scholarly journals Modelling COVID-19 cases in Nigeria: Forecasts, uncertainties, projections and the link with weather

2020 ◽  
Author(s):  
Adeyeri O.E. ◽  
Oyekan K.S.A. ◽  
Ige S.O. ◽  
Akinbobola A. ◽  
Okogbue E.C.
Keyword(s):  
Correlation Coefficient ◽  
Exponential Growth ◽  
Reproduction Number ◽  
Correlation Coefficients ◽  
World Health ◽  
Estimation Methods ◽  
Maximum Temperature ◽  
Religious Activities ◽  
Epidemic Curve ◽  
First Case

Abstract The World Health Organization (WHO) declared COVID-19 a global pandemic on 11 March 2020 due to its global spread. In Nigeria, the first case was documented on 27 February 2020. Since then, it has spread to most parts of the country. This study models, forecasts and projects COVID-19 incidence, cumulative incidence and death cases in Nigeria using six estimation methods i.e. the attack rate, maximum likelihood, exponential growth, Markov chain monte Carlo (MCMC), time-dependent and the sequential Bayesian approaches. A sensitivity analysis with respect to the mean generation time is used to quantify the associated reproduction number uncertainties. The relationship between the COVID-19 incidence and five meteorological variables are further assessed. The result shows that the highest incidences are recorded in days with either religious activities or market days while the weekday trend decreases towards the weekend. It is also established that COVID-19 incidence significantly increases with increasing sea level pressure (0.7 correlation coefficient) and significantly decreases with increasing maximum temperature (-0.3 correlation coefficient). Also, selecting an optimal period for reproduction number estimates reduces the variability between estimates. As an example, in the EG approach, the epidemic curve that optimally fits the exponential growth is between 1- and 53-time units with reproduction number estimate of 1.60 [1.58; 1.62] at 95% confidence interval. However, this optimal reproduction number estimate is different from the default reproduction number estimate. Using the MCMC approach, the correlation coefficients between the observed and forecasted incidence, cumulative death and cumulative confirmed cases are 0.66, 0.92 and 0.90 respectively. The projections till December shows values approaching 1,000,000, 120,000 and 3,000,000 respectively. Therefore, timely intervention and effective preventive measures are immediately needed to mitigate a full-scale epidemic in the country.

scholarly journals Comparative estimation of the reproduction number for pandemic influenza from daily case notification data

2006 ◽  
Vol 4 (12) ◽  
pp. 155-166 ◽  
Author(s):  
Gerardo Chowell ◽  
Hiroshi Nishiura ◽  
Luís M.A Bettencourt
Keyword(s):  
Growth Phase ◽  
Pandemic Influenza ◽  
Exponential Growth ◽  
Goodness Of Fit ◽  
Reproduction Number ◽  
Exponential Growth Phase ◽  
Initial Growth ◽  
Epidemic Curve ◽  
Spanish Flu ◽  
The City

The reproduction number, , defined as the average number of secondary cases generated by a primary case, is a crucial quantity for identifying the intensity of interventions required to control an epidemic. Current estimates of the reproduction number for seasonal influenza show wide variation and, in particular, uncertainty bounds for for the pandemic strain from 1918 to 1919 have been obtained only in a few recent studies and are yet to be fully clarified. Here, we estimate using daily case notifications during the autumn wave of the influenza pandemic (Spanish flu) in the city of San Francisco, California, from 1918 to 1919. In order to elucidate the effects from adopting different estimation approaches, four different methods are used: estimation of using the early exponential-growth rate (Method 1), a simple susceptible–exposed–infectious–recovered (SEIR) model (Method 2), a more complex SEIR-type model that accounts for asymptomatic and hospitalized cases (Method 3), and a stochastic susceptible–infectious–removed (SIR) with Bayesian estimation (Method 4) that determines the effective reproduction number at a given time t . The first three methods fit the initial exponential-growth phase of the epidemic, which was explicitly determined by the goodness-of-fit test. Moreover, Method 3 was also fitted to the whole epidemic curve. Whereas the values of obtained using the first three methods based on the initial growth phase were estimated to be 2.98 (95% confidence interval (CI): 2.73, 3.25), 2.38 (2.16, 2.60) and 2.20 (1.55, 2.84), the third method with the entire epidemic curve yielded a value of 3.53 (3.45, 3.62). This larger value could be an overestimate since the goodness-of-fit to the initial exponential phase worsened when we fitted the model to the entire epidemic curve, and because the model is established as an autonomous system without time-varying assumptions. These estimates were shown to be robust to parameter uncertainties, but the theoretical exponential-growth approximation (Method 1) shows wide uncertainty. Method 4 provided a maximum-likelihood effective reproduction number 2.10 (1.21, 2.95) using the first 17 epidemic days, which is consistent with estimates obtained from the other methods and an estimate of 2.36 (2.07, 2.65) for the entire autumn wave. We conclude that the reproduction number for pandemic influenza (Spanish flu) at the city level can be robustly assessed to lie in the range of 2.0–3.0, in broad agreement with previous estimates using distinct data.

