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Sci Rep ; 11(1): 17689, 2021 09 03.
Artículo en Inglés | MEDLINE | ID: mdl-34480062


COVID-19, a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus, has claimed millions of lives worldwide. Amid soaring contagion due to newer strains of the virus, it is imperative to design dynamic, spatiotemporal models to contain the spread of infection during future outbreaks of the same or variants of the virus. The reliance on existing prediction and contact tracing approaches on prior knowledge of inter- or intra-zone mobility renders them impracticable. We present a spatiotemporal approach that employs a network inference approach with sliding time windows solely on the date and number of daily infection numbers of zones within a geographical region to generate temporal networks capturing the influence of each zone on another. It helps analyze the spatial interaction among the hotspot or spreader zones and highly affected zones based on the flow of network contagion traffic. We apply the proposed approach to the daily infection counts of New York State as well as the states of USA to show that it effectively measures the phase shifts in the pandemic timeline. It identifies the spreaders and affected zones at different time points and helps infer the trajectory of the pandemic spread across the country. A small set of zones periodically exhibit a very high outflow of contagion traffic over time, suggesting that they act as the key spreaders of infection. Moreover, the strong influence between the majority of non-neighbor regions suggests that the overall spread of infection is a result of the unavoidable long-distance trips by a large number of people as opposed to the shorter trips at a county level, thereby informing future mitigation measures and public policies.

COVID-19 , Trazado de Contacto , Bases de Datos Factuales , Pandemias , COVID-19/epidemiología , COVID-19/transmisión , Humanos , New York/epidemiología , Salud Pública , SARS-CoV-2
Sci Rep ; 11(1): 16522, 2021 08 13.
Artículo en Inglés | MEDLINE | ID: mdl-34389789


Inflammatory bowel diseases (IBD), namely Crohn's disease (CD) and ulcerative colitis (UC) are chronic inflammation within the gastrointestinal tract. IBD patient conditions and treatments, such as with immunosuppressants, may result in a higher risk of viral and bacterial infection and more severe outcomes of infections. The effect of the clinical and demographic factors on the prognosis of COVID-19 among IBD patients is still a significant area of investigation. The lack of available data on a large set of COVID-19 infected IBD patients has hindered progress. To circumvent this lack of large patient data, we present a random sampling approach to generate clinical COVID-19 outcomes (outpatient management, hospitalized and recovered, and hospitalized and deceased) on 20,000 IBD patients modeled on reported summary statistics obtained from the Surveillance Epidemiology of Coronavirus Under Research Exclusion (SECURE-IBD), an international database to monitor and report on outcomes of COVID-19 occurring in IBD patients. We apply machine learning approaches to perform a comprehensive analysis of the primary and secondary covariates to predict COVID-19 outcome in IBD patients. Our analysis reveals that age, medication usage and the number of comorbidities are the primary covariates, while IBD severity, smoking history, gender and IBD subtype (CD or UC) are key secondary features. In particular, elderly male patients with ulcerative colitis, several preexisting conditions, and who smoke comprise a highly vulnerable IBD population. Moreover, treatment with 5-ASAs (sulfasalazine/mesalamine) shows a high association with COVID-19/IBD mortality. Supervised machine learning that considers age, number of comorbidities and medication usage can predict COVID-19/IBD outcomes with approximately 70% accuracy. We explore the challenge of drawing demographic inferences from existing COVID-19/IBD data. Overall, there are fewer IBD case reports from US states with poor health ranking hindering these analyses. Generation of patient characteristics based on known summary statistics allows for increased power to detect IBD factors leading to variable COVID-19 outcomes. There is under-reporting of COVID-19 in IBD patients from US states with poor health ranking, underpinning the perils of using the repository to derive demographic information.

COVID-19/mortalidad , Enfermedades Inflamatorias del Intestino , Aprendizaje Automático , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Antiinflamatorios no Esteroideos , Niño , Preescolar , Bases de Datos Factuales , Femenino , Humanos , Lactante , Recién Nacido , Enfermedades Inflamatorias del Intestino/tratamiento farmacológico , Enfermedades Inflamatorias del Intestino/epidemiología , Masculino , Mesalamina/efectos adversos , Mesalamina/uso terapéutico , Persona de Mediana Edad , Sulfasalazina/efectos adversos , Sulfasalazina/uso terapéutico , Estados Unidos/epidemiología , Adulto Joven
Soc Sci Humanit Open ; 3(1): 100098, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34173505


Lockdown measures to curb the spread of COVID-19 has brought the world economy on the brink of a recession. It is imperative that nations formulate administrative policies based on the changing economic landscape. In this work, we apply a statistical approach, called topic modeling, on text documents of job loss notices of 26 US states to identify the specific states and industrial sectors affected economically by this ongoing public health crisis. Our analysis reveals that there is a considerable incongruity in job loss patterns between the pre- and during-COVID timelines in several states and the recreational and philanthropic sectors register high job losses. It further shows that the interplay among several possible socioeconomic factors would lead to job losses in many sectors, while also creating new job opportunities in other sectors such as public service, pharmaceuticals and media, making the job loss trends a key indicator of the world economy. Finally, we compare the low income job loss rates against overall job losses due to COVID-19 in the US counties, and discuss the implications of press reports on reopening businesses and the unemployed workforce being absorbed by other sectors.

