A data-driven assessment of early travel restrictions related to the spreading of the novel COVID-19 within mainland China

Two months after it was firstly reported, the novel coronavirus disease COVID-19 has already spread worldwide. However, the vast majority of reported infections have occurred in China. To assess the effect of early travel restrictions adopted by the health authorities in China, we have implemented an epidemic metapopulation model that is fed with mobility data corresponding to 2019 and 2020. This allows to compare two radically different scenarios, one with no travel restrictions and another in which mobility is reduced by a travel ban. Our findings indicate that i) travel restrictions are an effective measure in the short term, however, ii) they are ineffective when it comes to completely eliminate the disease. The latter is due to the impossibility of removing the risk of seeding the disease to other regions. Our study also highlights the importance of developing more realistic models of behavioral changes when a disease outbreak is unfolding.

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Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)

Science ◽

10.1126/science.abb3221 ◽

2020 ◽

Vol 368 (6490) ◽

pp. 489-493 ◽

Cited By ~ 1023

Author(s):

Ruiyun Li ◽

Sen Pei ◽

Bin Chen ◽

Yimeng Song ◽

Tao Zhang ◽

...

Keyword(s):

Bayesian Inference ◽

Transmission Rate ◽

Credible Interval ◽

Metapopulation Model ◽

Mobility Data ◽

Geographic Spread ◽

Travel Restrictions ◽

Rapid Dissemination ◽

Novel Coronavirus ◽

Epidemiological Characteristics

Estimation of the prevalence and contagiousness of undocumented novel coronavirus [severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2)] infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here, we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model, and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV-2, including the fraction of undocumented infections and their contagiousness. We estimate that 86% of all infections were undocumented [95% credible interval (CI): 82–90%] before the 23 January 2020 travel restrictions. The transmission rate of undocumented infections per person was 55% the transmission rate of documented infections (95% CI: 46–62%), yet, because of their greater numbers, undocumented infections were the source of 79% of the documented cases. These findings explain the rapid geographic spread of SARS-CoV-2 and indicate that containment of this virus will be particularly challenging.

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The Impact of Mobility Patterns on the Spread of the COVID-19 in Indonesia

Journal of Information Systems Engineering and Business Intelligence ◽

10.20473/jisebi.7.1.31-41 ◽

2021 ◽

Vol 7 (1) ◽

pp. 31

Author(s):

Syafira Fitri Auliya ◽

Nurcahyani Wulandari

Keyword(s):

Short Term Memory ◽

The Novel ◽

Mobility Patterns ◽

Short Term ◽

Social Distancing ◽

Mobility Data ◽

Rapid Transmission ◽

Long Short Term Memory ◽

Novel Coronavirus ◽

The Impact

Background: The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly across the world and infected millions of people, many of whom died. As part of the response plans, many countries have been attempting to restrict people’s mobility by launching social distancing protocol, including in Indonesia. It is then necessary to identify the campaign’s impact and analyze the influence of mobility patterns on the pandemic’s transmission rate.Objective: Using mobility data from Google and Apple, this research discovers that COVID-19 daily new cases in Indonesia are mostly related to the mobility trends in the previous eight days.Methods: We generate ten-day predictions of COVID-19 daily new cases and Indonesians’ mobility by using Long-Short Term Memory (LSTM) algorithm to provide insight for future implementation of social distancing policies.Results: We found that all eight-mobility categories result in the highest accumulation correlation values between COVID-19 daily new cases and the mobility eight days before. We forecast of the pandemic daily new cases in Indonesia, DKI Jakarta and worldwide (with error on MAPE 6.2% - 9.4%) as well as the mobility trends in Indonesia and DKI Jakarta (with error on MAPE 6.4 - 287.3%).Conclusion: We discover that the driver behind the rapid transmission in Indonesia is the number of visits to retail and recreation, groceries and pharmacies, and parks. In contrast, the mobility to the workplaces negatively correlates with the pandemic spread rate.