scholarly journals Deep Learning Detection of COVID-19, Temporal Variation and its Link with Temperature in Nigeria

2021 ◽  
pp. 55-62
Author(s):  
Ebiendele Eromosele Precious
Keyword(s):  
Deep Learning ◽  
Health Workers ◽  
World Health ◽  
Religious Activities ◽  
X Ray ◽  
Global Pandemic ◽  
First Case ◽  
Average Accuracy ◽  
Maximum Temperatures ◽  
Health Organization

COVID-19 was announced as a global pandemic on 11 March 2020 by the World Health Organization due to its spread globally.  Nigeria recorded its first case on 27 February 2020. Since then, it has spread to all parts of the country. In this paper we study the effectiveness and skill performance of deep learning architectures in assisting health workers in detecting COVID-19 infected patient through X-ray images. Analytical deductions obtained from 500 X-ray images of both infected and non-infected patients confirmed that our proposed model InceptionV3 is effective in detecting COVID-19 and attain an average accuracy of 92%. The relationship or link between the COVID-19 daily occurrence and two meteorological variables (minimum and maximum temperatures) are further assessed. The result also indicated that the cases recorded in Wednesdays and Fridays are observed to be higher than other days which usually coincide with either religious activities or market days in the country, while a progressively decline in weekday cases is observed towards the weekend with Sundays (ranging from 152 to 280 cases) having the lowest cases. The study further indicated statistically that COVID-19 daily cases significantly decline when maximum and minimum temperature are increasing (-0.79 and -0.44 correlation coefficient).

scholarly journals Predicting the COVID-19 positive cases in India with concern to Lockdown by using Mathematical and Machine Learning based Models

2020 ◽  
Author(s):  
Ajit Kumar Pasayat ◽  
Satya Narayan Pati ◽  
Aashirbad Maharana
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Model ◽  
Growth Model ◽  
Linear Regression Model ◽  
Exponential Growth ◽  
Time Window ◽  
Epidemic Curve ◽  
First Case ◽  
Exponential Growth Model

In this study, we analyze the number of infected positive cases of COVID-19 outbreak with concern to lockdown in India in the time window of February 11th 2020 to Jun 30th 2020. The first case in India was reported in Kerala on January 30th 2020. To break the chain of spreading, Government announced a nationwide lockdown on March 24th 2020, which is increased two times. The Ongoing lockdown 3.0 is over on May 18th, 2020. We derived how the lockdown relaxation is going to impact on containment of the outbreak. Here the Exponential Growth Model has been used to derive the epidemic curve based on the data collected from February 11th 2020, to May 11th 2020, and the Machine Learning based Linear Regression model that gives the epidemic curve to predict the cases with the continuous flow of the lockdown. We estimate that if the lockdown is continuing with more relaxation, then the estimated infected cases reach up to 1.16 crores by June 30th 2020, and the lockdown would persist with current restriction, then the expected predicted infected cases are 5.69 lacs. The Exponential Growth Model and the Linear Regression Model are advantageous to predict the number of affected cases of COVID-19. These models can be used for forecasting in long term intervals. It shows from our result that lockdown with certain restriction has a vital role in preventing the spreading of this epidemic in this current situation.

