scholarly journals Predication of Pandemic COVID-19 situation in Maharashtra, India

2020 ◽  
Author(s):  
Sunny Kumar
Keyword(s):  
Public Health ◽  
Early Stage ◽  
Sir Model ◽  
Spreading Rate ◽  
Epidemic Spreading ◽  
Current Spreading ◽  
Infected People ◽  
The World ◽  
Effective Models ◽  
Virus Spreading

AbstractPresently, the world is infected by COVID 19 virus which has created an emergency for public health. For controlling the spreading of the virus, we have to prepare for precaution and futuristic calculation for infection spreading. The coronavirus affects the population of the world including Inia. Here, we are the study the virus spreading rate on the Maharashtra state which is part of India. We are predicting the infected people by the SIR model. SIR model is one of the most effective models which can predict the spreading rate of the virus. We have validated the model with the current spreading rate with this SIR model. This study will help to stop the epidemic spreading because it is in the early stage in the Maharashtra region.

scholarly journals The importance of superspreaders on the spread of the global COVID-19 pandemic

2021 ◽  
Author(s):  
Jaan Kalda ◽  
Mart Ratas ◽  
Taavet Kalda ◽  
Azer Ramazanli ◽  
Heiko Herrmann ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Early Stage ◽  
Control Strategies ◽  
Indirect Evidence ◽  
Spreading Rate ◽  
Rapid Decrease ◽  
Scale Free ◽  
The World ◽  
Virus Spreading ◽  
Key Parameter

Abstract The dynamics of pandemics is most often analyzed using a variation of the SIR (Susceptible-Infected-Recovered) model1, the key parameter of which is the basic reproduction number R0. Some evidences suggest that the contagion-spreading networks are scale-free, with the biggest nodes corresponding to superspreaders2,3. However, current understanding of the scale-free topology of these networks, and of the implications of such topology for the dynamics of pandemics is incomplete. Here we show that the world-wide spreading rate of COVID-19 gives an indirect evidence that the underlying virus-spreading network is scale-free, with the degree distribution exponent close to 2. Furthermore, our results show that the spreading rate of a virus is predominantly controlled by superspreaders who typically get infected and acquire immunity during the initial outbreak stage of the pandemic. Thereby the biggest nodes get immune and hence, removed from the network, resulting in a rapid decrease of the effective reproduction number. These findings are important for understanding the dynamics of pandemics, and for designing optimal virus control strategies. In particular, screening a population for the number of antibodies of a set of viruses can reveal potential superspreaders, the vaccination or isolation of whom can impede a pandemic at its early stage.

scholarly journals Analysis and Prediction for the Spreading of Covid-19 Pandemic in India Using Mathematical Modeling

2020 ◽  
Author(s):  
Nanda Poddar ◽  
Subham Dhar ◽  
Kajal Kumar Mondal ◽  
Gourab Saha
Keyword(s):  
Mathematical Modeling ◽  
Applied Mathematics ◽  
Medical Science ◽  
Spreading Rate ◽  
Living Room ◽  
Daily Lives ◽  
Infected People ◽  
Effective Strategies ◽  
The World ◽  
Novel Coronavirus

In the present time, the biggest problem of the world is the outbreak of novel coronavirus. Novel coronavirus (COVID-19), this one name has become a part of our daily lives over the past few months. Beyond the boundaries of medical science, coronavirus is now the main subject of research in all fields like Applied Mathematics, Economy, Philosophy, Sociology, Politics upto living room. The epidemic has brought unimaginable changes in our traditional habits and daily routines. Thousands of people in our country are fighting with the rest of the world to survive in various new situations. There are different kinds of coronavirus appeared in different times. In this time, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is responsible for the coronavirus disease of 2019 (COVID-19). This virus was first identified towards the end of 2019 in the city of Wuhan in the province of Hubei in China. Within very short duration of time and very fast, it has spread throughout a large part of the world. In this study, the main aim is to investigate the spreading rate, death rate, recovery rate due to corona virus infection and to study the future of the coronavirus in India by using mathematical modeling based on the previous data. Mathematical models, in this situation, are the important tools in recruiting effective strategies to fight this epidemic. India is at high risk of spreading the disease and is facing many losses in socio-economic aspects. With current infection rates and existing levels of personal alertness, the number of infected people in India will increase at least in the next three months. Proper social awareness, maintain of social distance, large rate of testing and separation may break the chain of the Coronavirus-2.

