Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches

Keshavamurthy, Ravikiran; Dixon, Samuel L.; Pazdernik, Karl; Charles, Lauren

doi:10.1016/j.onehlt.2022.100439

Cited by 34 publications

(13 citation statements)

References 44 publications

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“…Despite these challenges, the role of predictive modelling in public health is indispensable. As models become more sophisticated and data sources more diverse, the potential for predictive modelling to save lives and prevent disease spread only grows, underscoring the need for continued innovation and research in this critical field (Keshavamurthy, Dixon, Pazdernik, & Charles, 2022;Ramos et al, 2024;Wu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling for Disease Outbreaks: A Review of Data Sources and Accuracy

Scholastica Ijeh,

Chioma Anthonia Okolo,

Jeremiah Olawumi Arowoogun

et al. 2024

imsrj

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This review explores the dynamic field of predictive modelling for disease outbreaks, focusing on the data sources, modelling techniques, accuracy, challenges, and future directions integral to its advancement. It underscores the significance of diverse data sources, including epidemiological, environmental, social media, and mobility data. It also discusses various modelling approaches, from statistical models to advanced machine learning algorithms and network analysis. The review highlights the critical role of accuracy and validation in predictive models, alongside the challenges posed by data quality, model complexity, and the ethical use of personal data. It outlines promising research avenues, such as improving data collection methods, integrating novel data sources like genomic data, and leveraging emerging technologies such as AI and IoT to enhance predictive capabilities. This comprehensive overview emphasizes the importance of predictive modelling in informing public health decisions and the continuous need for innovation and collaboration to address the complex dynamics of disease outbreaks. Keywords: Predictive Modeling, Disease Outbreaks, Data Sources, Machine Learning, Public Health, Emerging Technologies.

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Section: Introductionmentioning

confidence: 99%

Predictive Modeling for Disease Outbreaks: A Review of Data Sources and Accuracy

Scholastica Ijeh,

Chioma Anthonia Okolo,

Jeremiah Olawumi Arowoogun

et al. 2024

imsrj

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“…Given these limitations, there is a clear need for a more comprehensive, data-driven approach to predict the risk of DF in Singapore. Machine learning models have the potential to uncover complex, non-linear relationships and patterns within data that are not readily apparent using traditional statistical methods [ 12 , 13 ]. The proposed study aims to address these gaps by developing a precision model for risk prediction based on machine learning algorithms using meteorological data.…”

Section: Introductionmentioning

confidence: 99%

Precision Prediction for Dengue Fever in Singapore: A Machine Learning Approach Incorporating Meteorological Data

Tian,

Zheng,

et al. 2024

TropicalMed

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Objective: This study aimed to improve dengue fever predictions in Singapore using a machine learning model that incorporates meteorological data, addressing the current methodological limitations by examining the intricate relationships between weather changes and dengue transmission. Method: Using weekly dengue case and meteorological data from 2012 to 2022, the data was preprocessed and analyzed using various machine learning algorithms, including General Linear Model (GLM), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared () were employed. Results: From 2012 to 2022, there was a total of 164,333 cases of dengue fever. Singapore witnessed a fluctuating number of dengue cases, peaking notably in 2020 and revealing a strong seasonality between March and July. An analysis of meteorological data points highlighted connections between certain climate variables and dengue fever outbreaks. The correlation analyses suggested significant associations between dengue cases and specific weather factors such as solar radiation, solar energy, and UV index. For disease predictions, the XGBoost model showed the best performance with an MAE = 89.12, RMSE = 156.07, and = 0.83, identifying time as the primary factor, while 19 key predictors showed non-linear associations with dengue transmission. This underscores the significant role of environmental conditions, including cloud cover and rainfall, in dengue propagation. Conclusion: In the last decade, meteorological factors have significantly influenced dengue transmission in Singapore. This research, using the XGBoost model, highlights the key predictors like time and cloud cover in understanding dengue’s complex dynamics. By employing advanced algorithms, our study offers insights into dengue predictive models and the importance of careful model selection. These results can inform public health strategies, aiming to improve dengue control in Singapore and comparable regions.

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“…Using novel, complex, and occasionally opaque algorithms, data scientists generate new insights and generalizable knowledge. Examples of data science applications include combination of diverse data streams to develop bio-preparedness, monitoring, and response strategies for infectious diseases outbreaks in human health and in agriculture 1 . Other examples include the use of Geographical Information System (GIS) data to map spatial variations in the determinants, incidence, prevalence, and outcomes of disease, and the response of healthcare systems 2 , 3 .…”

Section: Introductionmentioning

confidence: 99%

The promise of data science for health research in Africa

Adebamowo,

Callier,

Akintola

et al. 2023

Nat Commun

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Data science health research promises tremendous benefits for African populations, but its implementation is fraught with substantial ethical governance risks that could thwart the delivery of these anticipated benefits. We discuss emerging efforts to build ethical governance frameworks for data science health research in Africa and the opportunities to advance these through investments by African governments and institutions, international funding organizations and collaborations for research and capacity development.

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Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches

Cited by 34 publications

References 44 publications

Predictive Modeling for Disease Outbreaks: A Review of Data Sources and Accuracy

Predictive Modeling for Disease Outbreaks: A Review of Data Sources and Accuracy

Precision Prediction for Dengue Fever in Singapore: A Machine Learning Approach Incorporating Meteorological Data

The promise of data science for health research in Africa

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