scholarly journals The current state of COVID-19 in Australia: importation and spread

2020 ◽  
Author(s):  
Jessica Liebig ◽  
Raja Jurdak ◽  
Ahmad El Shoghri ◽  
Dean Paini
Keyword(s):  
World Health Organisation ◽  
The United States ◽  
World Health ◽  
Epidemic Curve ◽  
Current State ◽  
Local Spread ◽  
First Case ◽  
Travel Restrictions ◽  
Passenger Flows ◽  
Returning Travellers

AbstractBackgroundThe rapid global spread of coronavirus disease (COVID-19) is unprecedented. The outbreak has quickly spread to more than 100 countries reporting over 100,000 confirmed cases. Australia reported its first case of COVID-19 on 25th January 2020 and has since implemented travel restrictions to stop further introduction of the virus.MethodsWe analysed daily global COVID-19 data published by the World Health Organisation to investigate the spread of the virus thus far. To assess the current risk of COVID-19 importation and local spread in Australia we predict international passenger flows into Australia during 2020.FindingsOur analysis of global data shows that Australia can expect a similar growth rate of reported cases as observed in France and the United States. We identify travel patterns of Australian citizens/residents and foreign travellers that can inform the implementation of new and the alteration of existing travel restrictions related to COVID-19.InterpretationOur findings identify the risk reduction potential of current travel bans, based on the proportion of returning travellers to Australia that are residents or visitors. The similarity of the exponential growth in the epidemic curve in Australia to other countries guides forecasts of COVID-19 growth in Australia, and opportunities for drawing lessons from other countries with more advanced outbreaks.

scholarly journals Transmission dynamics and control of COVID-19 in Chile, March-October, 2020

2020 ◽  
Author(s):  
Amna Tariq ◽  
Eduardo A. Undurraga ◽  
Carla Castillo Laborde ◽  
Katia Vogt-Geisse ◽  
Ruiyan Luo ◽  
...  
Keyword(s):  
Low Income ◽  
Exponential Growth ◽  
Reproduction Number ◽  
Growth Parameter ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Virus Spread ◽  
Growth Trend ◽  
Social Distancing ◽  
First Case

Since the detection of the first case of COVID-19 in Chile on March 3rd, 2020, a total of 513188 cases, including ~14302 deaths have been reported in Chile as of November 2nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile's incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, R, estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96( 95% CI: 0.95, 0.98) as of November 2nd, 2020. The early sub-exponential growth trend (p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p ~0.5) as of July 7th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.

Spread of coronavirus disease 2019 (COVID-19) during the lockdown in the Indian population and preventive measures

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 328-332
Author(s):  
Milind Abhimanyu Nisargandha ◽  
Shweta DadaraoParwe
Keyword(s):  
Indian Population ◽  
World Health Organisation ◽  
World Health ◽  
Indian Government ◽  
Epidemic Curve ◽  
First Case ◽  
Number Of Patients ◽  
The World ◽  
Beneficial Impact ◽  
The Government

Coronavirus disease 2019 (COVID -19) is the newly found virus in Indian population spreading all over the world through the seafood market of Wuhan, Hubei, China. Due to the spreading of coronavirus in Indian Government facing difficulty after the lockdown of one month in two phases, the number of patients is increased day by day. This is a very challenging task for the Indian Government, people are not strictly following the guidelines of the World Health Organisation. In India reported 26585 confirmed cases and 833 deaths due to COVID -19 in 31 states and union territories when the first case was found on 30th January 2020. The Government decided immediately to lockdown and closed all international borders, as per the WHO guidelines for a pandemic. The future directions to choose for people can fight with such type of pandemic. The present reviewemphasis is strictlyon the WHO guideline's to prohibit spreading coronavirus in India. There is some gap of awareness in people which enhance spreading coronavirus even during the lockdown. This finding has cause to concern about the spread of coronavirus in thisscenarioduring the lockdown, what farther primary prevention to be taken to avoid such transmission. The lockdown is already having a beneficial impact of flattening the epidemic curve for spreading this transmission and During Lockdown period in each state. Each state having sufficient time for finding COVID -19 Patient, people come in contact with the patient keeps them institutional isolation and declared that area infection hotspot at the district level.

