Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

Lima, Clarisse Lins de; Silva, Ana Clara Gomes da; Moreno, Giselle Machado Magalhães; Silva, Cecilia Cordeiro da; Musah, Anwar; Aldosery, Aisha; Dutra, Lívia Márcia Mosso; Ambrizzi, Tércio; Borges, Iuri Valério Graciano; Tunalı, Merve; Basibuyuk, Selma; Yenigün, Orhan; Massoni, Tiago; Browning, Ella; Jones, Kate E.; Campos, Luiza C.; Kostkova, Patty; Filho, Abel Guilhermino da Silva; Santos, Wellington Pinheiro dos

doi:10.3389/fpubh.2022.900077

Cited by 4 publications

(2 citation statements)

References 115 publications

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“…Due to its inherent capability to perform prediction tasks, ML techniques have been increasingly utilized in the study of diseases in populations, being successfully employed in public health research, with notable examples being found in the study of air pollution 23 , arboviruses 24 , COVID-19 25 and TB 26 . Similarly, we employed ML models to better understand the most influential TB surveillance variables in the incidence of disease in the region.…”

Section: Discussionmentioning

confidence: 99%

Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning

Silva,

Motta,

Eberly

2024

Rev. bras. epidemiol.

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Objective: Tuberculosis (TB) is the second most deadly infectious disease globally, posing a significant burden in Brazil and its Amazonian region. This study focused on the “riverine municipalities” and hypothesizes the presence of TB clusters in the area. We also aimed to train a machine learning model to differentiate municipalities classified as hot spots vs. non-hot spots using disease surveillance variables as predictors. Methods: Data regarding the incidence of TB from 2019 to 2022 in the riverine town was collected from the Brazilian Health Ministry Informatics Department. Moran’s I was used to assess global spatial autocorrelation, while the Getis-Ord GI* method was employed to detect high and low-incidence clusters. A Random Forest machine-learning model was trained using surveillance variables related to TB cases to predict hot spots among non-hot spot municipalities. Results: Our analysis revealed distinct geographical clusters with high and low TB incidence following a west-to-east distribution pattern. The Random Forest Classification model utilizes six surveillance variables to predict hot vs. non-hot spots. The machine learning model achieved an Area Under the Receiver Operator Curve (AUC-ROC) of 0.81. Conclusion: Municipalities with higher percentages of recurrent cases, deaths due to TB, antibiotic regimen changes, percentage of new cases, and cases with smoking history were the best predictors of hot spots. This prediction method can be leveraged to identify the municipalities at the highest risk of being hot spots for the disease, aiding policymakers with an evidenced-based tool to direct resource allocation for disease control in the riverine municipalities.

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Section: Discussionmentioning

confidence: 99%

Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning

Silva,

Motta,

Eberly

2024

Rev. bras. epidemiol.

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“…Previous systematic reviews have been conducted to identify and characterise dengue transmission models focused on predicting trends over time (hindcasting with the goal of developing forecasting systems) as opposed to spatially explicit prediction (risk mapping) [15][16][17]. Some of these systematic reviews included risk mapping studies but they have been limited to just a single arbovirus, usually dengue [7,[18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A systematic review of the data, methods and environmental covariates used to mapAedes-borne arbovirus transmission risk

Lim

Jafari

Caldwell

et al. 2023

Preprint

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Background Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. Methods We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc). Results We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 183 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, ii) regional models used to predict the spread of major epidemics between countries and iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 33/148) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc) and only 48% of studies assessed predictive performance via out-of-sample validation procedures. Conclusions Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We outline specific recommendations for future studies regarding aims and data choice, covariate selection, model formulation and evaluation.

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Systematic reviews of machine learning in healthcare: a literature review

Kolasa,

Admassu,

Hołownia-Voloskova

et al. 2023

Expert Review of Pharmacoeconomics & Outcomes Research

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Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

Cited by 4 publications

References 115 publications

Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning

Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning

A systematic review of the data, methods and environmental covariates used to mapAedes-borne arbovirus transmission risk

Systematic reviews of machine learning in healthcare: a literature review

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