Spatiotemporal Bayesian networks for malaria prediction

Haddawy, Peter; Hasan, Ali Hussein; Kasantikul, Rangwan; Lawpoolsri, Saranath; Sa-angchai, Patiwat; Kaewkungwal, Jaranit; Singhasivanon, Pratap

doi:10.1016/j.artmed.2017.12.002

Cited by 54 publications

(27 citation statements)

References 20 publications

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“…In modeling relationships with a machine learning approach, the computer incorporates connections not obvious to human beings to successfully predict an outcome of interest. Machine learning is applicable in many fields and has been previously used in medical applications, to estimate clinical risk, guide triage or diagnose disease [23,[26][27][28]. Clinical applications of machine learning for arboviral illnesses, specifically, have included analysis of patient genomes for dengue prognosis [29], scanning of patient sera for DENV [30] or Zika diagnosis [31], thermal image scanning for detection of hemodynamic shock [32], analysis of body temperature patterns for diagnosis of undifferentiated fever etiology [33], and analysis of patient data for dengue fever diagnosis [27].…”

Section: Introductionmentioning

confidence: 99%

Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

et al. 2020

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Background Dengue, chikungunya, and Zika are arboviruses of major global health concern. Decisions regarding the clinical management of suspected arboviral infection are challenging in resource-limited settings, particularly when deciding on patient hospitalization. The objective of this study was to determine if hospitalization of individuals with suspected arboviral infections could be predicted using subject intake data. Methodology/Principal findings Two prediction models were developed using data from a surveillance study in Machala, a city in southern coastal Ecuador with a high burden of arboviral infections. Data were obtained from subjects who presented at sentinel medical centers with suspected arboviral infection (November 2013 to September 2017). The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-used only demographic and symptom data. The second prediction model-called the Severity Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These models were selected by comparing the prediction ability of seven machine learning algorithms; the area under the receiver operating characteristic curve from the prediction of a test dataset was used to

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Section: Introductionmentioning

confidence: 99%

Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

et al. 2020

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“…Among non-Thais, migrant workers from Myanmar represent the largest population of foreign workers [ 25 , 36 ]. There are also displaced people without a nationality and illegal immigrants in considerable numbers [ 37 , 38 ]. A study of mosquito vectors in these study areas found that P. falciparum infections were more concentrated seasonally among the recent migrant population while P. vivax cases were significantly associated with the dynamics of the local mosquito population and less with migrant status [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Bayesian spatiotemporal analysis of malaria infection along an international border: Hlaingbwe Township in Myanmar and Tha-Song-Yang District in Thailand

Thway

Rotejanaprasert

Sattabongkot

et al. 2018

Malar J

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BackgroundOne challenge in moving towards malaria elimination is cross-border malaria infection. The implemented measures to prevent and control malaria re-introduction across the demarcation line between two countries require intensive analyses and interpretation of data from both sides, particularly in border areas, to make correct and timely decisions. Reliable maps of projected malaria distribution can help to direct intervention strategies. In this study, a Bayesian spatiotemporal analytic model was proposed for analysing and generating aggregated malaria risk maps based on the exceedance probability of malaria infection in the township-district adjacent to the border between Myanmar and Thailand. Data of individual malaria cases in Hlaingbwe Township and Tha-Song-Yang District during 2016 were extracted from routine malaria surveillance databases. Bayesian zero-inflated Poisson model was developed to identify spatial and temporal distributions and associations between malaria infections and risk factors. Maps of the descriptive statistics and posterior distribution of predicted malaria infections were also developed.ResultsA similar seasonal pattern of malaria was observed in both Hlaingbwe Township and Tha-Song-Yang District during the rainy season. The analytic model indicated more cases of malaria among males and individuals aged ≥ 15 years. Mapping of aggregated risk revealed consistently high or low probabilities of malaria infection in certain village tracts or villages in interior parts of each country, with higher probability in village tracts/villages adjacent to the border in places where it could easily be crossed; some border locations with high mountains or dense forests appeared to have fewer malaria cases. The probability of becoming a hotspot cluster varied among village tracts/villages over the year, and some had close to no cases all year.ConclusionsThe analytic model developed in this study could be used for assessing the probability of hotspot cluster, which would be beneficial for setting priorities and timely preventive actions in such hotspot cluster areas. This approach might help to accelerate reaching the common goal of malaria elimination in the two countries.Electronic supplementary materialThe online version of this article (10.1186/s12936-018-2574-0) contains supplementary material, which is available to authorized users.

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“…It is revealed as the best method for complex biological regulatory networks modelling 13,14 . Because the surveillance data of infectious disease also has four similar characteristics: high noise, nonlinear correlation, small sample and latent variables as the gene expression data 15 , the surveillance data of infectious disease can also be fitted to the DBN model theoretically, and it has been initially proved a good fit handling four challenges mentioned in infectious disease surveillance data 15 , and proved a good function in infectious disease prediction model 16,17 .…”

Section: Dynamic Bayesian Networkmentioning

confidence: 99%

Spatial Transmission Network Construction of Influenza-Like Illness Using Dynamic Bayesian Network and Vector-Autoregressive Moving Average Model

Qiu

Wang

Zhang

et al. 2020

Preprint

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Background: Influenza is an acute respiratory infection caused by an influenza virus, and the primary intervention strategy is seasonal vaccine. Due to various influenza strains and their rapid mutation each year, how to recognize the key population and timing of the vaccination becomes essential. Considering the importance of finding possible spreading directions and effects of influenza between cities for department of influenza prevention, the construction of influenza transmission network becomes meaningful.Methods: 21 cities in Sichuan province were divided into different learning communities according to whether they were adjacent to each other or not. In each community, the first-order conditional dependencies approximation algorithm was performed to learn the possible structure of the time-lagged correlations between different time series vectors of the ILI estimated weekly number, and the vector autoregressive moving average models were performed for learning the lag orders and parameters of the time-lagged correlations between different time series vectors in each community.Results: It detected a number of significant time-lagged correlations between cities in Sichuan province using two models, and the lag was from 1 week to 3 weeks. The parameters indicating the suspected propagation relationship were between -0.90 and 0.75, and the proportion of the negative values in parameters increased with time. Furthermore, the spreading routes learning from two models were almost in accordance with the traffic network of Sichuan province.Conclusions: This study proposed an innovative framework for exploring the potentially stable transmission routes between different regions and measuring specific size of the transmission effect. It could be used for the infectious disease key area confirmation by considering their adjacent areas’ incidence and the transmission relationship.

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Spatiotemporal Bayesian networks for malaria prediction

Cited by 54 publications

References 20 publications

Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

Bayesian spatiotemporal analysis of malaria infection along an international border: Hlaingbwe Township in Myanmar and Tha-Song-Yang District in Thailand

Spatial Transmission Network Construction of Influenza-Like Illness Using Dynamic Bayesian Network and Vector-Autoregressive Moving Average Model

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