A Remote Sensing Data Based Artificial Neural Network Approach for Predicting Climate-Sensitive Infectious Disease Outbreaks: A Case Study of Human Brucellosis

Abstract

Remote sensing technologies can accurately capture environmental characteristics, and together with environmental modeling approaches, help to predict climate-sensitive infectious disease outbreaks. Brucellosis remains rampant worldwide in both domesticated animals and humans. This study used human brucellosis (HB) as a test case to identify important environmental determinants of the disease and predict its outbreaks. A novel artificial neural network (ANN) model was developed, using annual county-level numbers of HB cases and data on 37 environmental variables, potentially associated with HB in Inner Mongolia, China. Data from 2006 to 2008 were used to train, validate and test the model, while data for 2009-2010 were used to assess the model's performance. The Enhanced Vegetation Index was identified as the most important predictor of HB incidence, followed by land surface temperature and other temperature-and precipitation-related variables. The suitable ecological niche of HB was modeled based on these predictors. Model estimates were found to be in good agreement with reported numbers of HB cases in both the model development and assessment phases. The study suggests that HB outbreaks may be predicted, with a reasonable degree of accuracy, using the ANN model and environmental variables obtained from satellite data. The study deepened the understanding of environmental determinants of HB and advanced the methodology for prediction of climate-sensitive infectious disease outbreaks.

Prediction of Human Brucellosis in China Based on Temperature and NDVI

“…Previous publications have focused on the epidemiological characteristics and spatial distribution of the disease [15,25,26,27], while time series-based predictive models have been used to provide early warnings of disease occurrence. Common models include residual autoregression models, the exponential smoothing method (ES) [28], grayscale models, negative binomial regression models, artificial neural network models [26], autoregressive integrated moving average models (ARIMAs) [29,30], and their syntheses [31]. ARIMA models are optimal when the time series contains long-term trend, periodicity, and disturbance terms [32,33,34,35].…”

“…Therefore, this study aims to fill this research gap. Brucellosis bacteria are particularly sensitive to temperature [26], especially surface temperature. In addition, vegetation cover can provide suitable hydrothermal conditions for bacterial survival.…”

How decision makers can use quantitative approaches to guide outbreak responses

“…It is possible that these applications could be extended to analyse data from social media, purchasing patterns, travel data and qualitative data from other sources to gain greater insights into behavioural risk factors for outbreak control. In the future, ML could also be helpful for analysing large datasets from large or complex non-health sector data sources that may help us understand transmission risk factors, such as mobile phone data for movement patterns or remote sensing data for environmental exposures [21,22]. Combining these data sources with the analysis of other epidemiological data can increase our understanding of outbreaks.…”

A survey on prediction approaches for epidemic disease outbreaks based on social media data

“…The examination expanded the understanding of environmental determinants of HB and moved the system for forecast of air that delicate powerful sickness flare-ups. Wang et.al [30] Y. Zhang et.al [31] proposes a Poisson-relapse based model first with the intra-common and between nearby factors included and the Empirical evaluations are coordinated subject to a certified enlightening list which records the 16-days-declared cases in the Yunnan zone of China for quite a while, from 2005 to 2011. To get familiar with the structure of the dissemination lattice, he proposes two methodologies -using somewhere in the range of from the earlier learning and assessing it sans preparation by means of a scanty structure suspicion.…”