Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak

Effect of non-pharmaceutical interventions in the early phase of the COVID-19 epidemic in Saudi Arabia

Non-pharmaceutical interventions have been widely employed to control the COVID-19 pandemic. Their associated effect on SARS-CoV-2 transmission have however been unequally studied across regions. Few studies have focused on the Gulf states despite their potential role for global pandemic spread, in particular in the Kingdom of Saudi Arabia through religious pilgrimages. We study the association between NPIs and SARS-CoV-2 transmission in the Kingdom of Saudi Arabia during the first pandemic wave between March and October 2020. We infer associations between NPIs introduction and lifting through a spatial SEIR-type model that allows for inferences of region-specific changes in transmission intensity. We find that reductions in transmission were associated with NPIs implemented shortly after the first reported case including Isolate and Test with School Closure (region-level mean estimates of the reduction in R0 ranged from 25-41%), Curfew (20-70% reduction), and Lockdown (50-60% reduction), although uncertainty in the estimates was high, particularly for the Isolate and Test with School Closure NPI (95% Credible Intervals from 1% to 73% across regions). Transmission was found to increase progressively in most regions during the last part of NPI relaxation phases. These results can help informing the policy makers in the planning of NPI scenarios as the pandemic evolves with the emergence of SARS-CoV-2 variants and the availability of vaccination.

COVID-19 prevention measures reduce dengue spread in Yunnan Province, China, but do not reduce established outbreak

The COVID-19 pandemic and measures against it provided a unique opportunity to understand the transmission of other infectious diseases and to evaluate the efficacy of COVID-19 prevention measures on them. Here we show a dengue epidemic in Yunnan, China, during the pandemic of COVID-19 was dramatically reduced compared to non-pandemic years and, importantly, spread was confined to only one city, Ruili. Three key features characterized this dengue outbreak: (i) the urban-to-suburban spread was efficiently blocked; (ii) the scale of epidemic in urban region was less affected; (iii) co-circulation of multiple strains was attenuated. These results suggested that countermeasures taken during COVID-19 pandemic are efficient to prevent dengue transmission between cities and from urban to suburban, as well to reduce the co-circulation of multiple serotypes or genotypes. Nevertheless, as revealed by the spatial analysis, once the dengue outbreak was established, its distribution was very stable and resistant to measures against COVID-19, implying the possibility to develop a precise prediction method.

Non-Pharmacological Infection Prevention and Control Interventions in COVID-19: What Does the Current Evidence Say?

Coronavirus disease-19 (COVID-19), a major global public health emergency has significantly impacted human health and livelihoods. The pandemic continues to spread and treatments and vaccines are at different stages of development. Mass vaccination has been rolled out worldwide. This review article provides a narrative summary of the evidence on various non-pharmacological interventions (NPIs) for COVID-19 containment. The authors reviewed the evidence published by the Norwegian Institute of Public Health map of COVID-19 evidence. Additional literature was identified from PubMed and Google Scholar, preprint sites, and news media. The search terms included "Social distancing measures" and "COVID 19", "Non-pharmacological interventions'' and "COVID 19", "COVID-19", "non-pharmacological interventions", "face mask", etc. The strength of the evidence for most studies on NPIs was 'weak to moderate' for restrictive NPIs. Ascertaining the impact of each NPI as a standalone intervention is difficult since NPIs are implemented simultaneously with other measures. Varying testing and reporting strategies across the countries and classification of deaths directly caused by COVID-19 create challenges in assessing the impact of restrictive NPIs on the case numbers and deaths. Evidence on hygiene measures such as face mask is more robust in design providing credible evidence on prevention of COVID-19 infection. Evidence from modeling studies, natural before-after studies, and anecdotal evidence from the strategies adopted by 'role model' countries suggests that continued use of NPIs is the only containment strategy until 'herd immunity' is achieved to reduce the severe disease and mortality.

Quarantine and testing strategies to ameliorate transmission due to travel during the COVID-19 pandemic: a modelling study

BACKGROUND

Numerous countries imposed strict travel restrictions, contributing to the large socioeconomic burden during the COVID-19 pandemic. The long quarantines that apply to contacts of cases may be excessive for travel policy.

METHODS

We developed an approach to evaluate imminent countrywide COVID-19 infections after 0-14-day quarantine and testing. We identified the minimum travel quarantine duration such that the infection rate within the destination country did not increase compared to a travel ban, defining this minimum quarantine as "sufficient."

FINDINGS

We present a generalised analytical framework and a specific case study of the epidemic situation on November 21, 2021, for application to 26 European countries. For most origin-destination country pairs, a three-day or shorter quarantine with RT-PCR or antigen testing on exit suffices. Adaptation to the European Union traffic-light risk stratification provided a simplified policy tool. Our analytical approach provides guidance for travel policy during all phases of pandemic diseases.

INTERPRETATION

For nearly half of origin-destination country pairs analysed, travel can be permitted in the absence of quarantine and testing. For the majority of pairs requiring controls, a short quarantine with testing could be as effective as a complete travel ban. The estimated travel quarantine durations are substantially shorter than those specified for traced contacts.

FUNDING

EasyJet (JPT and APG), the Elihu endowment (JPT), the Burnett and Stender families' endowment (APG), the Notsew Orm Sands Foundation (JPT and APG), the National Institutes of Health (MCF), Canadian Institutes of Health Research (SMM) and Natural Sciences and Engineering Research Council of Canada EIDM-MfPH (SMM).

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic

Background

Currently, the knowledge of associations among newly recovered cases (NR), newly healed cases (NH), newly confirmed cases (NC), and newly dead cases (ND) can help to monitor, evaluate, predict, control, and curb the spreading of coronavirus disease 2019 (COVID-19). This study aimed to explore the panel associations of ND, NH, and NR with NC.

Methods

Data from China Data Lab in Harvard Dataverse with China (January 15, 2020 to January 14, 2021), the United States of America (the USA, January 21, 2020 to April 5, 2021), and the World (January 22, 2020 to March 20, 2021) had been analyzed. The main variables included in the present analysis were ND, NH, NR, and NC. Pooled regression, stacked within-transformed linear regression, quantile regression for panel data, random-effects negative binomial regression, and random-effects Poisson regression were conducted to reflect the associations of ND, NH, and NR with NC. Event study analyses were performed to explore how the key events influenced NC.

Results

Descriptive analyses showed that mean value of ND/NC ratio regarding China was more than those regarding the USA and the World. The results from tentative analysis reported the significant relationships among ND, NH, NR, and NC regarding China, the USA, and the World. Panel regressions confirmed associations of ND, NH, and NR with NC regarding China, the USA, and the World. Panel event study showed that key events influenced NC regarding USA and the World more greatly than that regarding China.

Conclusion

The findings in this study confirmed the panel associations of ND, NH, and NR with NC in the three datasets. The efficiencies of various control strategies of COVID-19 pandemic across the globe were compared by the regression outcomes. Future direction of research work could explore the influencing mechanisms of the panel associations.

Challenges of routine diabetes care during COVID-19 era: A systematic search and narrative review

BACKGROUND

The world is facing the current COVID-19 pandemic. The pandemic response is affecting routine health care provision all over the world. We aimed to review the relevant literature and highlight challenges in the provision of routine care for patients with diabetes during the COVID-19 outbreak.

METHODS

We systematically searched PubMed, ScienceDirect, and Embase databases up till August 13, 2020 and retrieved relevant articles published on difficulties on routine diabetes management during the COVID-19 pandemic.

RESULTS

Through our reading of the recent literature discussing the difficulties of routine healthcare provision for patients with diabetes amid the COVID-19 pandemic, we have identified nine themes as follows: lockdown of standard outpatient clinics, decreased inpatient capacity, staff shortage, medicine shortage, unaffordable medicine, delayed care seeking, limited self-care practice, transport difficulties, and undiagnosed cases/events.

