Curve Estimation Modeling for Predictions of the Novel Coronavirus (Covid-19) Epidemic in Nigeria

2020 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Christian Ebere Enyoh ◽  
Andrew Wirnkor Verla ◽  
Chidi Edbert Duru ◽  
Emmanuel Chinedu Enyoh ◽  
Budi Setiawan
Keyword(s):  
State Governments ◽  
The Novel ◽  
Curve Estimation ◽  
Exponential Models ◽  
Inverse Models ◽  
Forward Prediction ◽  
S Curve ◽  
Novel Coronavirus ◽  
Best Fit ◽  
Official Data

Based on the official Nigeria Centre for Disease Control (NCDC) data, the current research paper modeled the confirmed cases of the novel coronavirus disease 2019 (COVID-19) in Nigeria. Ten different curve regression models including linear, logarithmic, inverse, quadratic, cubic, compound, power, S-curve, growth, and exponential were used to fit the obtained official data. The cubic (R2 = 0.999) model gave the best fit for the entire country. However, the growth and exponential had the lowest standard error of estimate (0.958) and thus may best be used. The equations for these models were e0.78897+0.0944x and 2.2011e0.0944x respectively. In terms of confirmed cases in individual State, quadratic, cubic, compound, growth, power and exponential models generally best describe the official data for many states except for the state of Kogi which is best fitted with S-curve and inverse models.  The error between the model and the official data curve is quite small especially for compound, power, growth and exponential models. The computed models will help to realized forward prediction and backward inference of the epidemic situation in Nigeria, and the relevant analysis help Federal and State governments to make vital decisions on how to manage the lockdown in the country.

scholarly journals Dynamics based on analysis of public data for spreading of disease

2021 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Planck Equation ◽  
The Novel ◽  
Epidemic Spreading ◽  
Data Set ◽  
Public Health Agencies ◽  
The Public ◽  
Public Data ◽  
Health Agencies ◽  
Novel Coronavirus ◽  
Official Data

Abstract The stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supported by the public health agencies in countries as Brazil, EUA and India is investigated. We perform the numerical analysis using the stochastic differential equation in Itô’s calculus (SDE) for the estimating of novel cases daily as well as analytical calculations solving the correspondent Fokker-Planck equation for the density probability distribution of novel cases, P(N(t); t). Our results display that the model based in the Itô diffusion fits well to the results due to uncertain in the official data and to the number of tests realized in the populations of each country.

scholarly journals Modeling of spreading of the novel coronavirus based in the stochastic dynamic

2020 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Differential Equation ◽  
Mathematical Model ◽  
Stochastic Model ◽  
Planck Equation ◽  
Stochastic Dynamic ◽  
The Novel ◽  
Model Based ◽  
The Mean ◽  
Novel Coronavirus ◽  
Official Data

Abstract In this paper, one proposes a stochastic model based on Itô diffusion as mathematical model for time evolution of novel cases N(t) of the SARS-CoV-2 (COVID-19) in each day t. I propose a correspondent stochastic differential equation (SDE) analogous to classical differential equation for epidemic growing for some diseases as smallpox and typhoid fever. Furthermore, we made an analysis using the Fokker-Planck equation giving an estimating of the novel cases in the day t as the mean half-width of the distribution P(N,t) of novel cases. My results display that the model based on Itô diffusion fits well to the results supported by healthy Brazilian agencies due to large uncertainly in the official data generated by the low number of tests realized generating so a strong randomness in the official data.

scholarly journals CORONA CASE PREDICTOR

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Sumer Sharma ◽  
Namita Goyal
Keyword(s):  
Data Model ◽  
Diagnostic Techniques ◽  
The Novel ◽  
Accurate Analysis ◽  
Global Migration ◽  
Time Period ◽  
The One ◽  
Novel Coronavirus ◽  
Second Wave ◽  
Official Data