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INDEPENDENT ASSOCIATION OF METEOROLOGICAL CHARACTERISTICS WITH INITIAL SPREAD OF COVID-19 IN INDIA

10.1101/2020.07.20.20157784 ◽

2020 ◽

Author(s):

Hemant Kulkarni ◽

Harshwardhan Vinod Khandait ◽

Uday Wasudeorao Narlawar ◽

Pragati G Rathod ◽

Manju Mamtani

Keyword(s):

Wind Speed ◽

Air Temperature ◽

Meteorological Data ◽

Reproduction Rate ◽

The Novel ◽

Total N ◽

Mobility Data ◽

Basic Reproduction Rate ◽

Independent Association ◽

Novel Coronavirus

Whether weather plays a part in the transmissibility of the novel COronaVIrus Disease-19 (COVID-19) is still not established. We tested the hypothesis that meteorological factors (air temperature, relative humidity, air pressure, wind speed and rainfall) are independently associated with transmissibility of COVID-19 quantified using the basic reproduction rate (R0). We used publicly available datasets on daily COVID-19 case counts (total n = 108,308), three-hourly meteorological data and community mobility data over a three-month period. Estimated R0 varied between 1.15-1.28. Mean daily air temperature (inversely) and wind speed (positively) were significantly associated with time dependent R0, but the contribution of countrywide lockdown to variability in R0 was over three times stronger as compared to that of temperature and wind speed combined. Thus, abating temperatures and easing lockdown may concur with increased transmissibility of COVID-19.

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Mobility-Guided Modeling of the COVID-19 Pandemic in Metro Manila

10.1101/2020.05.26.20111617 ◽

2020 ◽

Author(s):

Bernhard Egwolf ◽

O.P. Nicanor Austriaco

Keyword(s):

The Philippines ◽

The Novel ◽

National Capital Region ◽

Viral Spread ◽

Mobility Data ◽

National Capital ◽

Metro Manila ◽

The Government ◽

Novel Coronavirus ◽

The City

ABSTRACTCOVID-19 is a novel respiratory disease first identified in Wuhan, China, that is caused by the novel coronavirus, SARS-CoV-2. To better understand the dynamics of the COVID-19 pandemic in the Philippines, we have used real-time mobility data to modify the DELPHI Epidemiological Model recently developed at M.I.T., and to simulate the pandemic in Metro Manila. We have chosen to focus on the National Capital Region, not only because it is the nation’s demographic heart where over a tenth of the country’s population live, but also because it has been the epidemiological epicenter of the Philippine pandemic. Our UST CoV-2 model suggests that the government-imposed enhanced community quarantine (ECQ) has successfully limited the spread of the pandemic. It is clear that the initial wave of the pandemic is flattening, though suppression of viral spread has been delayed by the local pandemics in the City of Manila and Quezon City. Our data also reveals that replacing the ECQ with a General Community Quarantine (GCQ) will increase the forecasted number of deaths in the nation’s capital unless rigorous tracing and testing can be implemented to prevent a second wave of the pandemic.

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How to Better Manage Tourism on Pandemic-Times? Moving Forward on a Discussion We Should Have Had Before the 2020 Crisis

10.20944/preprints202010.0487.v1 ◽

2020 ◽

Author(s):

Laura Sinay ◽

Maria Cristina Fogliatti de Sinay

Keyword(s):

Literature Review ◽

Research Funding ◽

Tourism Industry ◽

The Novel ◽

Scholarly Community ◽

Pandemic Virus ◽

Funding Agencies ◽

Travel Restrictions ◽

Novel Coronavirus

Taking advantage of tourists’ intensive flow, the SARS-CoV-2 virus rapidly spread causing thousands of deaths globally. Trying to contain the already pandemic virus, government travel restrictions were suddenly imposed. Consequently, the tourism industry, which at that moment employed one in ten workers globally, suddenly collapsed. Hundreds of thousands of workers immediately lost their income. Flights were cancelled, and thousands of tourists were stuck abroad with no means to return to their home countries. The gravity of the situation raised the question of whether there was scholarly knowledge that could have helped manage tourism during the current pandemic. To answer this question, a methodical literature review was performed, allowing for up to 900 publications to be analysed. Keywords used were pandemic, tourism, tourist and travel. Based on this process, 63 publications were selected for further analysis. Among these, less than 5% were focused on the tourism side of the problem. As such, this research concludes that, by the time the novel coronavirus emerged, there was, virtually, no scholarly knowledge on how to manage tourism during pandemic times so as to avoid chaos, and that the scholarly community studying related issues is very small. Moving forward, this article recommends that research funding agencies and universities encourage the sound development of this area of knowledge. Aspects that should be investigated include when, how and by whom should tourism be halted, as well as the feasibility of a Tourism World Fund for supporting related costs.

