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

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

10.20944/preprints202004.0267.v1 ◽

2020 ◽

Author(s):

Andrea Maugeri ◽

Martina Barchitta ◽

Sebastiano Battiato ◽

Antonella Agodi

Keyword(s):

Transmission Rate ◽

Control Measures ◽

Transmission Dynamics ◽

The Novel ◽

Epidemic Spread ◽

Contact Precautions ◽

Its Regions ◽

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 small fraction of unreported SARS-CoV-2 cases (19.5%; 95%CI=0%-34.7%) before 10 March lockdown. Interestingly, we estimated that the first set of restrictions reduced transmission rate in the community by 42% (95%CI=38%-46%), and that more stringent measures adopted on 23 March succeeded to drastically curb the transmission rate by 84% (95%CI=80%-88%). Thus, our estimates delineated the characteristics of SARS-CoV2 epidemic before restrictions taking into account unreported data. Further modeling after the adoption of control measures, moreover, indicated that restrictions reduced SARS-CoV2 transmission considerably.

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Early dynamics of transmission and control of COVID-19: a mathematical modelling study

10.1101/2020.01.31.20019901 ◽

2020 ◽

Cited By ~ 184

Author(s):

Adam J Kucharski ◽

Timothy W Russell ◽

Charlie Diamond ◽

Yang Liu ◽

John Edmunds ◽

...

Keyword(s):

Reproduction Number ◽

Control Measures ◽

Transmission Model ◽

The Novel ◽

Transmission Potential ◽

Travel Restrictions ◽

Gates Foundation ◽

Novel Coronavirus ◽

And Control ◽

Over Time

AbstractBackgroundAn outbreak of the novel coronavirus SARS-CoV-2 has led to 46,997 confirmed cases as of 13th February 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas.MethodsWe combined a stochastic transmission model with data on cases of novel coronavirus disease (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January and February 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas.FindingsWe estimated that the median daily reproduction number, Rt, declined from 2.35 (95% CI: 1.15-4.77) one week before travel restrictions were introduced on 23rd January to 1.05 (95% CI: 0.413-2.39) one week after. Based on our estimates of Rt,we calculated that in locations with similar transmission potential as Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population.InterpretationOur results show that COVID-19 transmission likely declined in Wuhan during late January 2020, coinciding with the introduction of control measures. As more cases arrive in international locations with similar transmission potential to Wuhan pre-control, it is likely many chains of transmission will fail to establish initially, but may still cause new outbreaks eventually.FundingWellcome Trust (206250/Z/17/Z, 210758/Z/18/Z), HDR UK (MR/S003975/1), Gates Foundation (INV-003174), NIHR (16/137/109)

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The lockdown of Hubei Province causing different transmission dynamics of the novel coronavirus (2019-nCoV) in Wuhan and Beijing

10.1101/2020.02.09.20021477 ◽

2020 ◽

Cited By ~ 18

Author(s):

Xinhai Li ◽

Xumao Zhao ◽

Yuehua Sun

Keyword(s):

Virus Transmission ◽

Control Policy ◽

Hubei Province ◽

Control Measures ◽

Capital City ◽

Transmission Dynamics ◽

The Novel ◽

Real Situation ◽

Seir Model ◽

Novel Coronavirus

AbstractBackgroundAfter the outbreak of novel coronavirus (2019-nCoV) starting in late 2019, a number of researchers have reported the predicted the virus transmission dynamics. However, under the strict control policy the novel coronavirus does not spread naturally outside Hubei Province, and none of the prediction closes to the real situation.Methods and findingsWe used the traditional SEIR model, fully estimated the effect of control measures, to predict the virus transmission in Wuhan, the capital city of Hubei Province, and Beijing. We forecast that the outbreak of 2019-nCoV would reach its peak around March 6±10 in Wuhan and March 20±16 in Beijing, respectively. The infectious population in Beijing would be much less (only 0.3%) than those in Wuhan at the peak of this transmission wave. The number of confirmed cases in cities inside Hubei Province grow exponentially, whereas those in cities outside the province increase linearly.ConclusionsThe unprecedented province lockdown substantially suspends the national and global outbreak of 2019-nCoV.

