Is Nigeria really on top of COVID-19? Message from effective reproduction number

Following the importation of Covid-19 into Nigeria on the 27 February 2020 and then the outbreak, the question is: how do we anticipate the progression of the ongoing epidemics following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of Covid-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R_0). This also enables us to estimate the daily transmission rate (β) that determines the effect of social distancing. We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on Covid-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 1.78. Most importantly, the R(t) is strictly greater than one from April 13 till 7 May 2020. The R_0 is estimated to be 2.42 with credible interval: (2.37, 2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below one. Also, the estimated fractional reported symptomatic cases are between 10 to 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

Download Full-text

Is Nigeria really on top of COVID-19? Message from effective reproduction number

Epidemiology and Infection ◽

10.1017/s0950268820001740 ◽

2020 ◽

Vol 148 ◽

Cited By ~ 3

Author(s):

A. I. Adekunle ◽

O. A. Adegboye ◽

E. Gayawan ◽

E. S. McBryde

Keyword(s):

Public Health ◽

Compartmental Model ◽

Reproduction Number ◽

Transmission Rate ◽

Credible Interval ◽

Control Measures ◽

Maximum Value ◽

Intervention Measures ◽

Death Cases ◽

Epidemiological Parameters

Abstract Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R0) – this also enables us to estimate the initial daily transmission rate (β0). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 is estimated to be 2.42 with credible interval: (2.37–2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

Download Full-text

Real-time tracking and forecasting of the of COVID-19 outbreak in Kuwait: a mathematical modeling study

10.1101/2020.05.03.20089771 ◽

2020 ◽

Author(s):

Abdullah A. Al-Shammari ◽

Hamad Ali ◽

Barrak Al-Ahmad ◽

Faisal H. Al-Refaei ◽

Salman Al-Sabah ◽

...

Keyword(s):

Public Health ◽

Mathematical Modeling ◽

Maximum Likelihood ◽

Real Time ◽

Reproduction Number ◽

Control Measures ◽

List Type ◽

Imported Case ◽

Real Time Tracking ◽

Death Cases

AbstractKuwait has been experiencing a COVID-19 outbreak since the first imported case on Feb 24, 2020. Analysis of data from the early stage of COVID-19 outbreak in Kuwait can provide important information about the potential epidemic and healthcare burdens as well as assist in evaluating the potential impact of various outbreak control measures. Such control measures are essentially implemented to achieve a sufficient reduction in the effective reproduction number during an outbreak. In this study, we use a mathematical modeling framework to simulate the outbreak dynamics of SARS-CoV-2 transmission in Kuwait and forecast the potential burden on the healthcare system. We calibrate the model against daily numbers of detected infection and death cases using a maximum likelihood framework and estimate both the basic and effective reproduction numbers. Our results indicate that the early control measures implemented in Kuwait had the effect of delaying the intensity of the outbreak but were unsuccessful in reducing Rt below 1. This highlights a need for a systematic investigation of the current public health interventions as well as a scientific surveillance tool that is sufficiently sensitive to outbreak temporal dynamics. Meanwhile, the developed model can serve as a public health tool to control the current outbreak and can be used to anticipate effective measures should a second wave re-emerge in Kuwait.HighlightsKuwait is experiencing a COVID-19 outbreak since the first imported case on Feb 24, 2020.We develop a mathematical model of disease transmission to provide a real-time tracking and forecasting tool for the epidemic outbreak in Kuwait as well as assess the potential epidemic and healthcare burdens and the effectiveness of early control measures.We calibrate the model against daily numbers of detected infection and death cases using a maximum likelihood framework.We find that early control measures had the effect of delaying and lowering the intensity of the outbreak but were unsuccessful in reducing the effective reproduction number below 1.

Download Full-text

Interpreting the transmissibility of the avian influenza A(H7N9) infection from 2013 to 2015 in Zhejiang Province, China

Epidemiology and Infection ◽

10.1017/s0950268815002812 ◽

2015 ◽

Vol 144 (8) ◽

pp. 1584-1591 ◽

Cited By ~ 8

Author(s):

K. C. CHONG ◽

X. WANG ◽

S. LIU ◽

J. CAI ◽

X. SU ◽

...

Keyword(s):

Influenza A ◽

Transmission Rate ◽

Credible Interval ◽

Zhejiang Province ◽

Control Measures ◽

Posterior Mean ◽

Key Indicator ◽

Intervention Measures ◽

Live Poultry ◽

H7n9 Infection

SUMMARYThree epidemic waves of human influenza A(H7N9) were documented in several different provinces in China between 2013 and 2015. With limited understanding of the potential for human-to-human transmission, it was difficult to implement control measures efficiently or to inform the public adequately about the application of interventions. In this study, the human-to-human transmission rate for the epidemics that occurred between 2013 and 2015 in Zhejiang Province, China, was analysed. The reproduction number (R), a key indicator of transmission intensity, was estimated by fitting the number of infections from poultry to humans and from humans to humans into a mathematical model. The posterior mean R for human-to-human transmission was estimated to be 0·27, with a 95% credible interval of 0·14–0·44 for the first wave, whereas the posterior mean Rs decreased to 0·15 in the second and third waves. Overall, these estimates indicate that a human H7N9 pandemic is unlikely to occur in Zhejiang. The reductions in the viral transmissibility and the number of poultry-transmitted infections after the first epidemic may be attributable to the various intervention measures taken, including changes in the extent of closures of live poultry markets.

