scholarly journals Who Should Get Vaccinated First? An effective Network Information-driven Priority Vaccination Strategy

2021 ◽  
Author(s):  
Dong Liu ◽  
Chi Kong Tse ◽  
Rosa H. M. Chan ◽  
Choujun Zhan
Keyword(s):  
Large Scale ◽  
Early Stage ◽  
Vaccination Strategy ◽  
Cumulative Number ◽  
The Novel ◽  
Vaccination Programs ◽  
Network Information ◽  
Network Properties ◽  
Effective Network ◽  
Novel Coronavirus

Abstract Approval of emergency use of the Novel Coronavirus Disease 2019 (COVID-19) vaccines in many countries has brought hope to ending the COVID-19 pandemic sooner. Considering the limited vaccine supply in the early stage of COVID-19 vaccination programs in most countries, a highly relevant question to ask is: who should get vaccinated first? In this article we propose a network information- driven vaccination strategy where a small number of people in a network (population) are categorized, according to a few key network properties, into priority groups. Using a network-based SEIR model for simulating the pandemic progression, the network information-driven vaccination strategy is compared with a random vaccination strategy. Results for both large-scale synthesized networks and real social networks have demonstrated that the network information-driven vaccination strategy can significantly reduce the cumulative number of infected individuals and lead to a more rapid containment of the pandemic. The results provide insight for policymakers in designing an effective early-stage vaccination plan.

scholarly journals Who should get vaccinated first? An effective network information-driven priority vaccination strategy

2021 ◽  
Author(s):  
Dong Liu ◽  
Chi K. Tse ◽  
Rosa Ho Man Chan ◽  
Choujun Zhan
Keyword(s):  
Large Scale ◽  
Early Stage ◽  
Vaccination Strategy ◽  
Cumulative Number ◽  
The Novel ◽  
Vaccination Programs ◽  
Network Information ◽  
Network Properties ◽  
Effective Network ◽  
Novel Coronavirus

Approval of emergency use of the Novel Coronavirus Disease 2019 (COVID-19) vaccines in many countries has brought hope to ending the COVID-19 pandemic sooner. Considering the limited vaccine supply in the early stage of COVID-19 vaccination programs in most countries, a highly relevant question to ask is: who should get vaccinated first? In this article we propose a network information-driven vaccination strategy where a small number of people in a network (population) are categorized, according to a few key network properties, into priority groups. Using a network-based SEIR model for simulating the pandemic progression, the network information-driven vaccination strategy is compared with a random vaccination strategy. Results for both large-scale synthesized networks and real social networks have demonstrated that the network information-driven vaccination strategy can significantly reduce the cumulative number of infected individuals and lead to a more rapid containment of the pandemic. The results provide insight for policymakers in designing an effective early-stage vaccination plan.

scholarly journals The Impact of Armed Conflict on the Epidemiological Situation of COVID-19 in Libya, Syria and Yemen

2021 ◽  
Vol 9 ◽  
Author(s):  
Mohamed A. Daw
Keyword(s):  
Armed Conflict ◽  
Early Stage ◽  
Armed Conflicts ◽  
Cumulative Number ◽  
The Novel ◽  
Global Pandemic ◽  
Large Numbers ◽  
Public Data ◽  
Novel Coronavirus ◽  
The Impact

Background: Since the Arab uprising in 2011, Libya, Syria and Yemen have gone through major internal armed conflicts. This resulted in large numbers of deaths, injuries, and population displacements, with collapse of the healthcare systems. Furthermore, the situation was complicated by the emergence of COVID-19 as a global pandemic, which made the populations of these countries struggle under unusual conditions to deal with both the pandemic and the ongoing wars. This study aimed to determine the impact of the armed conflicts on the epidemiology of the novel coronavirus (SARS-CoV-2) within these war-torn countries and highlight the strategies needed to combat the spread of the pandemic and its consequences.Methods: Official and public data concerning the dynamics of the armed conflicts and the spread of SARS-COV-2 in Libya, Syria and Yemen were collected from all available sources, starting from the emergence of COVID-19 in each country until the end of December 2020. Datasets were analyzed by a set of statistical techniques and the weekly resolved data were used to probe the link between the intensity levels of the conflict and the prevalence of COVID-19.Results: The data indicated that there was an increase in the intensity of the violence at an early stage from March to August 2020, when it approximately doubled in the three countries, particularly in Libya. During that period, few cases of COVID-19 were reported, ranging from 5 to 53 cases/day. From September to December 2020, a significant decline in the intensity of the armed conflicts was accompanied by steep upsurges in the rate of COVID-19 cases, which reached up to 500 cases/day. The accumulative cases vary from one country to another during the armed conflict. The highest cumulative number of cases were reported in Libya, Syria and Yemen.Conclusions: Our analysis demonstrates that the armed conflict provided an opportunity for SARS-CoV-2 to spread. The early weeks of the pandemic coincided with the most intense period of the armed conflicts, and few cases were officially reported. This indicates undercounting and hidden spread during the early stage of the pandemic. The pandemic then spread dramatically as the armed conflict declined, reaching its greatest spread by December 2020. Full-blown transmission of the COVID-19 pandemic in these countries is expected. Therefore, urgent national and international strategies should be implemented to combat the pandemic and its consequences.

