More on COVID-19 modeling, please refer to here.
Differentiating behavioral impact with or without vaccination certification under mass vaccination and non-pharmaceutical interventions on mitigating COVID-19
Hu Cao and Longbing Cao. Scientific Reports, 14, 707 (2024). https://doi.org/10.1038/s41598-023-50421-9.
As COVID-19 vaccines became widely available worldwide, many countries implemented vaccination certification, also known as a “green pass”, to promote and expedite vaccination on containing virus spread from the latter half of 2021. This policy allowed those vaccinated to have more freedom in public activities compared to more constraints on the unvaccinated in addition to existing non-pharmaceutical interventions (NPIs). Accordingly, the vaccination certification also induced heterogeneous behaviors of unvaccinated and vaccinated groups. This makes it essential yet challenging to model the behavioral impact of vaccination certification on the two groups and the transmission dynamics of COVID-19 within and between the groups. Very limited quantitative work is available for addressing these purposes. Here we propose an extended epidemiological model SEIQRD^2 to effectively distinguish the behavioral impact of vaccination certification on unvaccinated and vaccinated groups through incorporating two contrastive transmission chains. SEIQRD^2 also quantifies the impact of the green pass policy. With the resurgence of COVID-19 in Greece, Austria, and Israel in 2021, our simulation results indicate that their implementation of vaccination certification brought about more than a 14-fold decrease in the total number of infections and deaths as compared to a scenario with no such a policy. Additionally, a green pass policy may offer a reasonable practical solution to strike the balance between public health and individual’s freedom during the pandemic.
How Have Global Scientists Responded to Tackling COVID-19?
Longbing Cao and Wenfeng Hou. Full technical report, pp. 1-125, University of Technology Sydney, 2022. medRxiv: https://doi.org/10.1101/2022.08.16.22278871
Since the outbreak of COVID-19, the global scientific communities across almost all countries have made urgent, intensive, and continuous effort on understanding, fighting and modeling the COVID-19 pandemic. COVID-19 research turns out to be the first overwhelming global scientific reaction to significant global crises and threats. This literature analysis report collects and summarizes the profiles, trends, quality and impact of this global scientific response. It collects and analyzes 346,267 scientific references in English, involving researchers from 189 countries and regions in 27 subject areas. The report generates a picture of how global scientists have responded to COVID-19 between Jan 2020 and Mar 2022 in terms of their publication quantity, impact, focused major problems, and research areas and methods over country/region, discipline and time and collaboratively. The report also captures broad-reaching distributions and trends of modeling COVID-19 by AI, data science, analytics, shallow and deep machine learning, epidemic modeling, applied mathematics, and social science methods, etc. We further show the correlations between publication quality and quantity and economic status and COVID-19 infections globally and in major countries and regions. The literature analysis results of this global scientific response to COVID-19 present a comprehensive global, regional and subject-specific picture of the significant cross-disciplinary, cross-country, cross-problem, and cross-technique profiles and differences of the COVID-19 publication quantity and quality. The report also discloses significant imbalances in the COVID-19 research across countries/regions, subject areas, problems, topics, methods, research collaborations, and economic statuses. We share the source and analytical data of this global literature analysis for further research on this unprecedented and future crises.
Access the COVID-19 global scientist response dataset and results at Kaggle.
COVID-19 Modeling: A Review
Longbing Cao and Qing Liu. doi: https://doi.org/10.1101/2022.08.22.22279022, 1-103, 2022 (new version).
The SARS-CoV-2 virus and COVID-19 disease have posed unprecedented and overwhelming demand, challenges and opportunities to domain, model and data driven modeling. This paper makes a comprehensive review of the challenges, tasks, methods, progresses, gaps and opportunities of modeling COVID-19 problems, data and objectives. It constructs a research landscape of the COVID-19 modeling tasks and methods, and further categorizes, summarizes, compares and discusses the related methods and progresses of modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and medical treatments, non-pharmaceutical interventions and their effects, drug and vaccine development, psychological, economic and social influence and impact, and misinformation, etc. The modeling methods involve mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, data-driven learning by shallow and deep machine learning, simulation modeling, social science methods, and hybrid modeling.
The SARS-CoV-2 virus, the COVID-19 disease, and the resulting pandemic have reshaped the entire world in an unprecedented manner. Massive efforts have been made by AI communities to combat the pandemic. What roles has AI played in tackling COVID-19? How has AI performed in the battle against COVID-19? Where are the gaps and opportunities? What lessons can we learn to enhance the ability of AI to battle future pandemics? These questions, despite being fundamental, are yet to be answered in full or systematically. They need to be addressed by AI communities as a priority despite the easing of the omicron infectiousness and threat. This article reviews these issues with reflections on global AI research and the literature on tackling COVID-19. It is envisaged that the demand and priority of developing “pandemic AI” will increase over time, with smart global epidemic early warning systems to be developed by a global collaborative AI effort.
How control and relaxation interventions and virus mutations influence the resurgence of COVID-19
Longbing Cao, Qing Liu. medRxiv, doi: https://doi.org/10.1101/2021.08.31.21262897, 1-29, 2021.
