scholarly journals SEIHCRD Model for COVID-19 Spread Scenarios, Disease Predictions and Estimates the Basic Reproduction Number, Case Fatality Rate, Hospital, and ICU Beds Requirement

2020 ◽  
Vol 125 (3) ◽  
pp. 991-1031
Author(s):  
Avaneesh Singh ◽  
Manish Kumar Bajpai
Keyword(s):  
Basic Reproduction Number ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
Fatality Rate ◽  
Number Case ◽  
The Basic Reproduction Number

scholarly journals SEIHCRD Model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement

2020 ◽  
Author(s):  
Avaneesh Singh ◽  
Manish Kumar Bajpai
Keyword(s):  
Basic Reproduction Number ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
The United States ◽  
Fatality Rate ◽  
Hospital Beds ◽  
The Impact ◽  
Over Time ◽  
The Basic Reproduction Number

We have proposed a new mathematical method, SEIHCRD-Model that is an extension of the SEIR-Model adding hospitalized and critical twocompartments. SEIHCRD model has seven compartments: susceptible (S), exposed (E), infected (I), hospitalized (H), critical (C), recovered (R), and deceased or death (D), collectively termed SEIHCRD. We have studied COVID- 19 cases of six countries, where the impact of this disease in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. SEIHCRD model is estimating COVID-19 spread and forecasting under uncertainties, constrained by various observed data in the present manuscript. We have first collected the data for a specific period, then fit the model for death cases, got the values of some parameters from it, and then estimate the basic reproduction number over time, which is nearly equal to real data, infection rate, and recovery rate of COVID-19. We also compute the case fatality rate over time of COVID-19 most affected countries. SEIHCRD model computes two types of Case fatality rate one is CFR daily and the second one is total CFR. We analyze the spread and endpoint of COVID-19 based on these estimates. SEIHCRD model is time-dependent hence we estimate the date and magnitude of peaks of corresponding to the number of exposed cases, infected cases, hospitalized cases, critical cases, and the number of deceased cases of COVID-19 over time. SEIHCRD model has incorporated the social distancing parameter, different age groups analysis, number of ICU beds, number of hospital beds, and estimation of how much hospital beds and ICU beds are required in near future.

scholarly journals Estimation of the basic reproduction number, average incubation time, asymptomatic infection rate, and case fatality rate for COVID-19: Meta-analysis and sensitivity analysis

2020 ◽  
Author(s):  
Wenqing He ◽  
Grace Y. Yi ◽  
Yayuan Zhu
Keyword(s):  
Confidence Interval ◽  
Basic Reproduction Number ◽  
Incubation Time ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Meta Analysis ◽  
Case Fatality ◽  
Asymptomatic Infection ◽  
Fatality Rate ◽  
The Basic Reproduction Number

AbstractThe coronavirus disease 2019 (COVID-19) has been found to be caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensive knowledge of COVID-19 remains incomplete and many important features are still unknown. This manuscripts conduct a meta-analysis and a sensitivity study to answer the questions: What is the basic reproduction number? How long is the incubation time of the disease on average? What portion of infections are asymptomatic? And ultimately, what is the case fatality rate? Our studies estimate the basic reproduction number to be 3.15 with the 95% interval (2.41, 3.90), the average incubation time to be 5.08 days with the 95% confidence interval (4.77, 5.39) (in day), the asymptomatic infection rate to be 46% with the 95% confidence interval (18.48%, 73.60%), and the case fatality rate to be 2.72% with 95% confidence interval (1.29%, 4.16%) where asymptomatic infections are accounted for.

scholarly journals The first wave of the SARS-CoV-2 epidemic in Tuscany (Italy): A SI2R2D compartmental model with uncertainty evaluation

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250029
Author(s):  
Michela Baccini ◽  
Giulia Cereda ◽  
Cecilia Viscardi
Keyword(s):  
Basic Reproduction Number ◽  
Compartmental Model ◽  
Reproduction Number ◽  
Case Fatality ◽  
Parametric Bootstrap ◽  
Model Parameters ◽  
Fatality Rate ◽  
Epidemic Curve ◽  
Plausible Values ◽  
The Basic Reproduction Number