scholarly journals Estimating the under-ascertainment number of COVID-19 cases in Kano, Nigeria in the fourth week of April 2020: a modelling analysis of the early outbreak

2020 ◽  
Author(s):  
Salihu S Musa ◽  
Shi Zhao ◽  
Nafiu Hussaini ◽  
Zian Zuang ◽  
Yushan Wu ◽  
...  
Keyword(s):  
Exponential Growth ◽  
Reproduction Number ◽  
Arima Model ◽  
Likelihood Estimation ◽  
Reporting Rate ◽  
Initial Growth ◽  
Short Term ◽  
First Case ◽  
Modelling Analysis ◽  
The Basic Reproduction Number

Abstract Background: The coronavirus disease 2019 (known as COVID-19) pandemic caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) appeared in Wuhan, China has rapidly spread to over 200 countries and territories. In Nigeria, the Kano State Ministry of Health has confirmed its first case of COVID-19 on April 11, 2020, and since then there might have been issues of under-ascertainment that occurred roughly from 22 to 27 April 2020. As of 4 October 2020, there were 1738 reported COVID-19 cases in Kano with 54 associated deaths. In this work, we estimate the number of under-ascertainment cases and the basic reproduction number, B, of COVID-19 in Kano, Nigeria. We also predict the number of COVID-19 cases in the short term.Methods: We employ the exponential growth and modelled the outbreak curve of COVID-19 cases, in Kano, Nigeria from 11 to 30 April 2020. We estimated the number of under-ascertainment cases using the maximum likelihood estimation. We adopted the SI estimated for Hong Kong as approximations of the unknown SI for COVID-19 in Kano to estimate the a. We use ARIMA model to provide a short term (15 days) prediction of the COVID-19 cases in Kano, Nigeria.Results: We revealed that the initial growth phase mimic an exponential growth pattern. We found that the under-ascertainment was likely to have resulted in 213 (95% CI: 106−346) unreported cases from 22 to 27 April 2020. The reporting rate after 27 April 2020 increase up to 10-fold compared to the scenario from 22 to 27 April 2020 on average. We estimated the c of COVID-19 in Kano as 2.74 (95% CI: 2.53−2.96). We forecasted that the total number of COVID-19 cases in Kano to be 1067 (95% CI: 883, 2137) by June 6, 2020.Conclusion: The under-ascertainment likely exists during the fourth week of April, 2020 and should be regarded in the future analysis/investigation.

scholarly journals Characterizing the effective reproduction number during the COVID-19 epidemic: Insights from Qatar experience

2021 ◽  
Author(s):  
Raghid Bsat ◽  
Hiam Chemaitelly ◽  
Peter Coyle ◽  
Patrick Tang ◽  
Mohammad Rubayet Hasan ◽  
...  
Keyword(s):  
Hospital Admissions ◽  
Reproduction Number ◽  
Acute Care Hospital ◽  
Correlation Coefficients ◽  
Sensitivity Analyses ◽  
Estimation Methods ◽  
Care Hospital ◽  
Public Health Response ◽  
Robert Koch Institute ◽  
Epidemic Dynamics

Background: The effective reproduction number, Rt, is a tool to track and understand epidemic dynamics. This investigation of Rt estimations was conducted to guide the national COVID-19 response in Qatar, from the onset of the epidemic until August 18, 2021. Methods: Real-time empirical RtEmpirical was estimated using five methods, including the Robert Koch Institute, Cislaghi, Systrom-Bettencourt and Ribeiro, Wallinga and Teunis, and Cori et al. methods. Rt was also estimated using a transmission dynamics model (RtModel-based). Uncertainty and sensitivity analyses were conducted. Agreements between different Rt estimates were assessed by calculating correlation coefficients. Results: RtEmpirical captured the evolution of the epidemic through three waves, public health response landmarks, effects of major social events, transient fluctuations coinciding with significant clusters of infection, and introduction and expansion of the B.1.1.7 variant. The various estimation methods produced consistent and overall comparable RtEmpirical estimates with generally large correlation coefficients. The Wallinga and Teunis method was the fastest at detecting changes in epidemic dynamics. RtEmpirical estimates were consistent whether using time series of symptomatic PCR-confirmed cases, all PCR-confirmed cases, acute-care hospital admissions, or ICU-care hospital admissions, to proxy trends in true infection incidence. RtModel-based correlated strongly with RtEmpirical and provided an average RtEmpirical. Conclusions: Rt estimations were robust and generated consistent results regardless of the data source or the method of estimation. Findings affirmed an influential role for Rt estimations in guiding national responses to the COVID-19 pandemic, even in resource-limited settings.

