About: Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model

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About: Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model
Creator	Cheng, Chaonan Mao, Qiang Yan, Wu Zhang, Kai
Source	Elsevier; Medline; PMC
abstract	Abstract Objectives The aims of this study were to develop a forecasting model for the incidence of tuberculosis (TB) and analyze the seasonality of infections in China; and to provide a useful tool for formulating intervention programs and allocating medical resources. Methods Data for the monthly incidence of TB from January 2004 to December 2015 were obtained from the National Scientific Data Sharing Platform for Population and Health (China). The Box–Jenkins method was applied to fit a seasonal auto-regressive integrated moving average (SARIMA) model to forecast the incidence of TB over the subsequent six months. Results During the study period of 144 months, 12,321,559 TB cases were reported in China, with an average monthly incidence of 6.4426 per 100,000 of the population. The monthly incidence of TB showed a clear 12-month cycle, and a seasonality with two peaks occurring in January and March and a trough in December. The best-fit model was SARIMA (1,0,0)(0,1,1)12, which demonstrated adequate information extraction (white noise test, p> 0.05). Based on the analysis, the incidence of TB from January to June 2016 were 6.6335, 4.7208, 5.8193, 5.5474, 5.2202 and 4.9156 per 100,000 of the population, respectively. Conclusions According to the seasonal pattern of TB incidence in China, the SARIMA model was proposed as a useful tool for monitoring epidemics.
has issue date	2018-10-31(xsd:dateTime)
bibo:doi	10.1016/j.jiph.2018.04.009
bibo:pmid	29730253
has license	els-covid
sha1sum (hex)	b46f553f3346007b177c8af1acc290940fccf35c
schema:url	https://doi.org/10.1016/j.jiph.2018.04.009
resource representing a document's title	Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model
has PubMed Central identifier	PMC7102794
has PubMed identifier	29730253
schema:publication	Journal of Infection and Public Health
resource representing a document's body	covid:b46f553f3346007b177c8af1acc290940fccf35c#body_text
is schema:about of	named entity 'REGRESSIVE' named entity 'population' named entity 'clear' named entity 'moving average' named entity 'China' named entity 'FORECASTING' named entity 'PERIOD' named entity '144' named entity 'BOX' named entity 'NOISE' named entity 'BEST' named entity 'DEMONSTRATED' named entity 'METHODS' named entity 'MODEL' named entity 'MARCH' named entity 'MEDICAL' named entity 'BASED' named entity 'OBTAINED' named entity 'CHINA' named entity 'JUNE' named entity 'NATIONAL' named entity 'METHOD' named entity 'HEALTH' named entity 'FIT' named entity 'JANUARY' named entity '2015' named entity 'SCIENTIFIC DATA' named entity 'APPLIED' named entity 'MOVING AVERAGE' named entity 'CONCLUSIONS' named entity 'MODEL' named entity 'TUBERCULOSIS' named entity 'FORECASTING' named entity 'INCIDENCE' named entity 'TEST' named entity 'RESOURCES' named entity 'PATTERN' named entity 'CLEAR' named entity 'ADEQUATE' named entity 'FORECAST' named entity 'REPORTED' named entity 'POPULATION' named entity 'STUDY' named entity 'PROPOSED' named entity 'AIMS' named entity 'DATA' named entity 'ACCORDING' named entity 'MOVING AVERAGE' named entity 'CHINA' named entity 'INTEGRATED' named entity 'PEAKS' named entity 'INTEGRATED' named entity 'MONTHLY' named entity 'SEASONALITY' named entity 'USEFUL' named entity 'OBJECTIVES' named entity 'AVERAGE' named entity 'SUBSEQUENT' named entity 'CASES' named entity '100%' named entity 'TROUGH' named entity 'EPIDEMICS' named entity 'MONTHS' named entity 'SEASONAL' named entity 'TOOL' named entity 'INFECTIONS' named entity 'AUTO' named entity 'SIX MONTHS' named entity 'TO PROVIDE' named entity 'PLATFORM' named entity 'ANALYSIS' named entity 'AUTO' named entity 'INCIDENCE' named entity 'USING' named entity '0.05' named entity 'SARIMA' named entity 'MONTH' named entity 'TUBERCULOSIS'

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