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

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, the indiscriminate nature of mitigation - applying to all individuals irrespective of disease status - has come with substantial socioeconomic costs. Here, we explore how to leverage the increasing reliability and scale of both molecular and serological tests to balance transmission risks with economic costs involved in responding to Covid-19 epidemics. First, we introduce an optimal control approach that identifies personalized interaction rates according to an individual's test status; such that infected individuals isolate, recovered individuals can elevate their interactions, and activity of susceptible individuals varies over time. Critically, the extent to which susceptible individuals can return to work depends strongly on isolation efficiency. As we show, optimal control policies can yield mitigation policies with similar infection rates to total shutdown but lower socioeconomic costs. However, optimal control policies can be fragile given mis-specification of parameters or mis-estimation of the current disease state. Hence, we leverage insights from the optimal control solutions and propose a feedback control approach based on monitoring of the epidemic state. We utilize genetic algorithms to identify a 'switching' policy such that susceptible individuals (both PCR and serological test negative) return to work after lockdowns insofar as recovered fraction is much higher than the circulating infected prevalence. This feedback control policy exhibits similar performance results to optimal control, but with greater robustness to uncertainty. Overall, 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.

Estimating the establishment of local transmission and the cryptic phase of the COVID-19 pandemic in the USA

We use a global metapopulation transmission model to study the establishment of sustained and undetected community transmission of the COVID-19 pandemic in the United States. The model is calibrated on international case importations from mainland China and takes into account travel restrictions to and from international destinations. We estimate widespread community transmission of SARS-CoV-2 in February, 2020. Modeling results indicate international travel as the key driver of the introduction of SARS-CoV-2 in the West and East Coast metropolitan areas that could have been seeded as early as late-December, 2019. For most of the continental states the largest contribution of imported infections arrived through domestic travel flows.

COVID-19 epidemic monitoring after non-pharmaceutical interventions: The use of time-varying reproduction number in a country with a large migrant population

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R_t can be used to closely monitor the non-pharmaceutical interventions (NPI) of the COVID-19 epidemic.

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Activity of the epidemic in Oman is examined.

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Other factors, like the return of overseas students, have increased the epidemic activity.

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Responses to NPI are different between migrants and natives.

Background

COVID-19’s emergence carries with it many uncertainties and challenges, including strategies to manage the epidemic. Oman has implemented non-pharmaceutical interventions (NPIs) to mitigate the impact of COVID-19. However, responses to NPIs may be different across different populations within a country with a large number of migrants, such as Oman. This study investigated the different responses to NPIs, and assessed the use of the time-varying reproduction number (R_t) to monitor them.

Methods

Polymerase chain reaction (PCR) laboratory-confirmed COVID-19 data for Oman, from February 24 to June 3, 2020, were used alongside demographic and epidemiological information. Data were arranged into pairs of infector–infectee, and two main libraries of R software were used to estimate reproductive number (R_t). R_t was calculated for both Omanis and non-Omanis.

Findings

A total of 13,538 cases were included, 44.9% of which were Omanis. Among all these cases we identified 2769 infector–infectee pairs for calculating R_t. There was a sharp drop in R_t from 3.7 (95% confidence interval [CI] 2.8–4.6) in mid-March to 1.4 (95% CI 1.2–1.7) in late March in response to NPIs. R_t then decreased further to 1.2 (95% CI 1.1–1.3) in late April after which it rose, corresponding to the easing of NPIs. Comparing the two groups, the response to major public health controls was more evident in Omanis in reducing R_t to 1.09 (95% CI 0.84–1.3) by the end of March.

Interpretation

Use of real-time estimation of R_t allowed us to follow the effects of NPIs. The migrant population responded differently than the Omani population.

Effectiveness of interventions targeting air travellers for delaying local outbreaks of SARS-CoV-2

BACKGROUND

We evaluated if interventions aimed at air travellers can delay local severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) community transmission in a previously unaffected country.

METHODS

We simulated infected air travellers arriving into countries with no sustained SARS-CoV-2 transmission or other introduction routes from affected regions. We assessed the effectiveness of syndromic screening at departure and/or arrival and traveller sensitisation to the COVID-2019-like symptoms with the aim to trigger rapid self-isolation and reporting on symptom onset to enable contact tracing. We assumed that syndromic screening would reduce the number of infected arrivals and that traveller sensitisation reduces the average number of secondary cases. We use stochastic simulations to account for uncertainty in both arrival and secondary infections rates, and present sensitivity analyses on arrival rates of infected travellers and the effectiveness of traveller sensitisation. We report the median expected delay achievable in each scenario and an inner 50% interval.

RESULTS

Under baseline assumptions, introducing exit and entry screening in combination with traveller sensitisation can delay a local SARS-CoV-2 outbreak by 8 days (50% interval: 3-14 days) when the rate of importation is 1 infected traveller per week at time of introduction. The additional benefit of entry screening is small if exit screening is effective: the combination of only exit screening and traveller sensitisation can delay an outbreak by 7 days (50% interval: 2-13 days). In the absence of screening, with less effective sensitisation, or a higher rate of importation, these delays shrink rapidly to <4 days.

CONCLUSION

Syndromic screening and traveller sensitisation in combination may have marginally delayed SARS-CoV-2 outbreaks in unaffected countries.

The effectiveness of full and partial travel bans against COVID-19 spread in Australia for travellers from China during and after the epidemic peak in China

BACKGROUND

Australia implemented a travel ban on China on 1 February 2020, while COVID-19 was largely localized to China. We modelled three scenarios to test the impact of travel bans on epidemic control. Scenario one was no ban; scenario two and three were the current ban followed by a full or partial lifting (allow over 100 000 university students to enter Australia, but not tourists) from the 8th of March 2020.