scholarly journals Estimation of exponential growth rate and basic reproduction number of the coronavirus disease 2019 (COVID-19) in Africa

2020 ◽  
Author(s):  
Salihu S Musa ◽  
Shi Zhao ◽  
Maggie H Wang ◽  
Abdurrazaq G Habib ◽  
Umar T Mustapha ◽  
...  
Keyword(s):  
Growth Rate ◽  
Basic Reproduction Number ◽  
Exponential Growth ◽  
Initial Phase ◽  
Reproduction Number ◽  
Exponential Growth Rate ◽  
Preparedness Planning ◽  
First Case ◽  
Exponential Growth Model ◽  
The Basic Reproduction Number

Abstract Since the first case of coronavirus disease 2019 (COVID-19) was detected on February 14, 2020, the cumulative confirmations reached 834 including 17 deaths by March 19, 2020. We analyzed the initial phase of the epidemic of COVID-19 in Africa between 1 March and 19 March 2020, by using the simple exponential growth model. We estimated the exponential growth rate as 0.22 per day (95%CI: 0.20 – 0.24), and the basic reproduction number to be 2.37 (95%CI: 2.22-2.51) based on the assumption that the exponential growth starting from 1 March, 2020. Our estimates should be useful in preparedness planning.

scholarly journals Estimation of the Basic Reproduction Number of SARS-CoV-2 in Bangladesh Using Exponential Growth Method

2020 ◽  
Author(s):  
Riaz Mahmud ◽  
H. M. Abrar Fahim Patwari
Keyword(s):  
Standard Deviation ◽  
Basic Reproduction Number ◽  
Exponential Growth ◽  
Generation Time ◽  
Reproduction Number ◽  
First Case ◽  
The World ◽  
Growth Method ◽  
Novel Coronavirus ◽  
The Basic Reproduction Number

Objectives: In December 2019, a novel coronavirus (SARS-CoV-2) outbreak emerged in Wuhan, Hubei Province, China. Soon, it has spread out across the world and become an ongoing pandemic. In Bangladesh, the first case of novel coronavirus (SARS-CoV-2) was detected on March 8, 2020. Since then, not many significant studies have been conducted to understand the transmission dynamics of novel coronavirus (SARS-CoV-2) in Bangladesh. In this study, we estimated the basic reproduction number R0 of novel coronavirus (SARS-CoV-2) in Bangladesh. Methods: The data of daily confirmed cases of novel coronavirus (SARS-CoV-2) in Bangladesh and the reported values of generation time of novel coronavirus (SARS-CoV-2) for Singapore and Tianjin, China, were collected. We calculated the basic reproduction number R0 by applying the exponential growth (EG) method. Epidemic data of the first 76 days and different values of generation time were used for the calculation. Results: The basic reproduction number R0 of novel coronavirus (SARS-CoV-2) in Bangladesh is estimated to be 2.66 [95% CI: 2.58-2.75], optimized R0 is 2.78 [95% CI: 2.69-2.88] using generation time 5.20 with a standard deviation of 1.72 for Singapore. Using generation time 3.95 with a standard deviation of 1.51 for Tianjin, China, R0 is estimated to be 2.15 [95% CI: 2.09-2.20], optimized R0 is 2.22 [95% CI: 2.16-2.29]. Conclusions: The calculated basic reproduction number R0 of novel coronavirus (SARS-CoV-2) in Bangladesh is significantly higher than 1, which indicates its high transmissibility and contagiousness.

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