CONCLUSION

Diabetes management during lockdown is particularly challenging. This review specified a summary of difficulties of diabetes care during COVID-19 pandemic. Healthcare policy makers as well as healthcare providers could take advantage of the results of this review to mitigate the adverse effect of the crisis on provision of routine care for diabetes as well as other chronic conditions.

The emergence, genomic diversity and global spread of SARS-CoV-2

Since the first cases of COVID-19 were documented in Wuhan, China in 2019, the world has witnessed a devastating global pandemic, with more than 238 million cases, nearly 5 million fatalities and the daily number of people infected increasing rapidly. Here we describe the currently available data on the emergence of the SARS-CoV-2 virus, the causative agent of COVID-19, outline the early viral spread in Wuhan and its transmission patterns in China and across the rest of the world, and highlight how genomic surveillance, together with other data such as those on human mobility, has helped to trace the spread and genetic variation of the virus and has also comprised a key element for the control of the pandemic. We pay particular attention to characterizing and describing the international spread of the major variants of concern of SARS-CoV-2 that were first identified in late 2020 and demonstrate that virus evolution has entered a new phase. More broadly, we highlight our currently limited understanding of coronavirus diversity in nature, the rapid spread of the virus and its variants in such an increasingly connected world, the reduced protection of vaccines, and the urgent need for coordinated global surveillance using genomic techniques. In summary, we provide important information for the prevention and control of both the ongoing COVID-19 pandemic and any new diseases that will inevitably emerge in the human population in future generations.

Epidemiological associations with genomic variation in SARS-CoV-2

SARS-CoV-2 (CoV) is the etiological agent of the COVID-19 pandemic and evolves to evade both host immune systems and intervention strategies. We divided the CoV genome into 29 constituent regions and applied novel analytical approaches to identify associations between CoV genomic features and epidemiological metadata. Our results show that nonstructural protein 3 (nsp3) and Spike protein (S) have the highest variation and greatest correlation with the viral whole-genome variation. S protein variation is correlated with nsp3, nsp6, and 3′-to-5′ exonuclease variation. Country of origin and time since the start of the pandemic were the most influential metadata associated with genomic variation, while host sex and age were the least influential. We define a novel statistic—coherence—and show its utility in identifying geographic regions (populations) with unusually high (many new variants) or low (isolated) viral phylogenetic diversity. Interestingly, at both global and regional scales, we identify geographic locations with high coherence neighboring regions of low coherence; this emphasizes the utility of this metric to inform public health measures for disease spread. Our results provide a direction to prioritize genes associated with outcome predictors (e.g., health, therapeutic, and vaccine outcomes) and to improve DNA tests for predicting disease status.

Post-Coronavirus Disease-2019 (COVID-19): Toward a Severe Multi-Level Health Crisis?

There were already numerous challenges facing the healthcare system prior to the ongoing coronavirus disease-2019 (COVID-19) pandemic. Although we look forward to ending this pandemic, it is still expected that the healthcare system will face further challenges leading to a multi-level health crisis. Indeed, after the COVID-19 pandemic, there will still be COVID-19 active cases and those left with health problems following COVID-19 infection who will be of a particular impact. In addition, we also have the health problems that either emerged or worsened during COVID-19, especially with the reduced ability of the healthcare system to take care of many non COVID-19 patients during the COVID-19 pandemic. Such expected evolution of the situation highlights the necessity for the decision-makers to consider applying serious reforms and take quick measures to prevent a post-COVID-19 health crisis.

Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition

In this study, we proposed a new data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), an autoregressive integrated moving average (ARIMA), with a nonlinear autoregressive artificial neural network (NARANN), called the EEMD-ARIMA-NARANN model, to perform time series modeling and forecasting based on the COVID-19 prevalence and mortality data from 28 February 2020 to 27 June 2020 in South Africa and Nigeria. By comparing the accuracy level of forecasting measurements with the basic ARIMA and NARANN models, it was shown that this novel data-driven hybrid model did a better job of capturing the dynamic changing trends of the target data than the others used in this work. Our proposed mixture technique can be deemed as a helpful policy-supportive tool to plan and provide medical supplies effectively. The overall confirmed cases and deaths were estimated to reach around 176,570 [95% uncertainty level (UL) 173,607 to 178,476] and 3454 (95% UL 3384 to 3487), respectively, in South Africa, along with 32,136 (95% UL 31,568 to 32,641) and 788 (95% UL 775 to 804) in Nigeria on 12 July 2020 using this data-driven EEMD-ARIMA-NARANN hybrid technique. The contributions of this study include three aspects. First, the proposed hybrid model can better capture the dynamic dependency characteristics compared with the individual models. Second, this new data-driven hybrid model is constructed in a more reasonable way relative to the traditional mixture model. Third, this proposed model may be generalized to estimate the epidemic patterns of COVID-19 in other regions.

Genetic Analysis of SARS-CoV-2 Variants in Mexico during the First Year of the COVID-19 Pandemic

During the first year of the SARS-CoV-2 pandemic in Mexico, more than two million people were infected. In this study, we analyzed full genome sequences from 27 February 2020 to 28 February 2021 to characterize the geographical and temporal distribution of SARS-CoV-2 lineages and identify the most common circulating lineages during this period. We defined six different geographical regions with particular dynamics of lineage circulation. The Northeast and Northwest regions were the ones that exhibited the highest lineage diversity, while the Central south and South/Southeast regions presented less diversity with predominance of a certain lineage. Additionally, by late February 2021, lineage B.1.1.519 represented more than 89% of all circulating lineages in the country.

Importance of untested infectious individuals for interventions to suppress COVID-19

The impact of the extent of testing infectious individuals on suppression of COVID-19 is illustrated from the early stages of outbreaks in Germany, the Hubei province of China, Italy, Spain and the UK. The predicted percentage of untested infected individuals depends on the specific outbreak but we found that they typically represent 60-80% of all infected individuals during the early stages of the outbreaks. We propose that reducing the underlying transmission from untested cases is crucial to suppress the virus. This can be achieved through enhanced testing in combination with social distancing and other interventions that reduce transmission such as wearing face masks. Once transmission from silent carriers is kept under control by these means, the virus could have been fully suppressed through fast isolation and contact tracing of tested cases.

COVID-19 transmission and the safety of air travel during the pandemic: a scoping review

PURPOSE OF REVIEW

To examine the literature assessing safety of air travel relating to coronavirus disease 2019 (COVID-19) transmission from January 2020 to May 2021. The COVID-19 pandemic has had an unprecedented impact on air travel and global mobility, and various efforts are being implemented to determine a safe way forward. As the pandemic evolves, so do the challenges that force various stakeholders, including the aviation industry, health authorities, and governments, to reassess and adapt their practices to ensure the safety of travellers.

RECENT FINDINGS

The literature was reviewed for multiple aspects of air travel safety during the COVID-19 pandemic. Recurring themes that surfaced included the pivotal role of commercial air travel in the geographic spread of COVID-19, the efficacy of travel restrictions and quarantines, inflight transmission risk and the role of preventive measures, the utility of pre and post flight testing, the development of effective vaccines and subsequent challenges of vaccine passports, and the ongoing threat of novel highly transmissible variants.

SUMMARY

Much uncertainty lies ahead within the domains of these findings, and ongoing research, discourse and review will be necessary to navigate and determine the future direction and safety of air travel. Recovery will be slow, necessitating innovative, multipronged and collaborative solutions.

Mathematical modeling applied to epidemics: an overview

This work presents an overview of the evolution of mathematical modeling applied to the context of epidemics and the advances in modeling in epidemiological studies. In fact, mathematical treatments have contributed substantially in the epidemiology area since the formulation of the famous SIR (susceptible-infected-recovered) model, in the beginning of the 20th century. We presented the SIR deterministic model and we also showed a more realistic application of this model applying a stochastic approach in complex networks. Nowadays, computational tools, such as big data and complex networks, in addition to mathematical modeling and statistical analysis, have been shown to be essential to understand the developing of the disease and the scale of the emerging outbreak. These issues are fundamental concerns to guide public health policies. Lately, the current pandemic caused by the new coronavirus further enlightened the importance of mathematical modeling associated with computational and statistical tools. For this reason, we intend to bring basic knowledge of mathematical modeling applied to epidemiology to a broad audience. We show the progress of this field of knowledge over the years, as well as the technical part involving several numerical tools.