METHODS

We used disease incidence data from China and air travel passenger movements between China and Australia during and after the epidemic peak in China, derived from incoming passenger arrival cards. We used the estimated incidence of disease in China, using data on expected proportion of under-ascertainment of cases and an age-specific deterministic model to model the epidemic in each scenario.

RESULTS

The modelled epidemic with the full ban fitted the observed incidence of cases well, predicting 57 cases on March 6th in Australia, compared to 66 observed on this date; however, we did not account for imported cases from other countries. The modelled impact without a travel ban results in more than 2000 cases and about 400 deaths, if the epidemic remained localized to China and no importations from other countries occurred. The full travel ban reduced cases by about 86%, while the impact of a partial lifting of the ban is minimal and may be a policy option.

CONCLUSIONS

Travel restrictions were highly effective for containing the COVID-19 epidemic in Australia during the epidemic peak in China and averted a much larger epidemic at a time when COVID-19 was largely localized to China. This research demonstrates the effectiveness of travel bans applied to countries with high disease incidence. This research can inform decisions on placing or lifting travel bans as a control measure for the COVID-19 epidemic.

A retrospective analysis of the dynamic transmission routes of the COVID-19 in mainland China

The fourth outbreak of the Coronaviruses, known as the COVID-19, has occurred in Wuhan city of Hubei province in China in December 2019. We propose a time-varying sparse vector autoregressive (VAR) model to retrospectively analyze and visualize the dynamic transmission routes of this outbreak in mainland China over January 31-February 19, 2020. Our results demonstrate that the influential inter-location routes from Hubei have become unidentifiable since February 4, 2020, whereas the self-transmission in each provincial-level administrative region (location, hereafter) was accelerating over February 4-15, 2020. From February 16, 2020, all routes became less detectable, and no influential transmissions could be identified on February 18 and 19, 2020. Such evidence supports the effectiveness of government interventions, including the travel restrictions in Hubei. Implications of our results suggest that in addition to the origin of the outbreak, virus preventions are of crucial importance in locations with the largest migrant workers percentages (e.g., Jiangxi, Henan and Anhui) to controlling the spread of COVID-19.

Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID

This study has two goals. The first is to explain the geo-environmental determinants of the accelerated diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats similar to COVID-19 having an accelerated viral infectivity in society. Using data on sample of N = 55 Italian province capitals, and data of infected individuals at as of April 7th, 2020, results reveal that the accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution of cities measured with days exceeding the limits set for PM10 (particulate matter 10 μm or less in diameter) or ozone. In particular, hinterland cities with average high number of days exceeding the limits set for PM10 (and also having a low wind speed) have a very high number of infected people on 7th April 2020 (arithmetic mean is about 2200 infected individuals, with average polluted days greater than 80 days per year), whereas coastal cities also having days exceeding the limits set for PM10 or ozone but with high wind speed have about 944.70 average infected individuals, with about 60 average polluted days per year; moreover, cities having more than 100 days of air pollution (exceeding the limits set for PM10), they have a very high average number of infected people (about 3350 infected individuals, 7th April 2020), whereas cities having less than 100 days of air pollution per year, they have a lower average number of infected people (about 1014 individuals). The findings here also suggest that to minimize the impact of future epidemics similar to COVID-19, the max number of days per year that Italian provincial capitals or similar industrialized cities can exceed the limits set for PM10 or for ozone, considering their meteorological conditions, is about 48 days. Moreover, results here reveal that the explanatory variable of air pollution in cities seems to be a more important predictor in the initial phase of diffusion of viral infectivity (on 17th March 2020, b1 = 1.27, p < 0.001) than interpersonal contacts (b2 = 0.31, p < 0.05). In the second phase of maturity of the transmission dynamics of COVID-19, air pollution reduces intensity (on 7th April 2020 with b'1 = 0.81, p < 0.001) also because of the indirect effect of lockdown, whereas regression coefficient of transmission based on interpersonal contacts has a stable level (b'2 = 0.31, p < 0.01). This result reveals that accelerated transmission dynamics of COVID-19 is due to mainly to the mechanism of "air pollution-to-human transmission" (airborne viral infectivity) rather than "human-to-human transmission". Overall, then, transmission dynamics of viral infectivity, such as COVID-19, is due to systemic causes: general factors that are the same for all regions (e.g., biological characteristics of virus, incubation period, etc.) and specific factors which are different for each region and/or city (e.g., complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity) and health level of individuals (habits, immune system, age, sex, etc.). Lessons learned for COVID-19 in the case study here suggest that a proactive strategy to cope with future epidemics is also to apply especially an environmental and sustainable policy based on reduction of levels of air pollution mainly in hinterland and polluting cities- (having low wind speed, high percentage of moisture and number of fog days) -that seem to have an environment that foster a fast transmission dynamics of viral infectivity in society. Hence, in the presence of polluting industrialization in regions that can trigger the mechanism of air pollution-to-human transmission dynamics of viral infectivity, this study must conclude that a comprehensive strategy to prevent future epidemics similar to COVID-19 has to be also designed in environmental and socioeconomic terms, that is also based on sustainability science and environmental science, and not only in terms of biology, medicine, healthcare and health sector.

Prevention and control of Aedes transmitted infections in the post-pandemic scenario of COVID-19: challenges and opportunities for the region of the Americas

The coronavirus disease of 2019 (COVID-19) pandemic challenges public health systems around the world. Tropical countries will face complex epidemiological scenarios involving the simultaneous transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with viruses transmitted by Aedes aegypti. The occurrence of arboviral diseases with COVID-19 in the Latin America and the Caribbean (LAC) region presents challenges and opportunities for strengthening health services, surveillance and control programs. Financing of training, equipment and reconversion of hospital spaces will have a negative effect on already the limited resource directed to the health sector. The strengthening of the diagnostic infrastructure reappears as an opportunity for the national reference laboratories. Sharing of epidemiological information for the modeling of epidemiological scenarios allows collaboration between health, academic and scientific institutions. The fear of contagion by COVID-19 is constraining people with arboviral diseases to search for care which can lead to an increase in serious cases and could disrupt the operation of vector-control programs due to the reluctance of residents to open their doors to health personnel. Promoting intense community participation along with the incorporation of long lasting innovations in vector control offers new opportunities for control. The COVID-19 pandemic offers challenges and opportunities that must provoke positive behavioral changes and encourage more permanent self-care actions.

