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A data-driven approach to measuring epidemiological susceptibility risk around the world.

ABSTRACT: Epidemic outbreaks are extreme events that become more frequent and severe, associated with large social and real costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying information. We apply both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in the data. We conduct extensive in-sample model evaluations of 168 countries covering 17 indicators for the 2010-2019 period. The results show that the robust PCA method accounts for about 90% of total variability, whilst the DFM accounts for about 76% of the total variability. Our index could therefore provide the basis for developing risk assessments of epidemiological risk contagion. It could be also used by organizations to assess likely real consequences of epidemics with useful managerial implications.

SUBMITTER: Bitetto A

PROVIDER: S-EPMC8674252 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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A data-driven approach to measuring epidemiological susceptibility risk around the world.

Bitetto Alessandro A Cerchiello Paola P Mertzanis Charilaos C

Scientific reports 20211215 1

Epidemic outbreaks are extreme events that become more frequent and severe, associated with large social and real costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying information. We apply both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in ...[more]

PMID: 34911989

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Project description:BackgroundTaenia solium cysticercosis is a zoonotic neglected disease responsible for severe health disorders such as seizures and death. Understanding the epidemiology of human cysticercosis (HCC) in endemic regions will help to expose critical information about the transmission of the disease, which could be used to design efficient control programs. This review gathered serological data on apparent prevalence of T. solium circulating antigens and/or seroprevalence of T. solium antibodies, apparent prevalence of human taeniasis and risk factors for HCC from endemic communities in order to understand the differences in exposure to the parasite and active infections with T. solium metacestodes in endemic areas around the world.MethodsThree databases were used to search sero-epidemiological data from community-based studies conducted between 1989 and 2014 in cysticercosis endemic communities worldwide. The search focused on data obtained from T. solium circulating antigen detection by monoclonal antibody-based sandwich ELISA and/or T. solium antibody seroprevalence determined by Enzyme-linked Immunoelectrotransfer Blot (EITB). A meta-analysis was performed per continent.Principal findingsA total of 39,271 participants from 19 countries, described in 37 articles were studied. The estimates for the prevalence of circulating T. solium antigens for Africa, Latin America and Asia were: 7.30% (95% CI [4.23-12.31]), 4.08% (95% CI [2.77-5.95]) and 3.98% (95% CI [2.81-5.61]), respectively. Seroprevalence estimates of T. solium antibodies were 17.37% (95% CI [3.33-56.20]), 13.03% (95% CI [9.95-16.88]) and 15.68% (95% CI [10.25-23.24]) respectively. Taeniasis reported prevalences ranged from 0 (95% CI [0.00-1.62]) to 17.25% (95% CI [14.55-20.23]).SignificanceA significant variation in the sero-epidemiological data was observed within each continent, with African countries reporting the highest apparent prevalences of active infections. Intrinsic factors in the human host such as age and immunity were main determinants for the occurrence of infections, while exposure was mostly related to environmental factors which varied from community to community.

Project description:PurposeThe aim of this study was to leverage data-driven approaches, including a novel articulatory consonant distinctiveness space (ACDS) approach, to better understand speech motor control in amyotrophic lateral sclerosis (ALS).MethodElectromagnetic articulography was used to record tongue and lip movement data during the production of 10 consonants from healthy controls (n = 15) and individuals with ALS (n = 47). To assess phoneme distinctness, speech data were analyzed using two classification algorithms, Procrustes matching (PM) and support vector machine (SVM), and the area/volume of the ACDS. Pearson's correlation coefficient was used to examine the relationship between bulbar impairment and the ACDS. Analysis of variance was used to examine the effects of bulbar impairment on consonant distinctiveness and consonant classification accuracies in clinical subgroups.ResultsThere was a significant relationship between the ACDS and intelligible speaking rate (area, p = .003; volume, p = .010), and the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) bulbar subscore (area, p = .009; volume, p = .027). Consonant classification performance followed a consistent pattern with bulbar severity, where consonants produced by speakers with more severe ALS were classified less accurately (SVM = 75.27%; PM = 74.54%) than the healthy, asymptomatic, and mild-moderate groups. In severe ALS, area of the ACDS was significantly condensed compared to both asymptomatic (p = .004) and mild-moderate (p = .013) groups. There was no statistically significant difference in area between the severe ALS group and healthy speakers (p = .292).ConclusionsOur comprehensive approach is sensitive to early oromotor changes in response due to disease progression. The preserved articulatory consonant space may capture the use of compensatory adaptations to counteract influences of neurodegeneration.Supplemental materialhttps://doi.org/10.23641/asha.22044320.

Dataset Information

A data-driven approach to measuring epidemiological susceptibility risk around the world.

Publications

A data-driven approach to measuring epidemiological susceptibility risk around the world.

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