scholarly journals Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 126
Author(s):  
Hai-Feng Ling ◽  
Zheng-Lian Su ◽  
Xun-Lin Jiang ◽  
Yu-Jun Zheng
Keyword(s):  
Prevention And Control ◽  
Large Scale ◽  
Medical Services ◽  
The Novel ◽  
Multi Objective ◽  
Medical Supplies ◽  
Problem Instances ◽  
Novel Coronavirus ◽  
And Control ◽  
Minimum Ratio

In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these demands. Resources from military medical services, which are normally reserved for military use, can be an effective supplement to these demands. In this paper, we formulate a problem of integrated civilian-military scheduling of medical supplies for epidemic prevention and control, the aim of which is to simultaneously maximize the overall satisfaction rate of the medical supplies and minimize the total scheduling cost, while keeping a minimum ratio of medical supplies reservation for military use. We propose a multi-objective water wave optimization (WWO) algorithm in order to efficiently solve this problem. Computational results on a set of problem instances constructed based on real COVID-19 data demonstrate the effectiveness of the proposed method.

scholarly journals The basic reproduction number and prediction of the epidemic size of the novel coronavirus (COVID-19) in Shahroud, Iran

2020 ◽  
Vol 148 ◽  
Author(s):  
A. Khosravi ◽  
R. Chaman ◽  
M. Rohani-Rasaf ◽  
F. Zare ◽  
S. Mehravaran ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Early Stage ◽  
Screening Tests ◽  
The Novel ◽  
Epidemic Size ◽  
Serial Interval ◽  
Novel Coronavirus ◽  
Interval Distribution ◽  
The Basic Reproduction Number

Abstract The aim of this study was to estimate the basic reproduction number (R0) of COVID-19 in the early stage of the epidemic and predict the expected number of new cases in Shahroud in Northeastern Iran. The R0 of COVID-19 was estimated using the serial interval distribution and the number of incidence cases. The 30-day probable incidence and cumulative incidence were predicted using the assumption that daily incidence follows a Poisson distribution determined by daily infectiousness. Data analysis was done using ‘earlyR’ and ‘projections’ packages in R software. The maximum-likelihood value of R0 was 2.7 (95% confidence interval (CI): 2.1−3.4) for the COVID-19 epidemic in the early 14 days and decreased to 1.13 (95% CI 1.03–1.25) by the end of day 42. The expected average number of new cases in Shahroud was 9.0 ± 3.8 cases/day, which means an estimated total of 271 (95% CI: 178–383) new cases for the period between 02 April to 03 May 2020. By day 67 (27 April), the effective reproduction number (Rt), which had a descending trend and was around 1, reduced to 0.70. Based on the Rt for the last 21 days (days 46–67 of the epidemic), the prediction for 27 April to 26 May is a mean daily cases of 2.9 ± 2.0 with 87 (48–136) new cases. In order to maintain R below 1, we strongly recommend enforcing and continuing the current preventive measures, restricting travel and providing screening tests for a larger proportion of the population.

scholarly journals Situation analysis of novel Coronavirus (2019-nCoV) cases in Nepal

2020 ◽  
Vol 1 (1) ◽  
pp. 9-14
Author(s):  
Kiran Paudel ◽  
Prashamsa Bhandari ◽  
Yadav Prasad Joshi
Keyword(s):  
South Asian ◽  
Case Fatality ◽  
Cumulative Number ◽  
The Novel ◽  
Asian Countries ◽  
Primary Case ◽  
The Status ◽  
Exponential Trend ◽  
Using Data ◽  
Novel Coronavirus