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Coronavirus: Towards controlling of the pandemic - Indian scenario

International Journal of Research in Pharmaceutical Sciences ◽

10.26452/ijrps.v11ispl1.2813 ◽

2020 ◽

Vol 11 (SPL1) ◽

pp. 462-468

Author(s):

Latika kothari ◽

Sanskruti Wadatkar ◽

Roshni Taori ◽

Pavan Bajaj ◽

Diksha Agrawal

Keyword(s):

Control Measures ◽

The Novel ◽

Medical Treatments ◽

Health Crisis ◽

The Public ◽

Infection Control Measures ◽

The Social ◽

Asking Questions ◽

The Government ◽

Novel Coronavirus

Coronavirus disease 2019 (COVID-19) is a communicable infection caused by the novel coronavirus resulting in severe acute respiratory syndrome coronavirus 2 (SARS-CoV). It was recognized to be a health crisis for the general population of international concern on 30th January 2020 and conceded as a pandemic on 11th March 2020. India is taking various measures to fight this invisible enemy by adopting different strategies and policies. To stop the COVID-19 from spreading, the Home Affairs Ministry and the health ministry, of India, has issued the nCoV 19 guidelines on travel. Screening for COVID-19 by asking questions about any symptoms, recent travel history, and exposure. India has been trying to get testing kits available. The government of India has enforced various laws like the social distancing, Janata curfew, strict lockdowns, screening door to door to control the spread of novel coronavirus. In this pandemic, innovative medical treatments are being explored, and a proper vaccine is being hunted to deal with the situation. Infection control measures are necessary to prevent the virus from further spreading and to help control the current situation. Thus, this review illustrates and explains the criteria provided by the government of India to the awareness of the public to prevent the spread of COVID-19.

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Evaluating the effectiveness of measures to control the novel coronavirus disease 2019 in Jilin Province, China

BMC Infectious Diseases ◽

10.1186/s12879-021-05936-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Qinglong Zhao ◽

Yao Wang ◽

Meng Yang ◽

Meina Li ◽

Zeyu Zhao ◽

...

Keyword(s):

Prevention And Control ◽

Good Control ◽

Asymptomatic Infection ◽

Jilin Province ◽

Control Measures ◽

First Case ◽

Before And After ◽

Novel Coronavirus ◽

Reported Data ◽

And Control

Abstract Background Based on differences in populations and prevention and control measures, the spread of new coronary pneumonia in different countries and regions also differs. This study aimed to calculate the transmissibility of coronavirus disease 2019 (COVID-19), and to evaluate the effectiveness of measures to control the disease in Jilin Province, China. Methods The data of reported COVID-19 cases were collected, including imported and local cases from Jilin Province as of March 14, 2019. A Susceptible–Exposed–Infectious–Asymptomatic–Recovered/Removed (SEIAR) model was developed to fit the data, and the effective reproduction number (Reff) was calculated at different stages in the province. Finally, the effectiveness of the measures was assessed. Results A total of 97 COVID-19 infections were reported in Jilin Province, among which 45 were imported infections (including one asymptomatic infection) and 52 were local infections (including three asymptomatic infections). The model fit the reported data well (R2 = 0.593, P < 0.001). The Reff of COVID-19 before and after February 1, 2020 was 1.64 and 0.05, respectively. Without the intervention taken on February 1, 2020, the predicted cases would have reached a peak of 177,011 on October 22, 2020 (284 days from the first case). The projected number of cases until the end of the outbreak (on October 9, 2021) would have been 17,129,367, with a total attack rate of 63.66%. Based on the comparison between the predicted incidence of the model and the actual incidence, the comprehensive intervention measures implemented in Jilin Province on February 1 reduced the incidence of cases by 99.99%. Therefore, according to the current measures and implementation efforts, Jilin Province can achieve good control of the virus’s spread. Conclusions COVID-19 has a moderate transmissibility in Jilin Province, China. The interventions implemented in the province had proven effective; increasing social distancing and a rapid response by the prevention and control system will help control the spread of the disease.

