Co-infection of Dengue, Zika and Chikungunya in a group of pregnant women from Tuxtla Gutiérrez, Chiapas: Preliminary data. 2019

Background Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on Machine Learning (ML) and Deep Learning (DL) models. Method We carried out a Systematic Literature Review (SLR) in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and seven from a single backward snowballing procedure), only 15 relevant papers were identified. Results Results show that current research is focused on the binary classification of Dengue, primarily using tree-based ML algorithms. Only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its variants) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient’s quality of life.

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Section: Introductionmentioning

confidence: 99%

Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review

et al. 2022

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“…Maternal mortality was found to be significantly associated with dengue infection and dengue, chikungunya coinfection. Associations of flavivirus co-infections and mortality have also been reported earlier 11 and dengue is notoriously known to be associated with poor maternal and fetal outcomes. 6,20 Complications that have been observed previously in pregnant women include thrombocytopenia, oligohydramnios, ante-partum and post partum hemorrhages, preeclampsia, and eclampsia.…”

Section: Discussionmentioning

confidence: 57%

“…Maternal and fetal outcome were not evaluated in this study. 11 A case report from Columbia demonstrated the presence of nucleic acid of all the three viruses in a pregnant woman by a triple nested reverse transcription RTPCR, who improved clinically with proper treatment. 12 A travel advisory has also been issued in Switzerland for pregnant women against these viruses looking at their serious consequences.…”

Section: Discussionmentioning

confidence: 99%

Prevalence of Dengue, Chikungunya, and Zika Viruses in Febrile Pregnant Women: An Observational Study at a Tertiary Care Hospital in North India

Gupta

Jain

Venkatesh

et al. 2022

The American Journal of Tropical Medicine and Hygiene

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Dengue virus (DENV), chikungunya virus (CHIKV), and Zika virus (ZIKV) are arboviruses that can affect maternal and fetal outcome if acquired during pregnancy. This study was done to estimate the positivity of DENV, CHIKV, and ZIKV in febrile pregnant women attending a tertiary care hospital in north India. Symptomatic pregnant women were tested for these viruses by IgM ELISA and/or by Trioplex real-time polymerase chain reaction. Their symptoms and laboratory parameters were recorded and were followed up till delivery to know their immediate delivery outcome. Of 104 women tested, 50 (48.1%) were positive for viral markers. Of these, evidence of infection by DENV, CHIKV, and both was found in 34 (32.7%), 10 (9.6%), and 6 (5.8%), respectively. ZIKV was not detected in any woman. Maximum DENV positivity occurred in the third trimester of pregnancy and in women residing in urban than rural areas. Chills and rigors, arthralgia, retro-orbital pain, anemia, and vaginal bleeding were more commonly associated with DENV positivity. Backache, arthralgia, jaundice, and vaginal bleeding were more common in CHIKV positives but the difference between positives and nonpositives regarding these symptoms was not statistically significant. Dengue infections were associated with more frequent hospitalizations (OR = 8.38, 95% confidence intervals [CI] = 3.29–21.30) and mortality (OR = 19.0, 95% CI = 1.01–357.10). Hence, to conclude, in India wherever possible, all symptomatic pregnant women should be screened for DENV, CHIKV, and ZIKV as part of sentinel surveillance for ZIKV.

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Section: Introductionmentioning

confidence: 99%

“…In addition, Dengue and Chikungunya symptoms may include hemorrages and leukopenia/thrombocytopenia, while Chikungunya and Zika symptoms may include non-purulent conjunctivitis [22]. Thirdly, co-infection is also common increasing the difficulty of diagnosis of these conditions [5,21,24,25].Despite the difficulties in differential diagnosis, the progression and impact of these diseases varies significantly. After infection by Dengue, the disease may manifest asymptomatically and patients may not even know they are infected.…”

mentioning

confidence: 99%

Machine learning and deep learning techniques to support the clinical diagnosis of arboviral diseases: A systematic review

Neto¹,

Oliveira²,

Teixeira³

et al. 2021

Preprint

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Background: NTDs primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective: The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on ML and DL models. Method: We carried out a SLR in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and 7 from single backward snowballing technique), only 15 relevant papers were identified. Results: Results show that current research is focused on the binary classification of Dengue, primarily using Tree based ML algorithms and only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its levels) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions: The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient's quality of life.

show abstract

Co-infection of Dengue, Zika and Chikungunya in a group of pregnant women from Tuxtla Gutiérrez, Chiapas: Preliminary data. 2019

Cited by 19 publications

References 34 publications

Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review

Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review

Prevalence of Dengue, Chikungunya, and Zika Viruses in Febrile Pregnant Women: An Observational Study at a Tertiary Care Hospital in North India

Machine learning and deep learning techniques to support the clinical diagnosis of arboviral diseases: A systematic review

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