Evidence-Based Considerations Exploring Relations between SARS-CoV-2 Pandemic and Air Pollution: Involvement of PM2.5-Mediated Up-Regulation of the Viral Receptor ACE-2

The COVID-19/SARS-CoV-2 pandemic struck health, social and economic systems worldwide, and represents an open challenge for scientists -coping with the high inter-individual variability of COVID-19, and for policy makers -coping with the responsibility to understand environmental factors affecting its severity across different geographical areas. Air pollution has been warned of as a modifiable factor contributing to differential SARS-CoV-2 spread but the biological mechanisms underlying the phenomenon are still unknown. Air quality and COVID-19 epidemiological data from 110 Italian provinces were studied by correlation analysis, to evaluate the association between particulate matter (PM)2.5 concentrations and incidence, mortality rate and case fatality risk of COVID-19 in the period 20 February-31 March 2020. Bioinformatic analysis of the DNA sequence encoding the SARS-CoV-2 cell receptor angiotensin-converting enzyme 2 (ACE-2) was performed to identify consensus motifs for transcription factors mediating cellular response to pollutant insult. Positive correlations between PM2.5 levels and the incidence (r = 0.67, p < 0.0001), the mortality rate (r = 0.65, p < 0.0001) and the case fatality rate (r = 0.7, p < 0.0001) of COVID-19 were found. The bioinformatic analysis of the ACE-2 gene identified nine putative consensus motifs for the aryl hydrocarbon receptor (AHR). Our results confirm the supposed link between air pollution and the rate and outcome of SARS-CoV-2 infection and support the hypothesis that pollution-induced over-expression of ACE-2 on human airways may favor SARS-CoV-2 infectivity.

A network-based method with privacy-preserving for identifying influential providers in large healthcare service systems

In data science, networks provide a useful abstraction of the structure of many complex systems, ranging from social systems and computer networks to biological networks and physical systems. Healthcare service systems are one of the main social systems that can also be understood using network-based approaches, for example, to identify and evaluate influential providers. In this paper, we propose a network-based method with privacy-preserving for identifying influential providers in large healthcare service systems. First, the provider-interacting network is constructed by employing publicly available information on locations and types of healthcare services of providers. Second, the ranking of nodes in the generated provider-interacting network is conducted in parallel on the basis of four nodal influence metrics. Third, the impact of the top-ranked influential nodes in the provider-interacting network is evaluated using three indicators. Compared with other research work based on patient-sharing networks, in this paper, the provider-interacting network of healthcare service providers can be roughly created according to the locations and the publicly available types of healthcare services, without the need for personally private electronic medical claims, thus protecting the privacy of patients. The proposed method is demonstrated by employing Physician and Other Supplier Data CY 2017, and can be applied to other similar datasets to help make decisions for the optimization of healthcare resources in the response to public health emergencies.

Mapping the situation of research on coronavirus disease-19 (COVID-19): a preliminary bibliometric analysis during the early stage of the outbreak

BACKGROUND

The novel coronavirus, named as 2019-nCoV or coronavirus disease 2019 (COVID-19), has recently appeared in China and has spread worldwide, presenting a health threat to the global community. Therefore, it is important to understand the global scientific output of COVID-19 research during the early stage of the outbreak. Thus, to track the current hotspots, and highlight future directions, we performed a bibliometric analysis to obtain an approximate scenario of COVID-19 to date.

METHODS

Relevant studies to COVID-19 were obtained from the Scopus database during the early stage of the outbreak. We then analysed the data by using well-established bibliometric indices: document type, country, collaboration patterns, affiliation, journal name, and citation patterns. VOSviewer was applied to map and determine hot topics in this field.

RESULTS

The bibliometric analysis indicated that there were 19,044 publications on Scopus published on COVID-19 during the early stage of the outbreak (December 2019 up until June 19, 2020). Of all these publications, 9140 (48.0%) were articles; 4192 (22.0%) were letters; 1797 (9.4%) were reviews; 1754 (9.2%) were editorials; 1728 (9.1%) were notes; and 433 (2.3%) were others. The USA published the largest number of publications on COVID-19 (4479; 23.4%), followed by China (3310; 17.4%), Italy, (2314; 12.2%), and the UK (1981; 10.4%). British Medical Journal was the most productive. The Huazhong University of Science and Technology, Tongji Medical, and Harvard Medical School were the institutions that published the largest number of COVID-19 research. The most prevalent topics of research in COVID-19 include "clinical features studies", "pathological findings and therapeutic design", "care facilities preparation and infection control", and "maternal, perinatal and neonatal outcomes".

CONCLUSIONS

This bibliometric study may reflect rapidly emerging topics on COVID-19 research, where substantial research activity has already begun extensively during the early stage of the outbreak. The findings reported here shed new light on the major progress in the near future for hot topics on COVID-19 research including clinical features studies, pathological findings and therapeutic design, care facilities preparation and infection control, and maternal, perinatal and neonatal outcomes.

