#Simulating the Infected Population and #Spread #Trend of 2019 novel #Coronavirus Under Different  Policy by EIR Model (SSRN, abstract)

[Source: SSRN, full page: (LINK). Abstract, edited.]

Simulating the Infected Population and Spread Trend of 2019-nCov Under Different  Policy by EIR Model

11 Pages Posted: 13 Feb 2020

Hao Xiong, Hainan University – Department of Management Sciences; Huili Yan, Hainan University – Department of Tourism Management

 

Abstract

Background:

Chinese government has taken strong measures in response to the epidemic of new coronavirus (2019-nCoV) from Jan.23, 2020. The number of confirmed infected individuals are still increasing rapidly. Estimating the accurate infected population and the future trend of epidemic spreading under control measures is significant and urgent. However, the common forecasting models, such as SI, SIS, SIR, SIRS and SEIR, are only suit for scenarios without non-pharmaceutical prevention interventions. And the estimating infected populations from existing literature are too far more than the official reported data. Here, we provide a two-phase EI model integrated the epidemic spreading before and after control measures. Then, we estimate of the size of the epidemic and simulate the future development of the epidemics under strong prevention interventions.

Methods:

According to the spread characters of 2019-nCov, we construct a novel exposed-infected (EI) compartment system dynamics model. This model integrates two phases of the epidemic spreading: before intervention and after intervention. We assume that 2019-nCov is firstly spread without intervention then the government started to take strong quarantine measures. Use the latest reported data from National Health Commission of the People’s Republic of China, we estimate the basic parameters of the model and the basic reproduction number of 2019-nCov. Then, based on this model, we simulate the future spread of the epidemics. Both the infected population and the development time of 2019-nCov under different prevention policy scenarios are estimated. And, the influences of the quarantine rate and the intervention time point of prevention intervention policy are analyzed and compared.

Findings:

In our baseline scenario, the government takes the strict prevention actions, the estimate numbers fit the official numbers very well. There can be no doubt that the official numbers are accurate. We estimated that the basic reproductive number for 2019-nCoV was 2.985 and that the peak infected individuals will be 49093 at Feb.16, 2020. And then the epidemic spreading will fade out at the end of March 2020. The quarantine rate and the starting date point of intervention have great effect on the epidemic spreading. Furthermore, if the quarantine rate is reduced from 100% to less than 63%, which is the threshold of the quarantine rate to control the epidemic spreading, the epidemic spreading would not never be fade out. Finally, from the simulation of different action starting date under the strict prevention measures, if the starting date of intervention is delayed for 1 day than the current date Jan. 23, 2020, the peak infected population will increase about 6351. If the delay 3 days or 7 days the peak number would be 70714 and 115022 individuals, which means increasing 21621 and 65929 individuals.

Interpretation:

Given that 2019-nCoV could be controlled under the strong prevention measures of what China has taken and it will take about three months. The confirmed infected individuals will still keep quick increasing for 14 days (approximately equal to the sum of exposed period and infection period) after the start time point of control. The strong prevention measures should be insisted until the epidemic Coronavirus. Other domestic places and overseas have confirmed infected individuals should take strong interventions immediately. Earlier strong prevention measures could efficiently stop the independent self-sustaining outbreaks in other cities globally.

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Funding: This work was supported by National Natural Science Foundation of China (Grant No. 71761009, No. 71461007 and No. 71461006) and Hainan Province Planning Program of Philosophy and Social Science (HNSK(YB)19-06, HNSK(YB)19-11).

Declaration of Interest: We declare no competing interests.

Keywords: simulation; forecasting; 2019-nCoV; epidemic spreading; transmission model

Suggested Citation: Xiong, Hao and Yan, Huili, Simulating the Infected Population and Spread Trend of 2019-nCov Under Different Policy by EIR Model (2/8/2020). Available at SSRN: https://ssrn.com/abstract=3537083

Keywords: COVID-19; SARS-CoV-2.

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Giuseppe Michieli

I am an Italian blogger, active since 2005 with main focus on emerging infectious diseases such as avian influenza, SARS, antibiotics resistance, and many other global Health issues. Other fields of interest are: climate change, global warming, geological and biological sciences. My activity consists mainly in collection and analysis of news, public services updates, confronting sources and making decision about what are the 'signals' of an impending crisis (an outbreak, for example). When a signal is detected, I follow traces during the entire course of an event. I started in 2005 my blog ''A TIME'S MEMORY'', now with more than 40,000 posts and 3 millions of web interactions. Subsequently I added an Italian Language blog, then discontinued because of very low traffic and interest. I contributed for seven years to a public forum (FluTrackers.com) in the midst of the Ebola epidemic in West Africa in 2014, I left the site to continue alone my data tracking job.