scholarly journals A REVIEW ARTICLE ON: CORONA VIRUS A RAISING GAINT

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Suraj G Malpani ◽  
Shraddha T Nemane ◽  
Vishweshwar M Dharashive ◽  
Nilesh N Shinde ◽  
Sushil S Kore
Keyword(s):  
Public Health ◽  
Early Stage ◽  
Antiviral Treatment ◽  
Genome Structure ◽  
Respiratory Illness ◽  
Review Article ◽  
Public Health Emergency ◽  
Contact Tracing ◽  
Infected People ◽  
Mode Of Transmission

The 2019-nCoV has been identified as the reason of an outbreak of respiratory illness in Wuhan, Hubei Province, China beginning in December 2019. This outbreak had spread to 19 countries with 11,791 confirmed cases, including 213 deaths, as of January 31, 2020. The WHO declared it as a Public Health Emergency of International Concern. This study analyzed and discussed 70 research articles published until January 31, 2020 for a better understanding of the virology, pathogenesis, mode of transmission, classification, genome structure of this virus. Studies thus far have shown origination in link to a seafood market in Wuhan, but specific animal association has not been confirmed. The reported symptoms include fever, cough, fatigue, pneumonia, headache, diarrhea, hemoptysis, and dyspnea. Preventive measures like masks, hand hygiene practices, avoidance of public contact, case detection, contact tracing, and quarantines are being suggested for reducing the transmission. To date, no specific antiviral treatment is proven effective; hence, infected people primarily rely on symptomatic treatment and supportive care. Although these studies had relevance to control a public emergency, more research need to be conducted to provide valid and reliable ways to manage this kind of public health emergency in both short- and long- term. Coronaviruses (CoV) belong to the genus Coronavirus with its high mutation rate in the Corona viridae. The objective of this review article was to have a primary   opinion about the disease mode of transmission, virology in this early stage of COVID-19 outbreak. Keywords: 2019-nCoV, virology, pathogenesis, genome structure

scholarly journals Serious role of non-quarantined COVID-19 patients for random walk simulations

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Nariyuki Nakagiri ◽  
Kazunori Sato ◽  
Yukio Sakisaka ◽  
Kei-ichi Tainaka
Keyword(s):  
Random Walk ◽  
Total Population ◽  
Critical Value ◽  
Sir Model ◽  
Mixed Population ◽  
Total Population Size ◽  
Infected People ◽  
The World ◽  
Pcr Test

AbstractThe infectious disease (COVID-19) causes serious damages and outbreaks. A large number of infected people have been reported in the world. However, such a number only represents those who have been tested; e.g. PCR test. We focus on the infected individuals who are not checked by inspections. The susceptible-infected-recovered (SIR) model is modified: infected people are divided into quarantined (Q) and non-quarantined (N) agents. Since N-agents behave like uninfected people, they can move around in a stochastic simulation. Both theory of well-mixed population and simulation of random-walk reveal that the total population size of Q-agents decrease in spite of increasing the number of tests. Such a paradox appears, when the ratio of Q exceeds a critical value. Random-walk simulations indicate that the infection hardly spreads, if the movement of all people is prohibited ("lockdown"). In this case the infected people are clustered and locally distributed within narrow spots. The similar result can be obtained, even when only non-infected people move around. However, when both N-agents and uninfected people move around, the infection spreads everywhere. Hence, it may be important to promote the inspections even for asymptomatic people, because most of N-agents are mild or asymptomatic.