COVID-19 and SARS-CoV-2. Modeling the present, looking at the future

Since December 2019 the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has produced an outbreak of pulmonary disease which has soon become a global pandemic, known as COronaVIrus Disease-19 (COVID-19). The new coronavirus shares about 82% of its genome with the one which produced the 2003 outbreak (SARS CoV-1). Both coronaviruses also share the same cellular receptor, which is the angiotensin-converting enzyme 2 (ACE2) one. In spite of these similarities, the new coronavirus has expanded more widely, more faster and more lethally than the previous one. Many researchers across the disciplines have used diverse modeling tools to analyze the impact of this pandemic at global and local scales. This includes a wide range of approaches - deterministic, data-driven, stochastic, agent-based, and their combinations - to forecast the progression of the epidemic as well as the effects of non-pharmaceutical interventions to stop or mitigate its impact on the world population. The physical complexities of modern society need to be captured by these models. This includes the many ways of social contacts - (multiplex) social contact networks, (multilayers) transport systems, metapopulations, etc. - that may act as a framework for the virus propagation. But modeling not only plays a fundamental role in analyzing and forecasting epidemiological variables, but it also plays an important role in helping to find cures for the disease and in preventing contagion by means of new vaccines. The necessity for answering swiftly and effectively the questions: could existing drugs work against SARS CoV-2? and can new vaccines be developed in time? demands the use of physical modeling of proteins, protein-inhibitors interactions, virtual screening of drugs against virus targets, predicting immunogenicity of small peptides, modeling vaccinomics and vaccine design, to mention just a few. Here, we review these three main areas of modeling research against SARS CoV-2 and COVID-19: (1) epidemiology; (2) drug repurposing; and (3) vaccine design. Therefore, we compile the most relevant existing literature about modeling strategies against the virus to help modelers to navigate this fast-growing literature. We also keep an eye on future outbreaks, where the modelers can find the most relevant strategies used in an emergency situation as the current one to help in fighting future pandemics.

Association of Social Distancing, Population Density, and Temperature With the Instantaneous Reproduction Number of SARS-CoV-2 in Counties Across the United States

IMPORTANCE

Local variation in the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the United States has not been well studied.

OBJECTIVE

To examine the association of county-level factors with variation in the SARS-CoV-2 reproduction number over time.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 211 counties, representing state capitals and cities with at least 100 000 residents and including 178 892 208 US residents, in 46 states and the District of Columbia between February 25, 2020, and April 23, 2020.

EXPOSURES

Social distancing, measured by percentage change in visits to nonessential businesses; population density; and daily wet-bulb temperatures.

MAIN OUTCOMES AND MEASURES

Instantaneous reproduction number (Rt), or cases generated by each incident case at a given time, estimated from daily case incidence data.

RESULTS

The 211 counties contained 178 892 208 of 326 289 971 US residents (54.8%). Median (interquartile range) population density was 1022.7 (471.2-1846.0) people per square mile. The mean (SD) peak reduction in visits to nonessential business between April 6 and April 19, as the country was sheltering in place, was 68.7% (7.9%). Median (interquartile range) daily wet-bulb temperatures were 7.5 (3.8-12.8) °C. Median (interquartile range) case incidence and fatality rates per 100 000 people were approximately 10 times higher for the top decile of densely populated counties (1185.2 [313.2-1891.2] cases; 43.7 [10.4-106.7] deaths) than for counties in the lowest density quartile (121.4 [87.8-175.4] cases; 4.2 [1.9-8.0] deaths). Mean (SD) Rt in the first 2 weeks was 5.7 (2.5) in the top decile compared with 3.1 (1.2) in the lowest quartile. In multivariable analysis, a 50% decrease in visits to nonessential businesses was associated with a 45% decrease in Rt (95% CI, 43%-49%). From a relative Rt at 0 °C of 2.13 (95% CI, 1.89-2.40), relative Rt decreased to a minimum as temperatures warmed to 11 °C, increased between 11 and 20 °C (1.61; 95% CI, 1.42-1.84) and then declined again at temperatures greater than 20 °C. With a 70% reduction in visits to nonessential business, 202 counties (95.7%) were estimated to fall below a threshold Rt of 1.0, including 17 of 21 counties (81.0%) in the top density decile and 52 of 53 counties (98.1%) in the lowest density quartile.2.

CONCLUSIONS AND RELEVANCE

In this cohort study, social distancing, lower population density, and temperate weather were associated with a decreased Rt for SARS-CoV-2 in counties across the United States. These associations could inform selective public policy planning in communities during the coronavirus disease 2019 pandemic.

The Novel Coronavirus - Latest Findings

What was initially a lung infection epidemic in the metropolitan area of Wuhan followed by a now contained extension to mainland China has now spread to all continents as a major pandemic with current hotspots in Europe and the USA. This minireview is an update of an earlier report on this novel coronavirus infection (Brüssow, 2020, Microb Biotech 13, 607). I am now summarizing the research literature published between end of February to mid-April 2020.

Epidemiological Measures in the Context of the COVID-19 Pandemic

BACKGROUND

The various epidemiological indicators used to communicate the impact of COVID-19 have different strengths and limitations.

METHODS

We conducted a selective literature review to identify the indicators used and to derive appropriate definitions. We calculated crude and age-adjusted indicators for selected countries.

RESULTS

The proportion of deaths (case fatality proportion [CFP]; number of deaths/ total number of cases) is commonly used to estimate the severity of a disease. If the CFP is used for purposes of comparison, the existence of heterogeneity in the detection and registration of cases and deaths has to be taken into account. In the early phase of an epidemic, when case numbers rise rapidly, the CFP suffers from bias. For these reasons, variants have been proposed: the "confirmed CFP" (number of deaths/total number of confirmed cases), and the "delay-adjusted CFP," which considers the delay between infection with the disease and death from the disease. The indicator mortality (number of deaths/total population) has at first sight the advantage of being based on a defined denominator, the total population. During the outbreak of a disease, however, the cumulative deaths rise while the total population remains stable. The phase of the epidemic therefore has to be considered when using this indicator. In this context, R0 and R(t) are important indicators. R0 estimates the maximum rate of spread of a disease in a population, while R(t) describes the dynamics of the epidemic at a given time. Age-adjusted analysis of the CFP shows that the differences between countries decrease but do not dis - appear completely. If the test strategies depend on age or symptom severity, however, the bias cannot be entirely eliminated.