What will be the further impact of the novel coronavirus (COVID-19) in India? To answer this question, we need an accurate analysis of the rate of death and recovery. At the same time, since the future does not usually repeat itself in the same way as in the past, so there is no certainty. The COVID-19 epidemic after spreading its roots to 206 countries around the world, has started again with more deadly waves than previous. Though vaccines are available now but still no one knows for how much time period certain vaccine can provide antibodies. So, the battle is still going on. Disease and death not only threaten people but also their economic impact. Even though if one got recovered from disease but post covid symptoms are the one which are haunting even more. Based on the official data model, diagnostic techniques are used to create a predictable but decisive prediction model for the spread of COVID-19 in India. The second wave of COVID-19 hit in the states of India during March and has since spread again to all other provinces with a great havoc and the situation is getting worse in countries with high global migration.

scholarly journals Stochastic analysis using public data for forecasting of epidemic spreading of the novel coronavirus disease

2020 ◽  
Author(s):  
Leonardo dos Santos Lima
Keyword(s):  
Stochastic Model ◽  
Stochastic Analysis ◽  
Planck Equation ◽  
The Novel ◽  
Epidemic Spreading ◽  
Public Data ◽  
Health Agencies ◽  
The Mean ◽  
Novel Coronavirus ◽  
Official Data

Abstract We propose a stochastic model for epidemic spreading of the novel coronavirus based in data supported by the Brazilian health agencies. Furthermore, we performed an analysis using the Fokker-Planck equation estimating the novel cases in the day t as the mean half-width of the distribution of novel cases P(N,t). Our results display that the model based in the Itô diffusion adjusts well to the results supplied by health Brazilian agencies due to large uncertain in the official data and to the low number of tests realized in the population.

scholarly journals Dynamics based on analysis of public data for spreading of disease

2020 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Public Health ◽  
The Novel ◽  
Epidemic Spreading ◽  
Data Set ◽  
Public Health Agencies ◽  
The Public ◽  
Public Data ◽  
Health Agencies ◽  
Novel Coronavirus ◽  
Official Data

Abstract The stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supported by the public health agencies in countries as Brazil, EUA and India is investigated. We performed the numerical analysis using the stochastic differential equation for estimating of the novel cases diary as well as analytical calculations solving the correspondent partial equation for the distribution of novel cases P. Our results display that the model based in the Itô diffusion fits well to the results diary due to uncertain in the official data and to the number of tests realized in the populations of each country.

scholarly journals Analysis of COVID-19 spread in South Korea using the SIR model with time-dependent parameters and deep learning

2020 ◽  
Author(s):  
Hyeontae Jo ◽  
Hwijae Son ◽  
Hyung Ju Hwang ◽  
Se Young Jung
Keyword(s):  
South Korea ◽  
Numerical Algorithms ◽  
Sir Model ◽  
Time Dependent ◽  
The Novel ◽  
Time Varying Parameters ◽  
Coronavirus Infection ◽  
Novel Coronavirus ◽  
The Given ◽  
Best Fit

AbstractMathematical modeling is a process aimed at finding a mathematical description of a system and translating it into a relational expression. When a system is continuously changing over time (e.g., infectious diseases) differential equations, which may include parameters, are used for modeling the system. The process of finding those parameters that best fit the given data from the system is called an inverse problem. This study aims at analyzing the novel coronavirus infection (COVID-19) spread in South Korea using the susceptible-infected-recovered (SIR) model. We collect the data from Korea Centers for Disease Control & Prevention (KCDC). We assume that each parameter in the SIR model is a function of time so that we can compute important parameters, such as the basic reproduction number (R0), more delicately. Using neural networks, we propose a method to find the best time-varying parameters and the solution for the model simultaneously. Moreover, using time-dependent parameters, we find that traditional numerical algorithms, such as the Runge-Kutta methods, can successfully approximate the SIR model while fitting the COVID-19 data, thus modeling the propagation patterns of COVID-19 more precisely.

