Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data

Abstract

Background The goal of this research is to create a system that can use the available relevant information about the factors responsible for the spread of dengue and; use it to predict the occurrence of dengue within a geographical region, so that public health experts can prepare for, manage and control the epidemic. Our study presents new geospatial insights into our understanding and management of health, disease and health-care systems. Methods We present a machine learning-based methodology capable of providing forecast estimates of dengue prediction in each of the fifty districts of Thailand by leveraging data from multiple data sources. Using a set of prediction variables, we show an increase in prediction accuracy of the model with an optimal combination of predictors which include: meteorological data, clinical data, lag variables of disease surveillance, socioeconomic data and the data encoding spatial dependence on dengue transmission. We use Generalized Additive Models (GAMs) to fit the relationships between the predictors (with a lag of one month) and the clinical data of Dengue hemorrhagic fever (DHF) using the data from 2008 to 2012. Using the data from 2013 to 2015 and a comparative set of prediction models, we evaluate the predictive ability of the fitted models according to RMSE and SRMSE as well as using adjusted R-squared value, deviance explained and change in AIC. Results The model allows for combining different predictors to make forecasts with a lead time of one month and also describe the statistical significance of the variables used to characterize the forecast. The discriminating ability of the final model was evaluated against Bangkok specific constant threshold and WHO moving threshold of the epidemic in terms of specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). Conclusions The out-of-sample validation showed poorer results than the in-sample validation, however it demonstrated ability in detecting outbreaks up-to one month ahead. We also determine that for the predicting dengue outbreaks within a district, the influence of dengue incidences and socioeconomic data from the surrounding districts is statistically significant. This validates the influence of movement patterns of people and spatial heterogeneity of human activities on the spread of the epidemic.

Present and future climatic suitability for dengue fever in Africa

“…presence/absence of a vector, to model and predict the spatial heterogeneity in disease risk [22], whereas other studies only use epidemiological data, i.e. the presence/absence data on disease cases [4,23,24]. However, World Health Organization (WHO) recommends to integrate epidemiological and entomological information for the analyses of vector-borne diseases [25].…”

COVID-19 Outbreak Prediction with Machine Learning

“…3. Often to acquire information on class R, several novel models included data from social media or call data records (CDR), which showed promising results [18][19][20][21][22][23][24][25]. However, observation of the behavior of COVID-19 in several countries demonstrates a high degree of uncertainty and complexity [26].…”

Forecasting COVID-19 outbreak progression using hybrid polynomial-Bayesian ridge regression model

“…In [16] author proposed machine learning models (XGBoost and Multi-Output Regressor) to predict confirmed cases over the coming 24 days in every province of South Korea with 82.4% accuracy. As we have already discussed, a study in [10], proposed to control the syncytial virus in infants, same as for China, a modified SEIR and AI prediction of the trend of the epidemic of COVID-19 has been proposed in this study [17]. Different research also takes place on the cases of India but using different methods, and autoregression integrated moving average model (ARIMA) and Richard's model [18].…”