CONCLUSION

Various indicators of the impact of the COVID-19 epidemic at population level are used in daily communication. Considering the relevance of the pandemic and the importance of relevant communications, however, the strengths and the limitations of each parameter must be considered carefully.

Risk estimation of the SARS-CoV-2 acute respiratory disease outbreak outside China

Background

On December 31, 2019, the World Health Organization was alerted to the occurrence of cases of pneumonia in Wuhan, Hubei Province, China, that were caused by an unknown virus, which was later identified as a coronavirus and named the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to estimate the reproductive number of SARS-CoV-2 in the Hubei Province and evaluate the risk of an acute respiratory coronavirus disease (COVID-19) outbreak outside China by using a mathematical model and stochastic simulations.

Results

We constructed a mathematical model of SARS-CoV-2 transmission dynamics, estimated the rate of transmission, and calculated the reproductive number in Hubei Province by using case-report data from January 11 to February 6, 2020. The possible number of secondary cases outside China was estimated by stochastic simulations in various scenarios of reductions in the duration to quarantine and rate of transmission. The rate of transmission was estimated as 0.8238 (95% confidence interval [CI] 0.8095–0.8382), and the basic reproductive number as 4.1192 (95% CI 4.0473–4.1912). Assuming the same rate of transmission as in Hubei Province, the possibility of no local transmission is 54.9% with a 24-h quarantine strategy, and the possibility of more than 20 local transmission cases is 7% outside of China.

Conclusion

The reproductive number for SARS-CoV-2 transmission dynamics is significantly higher compared to that of the previous SARS epidemic in China. This implies that human-to-human transmission is a significant factor for contagion in Hubei Province. Results of the stochastic simulation emphasize the role of quarantine implementation, which is critical to prevent and control the SARS-CoV-2 outbreak outside China.

Global supply-chain effects of COVID-19 control measures

Countries have sought to stop the spread of coronavirus disease 2019 (COVID-19) by severely restricting travel and in-person commercial activities. Here, we analyse the supply-chain effects of a set of idealized lockdown scenarios, using the latest global trade modelling framework. We find that supply-chain losses that are related to initial COVID-19 lockdowns are largely dependent on the number of countries imposing restrictions and that losses are more sensitive to the duration of a lockdown than its strictness. However, a longer containment that can eradicate the disease imposes a smaller loss than shorter ones. Earlier, stricter and shorter lockdowns can minimize overall losses. A 'go-slow' approach to lifting restrictions may reduce overall damages if it avoids the need for further lockdowns. Regardless of the strategy, the complexity of global supply chains will magnify losses beyond the direct effects of COVID-19. Thus, pandemic control is a public good that requires collective efforts and support to lower-capacity countries.

Diagnostic Testing for Severe Acute Respiratory Syndrome-Related Coronavirus 2: A Narrative Review

Diagnostic testing to identify persons infected with severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infection is central to control the global pandemic of COVID-19 that began in late 2019. In a few countries, the use of diagnostic testing on a massive scale has been a cornerstone of successful containment strategies. In contrast, the United States, hampered by limited testing capacity, has prioritized testing for specific groups of persons. Real-time reverse transcriptase polymerase chain reaction-based assays performed in a laboratory on respiratory specimens are the reference standard for COVID-19 diagnostics. However, point-of-care technologies and serologic immunoassays are rapidly emerging. Although excellent tools exist for the diagnosis of symptomatic patients in well-equipped laboratories, important gaps remain in screening asymptomatic persons in the incubation phase, as well as in the accurate determination of live viral shedding during convalescence to inform decisions to end isolation. Many affluent countries have encountered challenges in test delivery and specimen collection that have inhibited rapid increases in testing capacity. These challenges may be even greater in low-resource settings. Urgent clinical and public health needs currently drive an unprecedented global effort to increase testing capacity for SARS-CoV-2 infection. Here, the authors review the current array of tests for SARS-CoV-2, highlight gaps in current diagnostic capacity, and propose potential solutions.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): a review

In recent years, the prevalence and spread of coronavirus has had a huge impact on global public health. Due to the incomplete understanding of the pathogenic mechanism of the virus, it is difficult for humans to fight against the virus quickly and effectively once the outbreak occurs. In early 2020, a novel coronavirus was discovered in Wuhan, China. Soon after, similar cases were found in other countries around the world, and the number of infected people increased rapidly. So far, the global cumulative number of infected people has exceeded 3 million, and more than 200,000 people have died, which has had a huge impact on global human health and economic development. Every outbreak of disease makes a deep impression on mankind. Herein, we summarize the virology, epidemiology, clinical manifestations, diagnosis, treatment and prevention of SARS-CoV-2, and hope that countries can control the outbreak as soon as possible to minimize the loss.