Comparative docking of SARS-CoV-2 receptors antagonists from repurposing drugs

2020 ◽  
Author(s):  
Micael Davi Lima de Oliveira ◽  
Kelson Mota Teixeira de Oliveira
Keyword(s):  
World Health Organisation ◽  
World Health ◽  
Lung Cells ◽  
The Novel ◽  
High Modulation ◽  
Main Protease ◽  
The World ◽  
Novel Coronavirus ◽  
Viral Receptors ◽  
Theoretical Results

According to the World Health Organisation, until 16 June, 2020, the number of confirmed and notified cases of COVID-19 has already exceeded 7.9 million with approximately 434 thousand deaths worldwide. This research aimed to find repurposing antagonists, that may inhibit the activity of the main protease (Mpro) of the SARS-CoV-2 virus, as well as partially modulate the ACE2 receptors largely found in lung cells, and reduce viral replication by inhibiting Nsp12 RNA polymerase. Docking molecular simulations were performed among a total of 60 structures, most of all, published in the literature against the novel coronavirus. The theoretical results indicated that, in comparative terms, paritaprevir, ivermectin, ledipasvir, and simeprevir, are among the most theoretical promising drugs in remission of symptoms from the disease. Furthermore, also corroborate indinavir to the high modulation in viral receptors. The second group of promising drugs includes remdesivir and azithromycin. The repurposing drugs HCQ and chloroquine were not effective in comparative terms to other drugs, as monotherapies, against SARS-CoV-2 infection.

SARS-COV2 virus and Coronavirus disease (COVID-19)

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 977-982
Author(s):  
Mohamed J. Saadh ◽  
Bashar Haj Rashid M ◽  
Roa’a Matar ◽  
Sajeda Riyad Aldibs ◽  
Hala Sbaih ◽  
...  
Keyword(s):  
Close Contact ◽  
Antiviral Agents ◽  
Health Concern ◽  
The Novel ◽  
Global Public Health ◽  
Fatal Disease ◽  
Mild Disease ◽  
Life Threatening ◽  
Novel Coronavirus

SARS-COV2 virus causes Coronavirus disease (COVID-19) and represents the causative agent of a potentially fatal disease that is of great global public health concern. The novel coronavirus (2019) was discovered in 2019 in Wuhan, the market of the wet animal, China with viral pneumonia cases and is life-threatening. Today, WHO announces COVID-19 outbreak as a pandemic. COVID-19 is likely to be zoonotic. It is transmitted from bats as intermediary animals to human. Also, the virus is transmitted from human to human who is in close contact with others. The computerized tomographic chest scan is usually abnormal even in those with no symptoms or mild disease. Treatment is nearly supportive; the role of antiviral agents is yet to be established. The SARS-COV2 virus spreads faster than its two ancestors, the SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), but has lower fatality. In this article, we aimed to summarize the transmission, symptoms, pathogenesis, diagnosis, treatment, and vaccine to control the spread of this fatal disease.

Coronavirus: Towards controlling of the pandemic - Indian scenario

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.

Association and treatment of diabetes in patients affected by Covid-19

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 1198-1201
Author(s):  
Syed Yasir Afaque
Keyword(s):  
Diabetes Mellitus ◽  
Prognostic Value ◽  
Heart Diseases ◽  
The Novel ◽  
The World ◽  
Treatment Of Diabetes ◽  
Coronavirus Infection ◽  
Composite Outcomes ◽  
Novel Coronavirus ◽  
The Impact

In December 2019, a unique coronavirus infection, SARS-CoV-2, was first identified in the province of Wuhan in China. Since then, it spread rapidly all over the world and has been responsible for a large number of morbidity and mortality among humans. According to a latest study, Diabetes mellitus, heart diseases, Hypertension etc. are being considered important risk factors for the development of this infection and is also associated with unfavorable outcomes in these patients. There is little evidence concerning the trail back of these patients possibly because of a small number of participants and people who experienced primary composite outcomes (such as admission in the ICU, usage of machine-driven ventilation or even fatality of these patients). Until now, there are no academic findings that have proven independent prognostic value of diabetes on death in the novel Coronavirus patients. However, there are several conjectures linking Diabetes with the impact as well as progression of COVID-19 in these patients. The aim of this review is to acknowledge about the association amongst Diabetes and the novel Coronavirus and the result of the infection in such patients.

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