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Trends and prediction in daily incidence of novel coronavirus infection in China, Hubei Province and Wuhan City: an application of Farr's law.
Am J Transl Res. 2020; 12(4):1355-1361.AJ

Abstract

BACKGROUND

The recent outbreak of novel coronavirus (2019-nCoV) has infected tens of thousands of patients in China. Studies have forecasted future trends of the incidence of 2019-nCoV infection, but appeared unsuccessful. Farr's law is a classic epidemiology theory/practice for predicting epidemics. Therefore, we used and validated a model based on Farr's law to predict the daily-incidence of 2019-nCoV infection in China and 2 regions of high-incidence.

METHODS

We extracted the 2019-nCoV incidence data of China, Hubei Province and Wuhan City from websites of the Chinese and Hubei health commissions. A model based on Farr's law was developed using the data available on Feb. 8, 2020, and used to predict daily-incidence of 2019-nCoV infection in China, Hubei Province and Wuhan City afterward.

RESULTS

We observed 50,995 (37,001 on or before Feb. 8) incident cases in China from January 16 to February 15, 2020. The daily-incidence has peaked in China, Hubei Providence and Wuhan City, but with different downward slopes. If no major changes occur, our model shows that the daily-incidence of 2019-nCoV will drop to single-digit by February 25 for China and Hubei Province, but by March 8 for Wuhan city. However, predicted 75% confidence intervals of daily-incidence in all 3 regions of interest had an upward trend. The predicted trends overall match the prospectively-collected data, confirming usefulness of these models.

CONCLUSIONS

This study shows the daily-incidence of 2019-nCoV in China, Hubei Province and Wuhan City has reached the peak and was decreasing. However, there is a possibility of upward trend.

Authors+Show Affiliations

Department of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine Shanghai, China.Department of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine Shanghai, China.Department of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine Shanghai, China.Department of Biostatistics and Epidemiology, Rutgers School of Public Health Piscataway, NJ, USA.Department of Pathology, Princeton Medical Center Plainsboro, NJ, USA. Department of Biological Sciences, Rutgers University Newark NJ, USA. Rutgers Cancer Institute of New Jersey New Brunswick, NJ, USA. Department of Chemical Biology, Rutgers Ernest Mario School of Pharmacy Piscataway, NJ, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32355547

Citation

Xu, Jie, et al. "Trends and Prediction in Daily Incidence of Novel Coronavirus Infection in China, Hubei Province and Wuhan City: an Application of Farr's Law." American Journal of Translational Research, vol. 12, no. 4, 2020, pp. 1355-1361.
Xu J, Cheng Y, Yuan X, et al. Trends and prediction in daily incidence of novel coronavirus infection in China, Hubei Province and Wuhan City: an application of Farr's law. Am J Transl Res. 2020;12(4):1355-1361.
Xu, J., Cheng, Y., Yuan, X., Li, W. V., & Zhang, L. (2020). Trends and prediction in daily incidence of novel coronavirus infection in China, Hubei Province and Wuhan City: an application of Farr's law. American Journal of Translational Research, 12(4), 1355-1361.
Xu J, et al. Trends and Prediction in Daily Incidence of Novel Coronavirus Infection in China, Hubei Province and Wuhan City: an Application of Farr's Law. Am J Transl Res. 2020;12(4):1355-1361. PubMed PMID: 32355547.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Trends and prediction in daily incidence of novel coronavirus infection in China, Hubei Province and Wuhan City: an application of Farr's law. AU - Xu,Jie, AU - Cheng,Yajiao, AU - Yuan,Xiaoling, AU - Li,Wei Vivian, AU - Zhang,Lanjing, Y1 - 2020/04/15/ PY - 2020/02/24/received PY - 2020/03/16/accepted PY - 2020/5/2/entrez PY - 2020/5/2/pubmed PY - 2020/5/2/medline KW - China KW - Trend KW - incidence KW - novel coronavirus KW - pandemic SP - 1355 EP - 1361 JF - American journal of translational research JO - Am J Transl Res VL - 12 IS - 4 N2 - BACKGROUND: The recent outbreak of novel coronavirus (2019-nCoV) has infected tens of thousands of patients in China. Studies have forecasted future trends of the incidence of 2019-nCoV infection, but appeared unsuccessful. Farr's law is a classic epidemiology theory/practice for predicting epidemics. Therefore, we used and validated a model based on Farr's law to predict the daily-incidence of 2019-nCoV infection in China and 2 regions of high-incidence. METHODS: We extracted the 2019-nCoV incidence data of China, Hubei Province and Wuhan City from websites of the Chinese and Hubei health commissions. A model based on Farr's law was developed using the data available on Feb. 8, 2020, and used to predict daily-incidence of 2019-nCoV infection in China, Hubei Province and Wuhan City afterward. RESULTS: We observed 50,995 (37,001 on or before Feb. 8) incident cases in China from January 16 to February 15, 2020. The daily-incidence has peaked in China, Hubei Providence and Wuhan City, but with different downward slopes. If no major changes occur, our model shows that the daily-incidence of 2019-nCoV will drop to single-digit by February 25 for China and Hubei Province, but by March 8 for Wuhan city. However, predicted 75% confidence intervals of daily-incidence in all 3 regions of interest had an upward trend. The predicted trends overall match the prospectively-collected data, confirming usefulness of these models. CONCLUSIONS: This study shows the daily-incidence of 2019-nCoV in China, Hubei Province and Wuhan City has reached the peak and was decreasing. However, there is a possibility of upward trend. SN - 1943-8141 UR - https://www.unboundmedicine.com/medline/citation/32355547/Trends_and_prediction_in_daily_incidence_of_novel_coronavirus_infection_in_China_Hubei_Province_and_Wuhan_City:_an_application_of_Farr's_law_ L2 - https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/32355547/ DB - PRIME DP - Unbound Medicine ER -
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