Chikungunya outbreak (2015) in the Colombian Caribbean: Latent classes and gender differences in virus infection

Abstract

Chikungunya virus (CHIKV), a mosquito-borne alphavirus of the Togaviridae family, is part of a group of emergent diseases, including arbovirus, constituting an increasing public health problem in tropical areas worldwide. CHIKV causes a severe and debilitating disease with high morbidity. The first Colombian autochthonous case was reported in the Colombian Caribbean region in September 2014. Within the next two to three months, the CHIKV outbreak reached its peak. Although the CHIKV pattern of clinical symptomatology has been documented in different epidemiological studies, understanding of the relationship between clinical symptomatology and variation in phenotypic response to CHIKV infection in humans remains limited. We performed a cross sectional study following 1160 individuals clinically diagnosed with CHIKV at the peak of the Chikungunya outbreak in the Colombian Caribbean region. We examined the relationship between symptomatology and diverse phenotypic responses. Latent Class Cluster Analysis (LCCA) models were used to characterize patients' symptomatology and further identify subgroups of individuals with differential phenotypic response. We found that most individuals presented fever (94.4%), headache (73.28%) and general discomfort (59.4%), which are distinct clinical symptoms of a viral infection. Furthermore, 11/26 (43.2%) of the categorized symptoms were more frequent in women than in men. LCCA disclosed seven distinctive phenotypic response profiles in this population of CHIKV infected individuals. Interestingly, 282 (24.3%) individuals exhibited a lower symptomatic "extreme" phenotype and 74 (6.4%) patients were within the severe complex "extreme" phenotype. Although clinical symptomatology may be diverse, there are distinct symptoms or group of symptoms that can be correlated with differential phenotypic response and perhaps susceptibility to CHIKV infection, especially in the female population. This suggests that, comparatively to men, women are a CHIKV at-risk population. Further study is needed to validate these results and determine whether the distinct LCCA profiles are a result of the immune response or a mixture of genetic, lifestyle and environmental factors. Our findings could contribute to the development of machine learning approaches to PLOS NEGLECTED TROPICAL DISEASES

Co-Infection of Dengue and Chikungunya Viruses: A Serological Study in a Tertiary Care Hospital in Warangal, Telangana, India

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A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer’s Disease

“…Via ML, it has been possible to develop models to identify individuals more susceptible to developing common and rare diseases [ 58 , 59 , 60 , 61 , 62 , 63 , 67 , 88 , 89 , 90 , 91 , 92 , 93 ] and determine diverse phenotypic response profiles in infectious diseases [ 94 , 95 , 96 ]. Considering that ML- and computational-based models have the potential to overcome the limitations of current established clinical models for the diagnosis and follow-up of neurodegenerative diseases, including AD [ 97 ], here we studied the feasibility of ML algorithms for predicting Alzheimer’s disease age of onset (ADAOO) in individuals from the Paisa genetic isolate. We argue that these ML-based predictive models will improve our understanding of the disease and provide a more accurate and precise definition of the AD natural history landmarks.…”

Machine Learning based Psychology: Advocating for A Data-Driven Approach

“…Subsequent work may include to develop ML predictive models that can classify new individuals to such derived groups (Roman, 2019). Interestingly, the combination unsupervised ML techniques may lead to the identification of individuals exhibiting differential clinical profiles (i.e., extreme phenotypes; Acosta et al, 2011;Arcos-Burgos et al, 2019;Elia et al, 2009;Pérez-Gracia et al, 2010;Vidal et al, 2020;Yu et al, 2017;Yu et al, 2018), hence contributing to the development of personalized interventions, treatments, and follow-up strategies. The combination of supervised and unsupervised ML techniques as well as the automation of the data analysis process could allow the development of data-driven Intelligent Systems supporting psychologists to make more accurate and timely decisions (de Mello & de Souza, 2019;Luxton, 2016).…”