With the aim of studying the spread of the SARS-CoV-2 infection in the Tuscany region of Italy during the first epidemic wave (February-June 2020), we define a compartmental model that accounts for both detected and undetected infections and assumes that only notified cases can die. We estimate the infection fatality rate, the case fatality rate, and the basic reproduction number, modeled as a time-varying function, by calibrating on the cumulative daily number of observed deaths and notified infected, after fixing to plausible values the other model parameters to assure identifiability. The confidence intervals are estimated by a parametric bootstrap procedure and a Global Sensitivity Analysis is performed to assess the sensitivity of the estimates to changes in the values of the fixed parameters. According to our results, the basic reproduction number drops from an initial value of 6.055 to 0 at the end of the national lockdown, then it grows again, but remaining under 1. At the beginning of the epidemic, the case and the infection fatality rates are estimated to be 13.1% and 2.3%, respectively. Among the parameters considered as fixed, the average time from infection to recovery for the not notified infected appears to be the most impacting one on the model estimates. The probability for an infected to be notified has a relevant impact on the infection fatality rate and on the shape of the epidemic curve. This stresses the need of collecting information on these parameters to better understand the phenomenon and get reliable predictions.

scholarly journals Exploring COVID-19 Daily Records of Diagnosed Cases and Fatalities Based on Simple Non-parametric Methods

2020 ◽  
Author(s):  
Hans H. Diebner ◽  
Nina Timmesfeld
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Time Lag ◽  
Case Fatality ◽  
Parametric Models ◽  
Intermediate Step ◽  
Parametric Methods ◽  
Temporal Behaviour ◽  
The Basic Reproduction Number ◽  
Non Parametric

Based on comprehensible non-parametric methods, estimates of crucial parameters that characterise the COVID-19 pandemic with a focus on the German epidemic are presented. Where appropriate, the estimates for Germany are compared with the results for six other countries (FR, IT, US, UK, ES, CH) to get an idea of the breadth of applicability and a relational understanding. Thereby, only prevalence data of daily reported new counts of diagnosed cases and fatalities provided by the ECDC are used. Where appropriate, the results are compared with conclusions drawn from using the dataset provided by the RKI. Drawing on uncertain a priori knowledge is avoided. Specifically, we present estimates for the duration from diagnosis to death being 13 days for Germany and about 2 days for Italy as the extremes. Furthermore, based on the knowledge of this time lag between diagnoses and deaths, properly delayed asymptotic as well as instantaneous fatality-case ratios are calculated having superiority compared to the commonly published case-fatality rate. The median of the time series of the instantaneous fatality-case ratio with proper delay of 13-days between cases and deaths for Germany turns out to be 0.024. Asymptotic values are presented for other countries with France ranking highest with a fatality-case ratio of almost 0.2 at its peak. The basic reproduction number, R_0, for Germany is estimated to be between 2.4 and 3.4. The uncertainty stems from uncertain knowledge of the generation time. A delay autocorrelation shows resonances at about 4 days and 7 days, where the latter resonance is at least partially attributable to the sampling process with weekly periodicity. The calculation of the basic reproduction number is based on an evaluation of cumulative numbers of cases yielding time-dependent doubling times as an intermediate step. This allows to infer to the reproduction number during the early phase of onset of the epidemic. In a second approach, the instantaneous basic reproduction number is derived from the incident (counts of new) cases and allows, in contrast to the first version, to infer to the temporal behaviour of the reproduction number during the later epidemic course. To conclude, by avoiding complicated parametric models we provide insights into basic features of the COVID-19 epidemic in an utmost transparent and comprehensible way.

scholarly journals Omicron Impact in India: An Early Analysis of the Ongoing COVID-19 Third Wave

2022 ◽  
Author(s):  
Rajesh Ranjan
Keyword(s):  
Rapid Growth ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
Sir Model ◽  
Third Wave ◽  
Fatality Rate ◽  
Sequencing Data ◽  
The Third ◽  
Second Wave