Sociodemographic Predictors of Health Risk Perception, Attitude and Behavior Practices Associated with Health-Emergency Disaster Risk Management for Biological Hazards: The Case of COVID-19 Pandemic in Hong Kong, SAR China

In addition to top-down Health-Emergency and Disaster Risk Management (Health-EDRM) efforts, bottom-up individual and household measures are crucial for prevention and emergency response of the COVID-19 pandemic, a Public Health Emergency of International Concern (PHEIC). There is limited scientific evidence of the knowledge, perception, attitude and behavior patterns of the urban population. A computerized randomized digital dialing, cross-sectional, population landline-based telephone survey was conducted from 22 March to 1 April 2020 in Hong Kong Special Administrative Region, China. Data were collected for socio-demographic characteristics, knowledge, attitude and risk perception, and various self-reported Health-EDRM behavior patterns associated with COVID-19. The final study sample was 765. Although the respondents thought that individuals (68.6%) had similar responsibilities as government (67.5%) in infection control, less than 50% had sufficient health risk management knowledge to safeguard health and well-being. Among the examined Health-EDRM measures, significant differences were found between attitude and practice in regards to washing hands with soap, ordering takeaways, wearing masks, avoidance of visiting public places or using public transport, and travel avoidance to COVID-19-confirmed regions. Logistic regression indicated that the elderly were less likely to worry about infection with COVID-19. Compared to personal and household hygiene practices, lower compliance was found for public social distancing.

SARS-CoV-2 infection serology: a useful tool to overcome lockdown?

The outbreak of 2019 novel coronavirus disease (Covid-19) caused by SARS-CoV-2 has spread rapidly, inducing a progressive growth in infected patients number. Social isolation (lockdown) has been assessed to prevent and control virus diffusion, leading to a worldwide financial and political crisis. Currently, SARS-CoV-2 RNA detection in nasopharyngeal swab takes place by real-time PCR (RT-qPCR). However, molecular tests can give some false-negative results. In this context, serological assays can be useful to detect IgG/IgM antibodies, to assess the degree of immunization, to trace the contacts, and to support the decision to re-admit people at work. A lot of serological diagnostic kits have been proposed on the market but validation studies have not been published for many of them. The aim of our work was to compare and to evaluate different assays analytical performances (two different immunochromatographic cards, an immunofluorescence chromatographic card, and a chemiluminescence-automated immunoassay) on 43 positive samples with RT-qPCR-confirmed SARS-CoV-2 infection and 40 negative control subjects. Our data display excellent IgG/IgM specificities for all the immunocromatographic card tests (100% IgG and 100% IgM) and for the chemiluminescence-automated assay (100% IgG and 94% IgM); IgG/IgM sensitivities are moderately lower for all methods, probably due to the assay viral antigen's nature and/or to the detection time of nasopharyngeal swab RT-qPCR, with respect to symptoms onset. Given that sensitivities (around 94% and 84% for IgG and IgM, respectively) implicate false-negative cases and given the lack of effective vaccines or treatments, the only currently available procedure to reduce SARS-CoV-2 transmission is to identify and isolate persons who are contagious. For this reason, we would like to submit a flowchart in which serological tests, integrated with nasopharyngeal swab RT-qPCR, are included to help social and work activities implementation after the pandemic acute phase and to overcome lockdown.

Second waves, social distancing, and the spread of COVID-19 across America

We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several of these (epidemic) models to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity-and the exchange of people between regions-and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.

COVID-19: A Relook at Healthcare Systems and Aged Populations

Using data from the WHO’s Situation Report on the COVID-19 pandemic from 21 January 2020 to 30 March 2020 along with other health, demographic, and macroeconomic indicators from the WHO’s Application Programming Interface and the World Bank’s Development Indicators, this paper explores the death rates of infected persons and their possible associated factors. Through the panel analysis, we found consistent results that healthcare system conditions, particularly the number of hospital beds and medical staff, have played extremely important roles in reducing death rates of COVID-19 infected persons. In addition, both the mortality rates due to different non-communicable diseases (NCDs) and rate of people aged 65 and over were significantly related to the death rates. We also found that controlling international and domestic travelling by air along with increasingly popular anti-COVID-19 actions (i.e., quarantine and social distancing) would help reduce the death rates in all countries. We conducted tests for robustness and found that the Driscoll and Kraay (1998) method was the most suitable estimator with a finite sample, which helped confirm the robustness of our estimations. Based on the findings, we suggest that preparedness of healthcare systems for aged populations need more attentions from the public and politicians, regardless of income level, when facing COVID-19-like pandemics.

CT imaging features of 34 patients infected with COVID-19

Objective

To retrospectively analyze the CT findings in patients infected with Coronavirus disease 2019 (COVID-19).

Materials and methods

The thirty-four cases, 15 females and 19 males, with an age ranging from 7 to 88 years old, confirmed by real-time reverse-transcriptase-polymerase chain reaction (RT-PCR), were used for our study. All thin-section CT scans of the lungs were performed in all of patients. The clinical, laboratory and CT imaging were available to evaluate in all patients.

Results

The patients present with fever (85.29%, n = 29), cough (67.65%, n = 23), fatigue or myalgia (26.47%, n = 9), and pharyngalgia (8.82%, n = 3). The 4 patients (11.76%) with no symptoms were identified during screening for close contacts, who had typical CT findings. On initial CT scans, the bilateral lung involved was shown in 24 cases (70.59%), while 29 (82.35%) cases were distributed in peripheral. The pure ground glass opacity (GGO) was shown in 18 cases (52.94%), the GGO with consolidation was in 12 cases (35.29%), and full consolidation only in 3 cases. The lesion with air bronchogram was seen in 14 (41.18%) cases, with enlarged blood vessel in 17 (50.00%) cases, with crazy-paving pattern in 8 (23.53%) cases, with fine reticular pattern in 4 (11.77%) cases, and with intralesional vacuole sign in 6 (17.65%) cases. The pleural effusion was seen in one patient. Follow-up imaging in 19 patients during the study time window demonstrated mild, moderate or severe progression of disease, as manifested by increasing extent and density of lung opacities.

Conclusions

The bilateral GGO with air bronchogram, enlarged blood vessel, fine reticular pattern, and peripheral distribution are the early CT findings of COVID-19. The crazy-paving pattern and intralesional vacuole sign are the features of progressive stage.