The Novel Coronavirus (2019-nCoV) is currently a major threat to global health in an unprecedented manner. The global pandemic of COVID-19 has affected 215 countries and territories including Nepal. Until 1st June 2020, altogether 1,811 COVID-19 positive cases were diagnosed using RT-PCR. This study aimed to analyze the status of COVID-19 cases in Nepal and South Asian countries. A retrospective study from 23rd January to 1st June 2020 was conducted using data of the Ministry of Home Affairs, Nepal and Worldometer homepages. The primary case records during the pre and post lockdown periods were examined. Spatial distribution was observed. An exponential trend line was plotted and COVID-19 situation in South Asian countries was assessed. Of 1,811 COVID-19 cases, the highest number (38.3%) was reported in Province 2. Out of 77 districts, 59 were affected. In Fifty-eight districts, primary cases appeared during the lockdown period. The cumulative number of COVID-19 cases showed the exponential pattern of distribution in Nepal. In South Asian countries, India had the highest number of cases and case fatality rate (CFR). There were no cases of CFR in Bhutan. The Novel Coronavirus emergence in Nepal has become a serious challenge to the various sectors including public health. The emergence of primary cases even in the lockdown period needs a detailed study in the future.

scholarly journals Pooling of samples to optimize SARS-CoV-2 diagnosis by RT-qPCR: comparative analysis of two protocols.

2020 ◽  
Author(s):  
Fabiana Volpato ◽  
Daiana Lima-Morales ◽  
Priscila Lamb Wink ◽  
Julia Willig ◽  
Fernanda de-Paris ◽  
...  
Keyword(s):  
Large Scale ◽  
Diagnostic Yield ◽  
Rna Extraction ◽  
Clinical Samples ◽  
The Novel ◽  
Positive Individual ◽  
Sample Pooling ◽  
Novel Coronavirus ◽  
The Individual ◽  
Pooled Samples

RT-qPCR for SARS-CoV-2 is the main diagnostic test used to identify the novel coronavirus. Several countries have used large scale SARS-CoV-2 RT-qPCR testing as one of the important strategies for combating the pandemic. In order to process the massive needs for coronavirus testing, the usual throughput of routine clinical laboratories has reached and often surpassed its limits and new approaches to cope with this challenge must be developed. This study has aimed to evaluate the use pool of samples as a strategy to optimize the diagnostic of SARS-CoV-2 by RT-qPCR in a general population. A total of 220 naso/orofaryngeal swab samples were collected and tested using two different protocols of sample pooling. In the first protocol (Protocol A); 10 clinical samples were pooled before RNA extraction. The second protocol (Protocol B) consisted of pooling the already extracted RNAs from 10 individual samples. Results from Protocol A were identical (100% agreement) with the individual results. However, for results from Protocol B, reduced agreement (91%) was observed in relation to results obtained by individual testing. Inconsistencies observed were related to RT-qPCR results with higher Cycle Thresholds (Ct > 32.73). Furthermore, in pools containing more than one positive individual, the Ct of the pool was equivalent to the lowest Ct among the individual results. These results provide additional evidence in favor of the clinical use of pooled samples for SARS-CoV-2 diagnosis by RT-qPCR and suggest that pooling of samples before RNA extraction is preferrable in terms of diagnostic yield.

scholarly journals Understanding the Anticontagion Process and Reopening of China during COVID-19 via Coevolution Network of Epidemic and Awareness

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lingbo Li ◽  
Ying Fan ◽  
An Zeng ◽  
Zengru Di
Keyword(s):  
Large Scale ◽  
The Novel ◽  
Large Scale Data ◽  
Effective Interventions ◽  
Human Movements ◽  
Simulation Based ◽  
The World ◽  
Novel Coronavirus ◽  
Prevention Methods ◽  
The Impact

The novel coronavirus (COVID-19) pandemic is intensifying all over the world, but some countries, including China, have developed extensive and successful experience in controlling this pandemic. In this context, some questions arise naturally: What can countries caught up in the epidemic learn from China’s experience? In regions where the outbreak is under control, what would lead to a resurgence of the epidemic? To address these issues, we investigate China’s experience in anticontagion interventions and reopening process, focusing on the coevolution of epidemic and awareness during COVID-19 outbreak. Through an empirical analysis based on large-scale data and simulation based on a metapopulation and multilayer network model, we ascertain the impact of human movements and awareness diffusion on the epidemic, elucidate the inherent patterns and effective interventions of different epidemic prevention methods, and highlight the crunch time of each measure. The results are also employed to analyze COVID-19 evolution in other countries so as to find unified rules in complex situations around the world and provide advice on anticontagion and reopening policies. Our findings explain some key mechanisms of epidemic prevention and may help the epidemic analysis and decision-making in various countries suffering from COVID-19.

scholarly journals Abdominal symptoms as an initial manifestation of COVID-19 infection: Report of two cases