scholarly journals Analysing and comparing the COVID-19 data: The closed cases of Hubei and South Korea, the dark March in Europe, the beginning of the outbreak in South America

2020 ◽  
Author(s):  
Stefano De Leo ◽  
Gabriel G. Maia ◽  
Leonardo Solidoro
Keyword(s):  
South America ◽  
South Korea ◽  
Early Stage ◽  
Spreading Rate ◽  
European Countries ◽  
Time Interval ◽  
Angular Coefficient ◽  
Infected People ◽  
Time Integral ◽  
Fitting Parameters

The present work is a statistical analysis of the COVID-19 pandemic. As the number of cases worldwide overtakes one million, data reveals closed outbreaks in Hubei and South Korea, with a new slight increase in the number of infected people in the latter. Both of these countries have reached a plateau in the number of Total Confirmed Cases per Million (TCCpM) residents, suggesting a trend to be followed by other affected regions. Using Hubei’s data as a basis of analysis, we have studied the spreading rate of COVID-19 and modelled the epidemic center for 10 European countries. We have also given the final TCCpM curves for Italy and Lombardia. The introduction of the α-factor allows us to analyse the different stages of the outbreak, compare the European countries amongst each other, and, finally, to confront the initial phase of the disease between Europe and South America.MethodsBy dividing the TCCpM curves in multiple sections spanning short time frames we were able to fit each section to a linear model. By pairing then the angular coefficient (α factor) of each section to the total number of confirmed infections at the center of the corresponding time interval, we have analysed how the spreading rate of Covid-19 changes as more people are infected. Also, by modelling the TCCpM curves with an asymmetrical time integral of a Normal Distribution, we were able to study, by fitting progressively larger data ensembles, how the fitting parameters change as more data becomes available.FindingsThe data analysis shows that the spreading rate of COVID-19 increases similarly for all countries in its early stage, but changes as the number of TCCpM in each country grows. Regarding the modelling of the TCCpM curves, we have found that the fitting parameters oscillate with time before reaching constant values. The estimation of such values allows the determination of better parameters for the model, which in turn leads to more trustworthy forecasts on the pandemic development.InterpretationThe analysis of the oscillating fitting parameters allows an early prediction of the TCC, epidemic center and standard deviation of the outbreak. The α factor and the recovered over confirmed cases ratio can be used to understand the pandemic development in each country and to compare the protective measures taken by local authorities and their impact on the spreading of the disease.FundingCNPq (grant number 2018/303911) and Fapesp (grant numebr 2019/06382-9).

scholarly journals COVID-19 Outcomes in Saudi Arabia and the UK: A Tale of Two Kingdoms

2020 ◽  
Author(s):  
Saleh Komies ◽  
Abdulelah M. Aldhahir ◽  
Mater Almehmadi ◽  
Saeed M. Alghamdi ◽  
Ali Alqarni ◽  
...  
Keyword(s):  
Public Health ◽  
Mathematical Model ◽  
Saudi Arabia ◽  
Sir Model ◽  
Logistic Growth ◽  
Mathematical Technique ◽  
The United Kingdom ◽  
The World ◽  
The Uk ◽  
Two Kingdoms