Do local characteristics act in a similar way for the first two waves of COVID-19? Analysis at intraurban level in Barcelona

Background

This paper concerns the spatial determinants of the first two waves of COVID-19 at the neighbourhood level.

Methods

Using data for the first and second waves of COVID-19 at the neighbourhood level in Barcelona, we analyse whether local characteristics acted in the same way during the two waves and identify typologies of areas depending on such determinants. Univariate and bivariate local Moran’s I and count data models are used.

Results

Some structural effects at the neighbourhood level consistently either boost (e.g. population density) or reduce (e.g. income) COVID-19 cases. Other effects differ between the two waves (i.e. age composition, schools and transport infrastructures).

Conclusions

Since certain characteristics influenced the virus diffusion in opposite ways between the two pandemic waves, territorial heterogeneity alone is insufficient to explain COVID-19 outbreaks—individual behaviour also needs to be factored in. Consequently, both econometric and spatial analysis techniques are recommended for tracking the spatiotemporal spread of this disease and for monitoring the effectiveness of policy measures across heterogeneous neighbourhoods.

Risk of SARS-CoV-2 Transmission among Air Passengers in China

BACKGROUND

Modern transportation plays a key role in the spread of SARS-CoV-2 and new variants. However, little is known about the exact transmission risk of the virus on airplanes.

METHODS

Using the itinerary and epidemiological data of COVID-19 cases and close contacts on domestic airplanes departing from Wuhan city in China before the lockdown on January 23, 2020, we estimated the upper and lower bounds of overall transmission risk of COVID-19 among travellers.

RESULTS

175 index cases were identified among 5797 passengers on 177 airplanes. The upper and lower attack rates (ARs) of a seat were 0.60% (34/5622, 95%CI 0.43%-0.84%) and 0.33% (18/5400, 95%CI 0.21%-0.53%), respectively. In the upper- and lower-bound risk estimates, each index case infected 0.19 (SD 0.45) and 0.10 (SD 0.32) cases respectively. The seats immediately adjacent to the index cases had an AR of 9.2% (95%CI 5.7%-14.4%), with a relative risk 27.8 (95%CI 14.4-53.7) compared to other seats in the upper limit estimation. The middle seat had the highest AR (0.7%, 95%CI 0.4%-1.2%). The upper-bound AR increased from 0.7% (95%CI 0.5%-1.0%) to 1.2% (95%CI 0.4%-3.3%) when the co-travel time increased from 2.0 hours to 3.3 hours.

CONCLUSIONS

The ARs among travellers varied by seat distance from the index case and joint travel time, but the variation was not significant between the types of aircraft. The overall risk of SARS-CoV-2 transmission during domestic travel on planes was relatively low. These findings can improve our understanding of COVID-19 spread during travel and inform response efforts in the pandemic.

SARS-CoV-2 Seroprevalence in a University Community: A Longitudinal Study of the Impact of Student Return to Campus on Infection Risk Among Community Members

BACKGROUND

Returning university students represent large-scale, transient demographic shifts and a potential source of transmission to adjacent communities during the COVID-19 pandemic.

METHODS

In this prospective longitudinal cohort study, we tested for IgG antibodies against SARS-CoV-2 in a non-random cohort of residents living in Centre County prior to the Fall 2020 term at the Pennsylvania State University and following the conclusion of the Fall 2020 term. We also report the seroprevalence in a non-random cohort of students collected at the end of the Fall 2020 term.

RESULTS

Of 1313 community participants, 42 (3.2%) were positive for SARS-CoV-2 IgG antibodies at their first visit between 07 August and 02 October 2020. Of 684 student participants who returned to campus for fall instruction, 208 (30.4%) were positive for SARS-CoV-2 antibodies between 26 October and 21 December. 96 (7.3%) community participants returned a positive IgG antibody result by 19 February. Only contact with known SARS-CoV-2-positive individuals and attendance at small gatherings (20-50 individuals) were significant predictors of detecting IgG antibodies among returning students (aOR, 95% CI: 3.1, 2.07-4.64; 1.52, 1.03-2.24; respectively).

CONCLUSIONS

Despite high seroprevalence observed within the student population, seroprevalence in a longitudinal cohort of community residents was low and stable from before student arrival for the Fall 2020 term to after student departure. The study implies that heterogeneity in SARS-CoV-2 transmission can occur in geographically coincident populations.

Dynamics of SARS-CoV-2 mutations reveals regional-specificity and similar trends of N501 and high-frequency mutation N501Y in different levels of control measures

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This disease has spread globally, causing more than 161.5 million cases and 3.3 million deaths to date. Surveillance and monitoring of new mutations in the virus' genome are crucial to our understanding of the adaptation of SARS-CoV-2. Moreover, how the temporal dynamics of these mutations is influenced by control measures and non-pharmaceutical interventions (NPIs) is poorly understood. Using 1,058,020 SARS-CoV-2 from sequenced COVID-19 cases from 98 countries (totaling 714 country-month combinations), we perform a normalization by COVID-19 cases to calculate the relative frequency of SARS-CoV-2 mutations and explore their dynamics over time. We found 115 mutations estimated to be present in more than 3% of global COVID-19 cases and determined three types of mutation dynamics: high-frequency, medium-frequency, and low-frequency. Classification of mutations based on temporal dynamics enable us to examine viral adaptation and evaluate the effects of implemented control measures in virus evolution during the pandemic. We showed that medium-frequency mutations are characterized by high prevalence in specific regions and/or in constant competition with other mutations in several regions. Finally, taking N501Y mutation as representative of high-frequency mutations, we showed that level of control measure stringency negatively correlates with the effective reproduction number of SARS-CoV-2 with high-frequency or not-high-frequency and both follows similar trends in different levels of stringency.

Optimal strategies for COVID-19 prevention from global evidence achieved through social distancing, stay at home, travel restriction and lockdown: a systematic review

BACKGROUND

Coronavirus disease (COVID-19) is a global public health agenda with high level of pandemicity. There is no effective treatment, but prevention strategies can alter the pandemic. However, the effectiveness of existing preventive measures and strategies is inconclusive. Therefore, this study aimed to review evidence related to COVID-19 prevention achieved through social distancing, stay at home, travel ban and lockdown in order to determine best practices.

METHODS/DESIGN

This review has been conducted in accordance with the PRISMA and Cochrane guideline. A systematic literature search of articles archived from major medical databases (MEDLINE, SCOPUS, CINAHL, PsycINFO, and Web of Science) and Google scholar was done. Observational and modeling researches published to date with information on COVID-19 prevention like social distancing, stay at home, travel ban and lockdown were included. The articles were screened by two experts. Risk of bias of included studies was assessed through ROBINS-I tool and the certainty of evidence was graded using the GRADE approach for the main outcomes. The findings were presented by narration and in tabular form.

RESULTS

A total of 25 studies was included in the review. The studies consistently reported the benefit of social distancing, stay at home, travel restriction and lockdown measures. Mandatory social distancing reduced the daily growth rate by 9.1%, contacts by 7-9 folds, median number of infections by 92% and epidemic resolved in day 90. Travel restriction and lockdown averted 70.5% of exported cases in china and doubling time was increased from 2 to 4 days. It reduced contacts by 80% and decreased the initial R0, and the number of infected individuals decreased by 91.14%. Stay at home was associated with a 48.6 and 59.8% reduction in weekly morbidity and fatality. Obligatory, long term and early initiated programs were more effective.

CONCLUSION

Social distancing, stay at home, travel restriction and lockdown are effective to COVID-19 prevention. The strategies need to be obligatory, initiated early, implemented in large scale, and for a longer period of time. Combinations of the programs are more effective. However, the income of individuals should be guaranteed and supported.