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Government management capacities and the containment of COVID-19: a repeated cross-sectional study across Chinese cities

BMJ Open ◽

10.1136/bmjopen-2020-041516 ◽

2021 ◽

Vol 11 (4) ◽

pp. e041516

Author(s):

Wenchao Li ◽

Jing Li ◽

Junjian Yi

Keyword(s):

Outcome Measures ◽

Cross Sectional Study ◽

Control Measures ◽

Secondary Outcome ◽

Cross Sectional ◽

Travel Restrictions ◽

Government Management ◽

The Government ◽

Laws And Policies ◽

Novel Coronavirus

ObjectivesBetter understanding of the dynamics of the COVID-19 (2019 novel coronavirus disease) pandemic to curb its spread is now a global imperative. While travel restrictions and control measures have been shown to limit the spread of the disease, the effectiveness of the enforcement of those measures should depend on the strength of the government. Whether, and how, the government plays a role in fighting the disease, however, has not been investigated. Here, we show that government management capacities are critical to the containment of the disease.SettingWe conducted a statistical analysis based on cross-city comparisons within China. China has undergone almost the entire cycle of the anticoronavirus campaign, which allows us to trace the full dynamics of the outbreak, with homogeneity in standards for statistics recording.Primary and secondary outcome measuresOutcome measures include city-specific COVID-19 case incidence and recoveries in China.ResultsThe containment of COVID-19 depends on the effectiveness of the enforcement of control measures, which in turn depends on the local government’s management capacities. Specifically, government efficiency, capacity for law enforcement, and the transparency of laws and policies significantly reduce COVID-19 prevalence and increase the likelihood of recoveries. The organisation size of the government, which is not closely related to its capacity for management, has a limited role.

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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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Potential impact of seasonal forcing on a SARS-CoV-2 pandemic

10.1101/2020.02.13.20022806 ◽

2020 ◽

Cited By ~ 10

Author(s):

Richard A. Neher ◽

Robert Dyrdak ◽

Valentin Druelle ◽

Emma B. Hodcroft ◽

Jan Albert

Keyword(s):

Seasonal Variation ◽

Infection Control ◽

Transmission Rate ◽

Health Care Systems ◽

Control Measures ◽

Rate Variation ◽

Small Peak ◽

Infection Control Measures ◽

Care Systems ◽

Novel Coronavirus

A novel coronavirus (SARS-CoV-2) first detected in Wuhan, China, has spread rapidly since December 2019, causing more than 80,000 confirmed infections and 2,700 fatalities (as of Feb 27, 2020). Imported cases and transmission clusters of various sizes have been reported globally suggesting a pandemic is likely.Here, we explore how seasonal variation in transmissibility could modulate a SARS-CoV-2 pandemic. Data from routine diagnostics show a strong and consistent seasonal variation of the four endemic coronaviruses (229E, HKU1, NL63, OC43) and we parameterize our model for SARS-CoV-2 using these data. The model allows for many subpopulations of different size with variable parameters. Simulations of different scenarios show that plausible parameters result in a small peak in early 2020 in temperate regions of the Northern Hemisphere and a larger peak in winter 2020/2021. Variation in transmission and migration rates can result in substantial variation in prevalence between regions.While the uncertainty in parameters is large, the scenarios we explore show that transient reductions in the incidence rate might be due to a combination of seasonal variation and infection control efforts but do not necessarily mean the epidemic is contained. Seasonal forcing on SARS-CoV-2 should thus be taken into account in the further monitoring of the global transmission. The likely aggregated effect of seasonal variation, infection control measures, and transmission rate variation is a prolonged pandemic wave with lower prevalence at any given time, thereby providing a window of opportunity for better preparation of health care systems.

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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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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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