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Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China

Journal of Clinical Medicine ◽

10.3390/jcm9020571 ◽

2020 ◽

Vol 9 (2) ◽

pp. 571 ◽

Cited By ~ 93

Author(s):

Péter Boldog ◽

Tamás Tekeli ◽

Zsolt Vizi ◽

Attila Dénes ◽

Ferenc A. Bartha ◽

...

Keyword(s):

Control Measure ◽

Mainland China ◽

Destination Country ◽

Control Measures ◽

Cumulative Number ◽

Travel Restrictions ◽

Imported Cases ◽

Major Outbreak ◽

Novel Coronavirus ◽

Local Reproduction

We developed a computational tool to assess the risks of novel coronavirus outbreaks outside of China. We estimate the dependence of the risk of a major outbreak in a country from imported cases on key parameters such as: (i) the evolution of the cumulative number of cases in mainland China outside the closed areas; (ii) the connectivity of the destination country with China, including baseline travel frequencies, the effect of travel restrictions, and the efficacy of entry screening at destination; and (iii) the efficacy of control measures in the destination country (expressed by the local reproduction number R loc ). We found that in countries with low connectivity to China but with relatively high R loc , the most beneficial control measure to reduce the risk of outbreaks is a further reduction in their importation number either by entry screening or travel restrictions. Countries with high connectivity but low R loc benefit the most from policies that further reduce R loc . Countries in the middle should consider a combination of such policies. Risk assessments were illustrated for selected groups of countries from America, Asia, and Europe. We investigated how their risks depend on those parameters, and how the risk is increasing in time as the number of cases in China is growing.

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SARS-CoV-2 and Coronavirus Disease 2019: What We Know So Far

Pathogens ◽

10.3390/pathogens9030231 ◽

2020 ◽

Vol 9 (3) ◽

pp. 231 ◽

Cited By ~ 125

Author(s):

Firas A. Rabi ◽

Mazhar S. Al Zoubi ◽

Ghena A. Kasasbeh ◽

Dunia M. Salameh ◽

Amjad D. Al-Nasser

Keyword(s):

Vaccine Development ◽

Current Knowledge ◽

Treatment Options ◽

The Novel ◽

Clinical Criteria ◽

Health Authorities ◽

Close Relationship ◽

Public Health Authorities ◽

Molecular Information ◽

Novel Coronavirus

In December 2019, a cluster of fatal pneumonia cases presented in Wuhan, China. They were caused by a previously unknown coronavirus. All patients had been associated with the Wuhan Wholefood market, where seafood and live animals are sold. The virus spread rapidly and public health authorities in China initiated a containment effort. However, by that time, travelers had carried the virus to many countries, sparking memories of the previous coronavirus epidemics, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), and causing widespread media attention and panic. Based on clinical criteria and available serological and molecular information, the new disease was called coronavirus disease of 2019 (COVID-19), and the novel coronavirus was called SARS Coronavirus-2 (SARS-CoV-2), emphasizing its close relationship to the 2002 SARS virus (SARS-CoV). The scientific community raced to uncover the origin of the virus, understand the pathogenesis of the disease, develop treatment options, define the risk factors, and work on vaccine development. Here we present a summary of current knowledge regarding the novel coronavirus and the disease it causes.