Download Full-text

Modest effects of contact reduction measures on the reproduction number of SARS-CoV-2 in the most affected European countries and the US

10.1101/2020.04.20.20067538 ◽

2020 ◽

Cited By ~ 1

Author(s):

Armin Ensser ◽

Pia Überla ◽

Klaus Überla

Keyword(s):

Reproduction Number ◽

Control Measures ◽

European Countries ◽

Case Definitions ◽

Testing Frequency ◽

Reproduction Numbers ◽

The Us ◽

Reliable Parameter ◽

Death Cases ◽

Epidemiological Parameters

AbstractPopulation density, behaviour and cultural habits strongly influence the spread of pathogens. Consequently, key epidemiological parameters may vary from country to country. Many estimates of SARS-CoV-2 and COVID-19 strongly depend on testing frequency and case definitions. The fatal cases due to SARS-CoV2 could be a more reliable parameter, since missing of deaths is less likely. We analysed the dynamics of new infection and death cases to estimate the daily reproduction numbers (Rt) and the effectiveness of control measures in the most affected European Countries and the US. In summary, calculating Rt based on the daily number of deaths as well as of new infections may lead to more reliable estimates than those based on infection cases alone, as death based Rt are expected to be less susceptible to testing bias or limited capacities.

Download Full-text

Integration of fuzzy logic in the SEIRD compartmental model for the analysis of intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics

International Journal of Modern Physics C ◽

10.1142/s0129183122500917 ◽

2022 ◽

Author(s):

Zakaria Shams Siam ◽

Rubyat Tasnuva Hasan ◽

Hossain Ahamed ◽

Samiya Kabir Youme ◽

Soumik Sarker Anik ◽

...

Keyword(s):

Fuzzy Logic ◽

Compartmental Model ◽

Fuzzy Rule ◽

Transmission Dynamics ◽

Intervention Measures ◽

Proposed Model ◽

Total Death ◽

Linguistic Intervention ◽

Fuzzy Linguistic ◽

Death Cases

Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we have proposed a fuzzy rule-based Susceptible-Exposed-Infectious-Recovered-Death ([Formula: see text]) compartmental model considering a new dynamic transmission possibility variable as a function of time and three different fuzzy linguistic intervention variables to delineate the intervention and transmission heterogeneity on SARS-CoV-2 viral infection. We have analyzed the datasets of active cases and total death cases of China and Bangladesh. Using our model, we have predicted active cases and total death cases for China and Bangladesh. We further presented the correspondence of different intervention measures in relaxing the transmission possibility. The proposed model delineates the correspondence between the intervention measures as fuzzy subsets and the predicted active cases and total death cases. The prediction made by our system fitted the collected dataset very well while considering different fuzzy intervention measures. The integration of fuzzy logic in the classical compartmental model also produces more realistic results as it generates a dynamic transmission possibility variable. The proposed model could be used to control the transmission of SARS-CoV-2 as it deals with the intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics.

Download Full-text

Modelling the Evolution of COVID-19 in High-Incidence European Countries and Regions: Estimated Number of Infections and Impact of Past and Future Intervention Measures

Journal of Clinical Medicine ◽

10.3390/jcm9061825 ◽

2020 ◽

Vol 9 (6) ◽

pp. 1825 ◽

Cited By ~ 3

Author(s):

Juan Fernández-Recio

Keyword(s):

Reproduction Number ◽

Mechanistic Model ◽

Control Measures ◽

European Countries ◽

Intervention Measures ◽

High Incidence ◽

Future Outcomes ◽

Long Term Impact ◽

The Impact

A previously developed mechanistic model of COVID-19 transmission has been adapted and applied here to study the evolution of the disease and the effect of intervention measures in some European countries and territories where the disease has had a major impact. A clear impact of the major intervention measures on the reproduction number (Rt) has been found in all studied countries and territories, as already suggested by the drop in the number of deaths over time. Interestingly, the impact of such major intervention measures seems to be the same in most of these countries. The model has also provided realistic estimates of the total number of infections, active cases and future outcomes. While the predictive capabilities of the model are much more uncertain before the peak of the outbreak, we could still reliably predict the evolution of the disease after a major intervention by assuming the subsequent reproduction number from the current study. A greater challenge is to foresee the long-term impact of softer intervention measures, but this model can estimate the outcome of different scenarios and help to plan changes for the implementation of control measures in a given country or region.