India is currently experiencing the third wave of COVID-19, which began on around 28 Dec. 2021. Although genome sequencing data of a sufficiently large sample is not yet available, the rapid growth in the daily number of cases, comparable to South Africa, United Kingdom, suggests that the current wave is primarily driven by the Omicron variant. The logarithmic regression suggests the growth rate of the infections during the early days in this wave is nearly four times than that in the second wave. Another notable difference in this wave is the relatively concurrent arrival of outbreaks in all the states; the effective reproduction number (Rt) although has significant variations among them. The test positivity rate (TPR) also displays a rapid growth in the last 10 days in several states. Preliminary estimates with the SIR model suggest that the peak to occur in late January 2022 with peak caseload exceeding that in the second wave. Although the Omicron trends in several countries suggest a decline in case fatality rate and hospitalizations compared to Delta, a sudden surge in active caseload can temporarily choke the already stressed healthcare India is currently experiencing the third wave of COVID-19, which began on around 28 Dec. 2021. Although genome sequencing data of a sufficiently large sample is not yet available, the rapid growth in the daily number of cases, comparable to South Africa, United Kingdom, suggests that the current wave is primarily driven by the Omicron variant. The logarithmic regression suggests the growth rate of the infections during the early days in this wave is nearly four times than that in the second wave. Another notable difference in this wave is the relatively concurrent arrival of outbreaks in all the states; the effective reproduction number (Rt) although has significant variations among them. The test positivity rate (TPR) also displays a rapid growth in the last 10 days in several states. Preliminary estimates with the SIR model suggest that the peak to occur in late January 2022 with peak caseload exceeding that in the second wave. Although the Omicron trends in several countries suggest a decline in case fatality rate and hospitalizations compared to Delta, a sudden surge in active caseload can temporarily choke the already stressed healthcare infrastructure. Therefore, it is advisable to strictly adhere to COVID-19 appropriate behavior for the next few weeks to mitigate an explosion in the number of infections.

scholarly journals Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases

2020 ◽  
Author(s):  
Sung-mok Jung ◽  
Andrei R. Akhmetzhanov ◽  
Katsuma Hayashi ◽  
Natalie M. Linton ◽  
Yichi Yang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Basic Reproduction Number ◽  
Cumulative Incidence ◽  
Reproduction Number ◽  
Early Stage ◽  
Case Fatality ◽  
Primary Case ◽  
Risk Of Death ◽  
Novel Coronavirus ◽  
The Basic Reproduction Number

AbstractThe exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside of China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number—the average number of secondary cases generated by a single primary case in a naïve population. We modeled epidemic growth either from a single index case with illness onset on 8 December, 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January, 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% CI: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data.

scholarly journals Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases

2020 ◽  
Vol 9 (2) ◽  
pp. 523 ◽  
Author(s):  
Sung-mok Jung ◽  
Andrei R. Akhmetzhanov ◽  
Katsuma Hayashi ◽  
Natalie M. Linton ◽  
Yichi Yang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Basic Reproduction Number ◽  
Cumulative Incidence ◽  
Reproduction Number ◽  
Early Stage ◽  
Case Fatality ◽  
Primary Case ◽  
Risk Of Death ◽  
Novel Coronavirus ◽  
The Basic Reproduction Number

The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number—the average number of secondary cases generated by a single primary case in a naïve population. We modeled epidemic growth either from a single index case with illness onset on 8 December 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% confidence interval [CI]: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data.

scholarly journals Effect of evidence updates on key determinants of measles vaccination impact: a DynaMICE modelling study in ten high-burden countries

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Han Fu ◽  
Kaja Abbas ◽  
Petra Klepac ◽  
Kevin van Zandvoort ◽  
Hira Tanvir ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Vaccine Efficacy ◽  
Reproduction Number ◽  
Case Fatality ◽  
Routine Immunisation ◽  
High Burden ◽  
Fatality Risk ◽  
Measles Vaccination ◽  
Case Fatality Risk ◽  
The Basic Reproduction Number