Analysis of Imported Cases of COVID-19 in Taiwan: A Nationwide Study

In the early stages of the 2019 novel coronavirus disease (COVID-19) pandemic, containment of disease importation from epidemic areas was essential for outbreak control. This study is based on publicly accessible data on confirmed COVID-19 cases in Taiwan extracted from the Taiwan Centers for Disease Control website. We analysed the characteristics, infection source, symptom presentation, and route of identification of the 321 imported cases that were identified from 21 January to 6 April 2020. They were mostly returned Taiwanese citizens who had travelled to one or more of 37 countries for tourism, business, work, or study. Half of these cases developed symptoms before arrival, most of the remainder developed symptoms 1–13 days (mean 4.0 days) after arrival, and 3.4% never developed symptoms. Three-quarters of the cases had respiratory symptoms, 44.9% had fever, 13.1% lost smell or taste, and 7.2% had diarrhoea. Body temperature and symptom screening at airports identified 32.7% of the cases. Of the remainder, 27.7% were identified during home quarantining, 16.2% were identified via contact tracing, and 23.4% were reported by hospitals. Under the strict enforcement of these measures, the incidence of locally acquired COVID-19 cases in Taiwan remains sporadic. In conclusion, proactive border control measures are effective for preventing community transmission of this disease.

Health risks and potential remedies during prolonged lockdowns for coronavirus disease 2019 (COVID-19)

As coronavirus disease 2019 (COVID-19) pandemic continues, an increasing number of countries and territories are adopting restrictive measures based on physical ("social") distancing, aimed at preventing human-to-human transmission and thereby limiting virus propagation. Nationwide lockdowns, encompassing mass quarantine under stay-at-home ordinances, have already been proven effective to contain the COVID-19 outbreak in some countries. Nevertheless, a prolonged homestay may also be associated with potential side effects, which may jeopardize people's health and thus must be recognized and mitigated in a way without violating local ordinances. Some of the most important undesirable consequences of prolonged homestay such as physical inactivity, weight gain, behavioral addiction disorders, insufficient sunlight exposure and social isolation will be critically addressed in this article, which also aims to provide some tentative recommendations for the alleviation of side effects.

Estimating required ‘lockdown’ cycles before immunity to SARS-CoV-2: model-based analyses of susceptible population sizes, ‘S0’, in seven European countries, including the UK and Ireland

Background: Following stringent social distancing measures, some European countries are beginning to report a slowed or negative rate of growth of daily case numbers testing positive for the novel coronavirus. The notion that the first wave of infection is close to its peak begs the question of whether future peaks or ‘second waves’ are likely. We sought to determine the current size of the effective (i.e. susceptible) population for seven European countries—to estimate immunity levels following this first wave. Methods: We used Bayesian model inversion to estimate epidemic parameters from the reported case and death rates from seven countries using data from late January 2020 to April 5th 2020. Two distinct generative model types were employed: first a continuous time dynamical-systems implementation of a Susceptible-Exposed-Infectious-Recovered (SEIR) model, and second a partially observable Markov Decision Process or hidden Markov model (HMM) implementation of an SEIR model. Both models parameterise the size of the initial susceptible population (‘S0’), as well as epidemic parameters. Results: Both models recapitulated the dynamics of transmissions and disease as given by case and death rates. Crucially, maximum a posteriori estimates of S0 for each country indicated effective population sizes of below 20% (of total population size), under both the continuous time and HMM models. Using a Bayesian weighted average across all seven countries and both models, we estimated that 6.4% of the total population would be immune. From the two models, the maximum percentage of the effective population was estimated at 19.6% of the total population for the UK, 16.7% for Ireland, 11.4% for Italy, 12.8% for Spain, 18.8% for France, 4.7% for Germany and 12.9% for Switzerland. Conclusion: Our results indicate that after the current wave, a large proportion of the total population will remain without immunity.

COVID-19 pandemic: Lessons learned and future directions

Emerging pandemics show that humans are not infallible and communities need to be prepared. Coronavirus outbreak was first reported towards the end of 2019 and has now been declared a pandemic by the World Health Organization. Worldwide countries are responding differently to the virus outbreak. A delay in detection and response has been recorded in China, as well as in other major countries, which led to an overburdening of the local health systems. On the other hand, some other nations have put in place effective strategies to contain the infection and have recorded a very low number of cases since the beginning of the pandemics. Restrictive measures like social distancing, lockdown, case detection, isolation, contact tracing, and quarantine of exposed had revealed the most efficient actions to control the disease spreading. This review will help the readers to understand the difference in response by different countries and their outcomes. Based on the experience of these countries, India responded to the pandemic accordingly. Only time will tell how well India has faced the outbreak. We also suggest the future directions that the global community should take to manage and mitigate the emergency.

Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics (Preprint).. [DOI: 10.2196/preprints.19331]

BACKGROUND

Coronavirus disease 2019 (COVID-19) is viral infection that generates a severe acute respiratory syndrome with serious pneumonia that may result in progressive respiratory failure and death.

OBJECTIVE

This study has two goals. The first is to explain the main factors determining the diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats with of accelerated viral infectivity in society.

METHODS

Correlation and regression analyses on on data of N=55 Italian province capitals, and data of infected individuals at as of April 2020.

