An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov)

Abstract

a b s t r a c tThe basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period, in a naïve, uninfected population. It is well-known that the estimation of this number may vary due to several methodological issues, including different assumptions and choice of parameters, utilized models, used datasets and estimation period. With the spreading of the novel coronavirus (2019-nCoV) infection, the reproduction number has been found to vary, reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate. Due to significant variations in the control strategies, which have been changing over time, and thanks to the introduction of detection technologies that have been rapidly improved, enabling to shorten the time from infection/symptoms onset to diagnosis, leading to faster confirmation of the new coronavirus cases, our previous estimations on the transmission risk of the 2019-nCoV need to be revised. By using time-dependent contact and diagnose rates, we refit our previously proposed dynamics transmission model to the data available until January 29th , 2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions. We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak. Our updated findings suggest that the best measure is persistent and strict self-isolation. The epidemics will continue to grow, and can peak soon with the peak time depending highly on the public health interventions practically implemented.

A retrospective analysis of the dynamic transmission routes of the COVID-19 in mainland China

“…According to [43], the average incubation period of 2019-nCoV is up to 10 days. Thus, our estimated dynamic transmission routes supports the significant effectiveness of the interventions taken by the Chinese authorities [14][15][16][17][18][19][20][21][22][23][24]. This is evidenced by On the other hand, from February 5 to 16, Table 1 suggests that the averaged magnitudes of self-transmission on each day were strengthening steadily.…”

“…On 23 January, the Chinese authorities introduced travel restrictions on five cities (Wuhan, Huanggang, Ezhou, Chibi and Zhijiang) of Hubei, shutting down the movement of more than 40 million people [14]. Among existing research, most argues that those interventions have effectively halted the spread of the 2019-nCoV [14][15][16][17][18][19][20][21][22][23][24].…”

Stochastic discrete epidemic modeling of COVID-19 transmission in the Province of Shaanxi incorporating public health intervention and case importation

“…In this study we extend our deterministic model framework [9,[18][19][20]] to a discrete system incorporating stochastic modeling of imported cases to represent the relatively small number of cases observed in the Shaanxi province, with about half of them being imported cases. It is worth noting that traced data is around 7 days ahead of the reported ones, as illustrated in Figure 1(A).…”

“…Since January 23 rd 2020, the Chinese authorities have been implementing unprecedented and increasingly stringent public health interventions, including a complete lock-down of Wuhan and neighboring cities, strict contact tracing and travel restrictions in the epicenter and across the country [13]. Incorporating these interventions into the classical epidemic models has resulted in reliable estimates of epidemiological characteristics of COVID-19 transmission in the epicenter and accurate near-casting the epidemic trend and peak time [18][19][20], in comparison with other earlier modeling studies focusing on analyzing the transmission risk based on data at the early outbreak stage [1,3,4,17]. One important lesson gained from our recent experience suggests the need of updating the model parameters and model setting to reflect the rapid improvement of screening, diagnosing and testing techniques/procedures, accompanied by intensive contact tracing, quarantine and isolation.…”

Risk estimation and prediction by modeling the transmission of the novel coronavirus (COVID-19) in mainland China excluding Hubei province

“…Furthermore, the value of R 0 may vary with key clinical parameters inferred from data which depend on the time period, quality, accuracy, and reliability. To better quantify the evolution of the interventions, Tang et al fitted the previously proposed model in [5] to the data available until January 29th, 2020 and re-estimated the effective daily reproduction ( [13]). There are also some literatures focusing on the prediction of COVID-19 development trend.…”