Should international borders re-open? The impact of travel restrictions on COVID-19 importation risk

Background

Novel coronavirus disease (COVID-19) has spread across the world at an unprecedented pace, reaching over 200 countries and territories in less than three months. In response, many governments denied entry to travellers arriving from various countries affected by the virus. While several industries continue to experience economic losses due to the imposed interventions, it is unclear whether the different travel restrictions were successful in reducing COVID-19 importations.

Methods

Here we develop a comprehensive probabilistic framework to model daily COVID-19 importations, considering different travel bans. We quantify the temporal effects of the restrictions and elucidate the relationship between incidence rates in other countries, travel flows and the expected number of importations into the country under investigation.

Results

As a cases study, we evaluate the travel bans enforced by the Australian government. We find that international travel bans in Australia lowered COVID-19 importations by 87.68% (83.39 - 91.35) between January and June 2020. The presented framework can further be used to gain insights into how many importations to expect should borders re-open.

Conclusions

While travel bans lowered the number of COVID-19 importations overall, the effectiveness of bans on individual countries varies widely and directly depends on the change in behaviour in returning residents and citizens. Authorities may consider the presented information when planning a phased re-opening of international borders.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12889-021-11616-9).

The effects of COVID-19 pandemic on patients with lower extremity peripheral arterial disease: A near miss disaster

BACKGROUND

The COVID-19 pandemic has led to widespread postponement and cancelation of elective vascular surgeries in Switzerland. The consequences of these decisions are poorly understood.

PATIENTS AND METHODS

In this observational, retrospective, single-center cohort study, we describe the impact of COVID-19 pandemic containment strategies on patients with lower extremity peripheral arterial disease (PAD) referred during the period March 11, to May 11, 2020, compared to the same time frames in 2018 to 2019. Patients admitted for acute limb ischemia (ALI) or chronic PAD and undergoing urgent or elective vascular surgery or primary amputation were included. Patients' characteristics, indications for admission, and surgical features were analyzed. The occurrence of 30 day outcomes was assessed, including length of stay, rates of major adverse cardiovascular events (MACE) and major adverse limb events (MALE), and procedural and hemodynamic success.

RESULTS

Overall, 166 patients were included. Fewer subjects per 10 day period were operated in 2020 compared to, 2018 to 2019 (6.7 vs. 10.5, respectively; P < 0.001). The former had higher rates of chronic obstructive pulmonary disease (COPD) (25% vs. 11.1%; P = 0.029), and ASA score (3.13 vs. 2.90; P = 0.015). The percentage of patients with ALI in 2020 was about double that of the same period in 2018 to 2019 (47.5% vs. 24.6%; P = 0.006). Overall, the types of surgery were similar between 2020 and 2018 to 2019, while palliative care and primary amputations occurred only in 2020 (5 out 40 cases). The rate of post-operative MACE was significantly higher in 2020 (10% vs. 2.4%; P = 0.037).

CONCLUSIONS

During the first state of emergency for COVID-19 pandemic in 2020, less regular medical follow-up and hindered hospital access could have resulted in more acute and advanced clinical presentations of patients with PAD undergoing surgery. Guidelines are needed to provide appropriate care to this vulnerable population and avoid a large-scale disaster.

Impact of travel ban implementation on COVID-19 spread in Singapore, Taiwan, Hong Kong and South Korea during the early phase of the pandemic: a comparative study

Background

The COVID-19 pandemic has elicited imposition of some form of travel restrictions by almost all countries in the world. Most restrictions currently persist, although some have been gradually eased. It remains unclear if the trade-off from the unprecedented disruption to air travel was well worth for pandemic containment.

Method

A comparative analysis was conducted on Singapore, Taiwan, Hong Kong and South Korea’s COVID-19 response. Data on COVID-19 cases, travel-related and community interventions, socio-economic profile were consolidated. Trends on imported and local cases were analyzed using computations of moving averages, rate of change, particularly in response to distinct waves of travel-related interventions due to the outbreak in China, South Korea, Iran & Italy, and Europe.

Results

South Korea’s travel restrictions were observed to be consistently more lagged in terms of timeliness and magnitude, with their first wave of travel restrictions on flights departing from China implemented 34 days after the outbreak in Wuhan, compared to 22–26 days taken by Singapore, Taiwan and Hong Kong. South Korea’s restrictions against all countries came after 91 days, compared to 78–80 days for the other three countries. The rate of change of imported cases fell by 1.08–1.43 across all four countries following the first wave of travel restrictions on departures from China, and by 0.22–0.52 in all countries except South Korea in the fifth wave against all international travellers. Delayed rate of change of local cases resulting from travel restrictions imposed by the four countries with intrinsic importation risk, were not observed.

Conclusions

Travel restriction was effective in preventing COVID-19 case importation in early outbreak phase, but may still be limited in preventing general local transmission. The impact of travel restrictions, regardless of promptness, in containing epidemics likely also depends on the effectiveness of local surveillance and non-pharmaceutical interventions concurrently implemented.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06449-1.

Decreased human respiratory syncytial virus activity during the COVID-19 pandemic in Japan: an ecological time-series analysis

BACKGROUND

Non-pharmaceutical interventions (NPIs), such as sanitary measures and travel restrictions, aimed at controlling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may affect the transmission dynamics of human respiratory syncytial virus (HRSV). We aimed to quantify the contribution of the sales of hand hygiene products and the number of international and domestic airline passenger arrivals on HRSV epidemic in Japan.

METHODS

The monthly number of HRSV cases per sentinel site (HRSV activity) in 2020 was compared with the average of the corresponding period in the previous 6 years (from January 2014 to December 2020) using a monthly paired t-test. A generalized linear gamma regression model was used to regress the time-series of the monthly HRSV activity against NPI indicators, including sale of hand hygiene products and the number of domestic and international airline passengers, while controlling for meteorological conditions (monthly average temperature and relative humidity) and seasonal variations between years (2014-2020).

RESULTS

The average number of monthly HRSV case notifications in 2020 decreased by approximately 85% (p < 0.001) compared to those in the preceding 6 years (2014-2019). For every average ¥1 billion (approximately £680,000/$9,000,000) spent on hand hygiene products during the current month and 1 month before there was a 0.29% (p = 0.003) decrease in HRSV infections. An increase of average 1000 domestic and international airline passenger arrivals during the previous 1-2 months was associated with a 3.8 × 10^- 4% (p < 0.001) and 1.2 × 10^- 3% (p < 0.001) increase in the monthly number of HRSV infections, respectively.

CONCLUSIONS

This study suggests that there is an association between the decrease in the monthly number of HRSV cases and improved hygiene and sanitary measures and travel restrictions for COVID-19 in Japan, indicating that these public health interventions can contribute to the suppression of HRSV activity. These findings may help in public health policy and decision making.

Quantifying COVID-19 importation risk in a dynamic network of domestic cities and international countries

Since its outbreak in December 2019, the novel coronavirus 2019 (COVID-19) has spread to 191 countries and caused millions of deaths. Many countries have experienced multiple epidemic waves and faced containment pressures from both domestic and international transmission. In this study, we conduct a multiscale geographic analysis of the spread of COVID-19 in a policy-influenced dynamic network to quantify COVID-19 importation risk under different policy scenarios using evidence from China. Our spatial dynamic panel data (SDPD) model explicitly distinguishes the effects of travel flows from the effects of transmissibility within cities, across cities, and across national borders. We find that within-city transmission was the dominant transmission mechanism in China at the beginning of the outbreak and that all domestic transmission mechanisms were muted or significantly weakened before importation posed a threat. We identify effective containment policies by matching the change points of domestic and importation transmissibility parameters to the timing of various interventions. Our simulations suggest that importation risk is limited when domestic transmission is under control, but that cumulative cases would have been almost 13 times higher if domestic transmissibility had resurged to its precontainment level after importation and 32 times higher if domestic transmissibility had remained at its precontainment level since the outbreak. Our findings provide practical insights into infectious disease containment and call for collaborative and coordinated global suppression efforts.

