From the index case to global spread: the global mobility based modelling of the COVID-19 pandemic implies higher infection rate and lower detection ratio than current estimates

An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic

The world still suffers from the COVID-19 pandemic, which was identified in late 2019. The number of COVID-19 confirmed cases are increasing every day, and many governments are taking various measures and policies, such as city lockdown. It seriously treats people’s lives and health conditions, and it is highly required to immediately take appropriate actions to minimise the virus spread and manage the COVID-19 outbreak. This paper aims to study the impact of the lockdown schedule on pandemic prevention and control in Ningbo, China. For this, machine learning techniques such as the K-nearest neighbours and Random Forest are used to predict the number of COVID-19 confirmed cases according to five scenarios, including no lockdown and 2 weeks, 1, 3, and 6 months postponed lockdown. According to the results, the random forest machine learning technique outperforms the K-nearest neighbours model in terms of mean squared error and R-square. The results support that taking an early lockdown measure minimises the number of COVID-19 confirmed cases in a city and addresses that late actions lead to a sharp COVID-19 outbreak.

Revealing spatiotemporal transmission patterns and stages of COVID-19 in China using individual patients’ trajectory data

Gauging viral transmission through human mobility in order to contain the COVID-19 pandemic has been a hot topic in academic studies and evidence-based policy-making. Although it is widely accepted that there is a strong positive correlation between the transmission of the coronavirus and the mobility of the general public, there are limitations to existing studies on this topic. For example, using digital proxies of mobile devices/apps may only partially reflect the movement of individuals; using the mobility of the general public and not COVID-19 patients in particular, or only using places where patients were diagnosed to study the spread of the virus may not be accurate; existing studies have focused on either the regional or national spread of COVID-19, and not the spread at the city level; and there are no systematic approaches for understanding the stages of transmission to facilitate the policy-making to contain the spread.

To address these issues, we have developed a new methodological framework for COVID-19 transmission analysis based upon individual patients’ trajectory data. By using innovative space–time analytics, this framework reveals the spatiotemporal patterns of patients’ mobility and the transmission stages of COVID-19 from Wuhan to the rest of China at finer spatial and temporal scales. It can improve our understanding of the interaction of mobility and transmission, identifying the risk of spreading in small and medium-sized cities that have been neglected in existing studies. This demonstrates the effectiveness of the proposed framework and its policy implications to contain the COVID-19 pandemic.

Modelling and forecasting of growth rate of new COVID-19 cases in top nine affected countries: Considering conditional variance and asymmetric effect

COVID-19 pandemic has affected more than a hundred fifty million people and killed over three million people worldwide over the past year. During this period, different forecasting models have tried to forecast time path of COVID-19 pandemic. Unlike the COVID-19 forecasting literature based on Autoregressive Integrated Moving Average (ARIMA) modelling, in this paper new COVID-19 cases were modelled and forecasted by conditional variance and asymmetric effects employing Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Threshold GARCH (TARCH) and Exponential GARCH (EGARCH) models. ARMA, ARMA-GARCH, ARMA-TGARCH and ARMA-EGARCH models were employed for one-day ahead forecasting performance for April, 2021 and three waves of COVID-19 pandemic in nine most affected countries -USA, India, Brazil, France, Russia, UK, Italy, Spain and Germany. Empirical results show that ARMA-GARCH models have better forecast performance than ARMA models by modelling both the conditional heteroskedasticity and the heavy-tailed distributions of the daily growth rate of the new confirmed cases; and asymmetric GARCH models show mixed results in terms of reducing the root mean squared error (RMSE).

The relationship between measures of individualism and collectivism and the impact of COVID-19 across nations

Background

The global COVID-19 pandemic has been characterized by marked variations in prevalence, mortality and case fatality across nations. The available evidence to date suggests that social factors significantly influence these variations. The sociological concepts of individualism and collectivism provide a broad explanatory framework for the study of these factors. There is evidence to suggest that cross-cultural variations in collectivism may have emerged via a process of natural selection, as a protective mechanism against infectious diseases. As a test of this hypothesis, this paper examined the association between indices of individualism and collectivism and the prevalence, mortality and case fatality rates of COVID-19 across nations.

Study design

This study was a population-level association study based on data in the public domain and from prior publications.

Methods

Data on four standard measures of individualism/collectivism were obtained from the original publications. These were correlated with estimates of the nation-wide prevalence, mortality and fatality rates for COVID-19 in 94 countries, obtained from the Johns Hopkins Medical University real-time dashboard.

Results

Individualism was positively correlated with COVID-19 prevalence, mortality and case fatality rates; conversely, measures of collectivism were negatively correlated with these parameters. The strongest association was between scores for individualism and mortality rate, and remained significant after correcting for several potential confounders.

Conclusions

These findings are consistent with the prior hypothesis of a relationship between individualism-collectivism and the impact of infectious disease across populations, and have implications in terms of social strategies aimed at minimizing the impact of COVID-19.

COVID-19 prediction models: a systematic literature review

As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature review is reported, based on a manual search of 1,196 papers published from January to December 2020. Various databases such as Google Scholar, Web of Science, and Scopus were searched. The search strategy was formulated and refined in terms of subject keywords, geographical purview, and time period according to a predefined protocol. Visualizations were created to present the data trends according to different parameters. The results of this systematic literature review show that the study findings are critically relevant for both healthcare managers and prediction model developers. Healthcare managers can choose the best prediction model output for their organization or process management. Meanwhile, prediction model developers and managers can identify the lacunae in their models and improve their data-driven approaches.

