Tetanus in Southern Vietnam: Current Situation

Thuy, Duong Bich; Campbell, James; Thanh, Tran Tan; Thuy, Cao Thu; Loan, Huỳnh Thị; Hảo, Nguyễn Văn; Minh, Yen Lam; Tan, Le Van; Boni, Maciej F.; Thwaites, C. Louise

doi:10.4269/ajtmh.16-0470

Cited by 22 publications

(24 citation statements)

References 11 publications

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“…Test results were found to be a more reliable predictor of where gaps in immunity led to outbreaks in 2014 than were national coverage data or data from the UNICEF Multiple Indicator Cluster Surveys or Demographic and Health Surveys ( 14 ). Similarly, samples from the OUCRU 13-site serum bank have been tested for tetanus antibodies in Ho Chi Minh City; the results showed gaps in immunity to tetanus, consistent with the observed age distribution of tetanus case-patients, which should be addressed by additional vaccination ( 31 ). …”

Section: Types Of Relevant Serosurveillance Collections Already Undersupporting

confidence: 59%

“…Convenience sampling introduces bias because the sampled population may not be representative. However, this type of sampling is less expensive and easier to undertake and therefore can be conducted more regularly and may still provide relevant information, as has been illustrated for measles and tetanus in Vietnam ( 14 , 31 ). …”

Section: Collection Types and Their Pros And Consmentioning

confidence: 99%

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Plans for Nationwide Serosurveillance Network in Vietnam

Anh¹,

Choisy²,

Clapham³

et al. 2020

Emerg. Infect. Dis.

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In recent years, serosurveillance has gained momentum as a way of determining disease transmission and immunity in populations, particularly with respect to vaccine-preventable diseases. At the end of 2017, the Oxford University Clinical Research Unit and the National Institute of Hygiene and Epidemiology held a meeting in Vietnam with national policy makers, researchers, and international experts to discuss current seroepidemiologic projects in Vietnam and future needs and plans for nationwide serosurveillance. This report summarizes the meeting and the plans that were discussed to set up nationwide serosurveillance in Vietnam.

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Section: Types Of Relevant Serosurveillance Collections Already Undersupporting

confidence: 59%

Section: Collection Types and Their Pros And Consmentioning

confidence: 99%

Plans for Nationwide Serosurveillance Network in Vietnam

Anh¹,

Choisy²,

Clapham³

et al. 2020

Emerg. Infect. Dis.

View full text Add to dashboard Cite

show abstract

“…The Fourier transform for the frequency-domain features (f l and f w ) pose a computational cost of O(L log (L)) associated with the FFT computation. In addition, we provide (1). The number of patients per class is shown in brackets.…”

Section: Complexity Analysismentioning

confidence: 99%

“…Tetanus is a severe disease, often necessitating lengthy hospital treatment (up to six weeks), which was estimated to have caused 48-80,000 deaths in 2015 [2]. It tends to affect the poorest in society in lowand middle-income countries where unvaccinated individuals, particularly manual workers and farmers, are at high risk of contracting it [1,[3][4][5]. A recent study showed that tetanus prevalence is still high in a part of Asia and that it is associated with high morbidity and mortality rates [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Severity detection tool for patients with infectious disease

Tadesse

Zhu

Thành

et al. 2020

Healthcare Technology Letters

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Hand foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low and middle income countries. Tetanus in particular has a high mortality rate and its treatment is resource-demanding. Furthermore, HFMD often affects a large number of infants and young children. As a result, its treatment consumes enormous healthcare resources, especially when outbreaks occur. Autonomic nervous system dysfunction (ANSD) is the main cause of death for both HFMD and tetanus patients. However, early detection of ANSD is a difficult and challenging problem. In this paper, we aim to provide a proof-of-principle to detect the ANSD level automatically by applying machine learning techniques to physiological patient data, such as electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms, which can be collected using low-cost wearable sensors. Efficient features are extracted that encode variations in the waveforms in the time and frequency domains. A support vector machine is employed to classify the ANSD levels. The proposed approach is validated on multiple datasets of HFMD and tetanus patients in Vietnam. Results show that encouraging performance is achieved in classifying ANSD levels. Moreover, the proposed features are simple, more generalisable and outperformed the standard heart rate variability (HRV) analysis. The proposed approach would facilitate both the diagnosis and treatment of infectious diseases in low and middle income countries, and thereby improve overall patient care.

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“…Infectious diseases, such as tetanus and hand, foot and mouth disease (HFMD), can still be life-threatening conditions for patients in low and middle income countries [1]. Tetanus often affects the poorest in society in low and middle income countries, and it was estimated to have caused 48-80,000 deaths in 2015 [1]- [4]. Tetanus cases can progress to severe conditions in many cases, and the subsequent hospital treatment is often lengthy (up to six weeks).…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Diagnosis of Infectious Diseases in the Developing World

Tadesse

Javed

Thành

et al. 2020

IEEE J. Biomed. Health Inform.

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In low and middle income countries, infectious diseases continue to have a significant impact, particularly amongst the poorest in society. Tetanus and hand foot and mouth disease (HFMD) are two such diseases and, in both, death is associated with autonomic nervous system dysfunction (ANSD). Currently, photoplethysmogram or electrocardiogram monitoring is used to detect deterioration in these patients, however expensive clinical monitors are often required. In this study, we employ low-cost and mobile wearable devices to collect patient vital signs unobtrusively; and we develop machine learning algorithms for automatic and rapid triage of patients that provide efficient use of clinical resources. Existing methods are mainly dependent on the prior detection of clinical features with limited exploitation of multimodal physiological data. Moreover, the latest developments in deep learning (e.g. cross-domain transfer learning) have not been sufficiently applied for infectious disease diagnosis. In this paper, we present a fusion of multi-modal physiological data to predict the severity of ANSD with a hierarchy of resource-aware decision making. First, an on-site triage process is performed using a simple classifier. Second, personalised longitudinal modelling is employed that takes the previous states of the patient into consideration. We have also employed a spectrogram representation of the physiological waveforms to exploit existing networks for cross-domain transfer learning, which avoids the laborious and data intensive process of training a network from scratch. Results show that the proposed framework has promising potential in supporting severity grading of infectious diseases in low-resources settings, such as in the developing world.

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Tetanus in Southern Vietnam: Current Situation

Cited by 22 publications

References 11 publications

Plans for Nationwide Serosurveillance Network in Vietnam

Plans for Nationwide Serosurveillance Network in Vietnam

Severity detection tool for patients with infectious disease

Multi-Modal Diagnosis of Infectious Diseases in the Developing World

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