Dataset Information

Neologisms are epidemic: Modeling the life cycle of neologisms in China 2008-2016.

ABSTRACT: This paper adopts models from epidemiology to account for the development and decline of neologisms based on internet usage. The research design focuses on the issue of whether a host-driven epidemic model is well-suited to explain human behavior regarding neologisms. We extracted the search frequency data from Google Trends that covers the ninety most influential Chinese neologisms from 2008-2016 and found that the majority of them possess a similar rapidly rising-decaying pattern. The epidemic model is utilized to fit the evolution of these internet-based neologisms. The epidemic model not only has good fitting performance to model the pattern of rapid growth, but also is able to predict the peak point in the neologism's life cycle. This result underlines the role of human agents in the life cycle of neologisms and supports the macro-theory that the evolution of human languages mirrors the biological evolution of human beings.

SUBMITTER: Jiang M

PROVIDER: S-EPMC7857598 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Neologisms are epidemic: Modeling the life cycle of neologisms in China 2008-2016.

Jiang Menghan M Shen Xiang Ying XY Ahrens Kathleen K Huang Chu-Ren CR

PloS one 20210203 2

This paper adopts models from epidemiology to account for the development and decline of neologisms based on internet usage. The research design focuses on the issue of whether a host-driven epidemic model is well-suited to explain human behavior regarding neologisms. We extracted the search frequency data from Google Trends that covers the ninety most influential Chinese neologisms from 2008-2016 and found that the majority of them possess a similar rapidly rising-decaying pattern. The epidemic ...[more]

PMID: 33534795

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Project description:BackgroundNewborn hearing screening (NHS) can reduce the economic and social burden of hearing impairment. To track the progress of the goal set by the National Program of Action for Child Development (NPACD) and to estimate the detection rates of hearing impairment, the present study examined NHS coverage in 2008-2010 and 2016 and the detection of hearing impairment across China in 2016.MethodsLicensed medical institutions across China were surveyed in 2012 and 2018 by the National Center for Birth Defects Monitoring of China to collect data for the 2008-2010 period and for 2016 on live births, initial screening rates (total and referral), secondary screening rates (total and referral), and rates of hearing impairment diagnosis among infants who were referred in the secondary screening. To calculate universal newborn hearing screening (UNHS) coverage, the number of newborns who received NHS within 4 weeks after birth was divided by the number of live births. The detection rate of hearing impairment was calculated by combining referral rates on primary and secondary screening with the rate of diagnosis.ResultsNational UNHS coverage increased from 29.9% in 2008 to 86.5% in 2016, with different regions showing different increases. During this period, the number of provinces with UNHS coverage over 90.0% increased from 2 to 17, with UNHS coverage in 2016 being substantially higher in eastern provinces (93.1%) than in western provinces (79.4%). In 2016, the detection rate of hearing impairment across the country was 0.23% (95% CI 0.15-0.25%), and it varied from 0.17% in western provinces to 0.22% in central provinces and 0.28% in eastern provinces. The lowest rate was 0.02% in Heilongjiang Province and the highest rate was 0.63% in Hainan Province.ConclusionsNational UNHS coverage increased substantially from 2008 to 2016, although provinces and regions still showed differences. The detection rate of infant hearing impairment in China is comparable to that in other countries. A national individual-level information system is urgently needed in China to facilitate the integration of screening, diagnosis and treatment of infant hearing impairment, which may also lead to a more accurate estimate of the detection rate.

Project description:BackgroundThe coronavirus disease 2019 (COVID-19) is rapidly spreading in China and more than 30 countries over last two months. COVID-19 has multiple characteristics distinct from other infectious diseases, including high infectivity during incubation, time delay between real dynamics and daily observed number of confirmed cases, and the intervention effects of implemented quarantine and control measures.MethodsWe develop a Susceptible, Un-quanrantined infected, Quarantined infected, Confirmed infected (SUQC) model to characterize the dynamics of COVID-19 and explicitly parameterize the intervention effects of control measures, which is more suitable for analysis than other existing epidemic models.ResultsThe SUQC model is applied to the daily released data of the confirmed infections to analyze the outbreak of COVID-19 in Wuhan, Hubei (excluding Wuhan), China (excluding Hubei) and four first-tier cities of China. We found that, before January 30, 2020, all these regions except Beijing had a reproductive number R > 1, and after January 30, all regions had a reproductive number R < 1, indicating that the quarantine and control measures are effective in preventing the spread of COVID-19. The confirmation rate of Wuhan estimated by our model is 0.0643, substantially lower than that of Hubei excluding Wuhan (0.1914), and that of China excluding Hubei (0.2189), but it jumps to 0.3229 after February 12 when clinical evidence was adopted in new diagnosis guidelines. The number of unquarantined infected cases in Wuhan on February 12, 2020 is estimated to be 3,509 and declines to 334 on February 21, 2020. After fitting the model with data as of February 21, 2020, we predict that the end time of COVID-19 in Wuhan and Hubei is around late March, around mid March for China excluding Hubei, and before early March 2020 for the four tier-one cities. A total of 80,511 individuals are estimated to be infected in China, among which 49,510 are from Wuhan, 17,679 from Hubei (excluding Wuhan), and the rest 13,322 from other regions of China (excluding Hubei). Note that the estimates are from a deterministic ODE model and should be interpreted with some uncertainty.ConclusionsWe suggest that rigorous quarantine and control measures should be kept before early March in Beijing, Shanghai, Guangzhou and Shenzhen, and before late March in Hubei. The model can also be useful to predict the trend of epidemic and provide quantitative guide for other countries at high risk of outbreak, such as South Korea, Japan, Italy and Iran.Supplementary materialsThe supplementary materials can be found online with this article at 10.1007/s40484-020-0199-0.

Dataset Information

Neologisms are epidemic: Modeling the life cycle of neologisms in China 2008-2016.

Publications

Neologisms are epidemic: Modeling the life cycle of neologisms in China 2008-2016.

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