Discovering dynamic models of COVID-19 transmission

Existing models about the dynamics of COVID‐19 transmission often assume the mechanism of virus transmission and the form of the differential equations. These assumptions are hard to verify. Due to the biases of country‐level data, it is inaccurate to construct the global dynamic of COVID‐19. This research aims to provide a robust data‐driven global model of the transmission dynamics. We apply sparse identification of nonlinear dynamics (SINDy) to model the dynamics of COVID‐19 global transmission. One advantage is that we can discover the nonlinear dynamics from data without assumptions in the form of the governing equations. To overcome the problem of biased country‐level data on the number of reported cases, we propose a robust global model of the dynamics by using maximin aggregation. Real data analysis shows the efficiency of our model.

An intra-host SARS-CoV-2 dynamics model to assess testing and quarantine strategies for incoming travelers, contact management, and de-isolation

Non-pharmaceutical interventions (NPIs) remain decisive tools to contain SARS-CoV-2. Strategies that combine NPIs with testing may improve efficacy and shorten quarantine durations. We developed a stochastic within-host model of SARS-CoV-2 that captures temporal changes in test sensitivities, incubation periods, and infectious periods. We used the model to simulate relative transmission risk for (1) isolation of symptomatic individuals, (2) contact person management, and (3) quarantine of incoming travelers. We estimated that testing travelers at entry reduces transmission risks to 21.3% ([20.7, 23.9], by PCR) and 27.9% ([27.1, 31.1], by rapid diagnostic test [RDT]), compared with unrestricted entry. We calculated that 4 (PCR) or 5 (RDT) days of pre-test quarantine are non-inferior to 10 days of quarantine for incoming travelers and that 8 (PCR) or 10 (RDT) days of pre-test quarantine are non-inferior to 14 days of post-exposure quarantine. De-isolation of infected individuals 13 days after symptom onset may reduce the transmission risk to <0.2% (<0.01, 6.0).

Mathematical modelling for decision making of lockdown during COVID-19

Due to the recent worldwide outbreak of COVID-19, there has been an enormous change in our lifestyle and it has a severe impact in different fields like finance, education, business, travel, tourism, economy, etc., in all the affected countries. In this scenario, people must be careful and cautious about the symptoms and should act accordingly. Accurate predictions of different factors, like the end date of the pandemic, duration of lockdown and spreading trend can guide us through the pandemic and precautions can be taken accordingly. Multiple attempts have been made to model the virus transmission, but none of them has investigated it at a global level. The novelty of the proposed work lies here. In this paper, first, authors have analysed spreading of the said disease using data collected from various platforms and then, have presented a predictive mathematical model for fifteen countries from first, second and third world for probable future projections of this pandemic. The prediction can be used by planning commission, healthcare organizations and the government agencies as well for creating suitable arrangements against this pandemic.

Shared mobility in post-COVID era: New challenges and opportunities

This study is aimed at exploring the challenges and opportunities that the COVID-19 outbreak presents to the sustainability of shared mobility. To date, the sustainability of shared mobility has received little attention in the literature, and this study determines its central constructs that are critical to the sustainability of shared mobility. We accordingly conducted a three-phase Delphi approach composed of both qualitative and quantitative methods. Feedback was obtained from 18 international experts who are very knowledgeable regarding civil engineering and shared mobility, initially finding 18 challenges and 18 opportunities. Finally, we identified 12 key constructs as highly critical to the sustainability of shared mobility. The current work is an attempt to address gaps in exploring the challenges and opportunities that the COVID-19 outbreak has created in shared mobility, particularly when a comprehensive examination is needed. This study will serve as an inspiration and catalog for new studies within this field.

The Effect of Strict State Measures on the Epidemiologic Curve of COVID-19 Infection in the Context of a Developing Country: A Simulation from Jordan

COVID-19 has posed an unprecedented global public health threat and caused a significant number of severe cases that necessitated long hospitalization and overwhelmed health services in the most affected countries. In response, governments initiated a series of non-pharmaceutical interventions (NPIs) that led to severe economic and social impacts. The effect of these intervention measures on the spread of the COVID-19 pandemic are not well investigated within developing country settings. This study simulated the trajectories of the COVID-19 pandemic curve in Jordan between February and May and assessed the effect of Jordan's strict NPI measures on the spread of COVID-19. A modified susceptible, exposed, infected, and recovered (SEIR) epidemic model was utilized. The compartments in the proposed model categorized the Jordanian population into six deterministic compartments: suspected, exposed, infectious pre-symptomatic, infectious with mild symptoms, infectious with moderate to severe symptoms, and recovered. The GLEAMviz client simulator was used to run the simulation model. Epidemic curves were plotted for estimated COVID-19 cases in the simulation model, and compared against the reported cases. The simulation model estimated the highest number of total daily new COVID-19 cases, in the pre-symptomatic compartmental state, to be 65 cases, with an epidemic curve growing to its peak in 49 days and terminating in a duration of 83 days, and a total simulated cumulative case count of 1048 cases. The curve representing the number of actual reported cases in Jordan showed a good pattern compatibility to that in the mild and moderate to severe compartmental states. The reproduction number under the NPIs was reduced from 5.6 to less than one. NPIs in Jordan seem to be effective in controlling the COVID-19 epidemic and reducing the reproduction rate. Early strict intervention measures showed evidence of containing and suppressing the disease.