|
[1]
|
Yu XJ, Liang MF, Zhang SY, Liu Y, Li JD, Sun YL, et al. Fever with thrombocytopenia associated with a novel bunyavirus in China. N Engl J Med 2011;364(16):1523 − 32.CrossRef
|
|
[2]
|
Li H, Lu QB, Xing B, Zhang SF, Liu K, Du J, et al. Epidemiological and clinical features of laboratory-diagnosed severe fever with thrombocytopenia syndrome in China, 2011-17: a prospective observational study. Lancet Infect Dis 2018;18(10):1127 − 37.CrossRef
|
|
[3]
|
Sun JM, Lu L, Liu KK, Yang J, Wu HX, Liu QY. Forecast of severe fever with thrombocytopenia syndrome incidence with meteorological factors. Sci Total Environ 2018;626:1188 − 92.CrossRef
|
|
[4]
|
Sun JM, Lu L, Wu HX, Yang J, Ren JP, Liu QY. The changing epidemiological characteristics of severe fever with thrombocytopenia syndrome in China, 2011-2016. Sci Rep 2017;7(1):9236.CrossRef
|
|
[5]
|
Li JC, Zhao J, Li H, Fang LQ, Liu W. Epidemiology, clinical characteristics, and treatment of severe fever with thrombocytopenia syndrome. Infect Med 2022;1(1):40 − 9.CrossRef
|
|
[6]
|
Mehand MS, Millett P, Al-Shorbaji F, Roth C, Kieny MP, Murgue B. World health organization methodology to prioritize emerging infectious diseases in need of research and development. Emerg Infect Dis 2018;24(9):e171427.CrossRef
|
|
[7]
|
Wang T, Li XL, Liu M, Song XJ, Zhang H, Wang YB, et al. Epidemiological characteristics and environmental risk factors of severe fever with thrombocytopenia syndrome in Hubei province, China, from 2011 to 2016. Front Microbiol 2017;8:387.CrossRef
|
|
[8]
|
Battineni G, Chintalapudi N, Amenta F. Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Brazil, India and Russia) by Fb-Prophet machine learning model. Appl Comput Inf 2020;6(1):1 − 10.CrossRef
|
|
[9]
|
Lv CR, Guo WQ, Yin XY, Liu L, Huang XL, Li SM, et al. Innovative applications of artificial intelligence during the COVID-19 pandemic. Infect Med 2024;3(1):100095.CrossRef
|
|
[10]
|
Xie C, Wen HY, Yang WW, Cai J, Zhang P, Wu R, et al. Trend analysis and forecast of daily reported incidence of hand, foot and mouth disease in Hubei, China by Prophet model. Sci Rep 2021;11(1):1445.CrossRef
|
|
[11]
|
Deng B, Rui J, Liang SY, Li ZF, Li KG, Lin SN, et al. Meteorological factors and tick density affect the dynamics of SFTS in jiangsu province, China. PLoS Negl Trop Dis 2022;16(5):e0010432.CrossRef
|
|
[12]
|
Jiang XL, Wang YG, Zhang XM, Pang B, Yao MX, Tian XY, et al. Factors associated with severe fever with thrombocytopenia syndrome in endemic areas of China. Front Public Health 2022;10:844220.CrossRef
|
|
[13]
|
Wang ZJ, Yang ST, Luo L, Guo XH, Deng B, Zhao ZY, et al. Epidemiological characteristics of severe fever with thrombocytopenia syndrome and its relationship with meteorological factors in Liaoning province, China. Parasit Vectors 2022;15(1):283.CrossRef
|
|
[14]
|
Luo ZX, Jia XC, Bao JZ, Song ZJ, Zhu HL, Liu MY, et al. A combined model of SARIMA and prophet models in forecasting AIDS incidence in Henan province, China. Int J Environ Res Public Health 2022;19(10):5910.CrossRef
|
|
[15]
|
Huang D, Grifoll M, Sanchez-Espigares JA, Zheng PJ, Feng HX. Hybrid approaches for container traffic forecasting in the context of anomalous events: the case of the Yangtze River Delta region in the COVID-19 pandemic. Transp Policy (Oxf) 2022;128:1 − 12.CrossRef
|