AbstractBackgroundWhile the number of COVID-19 cases and deaths around the world is starting to peak, it is essential to point out how different countries manage the outbreak and how different measures and experience resulted in different outcomes. This study aimed to compare the effect of the measures taken by Saudi Arabia and the United Kingdom (UK) governments on the outcome of the COVID-19 pandemic as predicted by a mathematical model.MethodData on the numbers of cases, deaths and government measures were collected from Saudi’s Ministry of Health and Public Health England. A prediction of the trend of cases, deaths and days to peak was then modelled using the mathematical technique, Exponential Logistic Growth and Susceptible Infectious Recovered (SIR) model. The measures taken by the governments and the predicted outcomes were compared to assess effectiveness.ResultWe found over three months that 22 fast and extreme measures had been taken in Saudi Arabia compared to eight slow and late measures in the UK. This resulted in a decline in numbers of current infected cases per day and mortality in Saudi Arabia compared to the UK. Based on the SIR model, the predicted number of COVID-19 cases in Saudi as of 31st of March was 2,064, while the predicted number of cases was 63012 in the UK. In addition, the pandemic is predicted to peak earlier on the 27th of March in Saudi Arabia compared to the 2nd of May 2020 in the UK. The end of transition phases for Saudi and UK according to the model, were predicted to be on 18th of April and 24th of May, respectively. These numbers relate to early and decisive measures adopted by the Saudi government.ConclusionWe show that early extreme measures, informed by science and guided by experience, helped reduce the spread and related deaths from COVID-19 in Saudi. Actions were taken by Saudi under the national slogan “We are all responsible” resulted in the observed reduced number of current and predicted cases and deaths compared to the UK approach “keep calm and carry on”.

Analysing and comparing the COVID-19 data: The closed cases of Hubei and South Korea, the dark March in Europe, the beginning of the outbreak in South America (Preprint)

2020 ◽  
Author(s):  
Stefano De Leo ◽  
Gabriel Gulak Maia ◽  
Leonardo Solidoro
Keyword(s):  
South America ◽  
South Korea ◽  
Early Stage ◽  
Spreading Rate ◽  
European Countries ◽  
Time Interval ◽  
Angular Coefficient ◽  
Infected People ◽  
Alpha Factor ◽  
Fitting Parameters

BACKGROUND The present work is a statistical analysis of the COVID-19 pandemic. As the number of cases worldwide overtakes one million, data reveals closed outbreaks in Hubei and South Korea, with a new slight increase in the number of infected people in the latter. Both of these countries have reached a plateau in the number of Total Confirmed Cases per Million (TCCpM) residents, suggesting a trend to be followed by other affected regions. OBJECTIVE Using Hubei's data as a basis of analysis, we have studied the spreading rate of COVID-19 and modelled the epidemic center for 10 European countries. We have also given the final TCCpM curves for Italy and Lombardia. The introduction of the $\boldsymbol{\alpha}$-factor allows us to analyse the different stages of the outbreak, compare the European countries amongst each other, and, finally, to confront the initial phase of the disease between Europe and South America. METHODS By dividing the TCCpM curves in multiple sections spanning short time frames we were able to fit each section to a linear model. By pairing then the angular coefficient ( factor) of each section to the total number of confirmed infections at the center of the corresponding time interval, we have analysed how the spreading rate of Covid-19 changes as more people are infected. Also, by modelling the TCCpM curves with an asymmetrical time integral of a Normal Distribution, we were able to study, by fitting progressively larger data ensembles, how the fitting parameters change as more data becomes available. RESULTS The data analysis shows that the spreading rate of COVID-19 increases similarly for all countries in its early stage, but changes as the number of TCCpM in each country grows. Regarding the modelling of the TCCpM curves, we have found that the fitting parameters oscillate with time before reaching constant values. The estimation of such values allows the determination of better parameters for the model, which in turn leads to more trustworthy forecasts on the pandemic development. CONCLUSIONS The analysis of the oscillating fitting parameters allows an early prediction of the TCC, epidemic center and standard deviation of the outbreak. The alpha factor and the recovered over confirmed cases ratio can be used to understand the pandemic development in each country and to compare the protective measures taken by local authorities and their impact on the spreading of the disease. INTERNATIONAL REGISTERED REPORT RR2-doi.org/10.1101/2020.04.06.20055327

scholarly journals COVID-19 emergency measures and the impending authoritarian pandemic

2020 ◽  
Author(s):  
Stephen Thomson ◽  
Eric C Ip
Keyword(s):  
Public Health ◽  
Civil Liberties ◽  
Healthcare Ethics ◽  
Infected People ◽  
Public Health Systems ◽  
Long Run ◽  
Humanitarian Crises ◽  
The World ◽  
Modern Era ◽  
Emergency Measures

