the number of dengue and dengue hemorrhagic fever cases collected over the period 1985-2010 in 32 federal states of Mexico. The empirical study proves the viability of the proposed strategy and confirms that it outperforms various state-of-the-art competitor methods formulated both in regression and in time series forecasting analysis

Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore

Background: Rainfall patterns are one of the main drivers of dengue transmission as mosquitoes require standing water to reproduce. However, excess rainfall can be disruptive to the Aedes reproductive cycle by “flushing out” aquatic stages from breeding sites. We developed models to predict the occurrence of such “flushing” events from rainfall data and to evaluate the effect of flushing on dengue outbreak risk in Singapore between 2000 and 2016. Methods: We used machine learning and regression models to predict days with “flushing” in the dataset based on entomological and corresponding rainfall observations collected in Singapore. We used a distributed lag nonlinear logistic regression model to estimate the association between the number of flushing events per week and the risk of a dengue outbreak. Results: Days with flushing were identified through the developed logistic regression model based on entomological data (test set accuracy = 92%). Predictions were based upon the aggregate number of thresholds indicating unusually rainy conditions over multiple weeks. We observed a statistically significant reduction in dengue outbreak risk one to six weeks after flushing events occurred. For weeks with five or more flushing events, compared with weeks with no flushing events, the risk of a dengue outbreak in the subsequent weeks was reduced by 16% to 70%. Conclusions: We have developed a high accuracy predictive model associating temporal rainfall patterns with flushing conditions. Using predicted flushing events, we have demonstrated a statistically significant reduction in dengue outbreak risk following flushing, with the time lag well aligned with time of mosquito development from larvae and infection transmission. Vector control programs should consider the effects of hydrological conditions in endemic areas on dengue transmission.Charles Stark Draper Laborator

Applications of big data approaches to topics in infectious disease epidemiology

The availability of big data (i.e., a large number of observations and variables per observation) and advancements in statistical methods present numerous exciting opportunities and challenges in infectious disease epidemiology. The studies in this dissertation address questions regarding the epidemiology of dengue and sepsis by applying big data and traditional epidemiologic approaches. In doing so, we aim to advance our understanding of both diseases and to critically evaluate traditional and novel methods to understand how these approaches can be leveraged to improve epidemiologic research. In the first study, we examined the ability of machine learning and regression modeling approaches to predict dengue occurrence in three endemic locations. When we utilized models with historical surveillance, population, and weather data, machine learning models predicted weekly case counts more accurately than regression models. When we removed surveillance data, regression models were more accurate. Furthermore, machine learning models were able to accurately forecast the onset and duration of dengue outbreaks up to 12 weeks in advance without using surveillance data. This study highlighted potential benefits that machine learning models could bring to a dengue early warning system. The second study utilized machine learning approaches to identify the rainfall conditions which lead to mosquito larvae being washed away from breeding sites occurring in roadside storm drains in Singapore. We then used conventional epidemiologic approaches to evaluate how the occurrence of these washout events affect dengue occurrence in subsequent weeks. This study demonstrated an inverse relationship between washout events and dengue outbreak risk. The third study compared algorithmic-based and conventional epidemiologic approaches used to evaluate variables for statistical adjustment. We used these approaches to identify what variables to adjust for when estimating the effect of autoimmune disease on 30-day mortality among ICU patients with sepsis. In this study, autoimmune disease presence was associated with an approximate 10-20% reduction in mortality risk. Risk estimates identified with algorithmic-based approaches were compatible with conventional approaches and did not differ by more than 9%. This study revealed that algorithmic-based approaches can approximate conventional selection methods, and may be useful when the appropriate set of variables to adjust for is unknown

Forecast analysis of any opportunistic infection among HIV positive individuals on antiretroviral therapy in Uganda

Data on monthly prevalence of any opportunistic infection among HIV positive individuals on HAART in TASO, Uganda (2004-2013). (XLS 34 kb

Correlation Study between Elevation, Population Density, and Dengue Hemorrhagic Fever in Kendari City in 2014–2018

BACKGROUND: The incidence of dengue hemorrhagic fever (DHF) has experienced rapid development throughout the world in recent decades. Indonesia was reported as the 2nd country with the largest DHF cases among 30 endemic countries. Dengue virus can develop properly based on certain regional conditions. The elevation is an important factor that can affect the presence of dengue vector mosquitoes. High population density contributes to dengue transmission by increasing the contact between infected mosquitoes and human hosts. AIM: This study aimed to determine the correlation between elevation and population density with the incidence of dengue in Kendari City in 2014–2018. METHODS: This research is an observational analytic study with ecological study design. Data incidence of DHF in 2014–2018, elevation and population density were respectively obtained from the Health Office of Kendari City, Meteorology, Climatology and Geophysics Agency of Kendari City, Statistics Agency of Kendari City. The analysis of the data used in the study is univariate and bivariate analysis. Bivariate analysis using Pearson correlation test was performed. RESULTS: The results showed that the correlation between elevation and DHF (p = 0.014, r = 0.339) and the correlation between population density and DHF (p = 0.186). CONCLUSION: It can be concluded that there is significant correlation with positive direction between elevation and the DHF, and there is no significant correlation between population density and DHF incidence in Kendari City in 2014–2018

Early clinical and laboratory risk factors of intensive care unit requirement during 2004–2008 dengue epidemics in Singapore: a matched case–control study

Background: Dengue infection can result in severe clinical manifestations requiring intensive care. Effective triage is critical for early clinical management to reduce morbidity and mortality. However, there is limited knowledge on early risk factors of intensive care unit (ICU) requirement. This study aims to identify early clinical and laboratory risk factors of ICU requirement at first presentation in hospital and 24 hours prior to ICU requirement. Method: A retrospective 1:4 matched case–control study was performed with 27 dengue patients who required ICU, and 108 dengue patients who did not require ICU from year 2004–2008, matched by year of dengue presentation. Univariate and multivariate conditional logistic regression were performed. Optimal predictive models were generated with statistically significant risk factors identified using stepwise forward and backward elimination method. Results: ICU dengue patients were significantly older (P=0.003) and had diabetes (P=0.031), compared with non-ICU dengue patients. There were seven deaths among ICU patients at median seven days post fever. At first presentation, the WHO 2009 classification of dengue severity was significantly associated (P<0.001) with ICU, but not the WHO 1997 classification. Early clinical risk factors at presentation associated with ICU requirement were hematocrit change ≥20% concurrent with platelet <50 K [95% confidence-interval (CI)=2.46-30.53], hypoproteinemia (95% CI=1.09-19.74), hypotension (95% CI=1.83-31.79) and severe organ involvement (95% CI=3.30-331). Early laboratory risk factors at presentation were neutrophil proportion (95% CI=1.04-1.17), serum urea (95% CI=1.02-1.56) and alanine aminotransferase level (95% CI=1.001-1.06). This predictive model has sensitivity and specificity up to 88%. Early laboratory risk factors at 24 hours prior to ICU were lymphocyte (95% CI=1.03-1.38) and monocyte proportions (95% CI=1.02-1.78), pulse rate (95% CI=1.002-1.14) and blood pressure (95% CI=0.92-0.996). This predictive model has sensitivity and specificity up to 88.9% and 78%, respectively. Conclusions: This is the first matched case–control study, to our best knowledge, that identified early clinical and laboratory risk factors of ICU requirement during hospitalization. These factors suggested differential pathophysiological background of dengue patients as early as first presentation prior to ICU requirement, which may reflect the pathogenesis of dengue severity. These risk models may facilitate clinicians in triage of patients, after validating in larger independent studies.Published versio