Meta-analysis and comprehensive study of coronavirus outbreaks: SARS, MERS and COVID-19

BACKGROUND

Zoonotic coronaviruses have caused several endemic and pandemic situations around the world. SARS caused the first epidemic alert at the beginning of this century, followed by MERS. COVID-19 appeared to be highly contagious, with human-to-human transmission by aerosol droplets, and reached nearly all countries around the world. A plethora of studies were performed, with reports being published within a short period of time by scientists and medical physicians. It has been difficult to find the relevant data to create an overview of the situation according to studies from accumulated findings and reports. In the present study we aimed to perform a comprehensive study in the context of the case fatality ratios (CFRs) of three major human Coronavirus outbreaks which occurred during the first twenty years of 21st century.

METHODS

In this study, we performed meta-analyses on SARS, MERS and COVID-19 outbreak events from publicly available records. Study analyses were performed with the help of highly reputable scientific databases such as PubMed, WOS and Scopus to evaluate and present current knowledge on zoonotic coronavirus outbreaks, starting from 2000 to the end of 2020.

RESULTS

A total of 250,194 research studies and records were identified with specific keywords and synonyms for the three viruses in order to cover all publications. In the end, 41 records were selected and included after applying several exclusion and inclusion criteria on identified datasets. SARS was found to have a nearly 11% case fatality ratio (CFR), which means the estimated number of deaths as a proportion of confirmed positive cases; Taiwan was the country most affected by the SARS outbreak based on the CFR analysis. MERS had CFRs of 35.8 and 26 in Saudi Arabia during the 2012 and 2015 outbreaks, respectively. COVID-19 resulted in a 2.2 CFR globally, and the USA reported the highest mortality ratio in the world in the end of first year of COVID-19 pandemic.

CONCLUSION

Some members of the Coronaviridae family can cause highly contagious and devastating infections among humans. Within the last two decades, the whole world has witnessed several deadly emerging infectious diseases, which are most commonly zoonotic in nature. We conclude that pre-existing immunity during the early stages of a pandemic might be important, but case control and management strategies should be improved to decrease CFRs. Finally, we have addressed several concerns in relation to outbreak events in this study.

Rapid greening response of China's 2020 spring vegetation to COVID-19 restrictions: Implications for climate change

The 2019 novel coronavirus pandemic (COVID-19) negatively affected global public health and socioeconomic development. Lockdowns and travel restrictions to contain COVID-19 resulted in reduced human activity and decreased anthropogenic emissions. However, the secondary effects of these restrictions on the biophysical environment are uncertain. Using remotely sensed big data, we investigated how lockdowns and traffic restrictions affected China's spring vegetation in 2020. Our analyses show that travel decreased by 58% in the first 18 days following implementation of the restrictions across China. Subsequently, atmospheric optical clarity increased and radiation levels on the vegetation canopy were augmented. Furthermore, the spring of 2020 arrived 8.4 days earlier and vegetation 17.45% greener compared to 2015-2019. Reduced human activity resulting from COVID-19 restrictions contributed to a brighter, earlier, and greener 2020 spring season in China. This study shows that short-term changes in human activity can have a relatively rapid ecological impact at the regional scale.

Disease-dependent interaction policies to support health and economic outcomes during the COVID-19 epidemic

Lockdowns and stay-at-home orders have partially mitigated the spread of Covid-19. However, en masse mitigation has come with substantial socioeconomic costs. In this paper, we demonstrate how individualized policies based on disease status can reduce transmission risk while minimizing impacts on economic outcomes. We design feedback control policies informed by optimal control solutions to modulate interaction rates of individuals based on the epidemic state. We identify personalized interaction rates such that recovered/immune individuals elevate their interactions and susceptible individuals remain at home before returning to pre-lockdown levels. As we show, feedback control policies can yield similar population-wide infection rates to total shutdown but with significantly lower economic costs and with greater robustness to uncertainty compared to optimal control policies. Our analysis shows that test-driven improvements in isolation efficiency of infectious individuals can inform disease-dependent interaction policies that mitigate transmission while enhancing the return of individuals to pre-pandemic economic activity.

Reopening International Borders without Quarantine: Contact Tracing Integrated Policy against COVID-19

With the COVID-19 vaccination widely implemented in most countries, propelled by the need to revive the tourism economy, there is a growing prospect for relieving the social distancing regulation and reopening borders in tourism-oriented countries and regions. This need incentivizes stakeholders to develop border control strategies that fully evaluate health risks if mandatory quarantines are lifted. In this study, we have employed a computational approach to investigate the contact tracing integrated policy in different border-reopening scenarios in Hong Kong, China. Explicitly, by reconstructing the COVID-19 transmission from historical data, specific scenarios with joint effects of digital contact tracing and other concurrent measures (i.e., controlling arrival population and community nonpharmacological interventions) are applied to forecast the future development of the pandemic. Built on a modified SEIR epidemic model with a 30% vaccination coverage, the results suggest that scenarios with digital contact tracing and quick isolation intervention can reduce the infectious population by 92.11% compared to those without contact tracing. By further restricting the inbound population with a 10,000 daily quota and applying moderate-to-strong community nonpharmacological interventions (NPIs), the average daily confirmed cases in the forecast period of 60 days can be well controlled at around 9 per day (95% CI: 7–12). Two main policy recommendations are drawn from the study. First, digital contact tracing would be an effective countermeasure for reducing local virus spread, especially when it is applied along with a moderate level of vaccination coverage. Second, implementing a daily quota on inbound travelers and restrictive community NPIs would further keep the local infection under control. This study offers scientific evidence and prospective guidance for developing and instituting plans to lift mandatory border control policies in preparing for the global economic recovery.

Analysis and Evaluation of Non-Pharmaceutical Interventions on Prevention and Control of COVID-19: A Case Study of Wuhan City

As the threat of COVID-19 increases, many countries have carried out various non-pharmaceutical interventions. Although many studies have evaluated the impact of these interventions, there is a lack of mapping between model parameters and actual geographic areas. In this study, a non-pharmaceutical intervention model of COVID-19 based on a discrete grid is proposed from the perspective of geography. This model can provide more direct and effective information for the formulation of prevention and control policies. First, a multi-level grid was introduced to divide the geographical space, and the properties of the grid boundary were used to describe the quarantine status and intensity in these different spaces; this was also combined with the model of hospital isolation and self-protection. Then, a process for the spatiotemporal evolution of the early COVID-19 spread is proposed that integrated the characteristics of residents’ daily activities. Finally, the effect of the interventions was quantitatively analyzed by the dynamic transmission model of COVID-19. The results showed that quarantining is the most effective intervention, especially for infectious diseases with a high infectivity. The introduction of a quarantine could effectively reduce the number of infected humans, advance the peak of the maximum infected number of people, and shorten the duration of the pandemic. However, quarantines only function properly when employed at sufficient intensity; hospital isolation and self-protection measures can effectively slow the spread of COVID-19, thus providing more time for the relevant departments to prepare, but an outbreak will occur again when the hospital reaches full capacity. Moreover, medical resources should be concentrated in places where there is the most urgent need under a strict quarantine measure.

A systematic review on AI/ML approaches against COVID-19 outbreak

A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.

Minimal and adaptive numerical strategy for critical resource planning in a pandemic

Current epidemiological models can in principle model the temporal evolution of a pandemic. However, any such model will rely on parameters that are unknown, which in practice are estimated using stochastic and poorly measured quantities. As a result, an early prediction of the long-term evolution of a pandemic will quickly lose relevance, while a late model will be too late to be useful for disaster management. Unless a model is designed to be adaptive, it is bound either to lose relevance over time, or lose trust and thus not have a second chance for retraining. We propose a strategy for estimating the number of infections and the number of deaths, that does away with time-series modeling, and instead makes use of a "phase portrait approach." We demonstrate that, with this approach, there is a universality to the evolution of the disease across countries, that can then be used to make reliable predictions. These same models can also be used to plan the requirements for critical resources during the pandemic. The approach is designed for simplicity of interpretation, and adaptivity over time. Using our model, we predict the number of infections and deaths in Italy and New York State, based on an adaptive algorithm which uses early available data, and show that our predictions closely match the actual outcomes. We also carry out a similar exercise for India, where in addition to projecting the number of infections and deaths, we also project the expected range of critical resource requirements for hospitalizations in a location.

