R0 and Re of COVID-19: Can We Predict When the Pandemic Outbreak will be Contained?

Achaiah, Nithya Chowrira; Subbarajasetty, Sindhu B; Shetty, Rajesh Mohan

doi:10.5005/jp-journals-10071-23649

Cited by 72 publications

(61 citation statements)

References 7 publications

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“…In this work, we used the extended Susceptible-Exposed-Removed (eSIR) compartmental model [22,23] to to the number of new infections generated by each infected person [16]. If R0 < 1, the disease will decline in the population, and if R0 > 1, the disease will spread faster [17]. However, foreseen risks include underestimation of the disease extend due to asymptomatic cases.…”

Section: Discussionmentioning

confidence: 99%

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Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa

Wamalwa

Tonnang

2021

Preprint

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BackgroundThe emergence of Coronavirus disease 2019 (COVID-19) as a global pandemic presents a serious health threat to African countries and the livelihoods of its people. To mitigate the impact of this disease, these countries implemented intervention measures including self-isolation, the closure of schools, banning of public gatherings, social distancing and border closures. Several epidemiological models have been used to improve our understanding of COVID-19 trajectory. This has helped inform decisions about pandemic planning, resource allocation, implementation of other non-pharmaceutical interventions (NPIs). This study presents estimates of the cases and fatalities due to COVID-19 and attempts to forecast the impact of governmental interventions in Burundi, Ethiopia, Kenya, Rwanda, South Sudan, Tanzania and Uganda. .MethodsWe used time series COVID-19 case and mortality data collated from the Johns Hopkins University (JHU) repository and an extended susceptible-infected-removed (eSIR) compartmental model incorporating quarantine and vaccination compartments to account for transmission dynamics and vaccine-induced immunity over time. The predication accuracy was evaluated using the root mean square error and mean absolute error.ResultsThe number of new and confirmed cases show an exponential trend since March 02 2020. The mean basic reproductive number (R0) was between 1.32 (95% CI, 1.17 - 1.49) in Rwanda and 8.52 (95% CI: 3.73 - 14.10) in Kenya, under exponential growth. There would be a total of 115,505 (95% CI:109,999 - 121,264), 7,072,584 (6,945,505 - 7,203,084), 18,248,566(18,100,299 - 18,391,438), 410,599 (399,776 - 421528), 386,020 (376,478 - 396244), 107,265 (95,757 - 119982), 3,145,602 (3,089,070 - 3205017) infected cases under the current country blockade by January 16/2022 in Burundi, Ethiopia, Kenya, Rwanda, South Sudan, Tanzania and Uganda respectively. We show that the low apparent morbidity and mortality observed in EACs, is likely biased by underestimation of infected and mortality cases.ConclusionThe current NPI measures can effectively reduce further spread of COVID-19 and should be strengthened. The observed reduction in R0 is consistent with intervention measures implemented in EACs, in particular, lockdowns and roll-out of vaccination programmes. Future work should account for the negative impact of the interventions to the economy and food systems.

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Section: Discussionmentioning

confidence: 99%

“…R0 = β/γ is the basic reproduction number assuming the whole population is susceptible. The Markov chain Monte Carlo (MCMC) algorithm is used to implement this model and provide the posterior estimates and credible intervals of the unknown parameters, R0, β, and γ [17,22].…”

Section: Introductionmentioning

confidence: 99%

Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa

Wamalwa

Tonnang

2021

Preprint

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“…As per his suggestion, if R0 < 1, the disease will perish in the population, and if R0 > 1, the disease will spread faster (Achaiah et al, 2020) . These calculations are as follows for countries that have successfully managed risk management.…”

Section: Sars-cov-2 Infection Prevention and Treatmentmentioning

confidence: 94%

Biological Disasters: An Overview of the Covid-19 Pandemic in the First Quarter of 2021

