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

Abstract

Before the lock-down of Wuhan/Hubei/China, on January 23 rd 2020, a large number of individuals infected by COVID-19 moved from the epicenter Wuhan and the Hubei province due to the Spring Festival, resulting in an epidemic in the other provinces including the Shaanxi province. The epidemic scale in Shaanxi was comparatively small and with half of cases being imported from the epicenter. Based on the complete epidemic data including the symptom onset time and transmission chains, we calculate the control reproduction number (1.48-1.69) in Xi'an. We could also compute the time transition, for each imported or local case, from the latent, to infected, to hospitalized compartment, as well as the effective reproduction number. This calculation enables us to revise our early deterministic transmission model to a stochastic discrete epidemic model with case importation and parameterize it. Our model-based analyses reveal that the newly generated infections decay to zero quickly; the cumulative number of case-driven quarantined individuals via contact tracing stabilize at a manageable level, indicating that the intervention strategies implemented in the Shaanxi province have been effective. Risk analyses, important for the consideration of "resumption of work", show that a large second outbreak is expected if the level of case importation remains at the same level as between January 10 th and February 4 th 2020. However, if the case importation decreases by 30%, 60% and 90%, the second outbreak if happening will be of small-scale assuming contact tracing and quarantine/isolation remain as effective as before. Finally, we consider the effects of intermittent inflow with a Poisson distribution on the likelihood of multiple outbreaks. We believe the developed methodology and stochastic model provide an important model framework for the evaluation of revising travel restriction rules in the consideration of resuming social-economic activities while managing the disease control with potential case importation.

Basic reproduction number and predicted trends of coronavirus disease 2019 epidemic in the mainland of China

“…It also could be seen that our estimated basic reproduction number before 11 February 2020 was slightly higher than that of some previous studies [15,18,[41][42][43]57]. This might be mainly caused by the following two reasons.…”

“…Therefore, to find more effective control efforts, the peak arrival time and the trend of the COVID-19 epidemic in the mainland of China should be predicted with a well-designed model [32,33]. Many studies have estimated the reproduction number in the early phase of COVID-19 outbreak in China [32][33][34][35][36][37][38][39][40][41][42][43].…”

Modelling the effect of lockdown on COVID-19 pandemic in 22 countries and cities

“…In the 22 countries or cities, before the lockdown, the basic reproduction numbers were between 1.3884-4.1926; after the lockdown, the basic reproduction numbers were between 1.2141-3.7829; the average basic reproduction numbers were between 1.5286-3.8067. The results were in good agreement with many existing results [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. The basic reproduction numbers in the 22 countries or cities decreased significantly after the lockdown, which suggested that lockdown measures indeed suppressed the epidemics.…”

“…Many studies estimated the basic reproduction number in the early phase of COVID-19 outbreak [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. Some results indicated that the basic reproduction number was greater than 3, [32] and so on.…”

Transmissibility of coronavirus disease 2019 in Chinese cities with different dynamics of imported cases

“…This may be due to the increased awareness on the use of personal protective measures against COVID-19 after noticing the unknown pneumonia outbreak in Wuhan in early January through social media. Similar transmission dynamics were also reported in Shaanxi province (Tang et al, 2020).…”