Testing at scale during the COVID-19 pandemic

Assembly and publication of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome in January 2020 enabled the immediate development of tests to detect the new virus. This began the largest global testing programme in history, in which hundreds of millions of individuals have been tested to date. The unprecedented scale of testing has driven innovation in the strategies, technologies and concepts that govern testing in public health. This Review describes the changing role of testing during the COVID-19 pandemic, including the use of genomic surveillance to track SARS-CoV-2 transmission around the world, the use of contact tracing to contain disease outbreaks and testing for the presence of the virus circulating in the environment. Despite these efforts, widespread community transmission has become entrenched in many countries and has required the testing of populations to identify and isolate infected individuals, many of whom are asymptomatic. The diagnostic and epidemiological principles that underpin such population-scale testing are also considered, as are the high-throughput and point-of-care technologies that make testing feasible on a massive scale.

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

COVID-19 infection among international travellers: a prospective analysis

OBJECTIVES

This report estimates the risk of COVID-19 importation and secondary transmission associated with a modified quarantine programme in Canada.

DESIGN AND PARTICIPANTS

Prospective analysis of international asymptomatic travellers entering Alberta, Canada.

INTERVENTIONS

All participants were required to receive a PCR COVID-19 test on arrival. If negative, participants could leave quarantine but were required to have a second test 6 or 7 days after arrival. If the arrival test was positive, participants were required to remain in quarantine for 14 days.

MAIN OUTCOME MEASURES

Proportion and rate of participants testing positive for COVID-19; number of cases of secondary transmission.

RESULTS

The analysis included 9535 international travellers entering Alberta by air (N=8398) or land (N=1137) that voluntarily enrolled in the Alberta Border Testing Pilot Programme (a subset of all travellers); most (83.1%) were Canadian citizens. Among the 9310 participants who received at least one test, 200 (21.5 per 1000, 95% CI 18.6 to 24.6) tested positive. Sixty-nine per cent (138/200) of positive tests were detected on arrival (14.8 per 1000 travellers, 95% CI 12.5 to 17.5). 62 cases (6.7 per 1000 travellers, 95% CI 5.1 to 8.5; 31.0% of positive cases) were identified among participants that had been released from quarantine following a negative test result on arrival. Of 192 participants who developed symptoms, 51 (26.6%) tested positive after arrival. Among participants with positive tests, four (2.0%) were hospitalised for COVID-19; none required critical care or died. Contact tracing among participants who tested positive identified 200 contacts; of 88 contacts tested, 22 were cases of secondary transmission (14 from those testing positive on arrival and 8 from those testing positive thereafter). SARS-CoV-2 B.1.1.7 lineage was not detected in any of the 200 positive cases.

CONCLUSIONS

21.5 per 1000 international travellers tested positive for COVID-19. Most (69%) tested positive on arrival and 31% tested positive during follow-up. These findings suggest the need for ongoing vigilance in travellers testing negative on arrival and highlight the value of follow-up testing and contact tracing to monitor and limit secondary transmission where possible.

The differential importation risks of COVID-19 from inbound travellers and the feasibility of targeted travel controls: A case study in Hong Kong

Background

Many countries/regions implemented strict border measures (e.g., 14-day quarantines) as a blanket policy to prevent COVID-19 importations, while proposed “travel bubbles” as an alternative to reduce the impact of border controls. We aim to examine the differential importation risks with departure origins and post-arrival controls.

Methods

We developed a Bayesian framework to model disease progress of COVID-19 and the effectiveness of travel measures and inferred the origin-specific disease prevalence among inbound travellers, using data on passengers arriving in Hong Kong and laboratory-confirmed imported cases. We estimated the origin-specific risks of releasing infectious travellers under different control strategies and traveller volumes. We also estimated the risk of having released infectious travellers when a resurgence occurs in departure locations with no imported cases during a certain period.

Findings

Under the then strict controls of 14-day quarantine and testing on day 12, the Philippines imposed the greatest importation risk among the studied countries/regions (95.8% of releasing at least one infectious traveller, 95% credible interval (CrI), 94.8-96.6%). This was higher than that from low prevalence countries/regions (e.g., 23.4%, 95% CrI, 21.6-25.3% for Taiwan) if controls relaxed (i.e., 7-day quarantine and test on day 5). Increased traveller volumes and resurgence in departure locations with low prevalence under relaxed controls did not impose a greater importation risk than high prevalence locations under stricter controls.

Interpretation

Moderate relaxation of control measures for travellers arriving from low prevalence locations did not impose higher risks of community outbreaks than strict controls on travellers from high prevalence locations.

Funding

Health and Medical Research Fund, Hong Kong.

Early Spread of COVID-19 in the Air-Polluted Regions of Eight Severely Affected Countries

COVID-19 escalated into a pandemic posing several humanitarian as well as scientific challenges. We here investigated the geographical character of the early spread of the infection and correlated it with several annual satellite and ground indexes of air quality in China, the United States, Italy, Iran, France, Spain, Germany, and the United Kingdom. The time of the analysis corresponded with the end of the first wave infection in China, namely June 2020. We found more viral infections in those areas afflicted by high PM 2.5 and nitrogen dioxide values. Higher mortality was also correlated with relatively poor air quality. In Italy, the correspondence between the Po Valley pollution and SARS-CoV-2 infections and induced mortality was the starkest, originating right in the most polluted European area. Spain and Germany did not present a noticeable gradient of pollution levels causing non-significant correlations. Densely populated areas were often hotspots of lower air quality levels but were not always correlated with a higher viral incidence. Air pollution has long been recognised as a high risk factor for several respiratory-related diseases and conditions, and it now appears to be a risk factor for COVID-19 as well. As such, air pollution should always be included as a factor for the study of airborne epidemics and further included in public health policies.

Current global research landscape on COVID-19 and depressive disorders: Bibliometric and visualization analysis

BACKGROUND

Coronavirus disease 2019 (COVID-19) has affected daily life globally dramatically over the last year. The impact of the COVID-19 epidemic on mental health is expected to be immense and likely to be long-lasting, raising a range of global problems that need to be addressed accordingly.

AIM

To analyze the Scopus-based depression research and COVID-19, explain the advancement of research nowadays, and comment on the possible hotspots of depression research and COVID-19 to obtain a more global perspective.

METHODS

In this report, bibliometric analysis and visualization are used to explain COVID-19's global research status on depression and provide researchers with a guide to identify future research directions. Relevant studies on depression and COVID-19 were retrieved from the Scopus database. Visualization maps were produced using the VOSviewer software, including research collaboration.

RESULTS

At the time of data collection (November 18, 2020), 77217 documents were released by Scopus to COVID-19 in all areas of research. By limiting the search to depression and COVID-19 (January 2020 up until November 18, 2020), there are 1274 published articles on depression and COVID-19 in the Scopus. The great majority of which are original articles (n = 1049, 82.34%), followed by 118 review articles (9.26%), 66 letters (5.18%). The United States had the highest number of publications at 282 (22.14%), followed by China (19.07%) at 243 and Italy at 121 (9.5%). The major two clusters are signified by mental health outcomes among the general population and mental health outcomes among health care workers.

CONCLUSION

The evidence from this study found that many articles focused on mental health outcomes among the general population and health care workers. With adequate psychological support offered by the government or community agencies, mental health in various communities should be put within the local and global public health agenda. This changing situation involves the scientific community's collaborative efforts to contribute to population monitoring during quarantine and COVID-19 outbreaks and to examine the short- and long-term adverse effects on psychological well-being.