Artik¹,

Cesur²,

Kenar³

et al. 2021

Afet Ve Risk Dergisi

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Disaster is defined as the holistic state of natural or human-induced events that develop suddenly, whose controllability requires a systematic approach, which interrupts or stops social life and causes loss of life, property and often cannot be overcome with local capacity. Biological disasters can be human-induced as well as naturally infectious diseases and epidemiological emergencies. Considering the potential of the 21st century, biological disasters have played a role in influenza infections such as Swine Flu (H1N1), Bird Influenza (H5N1), and the Coronavirus family. Considering the disasters in which medical CBRN agents are effective, we evaluated the data in this study to determine the risk management of biological disasters. Since the World Health Organization (WHO) declared a pandemic on March 11, 2020, the first 15 countries are included in the list based on the total cumulative order of the cases and the 12-week case of SARS-CoV-2 in the first quarter of 2021. We tried to evaluate the analysis of data, mortality rates, and the point reached in vaccination within this timeline to provide an overview of 2021 in this research study.

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“…R 0 is a measure of contagiousness of infectious agents, and it refers to the number of new infections generated by each infected person [ 18 ]. If R 0 < 1, the disease will decline spreading in the population, and if R 0 > 1, the disease will spread faster [ 19 ]. Moreover, compartmental models have long been used to model the dynamics of infectious diseases including influenza [ 20 – 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…The Markov chain Monte Carlo (MCMC) algorithm was used to implement this model in order to provide the posterior estimates and credible intervals of the unknown parameters, R 0 , β, and γ [ 19 , 25 ]. Previously, Mkhatshwa et al and Wangping et al reported that MCMC prior distributions can be initialized according to the SARS data from Hong Kong [ 26 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa

Wamalwa

Tonnang

2022

BMC Infect Dis

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Background The emergence of COVID-19 as a global pandemic presents a serious health threat to African countries and the livelihoods of its people. To mitigate the impact of this disease, intervention measures including self-isolation, schools and border closures were implemented to varying degrees of success. Moreover, there are a limited number of empirical studies on the effectiveness of non-pharmaceutical interventions (NPIs) to control COVID-19. In this study, we considered two models to inform policy decisions about pandemic planning and the implementation of NPIs based on case-death-recovery counts. Methods We applied an extended susceptible-infected-removed (eSIR) model, incorporating quarantine, antibody and vaccination compartments, to time series data in order to assess the transmission dynamics of COVID-19. Additionally, we adopted the susceptible-exposed-infectious-recovered (SEIR) model to investigate the robustness of the eSIR model based on case-death-recovery counts and the reproductive number (R0). The prediction accuracy was assessed using the root mean square error and mean absolute error. Moreover, parameter sensitivity analysis was performed by fixing initial parameters in the SEIR model and then estimating R0, β and γ. Results We observed an exponential trend of the number of active cases of COVID-19 since March 02 2020, with the pandemic peak occurring around August 2021. The estimated mean R0 values ranged from 1.32 (95% CI, 1.17–1.49) in Rwanda to 8.52 (95% CI: 3.73–14.10) in Kenya. The predicted case counts by January 16/2022 in Burundi, Ethiopia, Kenya, Rwanda, South Sudan, Tanzania and Uganda were 115,505; 7,072,584; 18,248,566; 410,599; 386,020; 107,265, and 3,145,602 respectively. We show that the low apparent morbidity and mortality observed in EACs, is likely biased by underestimation of the infected and mortality cases. Conclusion The current NPIs can delay the pandemic pea and effectively reduce further spread of COVID-19 and should therefore be strengthened. The observed reduction in R0 is consistent with the interventions implemented in EACs, in particular, lockdowns and roll-out of vaccination programmes. Future work should account for the negative impact of the interventions on the economy and food systems.

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R0 and Re of COVID-19: Can We Predict When the Pandemic Outbreak will be Contained?

Cited by 72 publications

References 7 publications

Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa

Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa

Biological Disasters: An Overview of the Covid-19 Pandemic in the First Quarter of 2021

Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa

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