ABSTRACT COVID-19 has brought the world grinding to a halt. As of early August 2020, the greatest public health emergency of the century thus far has registered almost 20 million infected people and claimed over 730,000 lives across all inhabited continents, bringing public health systems to their knees, and causing shutdowns of borders and lockdowns of cities, regions, and even nations unprecedented in the modern era. Yet, as this Article demonstrates—with diverse examples drawn from across the world—there are unmistakable regressions into authoritarianism in governmental efforts to contain the virus. Despite the unprecedented nature of this challenge, there is no sound justification for systemic erosion of rights-protective democratic ideals and institutions beyond that which is strictly demanded by the exigencies of the pandemic. A Wuhan-inspired all-or-nothing approach to viral containment sets a dangerous precedent for future pandemics and disasters, with the global copycat response indicating an impending ‘pandemic’ of a different sort, that of authoritarianization. With a gratuitous toll being inflicted on democracy, civil liberties, fundamental freedoms, healthcare ethics, and human dignity, this has the potential to unleash humanitarian crises no less devastating than COVID-19 in the long run.

scholarly journals Controlling the COVID-19 Pandemic in Vietnam: Lessons From a Limited Resource Country

2020 ◽  
Vol 32 (4) ◽  
pp. 161-162 ◽  
Author(s):  
Duc Minh Duong ◽  
Vui Thi Le ◽  
Bui Thi Thu Ha
Keyword(s):  
Public Health ◽  
Case Management ◽  
Early Stage ◽  
Close Contact ◽  
Limited Resource ◽  
Lessons Learned ◽  
Health Measures ◽  
The World ◽  
The Government ◽  
Operation Case

The lessons learned from Vietnam, a country that the world acclaimed for its management of the fight against COVID-19, could stand out as an example of how to do more with less. The Vietnamese government has acted swiftly at the very early stage of the pandemic with a focus on containment efforts and extensive public health measures, particularly (1) the commitment from the government with a multisectoral approach; (2) a timely, accurate, and transparent risk communication; (3) active surveillance and intensive isolation/quarantine operation, case management with tracing all new arrivals and close contact up to three clusters; and (4) suspension of flights, shutting schools, and all nonessential services.

scholarly journals Effects of Air Pollution on COVID-19 and Public Health

2020 ◽  
Author(s):  
Mario Coccia
Keyword(s):  
Public Health ◽  
Air Pollution ◽  
Wind Speed ◽  
Environmental Science ◽  
Negative Impact ◽  
Atmospheric Stability ◽  
Average Intensity ◽  
Infected People ◽  
Low Wind Speed ◽  
The World

Abstract The pandemic of coronavirus disease 2019 (COVID-19), generate by a novel virus SARS-CoV-2, is rapidly spreading all over the world, generating a high number of deaths. One of the current questions in the field of environmental science is to explain the relationships determining the diffusion of COVID-19 in specific regions of countries. The research here focuses on case study of Italy, one of the countries in the World to experience a rapid increase in confirmed cases and deaths. Results suggest that diffusion of COVID-19 is very high in cities with high air pollution generating severe negative effects on public health o. In particular, results reveal that, among Italian provincial capitals, the number of infected people was higher in cities with more than 100 days per year exceeding limits set for PM10 or ozone, cities located in hinterland zones (i.e. away from the coast), cities having a low average intensity of wind speed and cities with a lower temperature. In hinterland cities (mostly those bordering large urban conurbations) with a high number of days exceeding PM10 and ozone limits, coupled with low wind speed (atmospheric stability), the average number of infected people in April 2020 more than tripled those that had less than 100 days of excessive air pollution. In fact, results show that more than 75% of infected individuals and about 81% of deaths in Italy of COVID-19 are in regions with high air pollution. This study must conclude that a long-run strategy to constrain future epidemics similar to the COVID-19, reducing the negative impact on public health has also to be designed in terms of environmental and sustainability policies and not only in terms of efficient approaches in medicine.

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