Modelling the impact of travel restrictions on COVID-19 cases in Newfoundland and Labrador

In many jurisdictions, public health authorities have implemented travel restrictions to reduce coronavirus disease 2019 (COVID-19) spread. Policies that restrict travel within countries have been implemented, but the impact of these restrictions is not well known. On 4 May 2020, Newfoundland and Labrador (NL) implemented travel restrictions such that non-residents required exemptions to enter the province. We fit a stochastic epidemic model to data describing the number of active COVID-19 cases in NL from 14 March to 26 June. We predicted possible outbreaks over nine weeks, with and without the travel restrictions, and for contact rates 40-70% of pre-pandemic levels. Our results suggest that the travel restrictions reduced the mean number of clinical COVID-19 cases in NL by 92%. Furthermore, without the travel restrictions there is a substantial risk of very large outbreaks. Using epidemic modelling, we show how the NL COVID-19 outbreak could have unfolded had the travel restrictions not been implemented. Both physical distancing and travel restrictions affect the local dynamics of the epidemic. Our modelling shows that the travel restrictions are a plausible reason for the few reported COVID-19 cases in NL after 4 May.

Revisiting the Effects of High-Speed Railway Transfers in the Early COVID-19 Cross-Province Transmission in Mainland China

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic that was reported at the end of 2019 in Wuhan, China, and was rapidly disseminated to all provinces in around one month. The study aims to assess the changes in intercity railway passenger transport on the early spatial transmission of COVID-19 in mainland China. Examining the role of railway transport properties in disease transmission could help quantify the spatial spillover effects of large-scale travel restriction interventions. This study used daily high-speed railway schedule data to compare the differences in city-level network properties (destination arrival and transfer service) before and after the Wuhan city lockdown in the early stages of the spatial transmission of COVID-19 in mainland China. Bayesian multivariate regression was used to examine the association between structural changes in the railway origin-destination network and the incidence of COVID-19 cases. Our results show that the provinces with rising transfer activities after the Wuhan city lockdown had more confirmed COVID-19 cases, but changes in destination arrival did not have significant effects. The regions with increasing transfer activities were located in provinces neighboring Hubei in the widthwise and longitudinal directions. These results indicate that transfer activities enhance interpersonal transmission probability and could be a crucial risk factor for increasing epidemic severity after the Wuhan city lockdown. The destinations of railway passengers might not be affected by the Wuhan city lockdown, but their itinerary routes could be changed due to the replacement of an important transfer hub (Wuhan city) in the Chinese railway transportation network. As a result, transfer services in the high-speed rail network could explain why the provinces surrounded by Hubei had a higher number of confirmed COVID-19 cases than other provinces.

Accounting for imported cases in estimating the time-varying reproductive number of COVID-19 in Hong Kong

Estimating the time-varying reproductive number, R_t, is critical for monitoring transmissibility of an infectious disease. The impact of imported cases on the estimation is rarely explored. We developed a model to estimate separately the R_t for local cases and imported cases, with accounting for imperfect contact tracing of cases. We applied this framework to data on COVID-19 outbreaks in Hong Kong. The estimated R_t for local cases rise above 1 in late March, 2020, which was undetected by other commonly used methods. When imported cases accounted for a considerable proportion of all cases, their impact on estimating R_t is critical.

COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records

In 2019, COVID-19 quickly spread across the world, infecting billions of people and disrupting the normal lives of citizens in every country. Governments, organizations, and research institutions all over the world are dedicating vast resources to research effective strategies to fight this rapidly propagating virus. With virus testing, most countries publish the number of confirmed cases, dead cases, recovered cases, and locations routinely through various channels and forms. This important data source has enabled researchers worldwide to perform different COVID-19 scientific studies, such as modeling this virus's spreading patterns, developing prevention strategies, and studying the impact of COVID-19 on other aspects of society. However, one major challenge is that there is no standardized, updated, and high-quality data product that covers COVID-19 cases data internationally. This is because different countries may publish their data in unique channels, formats, and time intervals, which hinders researchers from fetching necessary COVID-19 datasets effectively, especially for fine-scale studies. Although existing solutions such as John's Hopkins COVID-19 Dashboard and 1point3acres COVID-19 tracker are widely used, it is difficult for users to access their original dataset and customize those data to meet specific requirements in categories, data structure, and data source selection. To address this challenge, we developed a toolset using cloud-based web scraping to extract, refine, unify, and store COVID-19 cases data at multiple scales for all available countries around the world automatically. The toolset then publishes the data for public access in an effective manner, which could offer users a real time COVID-19 dynamic dataset with a global view. Two case studies are presented about how to utilize the datasets. This toolset can also be easily extended to fulfill other purposes with its open-source nature.

Risk Assessment of Importation and Local Transmission of COVID-19 in South Korea: Statistical Modeling Approach

Background

Despite recent achievements in vaccines, antiviral drugs, and medical infrastructure, the emergence of COVID-19 has posed a serious threat to humans worldwide. Most countries are well connected on a global scale, making it nearly impossible to implement perfect and prompt mitigation strategies for infectious disease outbreaks. In particular, due to the explosive growth of international travel, the complex network of human mobility enabled the rapid spread of COVID-19 globally.

Objective

South Korea was one of the earliest countries to be affected by COVID-19. In the absence of vaccines and treatments, South Korea has implemented and maintained stringent interventions, such as large-scale epidemiological investigations, rapid diagnosis, social distancing, and prompt clinical classification of severely ill patients with appropriate medical measures. In particular, South Korea has implemented effective airport screenings and quarantine measures. In this study, we aimed to assess the country-specific importation risk of COVID-19 and investigate its impact on the local transmission of COVID-19.

Methods

The country-specific importation risk of COVID-19 in South Korea was assessed. We investigated the relationships between country-specific imported cases, passenger numbers, and the severity of country-specific COVID-19 prevalence from January to October 2020. We assessed the country-specific risk by incorporating country-specific information. A renewal mathematical model was employed, considering both imported and local cases of COVID-19 in South Korea. Furthermore, we estimated the basic and effective reproduction numbers.

Results

The risk of importation from China was highest between January and February 2020, while that from North America (the United States and Canada) was high from April to October 2020. The R₀ was estimated at 1.87 (95% CI 1.47-2.34), using the rate of α=0.07 for secondary transmission caused by imported cases. The R_t was estimated in South Korea and in both Seoul and Gyeonggi.

Conclusions

A statistical model accounting for imported and locally transmitted cases was employed to estimate R₀ and R_t. Our results indicated that the prompt implementation of airport screening measures (contact tracing with case isolation and quarantine) successfully reduced local transmission caused by imported cases despite passengers arriving from high-risk countries throughout the year. Moreover, various mitigation interventions, including social distancing and travel restrictions within South Korea, have been effectively implemented to reduce the spread of local cases in South Korea.

How much leeway is there to relax COVID-19 control measures?

Following successful non-pharmaceutical interventions (NPI) aiming to control COVID-19, many jurisdictions reopened their economies and borders. As little immunity had developed in most populations, re-establishing higher contact carried substantial risks, and therefore many locations began to see resurgence in COVID-19 cases. We present a Bayesian method to estimate the leeway to reopen, or alternatively the strength of change required to re-establish COVID-19 control, in a range of jurisdictions experiencing different COVID-19 epidemics. We estimated the timing and strength of initial control measures such as widespread distancing and compared the leeway jurisdictions had to reopen immediately after NPI measures to later estimates of leeway. Finally, we quantified risks associated with reopening and the likely burden of new cases due to introductions from other jurisdictions. We found widely varying leeway to reopen. After initial NPI measures took effect, some jurisdictions had substantial leeway (e.g., Japan, New Zealand, Germany) with > 0.99 probability that contact rates were below 80% of the threshold for epidemic growth. Others had little leeway (e.g., the United Kingdom, Washington State) and some had none (e.g., Sweden, California). For most such regions, increases in contact rate of 1.5-2 fold would have had high (> 0.7) probability of exceeding past peak sizes. Most jurisdictions experienced June-August trajectories consistent with our projections of contact rate increases of 1-2-fold. Under such relaxation scenarios for some regions, we projected up to ∼100 additional cases if just one case were imported per week over six weeks, even between jurisdictions with comparable COVID-19 risk. We provide an R package covidseir to enable jurisdictions to estimate leeway and forecast cases under different future contact patterns. Estimates of leeway can establish a quantitative basis for decisions about reopening. We recommend a cautious approach to reopening economies and borders, coupled with strong monitoring for changes in transmission.

