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In this paper, a SEIR epidemic model for the COVID-19 is built according to some general control strategies, such as hospital, quarantine and external input. Based on the data of Hubei province, the particle swarm optimization (PSO) algorithm is applied to estimate the parameters of the system. We found that the parameters of the proposed SEIR model are different for different scenarios. Then, the model is employed to show the evolution of the epidemic in Hubei province, which shows that it can be used to forecast COVID-19 epidemic situation. Moreover, by introducing the seasonality and stochastic infection the parameters, nonlinear dynamics including chaos are found in the system. Finally, we discussed the control strategies of the COVID-19 based on the structure and parameters of the proposed model.

During the COVID-19 pandemic, the Malaysian government prioritized health and economic stimulus packages for the sectors considered “key” for the economy and gave little recognition to the workers making contributions to the functioning of daily lives. The paper documents the impacts on key workers in healthcare, food and beverages, transportation and delivery, security and cleaning services. For these key workers, the nature of their jobs, labour force status and personal characteristics exposed them to greater job stressors and demands and exacerbated their vulnerabilities. While the fluidity of the coronavirus situation and the dynamic socio-political context of the country imply that change is on-going, the suggestions for recognizing, valuing, protecting and empowering key workers are necessary for helping the country to build back better.

Firstly, this paper describes the challenge of the new coronary pneumonia epidemic to the ability of information service support. Secondly, it analyzes the main problems and related concepts of cloud network convergence, and points out the new problems faced by cloud network converge. Thirdly, this paper summarizes the current status and significant characteristics of the construction and development of cloud network convergence, and points out the important role and performance of cloud network convergence in fighting the new crown epidemic through specific examples; Finally, it analyzes the risks of cloud network convergence and the countermeasures was proposed.

The COVID-19 pandemic has dealt a severe blow to human capital. This report presents new evidence and analysis to provide a comprehensive diagnostic of the effects of the pandemic on human capital outcomes and identify promising policy responses for governments faced with the task of rebuilding human capital in the wake of the pandemic. The report identifies the mechanisms through which COVID-19 affected the human capital of people at different points in the life cycle and provides estimates of the magnitude of these losses. This analysis underlines differences in impact across countries and groups within countries to understand how the reported blow on human capital has been unequal, exacerbating existing gaps and creating new ones. Grounded in the diagnostic, the report discusses policy responses that attend to afflicted groups in the short-term as well as the medium- to long-term agenda to build back better human capital and make systems more resilient. The long-term policy discussion recognizes COVID-19 as an inflection point, using the opportunity to reimagine systems and institutions, thinking in a completely different way about some key issues. In conclusion, the report reflects on what we have learned from failed policy responses as well as the innovations that proved successful across sectors in preventing or mitigating human capital losses associated with the COVID-19 crisis, and how these lessons can be incorporated across sectors going forward\"--

This research assesses the effects of COVID-19-associated shocks on financial constraints and sustainable development goal (SDG) performance to shed light on the impact of SDGs on economic recovery. We construct a large sample of Chinese listed firms from quarterly firm-level accounting data from the China Stock Market & Accounting Research Database for the period 2019Q1–2021Q1, matched with environmental, social, and governance (ESG) scores, SDG performance from the WIND Database, and complemented with data on cumulative and new cases of COVID-19 from the World Health Organization. We use difference-in-differences to investigate any causal effect from COVID-19. We find that COVID-19 induces financial constraints in firms. Further, differing from the existing literature on the determinants of SDGs, we explore the supportive role of SDG performance on firm financial performance and show that ESG can better describe SDG performance and alleviate financial constraints. Moreover, both internal and external financial intermediaries improve with enhanced ESG performance in overcoming financial constraints. Our findings strongly indicate that a sustainable development strategy facilitates efficient adaptation to financial challenges and assists in overcoming external shocks.

We extend the canonical epidemiology model to study the interaction between economic decisions and epidemics. Our model implies that people cut back on consumption and work to reduce the chances of being infected. These decisions reduce the severity of the epidemic but exacerbate the size of the associated recession. The competitive equilibrium is not socially optimal because infected people do not fully internalize the effect of their economic decisions on the spread of the virus. In our benchmark model, the best simple containment policy increases the severity of the recession but saves roughly half a million lives in the United States.

We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number R 0 for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors’ amplification.

Following the detection of the new coronavirus 1 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics of coronavirus disease 2019 (COVID-19). In response, many European countries have implemented non-pharmaceutical interventions, such as the closure of schools and national lockdowns. Here we study the effect of major interventions across 11 European countries for the period from the start of the COVID-19 epidemics in February 2020 until 4 May 2020, when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks previously, allowing for the time lag between infection and death. We use partial pooling of information between countries, with both individual and shared effects on the time-varying reproduction number ( R t ). Pooling allows for more information to be used, helps to overcome idiosyncrasies in the data and enables more-timely estimates. Our model relies on fixed estimates of some epidemiological parameters (such as the infection fatality rate), does not include importation or subnational variation and assumes that changes in R t are an immediate response to interventions rather than gradual changes in behaviour. Amidst the ongoing pandemic, we rely on death data that are incomplete, show systematic biases in reporting and are subject to future consolidation. We estimate that—for all of the countries we consider here—current interventions have been sufficient to drive R t below 1 (probability R t  < 1.0 is greater than 99%) and achieve control of the epidemic. We estimate that across all 11 countries combined, between 12 and 15 million individuals were infected with SARS-CoV-2 up to 4 May 2020, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions—and lockdowns in particular—have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control. Modelling based on pooled data from 11 European countries indicates that non-pharmaceutical interventions—particularly lockdowns—have had a marked effect on SARS-CoV-2 transmission, driving the reproduction number of the infection below 1.

PurposeMany supply chains have faced disruption during Covid-19. Artificial intelligence (AI) is one mechanism that can be used to improve supply chain resilience by developing business continuity capabilities. This study examines how firms employ AI and consider the opportunities for AI to enhance supply chain resilience by developing visibility, risk, sourcing and distribution capabilities.Design/methodology/approachThe authors have gathered rich data by conducting semistructured interviews with 35 experts from the e-commerce supply chain. The authors have adopted a systematic approach of coding using open, axial and selective methods to map and identify the themes that represent the critical elements of AI-enabled supply chain resilience.FindingsThe results of the study highlight the emergence of five critical areas where AI can contribute to enhanced supply chain resilience; (1) transparency, (2) ensuring last-mile delivery, (3) offering personalized solutions to both upstream and downstream supply chain stakeholders, (4) minimizing the impact of disruption and (5) facilitating an agile procurement strategy.Research limitations/implicationsThe study offers interesting implications for bridging the theory–practice gap by drawing on contemporary empirical data to demonstrate how enhancing dynamic capabilities via AI technologies further strengthens supply chain resilience. The study also offers suggestions for utilizing the findings and proposes a framework to strengthen supply chain resilience through AI.Originality/valueThe study presents the dynamic capabilities for supply chain resilience through the employment of AI. AI can contribute to readying supply chains to reduce their risk of disruption through enhanced resilience.