PMID- 34504277
OWN - NLM
STAT- MEDLINE
DCOM- 20210923
LR  - 20240403
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 11
IP  - 1
DP  - 2021 Sep 9
TI  - The effect of population size for pathogen transmission on prediction of COVID-19 
      spread.
PG  - 18024
LID - 10.1038/s41598-021-97578-9 [doi]
LID - 18024
AB  - Extreme public health interventions play a critical role in mitigating the local 
      and global prevalence and pandemic potential. Here, we use population size for 
      pathogen transmission to measure the intensity of public health interventions, 
      which is a key characteristic variable for nowcasting and forecasting of 
      COVID-19. By formulating a hidden Markov dynamic system and using nonlinear 
      filtering theory, we have developed a stochastic epidemic dynamic model under 
      public health interventions. The model parameters and states are estimated in 
      time from internationally available public data by combining an unscented filter 
      and an interacting multiple model filter. Moreover, we consider the computability 
      of the population size and provide its selection criterion. With applications to 
      COVID-19, we estimate the mean of the effective reproductive number of China and 
      the rest of the globe except China (GEC) to be 2.4626 (95% CI: 2.4142-2.5111) and 
      3.0979 (95% CI: 3.0968-3.0990), respectively. The prediction results show the 
      effectiveness of the stochastic epidemic dynamic model with nonlinear filtering. 
      The hidden Markov dynamic system with nonlinear filtering can be used to make 
      analysis, nowcasting and forecasting for other contagious diseases in the future 
      since it helps to understand the mechanism of disease transmission and to 
      estimate the population size for pathogen transmission and the number of hidden 
      infections, which is a valid tool for decision-making by policy makers for 
      epidemic control.
CI  - (c) 2021. The Author(s).
FAU - Zhang, Xuqi
AU  - Zhang X
AD  - School of Mathematics, Sichuan University, Chengdu, 610064, Sichuan, China.
FAU - Liu, Haiqi
AU  - Liu H
AUID- ORCID: 0000-0003-2203-137X
AD  - School of Mathematics, Sichuan University, Chengdu, 610064, Sichuan, China. 
      411566818@qq.com.
FAU - Tang, Hanning
AU  - Tang H
AD  - School of Mathematics, Sichuan University, Chengdu, 610064, Sichuan, China.
FAU - Zhang, Mei
AU  - Zhang M
AD  - School of Mathematics, Sichuan University, Chengdu, 610064, Sichuan, China.
FAU - Yuan, Xuedong
AU  - Yuan X
AD  - School of Computer Science, Sichuan University, Chengdu, 610064, Sichuan, China.
FAU - Shen, Xiaojing
AU  - Shen X
AD  - School of Mathematics, Sichuan University, Chengdu, 610064, Sichuan, China.
LA  - eng
PT  - Journal Article
DEP - 20210909
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - *Basic Reproduction Number
MH  - *COVID-19/epidemiology/prevention & control/transmission
MH  - China/epidemiology
MH  - Communicable Disease Control
MH  - Forecasting
MH  - Humans
MH  - Models, Statistical
MH  - *Population Density
MH  - Prevalence
MH  - Public Health
MH  - SARS-CoV-2
PMC - PMC8429718
COIS- The authors declare no competing interests.
EDAT- 2021/09/11 06:00
MHDA- 2021/09/24 06:00
PMCR- 2021/09/09
CRDT- 2021/09/10 06:51
PHST- 2020/12/18 00:00 [received]
PHST- 2021/08/24 00:00 [accepted]
PHST- 2021/09/10 06:51 [entrez]
PHST- 2021/09/11 06:00 [pubmed]
PHST- 2021/09/24 06:00 [medline]
PHST- 2021/09/09 00:00 [pmc-release]
AID - 10.1038/s41598-021-97578-9 [pii]
AID - 97578 [pii]
AID - 10.1038/s41598-021-97578-9 [doi]
PST - epublish
SO  - Sci Rep. 2021 Sep 9;11(1):18024. doi: 10.1038/s41598-021-97578-9.