A Heterogeneous Ensemble Forecasting Model for Disease Prediction

Sharma, Nonita; Dev, Jaiditya; Mangla, Monika; Wadhwa, Vaishali Mehta; Mohanty, Sachi Nandan; Kakkar, Deepti

doi:10.1007/s00354-020-00119-7

Cited by 31 publications

(13 citation statements)

References 31 publications

(27 reference statements)

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“…In addition, B. anthracis spores can survive in the environment for long periods, resulting in recurrent contamination of livestock and the existence of high‐risk areas for anthrax outbreaks (Hugh‐Jones & Blackburn, 2009; Romero‐Alvarez et al., 2020; Schmid & Kaufmann, 2002). Although the disease is distributed worldwide, it is endemic to Africa, the Middle East, South America and Central Asia, where outbreaks cause substantial public health and economic burdens (Hugh‐Jones & Blackburn, 2009; Shadomy et al., 2016; Sushma et al., 2021). Furthermore, in endemic, low‐resource areas, anthrax outbreaks in livestock often lead to secondary human infections through the practice of slaughtering sick animals to recoup income or food from lost animals (Bengis & Frean, 2014; Misgie et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Modelling the ecological niche of naturally occurring anthrax at global and circumpolar extents using an ensemble modelling framework

Deka

Vieira

Bower

2022

Transbounding Emerging Dis

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Bacillus anthracis, the causative agent of anthrax, is a spore‐forming bacterium that primarily affects herbivorous livestock, wildlife and humans exposed to direct contact with infected animal carcasses or products. To date, there are a limited number of studies that have delineated the potential global distribution of anthrax, despite the importance of the disease from both an economic and public health standpoint. This study compiled occurrence data (n = 874) of confirmed human and animal cases from 1954 to 2021 in 94 countries. Using an ensemble ecological niche model framework, we developed updated maps of the global predicted ecological suitability of anthrax to measure relative risk at multiple scales of analysis, including a model for circumpolar regions. Additionally, we produced maps quantifying the disease transmission risk associated with anthrax to cattle, sheep and goat populations. Environmental suitability for B. anthracis globally is concentred throughout Eurasia, sub‐Saharan Africa, the Americas, Southeast Asia, Australia and Oceania. Suitable environments for B. anthracis at the circumpolar scale extend above the Arctic Circle into portions of Russia, Canada, Alaska and northern Scandinavia. Environmental factors driving B. anthracis suitability globally include vegetation, land surface temperature, soil characteristics, primary climate conditions and topography. At the circumpolar scale, suitability is influenced by soil factors, topography and the derived climate characteristics. The greatest risk to livestock is concentrated within the Indian subcontinent, Australia, Anatolia, the Caucasus region, Central Asia, the European Union, Argentina, Uruguay, China, the United States, Canada and East Africa. This study expands on previous work by providing enhanced knowledge of the potential spatial distribution of anthrax in the Southern Hemisphere, sub‐Saharan Africa, Asia and circumpolar regions of the Northern Hemisphere. We conclude that these updated maps will provide pertinent information to guide disease control programs, inform policymakers and raise awareness at the global level to lessen morbidity and mortality among animals and humans located in environmentally suitable areas.

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Section: Introductionmentioning

confidence: 99%

Modelling the ecological niche of naturally occurring anthrax at global and circumpolar extents using an ensemble modelling framework

Deka

Vieira

Bower

2022

Transbounding Emerging Dis

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“…Still, it is established that Auto SARIMA can efficiently predict the cases of COVID-19 despite data deprivation. The researchers and healthcare professionals across the world have been continuously working hard to deal with this epidemic outbreak [31,32]. Moreover, the pandemic spread of such type of epidemic is also closely related to the social awareness as well as stringent policies put forth by the government.…”

Section: Discussionmentioning

confidence: 99%

Predicting mortality rate and associated risks in COVID-19 patients

et al. 2021

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The genesis of novel coronavirus (COVID-19) was from Wuhan city, China in December 2019, which was later declared as a global pandemic in view of its exponential rise and spread around the world. Resultantly, the scientific and medical research communities around the globe geared up to curb its spread. In this manuscript, authors claim competence of AI-mediated methods to predict mortality rate. Efficient prediction model enables healthcare professionals to be well prepared to handle this unpredictable situation. The prime focus of the study is to investigate efficient prediction model. In order to determine the most effective prediction model, authors perform comparative analysis of numerous models. The performance of various prediction models is compared using various error metrics viz. Root mean square error, mean absolute error, mean square error and R 2 . During comparative analysis, Auto seasonal auto regressive integrated moving average model proves its competence over comparative models.

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“…. .., n), then it will be stationary when its mean and variance are constant and its auto covariances does not depend on time t [20]. Non-stationary time series can be converted into stationary by different types of processes such as smoothing, transformation and differencing.…”

Section: Stationarity Testingmentioning

confidence: 99%

An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach

Verma¹,

Sharma²,

Singh³

et al. 2022

Computers, Materials &Amp; Continua

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This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease. In addition to forecasting the occurrences of conjunctivitis incidences, the proposed model also improves performance by using the ensemble model. Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December 2019. Pre-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets. A stacked generalization ensemble model based on Auto-ARIMA (Autoregressive Integrated Moving Average), NNAR (Neural Network Autoregression), ETS (Exponential Smoothing), HW (Holt Winter) is proposed and applied on the dataset. Predictive analysis is conducted on the collected dataset of conjunctivitis disease, and further compared for different performance measures. The result shows that the RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), ACF1 (Auto Correlation Function) of the proposed ensemble is decreased significantly. Considering the RMSE, for instance, error values are reduced by 39.23%, 9.13%, 20.42%, and 17.13% in comparison to Auto-ARIMA, NAR, ETS, and HW model respectively. This research concludes that the accuracy of the forecasting of diseases can be significantly increased by applying the proposed stack generalization ensemble model as it minimizes the prediction error and hence provides better prediction trends as compared to Auto-ARIMA, NAR, ETS, and HW model applied discretely.

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A Heterogeneous Ensemble Forecasting Model for Disease Prediction

Cited by 31 publications

References 31 publications

Modelling the ecological niche of naturally occurring anthrax at global and circumpolar extents using an ensemble modelling framework

Modelling the ecological niche of naturally occurring anthrax at global and circumpolar extents using an ensemble modelling framework

Predicting mortality rate and associated risks in COVID-19 patients

An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach

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