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Modeling the Novel Coronavirus (SARS-CoV-2) Outbreak in Sicily, Italy

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17144964 ◽

2020 ◽

Vol 17 (14) ◽

pp. 4964 ◽

Cited By ~ 5

Author(s):

Andrea Maugeri ◽

Martina Barchitta ◽

Sebastiano Battiato ◽

Antonella Agodi

Keyword(s):

Transmission Rate ◽

Control Measures ◽

Transmission Dynamics ◽

The Novel ◽

Epidemic Spread ◽

Transmission Rates ◽

Contact Precautions ◽

Travel Restrictions ◽

Novel Coronavirus ◽

Reported Data

Italy was the first country in Europe which imposed control measures of travel restrictions, quarantine and contact precautions to tackle the epidemic spread of the novel coronavirus (SARS-CoV-2) in all its regions. While such efforts are still ongoing, uncertainties regarding SARS-CoV-2 transmissibility and ascertainment of cases make it difficult to evaluate the effectiveness of restrictions. Here, we employed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model to assess SARS-CoV-2 transmission dynamics, working on the number of reported patients in intensive care unit (ICU) and deaths in Sicily (Italy), from 24 February to 13 April. Overall, we obtained a good fit between estimated and reported data, with a fraction of unreported SARS-CoV-2 cases (18.4%; 95%CI = 0–34.0%) before 10 March lockdown. Interestingly, we estimated that transmission rate in the community was reduced by 32% (95%CI = 23–42%) after the first set of restrictions, and by 80% (95%CI = 70–89%) after those adopted on 23 March. Thus, our estimates delineated the characteristics of SARS-CoV2 epidemic before restrictions taking into account unreported data. Moreover, our findings suggested that transmission rates were reduced after the adoption of control measures. However, we cannot evaluate whether part of this reduction might be attributable to other unmeasured factors, and hence further research and more accurate data are needed to understand the extent to which restrictions contributed to the epidemic control.

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How far Covid19 virus spread can be curbed by relaxing lockdown in different stages? -A study in Indian scenario

10.1101/2020.06.11.20129023 ◽

2020 ◽

Author(s):

Arindom Chakraborty ◽

Kalyan Das

Keyword(s):

Simulated Data ◽

The Novel ◽

Short Term ◽

Time Points ◽

Term Forecast ◽

Novel Coronavirus ◽

And Control ◽

Near Future ◽

Different Levels ◽

Indian Scenario

ABSTRACTAfter the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has rapidly spread out to other provinces, neighboring countries and finally has become a global terror. It is indeed a matter of serious concern to study the transmission dynamics of this virus. The potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity can be well understood by the unknown basic reproduction number. A stochastic model can be used to estimate this number with possible safeguard on uncertainties. It is essential to assess how the expensive, resource-intensive measures can contribute to the prevention and control of the 2019-nCoV infection and how long they should be maintained. A short-term forecast of incidences are often of high priority. The challenge is to forecast unseen “future” simulated data for three different scenarios at some time points. We estimate current levels of transmissibility, over variable time points under different levels of interventions and use that to forecast near-future incidence. The forecasted values of incidence can be used for determining the near future mortality also.

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Emergence of the first manic episode in recovered COVID-19 patients: A case series from Egypt

10.21203/rs.3.rs-964024/v1 ◽

2021 ◽

Author(s):

Hayam M. Elgohary ◽

Mohammad G. Sehlo ◽

Usama M. Youssef ◽

Mohamed Abdelghani

Keyword(s):

Neuropsychiatric Symptoms ◽

Gastrointestinal Symptoms ◽

Case Series ◽

Manic Episode ◽

Behavioral Changes ◽

Future Research ◽

The Novel ◽

Manic Symptoms ◽

Global Pandemic ◽

Novel Coronavirus

Abstract Objective In December 2019, the novel coronavirus (COVID-19) infection was first reported in Wuhan city, China, which had rapidly spread as a global pandemic. This infection was commonly presented by respiratory and /or gastrointestinal symptoms. However, it is still unclear whether COVID-19 infection could be associated with central nervous system (CNS) damage which would result in development of neuropsychiatric symptoms. Method A total of five cases of suddenly emerged manic episodes during the pandemic of COVID-19 were extensively described. We presented the symptoms and described the diagnosis, clinical course, and treatment of each case. Results All patients had positive findings of ribonucleic acid (RNA) tests for COVID-19 in specimens of their sputum. The patients later developed manic symptoms during and after the recovering period of their illness. Conclusions The case series of newly emerged manic symptoms associated with COVID-19 infection highlights the essential need for evaluation of mental health status and would contribute to our understanding of the potential risk of CNS affection by COVID-19 infection. The limited number of cases would limit the generalizability of association. Future research should investigate the behavioral changes accompanying and following COVID-19 infection.

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