Abstract Background Model-based estimates of measles burden and the impact of measles-containing vaccine (MCV) are crucial for global health priority setting. Recently, evidence from systematic reviews and database analyses have improved our understanding of key determinants of MCV impact. We explore how representations of these determinants affect model-based estimation of vaccination impact in ten countries with the highest measles burden. Methods Using Dynamic Measles Immunisation Calculation Engine (DynaMICE), we modelled the effect of evidence updates for five determinants of MCV impact: case-fatality risk, contact patterns, age-dependent vaccine efficacy, the delivery of supplementary immunisation activities (SIAs) to zero-dose children, and the basic reproduction number. We assessed the incremental vaccination impact of the first (MCV1) and second (MCV2) doses of routine immunisation and SIAs, using metrics of total vaccine-averted cases, deaths, and disability-adjusted life years (DALYs) over 2000–2050. We also conducted a scenario capturing the effect of COVID-19 related disruptions on measles burden and vaccination impact. Results Incorporated with the updated data sources, DynaMICE projected 253 million measles cases, 3.8 million deaths and 233 million DALYs incurred over 2000–2050 in the ten high-burden countries when MCV1, MCV2, and SIA doses were implemented. Compared to no vaccination, MCV1 contributed to 66% reduction in cumulative measles cases, while MCV2 and SIAs reduced this further to 90%. Among the updated determinants, shifting from fixed to linearly-varying vaccine efficacy by age and from static to time-varying case-fatality risks had the biggest effect on MCV impact. While varying the basic reproduction number showed a limited effect, updates on the other four determinants together resulted in an overall reduction of vaccination impact by 0.58%, 26.2%, and 26.7% for cases, deaths, and DALYs averted, respectively. COVID-19 related disruptions to measles vaccination are not likely to change the influence of these determinants on MCV impact, but may lead to a 3% increase in cases over 2000–2050. Conclusions Incorporating updated evidence particularly on vaccine efficacy and case-fatality risk reduces estimates of vaccination impact moderately, but its overall impact remains considerable. High MCV coverage through both routine immunisation and SIAs remains essential for achieving and maintaining low incidence in high measles burden settings.

scholarly journals The Comparison of Epidemiological Characteristics Between Confirmed and Clinically Diagnosed Cases With COVID-19 in Wuhan, China

2020 ◽  
Author(s):  
Fang Shi ◽  
Haoyu Wen ◽  
Rui Liu ◽  
Jianjun Bai ◽  
Fang Wang ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Clinical Diagnosis ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
Fatality Rate ◽  
Elderly Men ◽  
Epidemiological Characteristics ◽  
Over Time

Abstract Background: To put COVID-19 patients into hospital timely, the clinical diagnosis had been implemented in Wuhan in the early outbreak. Here we compared the epidemiological characteristics of laboratory-confirmed and clinically diagnosed cases with COVID-19 in Wuhan.Methods: Demographics, case severity and outcomes of 29886 confirmed cases and 21960 clinically diagnosed cases reported between December 2019 and February 24, 2020, were compared. The risk factors were estimated, and the effective reproduction number of SARS-CoV-2 (Rt) was also calculated.Results: The interval between symptom onset and diagnosis of confirmed and clinically diagnosed cases reduced gradually as time went by, and the proportion of severe and critical cases as well as case fatality rates of the two groups all decreased over time. The proportion of severe and critical cases (21.5% vs 14.0%, P<0.0001) and case fatality rates (5.2% vs 1.2%, P<0.0001) of confirmed cases were all higher than those of clinically diagnosed cases. Risk factors for death we observed in all two groups were older age, male, severe or critical cases. Rt showed a downward trend after the lockdown of Wuhan, it dropped below 1.0 on February 6 among confirmed cases, and February 8 among clinically diagnosed cases.Conclusion: Public health responses taken in Wuhan, including clinical diagnosis, have contributed to slow transmission. In cases where testing kits are insufficient, clinical diagnosis is effective, which is helpful to quarantine or treat infected cases as soon as possible, and prevent the epidemic from worsening. To decrease the case fatality rate of COVID-19, it is necessary to strengthen early warning and intervention of severe and critical elderly men.

Sign in / Sign up

Export Citation Format

Share Document