Soc Sci Humanit Open ; : 100163, 2021 May 11.
Artículo en Inglés | MEDLINE | ID: mdl-33997770


COVID-19, declared by the World Health Organization as a Public Health Emergency of International Concern, has claimed over 2.7 million lives worldwide. In the absence of vaccinations, social distancing and lockdowns emerged as the means to curb infection spread, with the downside of bringing the world economy to a standstill. In this work, we explore the epidemiological, socioeconomic and demographic factors affecting the unemployment rates of United States that may contribute towards policymaking to contain contagion and mortality while balancing the economy in the future. We identify the ethnic groups and job sectors that are affected by the pandemic and demonstrate that Gross Domestic Product (GDP), race, age group, lockdown severity and infected count are the key indicators of post-COVID job loss trends.

PLoS One ; 15(10): e0241165, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-33095811


BACKGROUND: After claiming nearly five hundred thousand lives globally, the COVID-19 pandemic is showing no signs of slowing down. While the UK, USA, Brazil and parts of Asia are bracing themselves for the second wave-or the extension of the first wave-it is imperative to identify the primary social, economic, environmental, demographic, ethnic, cultural and health factors contributing towards COVID-19 infection and mortality numbers to facilitate mitigation and control measures. METHODS: We process several open-access datasets on US states to create an integrated dataset of potential factors leading to the pandemic spread. We then apply several supervised machine learning approaches to reach a consensus as well as rank the key factors. We carry out regression analysis to pinpoint the key pre-lockdown factors that affect post-lockdown infection and mortality, informing future lockdown-related policy making. FINDINGS: Population density, testing numbers and airport traffic emerge as the most discriminatory factors, followed by higher age groups (above 40 and specifically 60+). Post-lockdown infected and death rates are highly influenced by their pre-lockdown counterparts, followed by population density and airport traffic. While healthcare index seems uncorrelated with mortality rate, principal component analysis on the key features show two groups: states (1) forming early epicenters and (2) experiencing strong second wave or peaking late in rate of infection and death. Finally, a small case study on New York City shows that days-to-peak for infection of neighboring boroughs correlate better with inter-zone mobility than the inter-zone distance. INTERPRETATION: States forming the early hotspots are regions with high airport or road traffic resulting in human interaction. US states with high population density and testing tend to exhibit consistently high infected and death numbers. Mortality rate seems to be driven by individual physiology, preexisting condition, age etc., rather than gender, healthcare facility or ethnic predisposition. Finally, policymaking on the timing of lockdowns should primarily consider the pre-lockdown infected numbers along with population density and airport traffic.

Betacoronavirus , Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/mortalidad , Neumonía Viral/epidemiología , Neumonía Viral/mortalidad , Formulación de Políticas , Densidad de Población , Cuarentena/métodos , Viaje , Adolescente , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , COVID-19 , Niño , Preescolar , Infecciones por Coronavirus/prevención & control , Infecciones por Coronavirus/virología , Femenino , Humanos , Lactante , Recién Nacido , Relaciones Interpersonales , Masculino , Persona de Mediana Edad , Pandemias/prevención & control , Neumonía Viral/prevención & control , Neumonía Viral/virología , SARS-CoV-2 , Aprendizaje Automático Supervisado , Factores de Tiempo , Estados Unidos/epidemiología , Adulto Joven
Sci Rep ; 10(1): 9628, 2020 06 15.
Artículo en Inglés | MEDLINE | ID: mdl-32541819


Analysis of the topology of transcriptional regulatory networks (TRNs) is an effective way to study the regulatory interactions between the transcription factors (TFs) and the target genes. TRNs are characterized by the abundance of motifs such as feed forward loops (FFLs), which contribute to their structural and functional properties. In this paper, we focus on the role of motifs (specifically, FFLs) in signal propagation in TRNs and the organization of the TRN topology with FFLs as building blocks. To this end, we classify nodes participating in FFLs (termed motif central nodes) into three distinct roles (namely, roles A, B and C), and contrast them with TRN nodes having high connectivity on the basis of their potential for information dissemination, using metrics such as network efficiency, path enumeration, epidemic models and standard graph centrality measures. We also present the notion of a three tier architecture and how it can help study the structural properties of TRN based on connectivity and clustering tendency of motif central nodes. Finally, we motivate the potential implication of the structural properties of motif centrality in design of efficient protocols of information routing in communication networks as well as their functional properties in global regulation and stress response to study specific disease conditions and identification of drug targets.

Regulación de la Expresión Génica , Redes Reguladoras de Genes , Factores de Transcripción/metabolismo , Regulación de la Expresión Génica/fisiología , Redes Reguladoras de Genes/fisiología , Genes , Factores de Transcripción/fisiología