Evaluating the impact of the travel ban within mainland China on the epidemic of the COVID-19

OBJECTIVES

The ongoing COVID-19 pandemic expanded its geographic distribution through the movement of humans and caused subsequent local outbreaks. Hence, it is essential to investigate how human mobility and travel ban affect the transmission and spatial spread while minimizing the impact on social activities and national economics.

METHODS

We developed a mobility network model for spatial epidemics, explicitly taking into account time-varying inter-province and inner-province population flows, spatial heterogeneity in terms of disease transmission, as well as the impact of media reports. The model is applied to study the epidemic of the dynamic network of 30 provinces of mainland China. The model was calibrated using the publicly available incidence and movement data.

RESULTS

We estimated that the second outbreak occurred approximately on February 24, 2020, and the cumulative number of cases as of March 15, 2020, increased by 290.1% (95% CI: (255.3%, 324.9%)) without a travel ban in mainland China (excluding Hubei and Tibet). We found that intra-province travel contributes more to the increase of cumulative number of cases than inter-province travel.

CONCLUSION

Our quantitative and qualitative research results suggest that the strict travel ban has successfully prevented a severe secondary outbreak in mainland China, which provides solutions for many countries and regions experiencing secondary outbreaks of COVID-19.

Continental transmission of emerging COVID-19 on the 38° north latitude

BACKGROUND

As COVID-19 has become a pandemic emerging infectious disease it is important to examine whether there was a spatiotemporal clustering phenomenon in the globe during the rapid spread after the first outbreak reported from southern China.

MATERIALS AND METHODS

The open data on the number of COVID-19 cases reported at daily basis form the globe were used to assess the evolution of outbreaks with international air link on the same latitude and also including Taiwan. The dynamic Susceptible-Infected-Recovered model was used to evaluate continental transmission from December 2019 to March 2020 before the declaration of COVID-19 pandemic with basic reproductive number and effective reproductive number before and after containment measurements.

RESULTS

For the initial COVID-19 outbreak in China, the estimated reproductive number was reduced from 2.84 during the overwhelming outbreaks in early January to 0.43 after the strict lockdown policy. It is very surprising to find there were three countries (including South Korea, Iran, and Italy) and the Washington state of the USA on the 38° North Latitude involved with large-scale community-acquired outbreaks since the first imported COVID-19 cases from China. The propagation of continental transmission was augmented from hotspot to hotspot with higher reproductive number immediately before the declaration of pandemic. By contrast, there was not any large community-acquired outbreak in Taiwan.

CONCLUSION

The propagated spatiotemporal transmission from China to other hotspots may explain the emerging pandemic that can only be exempted by timely border control and preparedness of containment measurements according to Taiwan experience.

Projecting the criticality of COVID-19 transmission in India using GIS and machine learning methods

There is a new public health catastrophe forbidding the world. With the advent and spread of 2019 novel coronavirus (2019-nCoV). Learning from the experiences of various countries and the World Health Organization (WHO) guidelines, social distancing, use of sanitizers, thermal screening, quarantining, and provision of lockdown in the cities being the effective measure that can contain the spread of the pandemic. Though complete lockdown helps in containing the spread, it generates complexity by breaking the economic activity chain. Besides, laborers, farmers, and workers may lose their daily earnings. Owing to these detrimental effects, the government has to open the lockdown strategically. Prediction of the COVID-19 spread and analyzing when the cases would stop increasing helps in developing a strategy. An attempt is made in this paper to predict the time after which the number of new cases stops rising, considering the strong implementation of lockdown conditions using three different techniques such as Decision Tree, Support Vector Machine, and Gaussian Process Regression algorithm are used to project the number of cases. Thus, the projections are used in identifying inflection points, which would help in planning the easing of lockdown in a few of the areas strategically. The criticality in a region is evaluated using the criticality index (CI), which is proposed by authors in one of the past of research works. This research work is made available in a dashboard to enable the decision-makers to combat the pandemic.

The effect of the synchronized multi-dimensional policies on imported COVID-19 curtailment in China

As countries are lifting restrictions and resuming international travels, the rising risk of COVID-19 importation remains concerning, given that the SARS-CoV-2 virus could be transmitted unintentionally through the global transportation network. To explore and assess the effective strategies for curtailing the epidemic risk from international importation nationwide, we evaluated "the joint prevention and control" mechanism, which made up of 19 containment policies, on how it impacted the change of medical observation and detection time from border arrival to laboratory confirmation of COVID-19 in its burst in China. Based on 1,314 epidemiological-survey cases from February 29 to May 25, 2020, we found that the synchronized approach of implementing multi-dimensional interventional policies, such as a centralized quarantine and nucleic acid testing (NAT), flight service adjustment and border closure, effectively facilitate early identification of infected case. Specifically, the implementation of the international flight service reduction was found to be associated with a reduction of the mean intervals of diagnosis from arrival to lab-confirmation by 0.44 days maximally, and the border closure was associated with a reduction of the diagnosis interval of imported cases by 0.69 days, from arrival to laboratory confirmation. The study suggests that a timely and synchronized implementation of multi-dimensional policies is compelling in preventing domestic spreading from importation.

Contagious Policies? Studying National Responses to a Global Pandemic in Europe

Not only Covid-19 has spread all over the world-the policies responding to this pandemic have also diffused rapidly across countries. In this research note, we present findings from an original dataset that features mobility restrictions in all EU/EFTA states as well as the United Kingdom during the first wave of the pandemic. We find that most countries adopted restrictions within a few days only and that restrictions on internal mobility had been introduced prior to restrictions on cross-border mobility, but that the latter have been more persistent. Furthermore, we observe an evolution from great variation of policy choices at the outset of the pandemic towards convergence. Analyzing the mobility restrictions through a policy diffusion lens, we find tentative evidence for interdependent policy-making especially in the temporal patterns of adoption. Our research note can serve a basis for future research on policy-making and policy diffusion in times of crisis.

Pandemic Prevention: Lessons from COVID-19

Coronavirus disease 2019 (COVID-19) is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which appeared in late 2019, generating a pandemic crisis with high numbers of COVID-19-related infected individuals and deaths in manifold countries worldwide. Lessons learned from COVID-19 can be used to prevent pandemic threats by designing strategies to support different policy responses, not limited to the health system, directed to reduce the risks of the emergence of novel viral agents, the diffusion of infectious diseases and negative impact in society.

Review of epidemic, containment strategies, clinical management, and economic evaluation of COVID-19 pandemic

The spread of the emerging pathogen, named as SARS-CoV-2, has led to an unprecedented COVID-19 pandemic since 1918 influenza pandemic. This review first sheds light on the similarity on global transmission, surges of pandemics, and the disparity of prevention between two pandemics. Such a brief comparison also provides an insight into the potential sequelae of COVID-19 based on the inference drawn from the fact that a cascade of successive influenza pandemic occurred after 1918 and also the previous experience on the epidemic of SARS and MERS occurring in 2003 and 2015, respectively. We then propose a systematic framework for elucidating emerging infectious disease (EID) such as COVID-19 with a panorama viewpoint from natural infection and disease process, public health interventions (non-pharmaceutical interventions (NPIs) and vaccine), clinical treatments and therapies (antivirals), until global aspects of health and economic loss, and economic evaluation of interventions with emphasis on mass vaccination. This review not only concisely delves for evidence-based scientific literatures from the origin of outbreak, the spread of SARS-CoV-2 to three surges of pandemic, and NPIs and vaccine uptakes but also provides a new insight into how to apply big data analytics to identify unprecedented discoveries through COVID-19 pandemic scenario embracing from biomedical to economic viewpoints.