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 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 ◽  
Maggie H Wang ◽  
...  
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. In this work, we estimate the number of under-ascertainment cases and the basic reproduction number, B, of COVID-19 in Kano, Nigeria. 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 Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak

2020 ◽  
Vol 9 (2) ◽  
pp. 388 ◽  
Author(s):  
Shi Zhao ◽  
Salihu S. Musa ◽  
Qianying Lin ◽  
Jinjun Ran ◽  
Guangpu Yang ◽  
...  
Keyword(s):  
Exponential Growth ◽  
Mainland China ◽  
Likelihood Estimation ◽  
Respiratory Illness ◽  
Reporting Rate ◽  
Initial Growth ◽  
Epidemic Curve ◽  
Foreign Countries ◽  
Modelling Analysis ◽  
Novel Coronavirus

Background: In December 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019-nCoV) emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries. The 2019-nCoV cases might have been under-reported roughly from 1 to 15 January 2020, and thus we estimated the number of unreported cases and the basic reproduction number, R0, of 2019-nCoV. Methods: We modelled the epidemic curve of 2019-nCoV cases, in mainland China from 1 December 2019 to 24 January 2020 through the exponential growth. The number of unreported cases was determined by the maximum likelihood estimation. We used the serial intervals (SI) of infection caused by two other well-known coronaviruses (CoV), Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) CoVs, as approximations of the unknown SI for 2019-nCoV to estimate R0. Results: We confirmed that the initial growth phase followed an exponential growth pattern. The under-reporting was likely to have resulted in 469 (95% CI: 403–540) unreported cases from 1 to 15 January 2020. The reporting rate after 17 January 2020 was likely to have increased 21-fold (95% CI: 18–25) in comparison to the situation from 1 to 17 January 2020 on average. We estimated the R0 of 2019-nCoV at 2.56 (95% CI: 2.49–2.63). Conclusion: The under-reporting was likely to have occurred during the first half of January 2020 and should be considered in future investigation.

scholarly journals Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak

2020 ◽  
Author(s):  
Shi Zhao ◽  
Qianyin Lin ◽  
Jinjun Ran ◽  
Salihu S Musa ◽  
Guangpu Yang ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Exponential Growth ◽  
Early Phase ◽  
Reproduction Number ◽  
Fold Increase ◽  
Reporting Rate ◽  
The Mean ◽  
Novel Coronavirus ◽  
The Impact ◽  
The Basic Reproduction Number

AbstractBackgroundsAn ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city of China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and countries. We present estimates of the basic reproduction number, R0, of 2019-nCoV in the early phase of the outbreak.MethodsAccounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate (γ), we estimated R0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI.FindingsThe early outbreak data largely follows the exponential growth. We estimated that the mean R0 ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R0.ConclusionThe mean estimate of R0 for the 2019-nCoV ranges from 2.24 to 3.58, and significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.

scholarly journals Estimation of the basic reproduction number of novel coronavirus (COVID-19) in Bangladesh: A 65-day outbreak data-driven analysis

2020 ◽  
Author(s):  
Md. Hasan ◽  
Akhtar Hossain ◽  
Wasimul Bari ◽  
Syed Shariful Islam
Keyword(s):  
Growth Model ◽  
Basic Reproduction Number ◽  
Exponential Growth ◽  
Reproduction Number ◽  
Fold Increase ◽  
Reporting Rate ◽  
Basic Reproductive Number ◽  
Exponential Growth Model ◽  
Novel Coronavirus ◽  
The Basic Reproduction Number

Abstract BackgroundThe outbreak of novel coronavirus disease (COVID-19), started from Wuhan, China, at the end of December 2019, hits almost the entire world. In Bangladesh, the first case was officially reported on March 8, 2020. We estimated the basic reproductive number, R0, of COVID-19 for Bangladesh using the first 65-day data of the outbreak.MethodsWith time-varying disease reporting rate, epidemic curves were estimated using the exponential growth model utilizing daily COVID-19 diagnosis data in Bangladesh from March 8 to May 11, 2020. We estimated R0 using the estimated intrinsic growth rate (γ). Serial intervals (SI) have been used from two well-known coronaviruses’ outbreaks, SARS and MERS; and the early estimate of SI of COVID-19 in Wuhan, China.ResultsThe COVID-19 epidemic in Bangladesh followed an exponential growth model. We found the R0 to be 1.84 [95% CI: 1.82–1.86], 1.82 [95% CI: 1.81–1.84], and 1.94 [95% CI: 1.92–1.96], for MERS, COVID-19, and SARS SI respectively without adjusting reporting rate. With the adjusted reporting rate, R0 reduced to 1.63 [95% CI: 1.62–1.65], 1.62 [95% CI: 1.61–1.64], and 1.71 [95% CI: 1.70–1.73] for a five-fold increase. Inverse association between the reporting rate and the basic reproduction number was observed.ConclusionThe R0 was found to be 1.87 for existing cases and was reduced to 1.65 for the five-fold increase of the early reporting rate. Findings suggest a continued COVID-19 outbreak in Bangladesh and immediate steps need to be taken to control.

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.

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 ◽  
Initial Growth ◽  
Exponential Growth Rate ◽  
Preparedness Planning ◽  
First Case ◽  
Exponential Growth Model

Abstract Background Since the first case of coronavirus disease 2019 (COVID-19) was detected on February 14, 2020, the cumulative confirmations reached 15207 including 831 deaths by April 13, 2020. Methods We analyzed the initial phase of the epidemic of COVID-19 in Africa between 1 March and 13 April 2020, by using the simple exponential growth model.Results We estimated the exponential growth rate as 0.22 per day (95%CI: 0.20 – 0.24), and the basic reproduction number, R0, to be 2.37 (95%CI: 2.22-2.51) based on the assumption that the exponential growth starting from 1 March 2020.Conclusion The initial growth of COVID-19 cases in Africa was rapid and showed large variations across countries. Our estimates should be useful in preparedness planning. Trial registration: NA

scholarly journals Prediction of confinement effects on the number of Covid-19 outbreak in Algeria

2020 ◽  
Vol 15 ◽  
pp. 37 ◽  
Author(s):  
Ali Moussaoui ◽  
Pierre Auger
Keyword(s):  
Reproduction Number ◽  
Control Measures ◽  
Cumulative Number ◽  
Time Data ◽  
Final Size ◽  
First Case ◽  
Algerian Population ◽  
Fast Change ◽  
Real Time Data ◽  
The Basic Reproduction Number

The first case of coronavirus disease 2019 (COVID-19) in Algeria was reported on 25 February 2020. Since then, it has progressed rapidly and the number of cases grow exponentially each day. In this article, we utilize SEIR modelling to forecast COVID-19 outbreak in Algeria under two scenarios by using the real-time data from March 01 to April 10, 2020. In the first scenario: no control measures are put into place, we estimate that the basic reproduction number for the epidemic in Algeria is 2.1, the number of new cases in Algeria will peak from around late May to early June and up to 82% of the Algerian population will likely contract the coronavirus. In the second scenario, at a certain date T, drastic control measures are taken, people are being advised to self-isolate or to quarantine and will be able to leave their homes only if necessary. We use SEIR model with fast change between fully protected and risky states. We prove that the final size of the epidemic depends strongly on the cumulative number of cases at the date when we implement intervention and on the fraction of the population in confinement. Our analysis shows that the longer we wait, the worse the situation will be and this very quickly produces.

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 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.

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