Download Full-text

Modelling the evolution of COVID-19 in high-incidence European countries and regions: estimated number of infections and impact of past and future intervention measures

10.1101/2020.05.09.20096735 ◽

2020 ◽

Author(s):

Juan Fernandez-Recio

Keyword(s):

Reproduction Number ◽

Mechanistic Model ◽

Control Measures ◽

European Countries ◽

Intervention Measures ◽

High Incidence ◽

Long Term Impact ◽

The Impact ◽

Over Time

A previously developed mechanistic model of COVID-19 transmission has been adapted and applied here to study the evolution of the disease and the effect of intervention measures in some European countries and territories where the disease had major impact. A clear impact of the major intervention measures on the reproduction number (Rt) has been found in all studied countries and territories, as already suggested by the drop in the number of deaths over time. Interestingly, the impact of such major intervention measures seems to be the same in most of these countries. The model has also provided realistic estimates of the total number of infections, active cases and future outcome. While the predictive capabilities of the model are much more uncertain before the peak of the outbreak, we could still reliably predict the evolution of the disease after a major intervention by assuming the afterwards reproduction number from current study. More challenging is to foresee the long-term impact of softer intervention measures, but this model can estimate the outcome of different scenarios and help planning changes in the implementation of control measures in a given country or region.

Download Full-text

Evolution of disease transmission rate during the course of SARS-COV-2: Patterns and determinants

10.21203/rs.3.rs-44647/v1 ◽

2020 ◽

Author(s):

Jie Zhu ◽

Blanca Gallego

Keyword(s):

Public Health ◽

Disease Transmission ◽

Reproduction Number ◽

Transmission Rate ◽

Region Of Interest ◽

Health Interventions ◽

Transmission Model ◽

Public Health Interventions ◽

Containment Measures ◽

The Impact

Abstract To date, many studies have argued the potential impact of public health interventions on flattening the epidemic curve of SARS-CoV-2. Most of them have focused on simulating the impact of interventions in a region of interest by manipulating contact patterns and key transmission parameters to reflect different scenarios. Our study looks into the evolution of the daily effective reproduction number during the epidemic via a stochastic transmission model. We found this measure (although model-dependent) provides an early signal of the efficacy of containment measures. This epidemiological parameter when updated in real-time can also provide better predictions of future outbreaks. Our results found a substantial variation in the effect of public health interventions on the dynamic of SARS-CoV-2 transmission over time and across countries, that could not be explained solely by the timing and number of the adopted interventions. This suggests that further knowledge about the idiosyncrasy of their implementation and effectiveness is required. Although sustained containment measures have successfully lowered growth in disease transmission, more than half of the 101 studied countries failed to maintain the effective reproduction number close to or below 1. This resulted in continued growth in reported cases. Finally, we were able to predict with reasonable accuracy which countries would experience outbreaks in the next 30 days.

Download Full-text

Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: a statistical analysis in Tunisia from February 29 to May 5, 2020

BMC Infectious Diseases ◽

10.1186/s12879-020-05577-4 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Khouloud Talmoudi ◽

Mouna Safer ◽

Hejer Letaief ◽

Aicha Hchaichi ◽

Chahida Harizi ◽

...

Keyword(s):

Credible Interval ◽

Emerging Diseases ◽

Control Measures ◽

Transmission Dynamics ◽

Contact Tracing ◽

Epidemic Threshold ◽

Illness Onset ◽

Intervention Measures ◽

Temporal Reproduction ◽

Serial Interval

Abstract Background Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29–May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results Four hundred ninety-one of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% Confidence Interval (CI) 4.66–5.95] and standard deviation 0.26 [95% CI 0.23–0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% Credible Interval (CrI) 2.73–3.69] to 1.77 [95% CrI 1.49–2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CrI 0.84–0.94]) by national lockdown measure. Conclusions Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.

Download Full-text

A Theoretical Linguistic Fuzzy Rule-Based Compartmental Modeling for the COVID-19 Pandemic

International Journal of Fuzzy System Applications ◽

10.4018/ijfsa.285553 ◽

2022 ◽

Vol 11 (1) ◽

pp. 1-22

Author(s):

Zakaria Shams Siam ◽

Rubyat Tasnuva Hasan ◽

Hossain Ahamed ◽

Samiya Kabir Youme ◽

Soumik Sarker Anik ◽

...

Keyword(s):

Compartmental Model ◽

Temporal Dynamics ◽

Reproduction Number ◽

Equilibrium Points ◽

Fuzzy Model ◽

Fuzzy Rule ◽

Rule Based ◽

Population Immunity ◽

Death Cases ◽

Over Time

Recently COVID-19 pandemic has affected the whole world quite seriously. The number of new infectious cases and death cases are rapidly increasing over time. In this study, a theoretical linguistic fuzzy rule-based Susceptible-Exposed-Infectious-Isolated-Recovered (SEIIsR) compartmental model has been proposed to predict the dynamics of the transmission of COVID-19 over time considering population immunity and infectiousness heterogeneity based on viral load in the model. The model’s equilibrium points have been calculated and stability analysis of the model’s equilibrium points has been conducted. Consequently, the fuzzy basic reproduction number, R0f of the fuzzy model has been formulated. Finally, the temporal dynamics of different compartmental populations with immunity and infectiousness heterogeneity using the fuzzy Mamdani model are delineated and some disease control policies have been suggested to get over the infection in no time.

Download Full-text