2020 ◽  
Author(s):  
PATRÍCIA YOKOO ◽  
Eduardo Kaiser Ururahy Nunes Fonseca ◽  
Marcelo Oranges Filho ◽  
Rodrigo Caruso Chate ◽  
Gilberto Szarf ◽  
...  
Keyword(s):  
Emergency Department ◽  
Respiratory Symptoms ◽  
Early Stage ◽  
The Novel ◽  
Initial Finding ◽  
Initial Manifestation ◽  
Abdominal Symptoms ◽  
Novel Coronavirus ◽  
Lung Base

Abstract The novel coronavirus (COVID-19) pandemic started in December 2019 in Wuhan (Hubei, China) and spread rapidly; therefore, it is essential to detect the disease at an early stage and immediately isolate the infected patients [1]. The most common symptoms of COVID-19 infection include fever, asthenia, cough and dyspnea [2]. However, some patients are asymptomatic from the respiratory symptoms, and may only present abdominal manifestations as an initial finding, what creates a diagnostic challenge.We describe two cases with diagnostic confirmations of COVID-19 who showed up at the Emergency Department with abdominal symptoms before presenting respiratory manifestations, and who had their initial suspicion based on the findings of the thoracoabdominal transition, demonstrating the importance of an adequate assessment of the lung base images.

scholarly journals How lethal is the novel coronavirus, and how many undetected cases there are? The importance of being tested

2020 ◽  
Author(s):  
Ugo Bastolla
Keyword(s):  
Death Rate ◽  
Cumulative Number ◽  
The Novel ◽  
Regularized Regression ◽  
Epidemiological Models ◽  
Infinite Time ◽  
Death Rates ◽  
Apparent Death ◽  
Minimum Number ◽  
Novel Coronavirus

AbstractThere is big concern for estimating the lethality and the extent of undetected infections associated with the novel coronavirus SARS-CoV2 outbreak. While detailed epidemiological models are certainly needed, I suggest here an orthogonal approach based on a minimum number of parameters robustly fitted from the cumulative data easily accessible for all countries at the John Hopkins University database that became the worldwide reference for the pandemics. I show that, after few days from the beginning of the outbreak, the apparent death rate can be extrapolated to infinite time through regularized regression such as rescaled ridge regression. The variation from country to country of these extrapolated death rates appears to depend almost only (r2 = 0.91) on the ratio between performed tests and detected cases even when the apparent instantaneous lethality rates are as different as 9% in Italy and 0.4% in Germany. Extrapolating to the limit of infinite number of tests, I obtain a death rate of 0.012 ± 0.012, in agreement with other estimates. The inverse relationship between the extrapolated death rate and the intensity tests allows estimating that more than 50% of cases were undetected in most countries, with more than 90% undetected cases in countries severely hit by the epidemics such as Italy. Finally, I propose to adopt the ratio between the cumulative number of recovered and deceased persons as an indicator that can anticipate the halting of the epidemics.

scholarly journals Pharmacotherapy for COVID-19: A Ray of Hope

2021 ◽  
Author(s):  
Mayank Kapoor ◽  
Prasan Kumar Panda ◽  
Vivek Mohanty
Keyword(s):  
Large Scale ◽  
Kinase Inhibitors ◽  
Viral Infections ◽  
Treatment Options ◽  
Interferon Alfa ◽  
The Novel ◽  
Plasma Therapy ◽  
Therapeutic Drugs ◽  
Novel Coronavirus ◽  
Multiple Drugs

Most viral infections have limited treatment options available and the same holds for COVID-19, its causative agent being the SARS-CoV-2 virus. Drugs used in the past against Severe Acute Respiratory Syndrome (SARS) or Middle East Respiratory Syndrome (MERS) viruses, which belong to the same family of viruses as the novel Coronavirus included ribavirin, interferon (alfa and beta), lopinavir-ritonavir combination, and corticosteroids. There remains controversy regarding their efficacy to date, except for the last one. Hence, large-scale multicentric trials are being conducted involving multiple drugs. Chloroquine and hydroxy-chloroquine were initially taking the race ahead but have now been rejected. Remdesivir was a promising candidate, for which the FDA had issued an emergency use authorization, but now is not recommended by the WHO. Convalescent plasma therapy had promising results in the early severe viremia phase, but the PLACID trial made an obscure end. Only corticosteroids have shown demonstrable benefits in improving mortality rates among severe COVID-19 cases. Many new modalities like monoclonal antibodies and tyrosine kinase inhibitors are discussed. In this chapter, we review the therapeutic drugs under investigation for the COVID-19 treatment, their mode of action, degree of effectiveness, and recommendations by different centers regarding their use in current settings.

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