After a year of the unprecedented COVID-19 pandemic in 2020, the world has been overwhelmed by COVID-19 resurgences in 2021. Resurgences usually cause longer, broader and higher waves of infection, with greater threat to societies and economies compared to first waves. They may be caused by late implementation or early relaxation of non-pharmaceutical interventions (NPIs) such as social restrictions, ineffective interventions, or virus mutations. Here we provide quantitative evidence to characterize epidemiological differences between waves, evaluate possible causes, and predict potential trends under virus mutations. We use an event-driven dynamic epidemiological model embedded with time-dependent intervention interactions to compare two waves of COVID-19 outbreaks, and we quantify the impacts of control or relaxation interventions (called events) on wave patterns. We show the second waves in late 2020 in Germany, France and Italy could have been better contained by either carrying forward the effective interventions from their first waves or implementing better controls and timing. We also obtain the quantitative effects of enforcing or relaxing interventions on various transmissibility levels of coronavirus mutants (like delta or lambda) in the second waves and in the next 30 days. Comprehensive analyses at four levels – vertical (between waves), horizontal (across countries), what-if (scenario simulations on second waves) and future (30-day trend) – in the two 2020 waves in Germany, France and Italy show that (1) intervention fatigue (government and community reluctance to interventions), early relaxations and lagging interventions may be common reasons for the resurgences observed in many countries; (2) timely strong interventions such as full lockdown will contain resurgence; and (3) in the absence of sufficient vaccination, herd immunity and effective antiviral pharmaceutical treatments and with more infectious mutations, the widespread early or fast relaxation of interventions including public activity restrictions will result in a COVID-19 resurgence.
Modeling COVID-19 uncertainties evolving over time and density-dependent social reinforcement and asymptomatic infections
Qing Liu and Longbing Cao, Sci Rep, 12(1):5891, 1-14, 2022.
The novel coronavirus disease 2019 (COVID-19) presents unique and unknown problem complexities and modeling challenges, where an imperative task is to model both its process and data uncertainties, represented in implicit and high-proportional undocumented infections, asymptomatic contagion, social reinforcement of infections, and various quality issues in the reported data. These uncertainties become even more phenomenal in the overwhelming mutation-dominated resurgences with vaccinated but still susceptible populations. Here we introduce a novel hybrid approach to (1) characterizing and distinguishing Undocumented (U) and Documented (D) infections commonly seen during COVID-19 incubation periods and asymptomatic infections by expanding the foundational compartmental epidemic Susceptible-Infected-Recovered (SIR) model with two compartments, resulting in a new Susceptible-Undocumented infected-Documented infected-Recovered (SUDR) model; (2) characterizing the probabilistic density of infections by empowering SUDR to capture exogenous processes like clustering contagion interactions, superspreading and social reinforcement; and (3) approximating the density likelihood of COVID-19 prevalence over time by incorporating Bayesian inference into SUDR. Different from existing COVID-19 models, SUDR characterizes the undocumented infections during unknown transmission processes. To capture the uncertainties of temporal transmission and social reinforcement during the COVID-19 contagion, the transmission rate is modeled by a time-varying density function of undocumented infectious cases. We solve the modeling by sampling from the mean-field posterior distribution with reasonable priors, making SUDR suitable to handle the randomness, noise and sparsity of COVID-19 observations widely seen in the public COVID-19 case data.
Quantifying COVID-19: Modeling and Evaluation
Qing Liu, PhD thesis, Feb 2022, UTS
The coronavirus disease 2019 (COVID-19) has evolved to a global pandemic and poses significant demands and challenges in modeling its complex epidemic transmission, infection, and contagion. Moreover, it has shown to be vastly different
from known epidemics. To address the COVID-19 pandemic, significant efforts have been made to model COVID-19 transmission, diagnoses, interventions, and pathological and influence analysis, etc. However, due to the unique and unknown problem and data complexity, the related studies of COVID-19 still face numerous challenges, including undocumented infections, asymptomatic contagion, uncertainty and quality issues in the reported data, flexible external non-pharmaceutical interventions, unknown resurgence patterns or periodicity, and multiple mutations.
This thesis aims to understand COVID-19 concerning the COVID-19 research landscape, transmission complexity, non-pharmaceutical interventions, and COVID-19 resurgence. Focusing on the COVID-19 challenges, this thesis first compares the key characteristics of COVID-19 disease with several known epidemics, and it summarizes the COVID-19 modeling complexities caused by these attributes. Starting from this basic knowledge, this thesis further explores COVID-19 modeling, which results in the following four contributions. (1) This thesis tracks the current COVID-19 modeling progress with natural language techniques and statistically summarizes the major facts of COVID- 19 disease and COVID-19 modelling. This work structures a transdisciplinary research landscape and provides a holistic picture of COVID-19 modeling. (2) It infers the possible quantity of undocumented infections in the early stage of the COVID-19 outbreak with the proposed density-based Bayesian probabilistic compartmental model. This work examines the COVID-19 transmission complexities, in other words, undocumented infections, contagion reinforcement, and the imperfect conditions existing in COVID-19 reported data, that is, noise, sparsity, and uncertainty. (3)With the proposed event-driven generalized Susceptible-Exposed-Infectious-Recovered compartmental model, this thesis studies the impact of external interventions and activities in the dynamic COVID-19 evolving process and quantifies the efficacy of control policies and relaxation measures. (4) This thesis compares the differences between multiple COVID-19 waves, including the epidemiological attributes and the countermeasures, and it simulates the possible scenarios with different interventions and virus mutations. This exploration illustrates the possible reasons for COVID-19 resurgence and provides reliable guidance for society resuscitation.
Extensive experiments, including mean-field Bayesian inference, backward-looking empirical evaluations, forward-looking simulations, and short-term forecast, demonstrate the effectiveness of the proposed methods for modeling the COVID-19 complexities aforementioned. The findings and quantitative results in this thesis indicate clues, evidence, and guidance for governments and policymakers to appropriately manage and mitigate the COVID-19 pandemic.