RESULTS

The main results are: o The accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution. o Hinterland cities have average days of exceeding the limits set for PM10 (particulate matter 10 micrometers or less in diameter) equal to 80 days, and an average number of infected more than 2,000 individuals as of April 1st, 2020, coastal cities have days of exceeding the limits set for PM10 equal to 60 days and have about 700 infected in average. o Cities that average number of 125 days exceeding the limits set for PM10, last year, they have an average number of infected individual higher than 3,200 units, whereas cities having less than 100 days (average number of 48 days) exceeding the limits set for PM10, they have an average number of about 900 infected individuals. o The results reveal that accelerated transmission dynamics of COVID-19 in specific environments is due to two mechanisms given by: air pollution-to-human transmission and human-to-human transmission; in particular, the mechanisms of air pollution-to-human transmission play a critical role rather than human-to-human transmission. o The finding here suggests that to minimize future epidemic similar to COVID-19, the max number of days per year in which cities can exceed the limits set for PM10 or for ozone, considering their meteorological condition, is less than 50 days. After this critical threshold, the analytical output here suggests that environmental inconsistencies because of the combination between air pollution and meteorological conditions (with high moisture%, low wind speed and fog) trigger a take-off of viral infectivity (accelerated epidemic diffusion) with damages for health of population, economy and society.

CONCLUSIONS

Considering the complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity, lessons learned for COVID-19 have to be applied for a proactive socioeconomic strategy to cope with future epidemics, especially an environmental policy based on reduction of air pollution mainly in hinterland zones of countries, having low wind speed, high percentage of moisture and fog that create an environment that can damage immune system of people and foster a fast transmission of viral infectivity similar to the COVID-19.

CLINICALTRIAL

not applicable

Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics.. [DOI: 10.1101/2020.04.06.20055657]

What is COVID-19?

Coronavirus disease 2019 (COVID-19) is viral infection that generates a severe acute respiratory syndrome with serious pneumonia that may result in progressive respiratory failure and death.

What are the goals of this investigation?

This study explains the geo-environmental determinants of the accelerated diffusion of COVID-19 in Italy that is generating a high level of deaths and suggests general lessons learned for a strategy to cope with future epidemics similar to COVID-19 to reduce viral infectivity and negative impacts in economic systems and society.

What are the results of this study?

The main results are:

What is a socioeconomic strategy to prevent future epidemics similar to COVID-19?

Considering the complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity, lessons learned for COVID-19 have to be applied for a proactive socioeconomic strategy to cope with future epidemics, especially an environmental policy based on reduction of air pollution mainly in hinterland zones of countries, having low wind speed, high percentage of moisture and fog that create an environment that can damage immune system of people and foster a fast transmission of viral infectivity similar to the COVID-19.

This study must conclude that a strategy to prevent future epidemics similar to COVID 19 has also to be designed in environmental and sustainability science and not only in terms of biology.

Preparing for the Perpetual Challenges of Pandemics of Coronavirus Infections with Special Focus on SARS-CoV-2

COVID-19, arising from novel, zoonotic coronavirus-2, has gripped the world in a pandemic. The present chapter discusses the current internationally implemented pandemic preparedness strategies succeeding/recommended to curb the COVID-19 threat to humankind. The updated phase-wise categorization of a pandemic as recommended by the WHO is described, and associated innovations in surveillance, response, and medical measures/advisory in practice across the globe are elaborated. From a bird’s eye view, the COVID-19 pandemic management relies on revolutionizing the disease surveillance by incorporating artificial intelligence and data analytics, boosting the response strategies—extensive testing, case isolation, contact tracing, and social distancing—and promoting awareness and access to pharmaceutical and non-pharmaceutical interventions, which are discussed in the present chapter. We also preview the economic bearing of the COVID-19 pandemic.

Mental Health Outcomes and Associations During the COVID-19 Pandemic: A Cross-Sectional Population-Based Study in the United States

Pandemic coronavirus disease 2019 (COVID-19) may lead to significant mental health stresses, potentially with modifiable risk factors. We performed an internet-based cross-sectional survey of an age-, sex-, and race-stratified representative sample from the US general population. Degrees of anxiety, depression, and loneliness were assessed using the 7-item Generalized Anxiety Disorder-7 scale (GAD-7), the 9-item Patient Health Questionnaire, and the 8-item UCLA Loneliness Scale, respectively. Unadjusted and multivariable logistic regression analyses were performed to determine associations with baseline demographic characteristics. A total of 1,005 finished surveys were returned of the 1,020 started, yielding a completion rate of 98.5% in the survey panel. The mean (standard deviation) age of the respondents was 45 (16) years, and 494 (48.8%) were male. Overall, 264 subjects (26.8%) met the criteria for an anxiety disorder based on a GAD-7 cutoff of 10; a cutoff of 7 yielded 416 subjects (41.4%), meeting the clinical criteria for anxiety. On multivariable analysis, male sex (odds ratio [OR] = 0.65, 95% confidence interval [CI] [0.49, 0.87]), identification as Black (OR = 0.49, 95% CI [0.31, 0.77]), and living in a larger home (OR = 0.46, 95% CI [0.24, 0.88]) were associated with a decreased odds of meeting the anxiety criteria. Rural location (OR 1.39, 95% CI [1.03, 1.89]), loneliness (OR 4.92, 95% CI [3.18, 7.62]), and history of hospitalization (OR = 2.04, 95% CI [1.38, 3.03]) were associated with increased odds of meeting the anxiety criteria. Two hundred thirty-two subjects (23.6%) met the criteria for clinical depression. On multivariable analysis, male sex (OR = 0.71, 95% CI [0.53, 0.95]), identifying as Black (OR = 0.62, 95% CI [0.40, 0.97]), increased time outdoors (OR = 0.51, 95% CI [0.29, 0.92]), and living in a larger home (OR = 0.35, 95% CI [0.18, 0.69]) were associated with decreased odds of meeting depression criteria. Having lost a job (OR = 1.64, 95% CI [1.05, 2.54]), loneliness (OR = 10.42, 95% CI [6.26, 17.36]), and history of hospitalization (OR = 2.42, 95% CI [1.62, 3.62]) were associated with an increased odds of meeting depression criteria. Income, media consumption, and religiosity were not associated with mental health outcomes. Anxiety and depression are common in the US general population in the context of the COVID-19 pandemic and are associated with potentially modifiable factors.