Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China

Abstract

As an emerging infectious disease, the 2019 coronavirus disease (COVID-19) has developed into a global pandemic. During the initial spreading of the virus in China, we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City. Second, we used an individual-level network-based model to reconstruct the epidemic dynamics in Hubei Province and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios. Our simulation results show that without continued control measures, the epidemic in Hubei Province could have become persistent. Only by continuing to decrease the infection rate through 1) protective measures and 2) social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation. Finally, we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories, compared to those obtained with Markov processes. Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes, future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories. In addition, shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

“…The first column describes the main categories, the second the sub-categories, the last the country/countries focus of each study and the references. Category Subcategory Country/countries of study and reference Compartmental SIR-like Brazil, China [48] ; China [49] , [50] , [51] ; China, Italy [52] ; China, France, Iran, Italy, South Korea, USA [53] ; France, Iran, Italy [54] ; Germany [55] ; India [56] ; Italy [57] ; USA [58] ; 187 countries [45] ; several European countries [44] , [59] SIR-like age structured China [60] , Italy [61] , 143 countries [47] SEIR-like Argentina, Japan, Indonesia, New Zealand, Spain, USA [62] ; Canada [63] , [64] ; Canada, Germany, Italy [65] ; China [66] , [67] , [68] , [69] ; China, Italy [70] ; China, UK [71] ; Germany [72] ; India [73] , [74] ; Indonesia [75] ; Ireland [76] ; Mexico [77] ; Pakistan [78] ; South Korea [79] , [80] ; Switzerland [81] ; USA [82] , [83] ...…”

“…In the sample of papers under review here, we find full fledged agent-based models used to model the COVID-19 and the effects of NPIs at the levels of countries, regions, or cities [63] , [66] , [135] , [137] , [138] , [157] . We also find more theoretical approaches modeling particular aspects of NPIs such as different strategies for isolation [136] , [139] , [140] .…”

“…Authors in Ref. [66] use a network-based model where a representative sample of different cities interacts via a contact network. The authors model interactions within and across cities.…”

Understanding the effects of contact structures and information sharing on the FMD transmission among beef cattle farms

“…To simulate the disease spreading between beef-cattle farms, we develop a farm-level, stochastic, individual-based epidemic model on GEMF, which can be used to simulate spreading processes on multilayer networks [25][26][27]. GEMF can numerically simulate any stochastic GEMF-based model, and is available in MATLAB, R, Python, and C programming language.…”

Combined measures to control the COVID-19 pandemic in Wuhan, Hubei, China: A narrative review

“…We sought to answer this question by performing a literature search, exploring how enhanced protective measures and social distancing measures were crucial to stopping the pandemic and how many lives were saved. 32 …”