@article {Xiong2020.02.10.20021519, author = {Xiong, Hao and Yan, Huili}, title = {Simulating the infected population and spread trend of 2019-nCov under different policy by EIR model}, elocation-id = {2020.02.10.20021519}, year = {2020}, doi = {10.1101/2020.02.10.20021519}, publisher = {Cold Spring Harbor Laboratory Press}, abstract = {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. There have been reports external icon of spread from an infected patient with no symptoms to a close contact, which means the incubation individuals may has the possibility of infectiousness. However, the traditional transmission model, Susceptible-Exposed-Infectious-Recovered (SEIR) model, assumes that the exposed individual is being infected but without infectiousness. Thus, the estimating infected populations based on SEIR model from the existing literatures seems too far more than the official reported data. Here, we inferred that the epidemic could be spread by exposed (incubation) individuals. Then, we provide a new Exposed-identified-Recovered (EIR) model, and simulated the epidemic spreading processes from free propagation phase to extremely control phase. Then, we estimate of the size of the epidemic and forecast the future development of the epidemics under strong prevention interventions. According to the spread characters of 2019-nCov, we construct a novel EIR 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 official data, 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 spreading trend of 2019-nCov under different prevention policy scenarios are estimated. The epidemic spreading trends under different quarantine rate and action starting date of prevention policy are simulated and compared.Competing Interest StatementThe authors have declared no competing interest.Clinical TrialThis study is not a medicinal technique research. All the medicinal knowledge needed in the manuscript are based on other literatures.Funding StatementThis 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).Author DeclarationsAll relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.YesAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll the original data are included manuscript. And the simulating data could be available if needed}, URL = {https://www.medrxiv.org/content/early/2020/02/12/2020.02.10.20021519}, eprint = {https://www.medrxiv.org/content/early/2020/02/12/2020.02.10.20021519.full.pdf}, journal = {medRxiv} }