Early Phasic Containment of COVID-19 in Substantially Affected States of India

Introduction: India is experiencing the global COVID-19 pandemic caused with the infection of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). To explore the early epidemic course and the effectiveness of lockdowns on COVID-19 pandemic in some worst-affected Indian states. Methods: Using publicly available real data and model-based prediction, the growth rate, case fatality rate, serial interval, and time-varying reproduction number (R) of COVID-19 were estimated, before and after lockdown implementation in India. Results: The spread of COVID-19 epidemic in some highly-impacted Indian states displayed a characteristic sub-exponential growth projected up to 3 May 2020, as a consequence of lockdown strategies, in addition to improvement of reproduction number (R), serial interval, and daily growth rate, but not case fatality rate (CFR). The effect of COVID-19 containment was more prominent in second phase of lockdown with declining R, which was still >1. Conclusion: The current findings suggest the requirement of sustained interventions for effective containment of COVID-19 pandemic in Indian context. Keywords: COVID-19, SARS-CoV-2, Indian states, epidemiological parameters, lockdown effect.

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Phasic containment of COVID-19 in substantially affected states of India

10.1101/2020.05.05.20092130 ◽

2020 ◽

Author(s):

Manisha Mandal ◽

Shyamapada Mandal

Keyword(s):

Case Fatality Rate ◽

Reproduction Number ◽

Case Fatality ◽

Second Phase ◽

Fatality Rate ◽

Daily Growth Rate ◽

Daily Growth ◽

Indian States ◽

Indian Context ◽

Serial Interval

AbstractThe spread of COVID-19 epidemic in some highly-impacted Indian states displayed a characteristic sub-exponential growth projected up to 3 May 2020, as a consequence of lockdown strategies, in addition to improvement of reproduction number (R), serial interval, and daily growth rate, but not case fatality rate (CFR). The effect of COVID-19 containment was more prominent in second phase of lockdown with declining R, which was still >1, suggesting the requirement of sustained interventions for effective containment of COVID-19 pandemic in Indian context.

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Short-Term Projection of COVID 19 Cases in Kenya using an Exponential Model

10.21203/rs.3.rs-21900/v2 ◽

2020 ◽

Author(s):

Samuel Kiruri Kirichu

Keyword(s):

Growth Rate ◽

Real Time ◽

Case Fatality Rate ◽

Reproduction Number ◽

Case Fatality ◽

Daily Growth Rate ◽

Daily Growth ◽

The Real ◽

Time Reproduction ◽

First Case

Abstract Introduction: The COVID-19 disease has spread to over 200 countries and territories since the first case was recorded in Wuhan, China in December 2019. In Kenya, the first case of COVID-19 was recorded on 13th March 2020 and since then over five thousand cases have been confirmed as of 26th June 2020. In the same period, one hundred and forty four mortality cases had been recorded in the country. With the rapid changing situation, timely and reliable data is required for monitoring, planning and rapid decision making with an aim of reversing the already deteriorating situation (economic, health, learning among others) in the country. Methods: The study used the exponential growth model to estimate the daily growth rate and the real-time-effective reproduction number. The study also estimated the naïve and the adjusted Case Fatality Rates. Results: The naïve-Case Fatality Rate of 26th June 2020 which was the 106 day after the first case was confirmed in Kenya was estimated as 2.5% while the adjusted Case Fatality Rate with a lag of 2 days was estimated as 2.6%. The daily exponential growth rate was estimated as 0.22 while the real-time reproduction number as of 26th June 2020 was estimated as 1.28 [95% CI: 1.27 – 1.29]. Conclusion: The daily growth rate and the real-time reproduction number indicated that the outbreak was still growing as of the time of analysis.

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Estimating COVID-19 pandemic severity in Indian context

10.21203/rs.3.rs-29484/v1 ◽

2020 ◽

Author(s):

Manisha Mandal ◽

Shyamapada Mandal

Keyword(s):

Case Fatality Rate ◽

Reproduction Number ◽

Case Fatality ◽

Fatality Rate ◽

Relative Susceptibility ◽

Indian Context ◽

Serial Interval ◽

Interval Reproduction ◽

Pandemic Severity ◽

Cumulative Recovery

Abstract The case fatality rate (CFR) represents one of the most vital factors in demonstrating the severity of novel infectious disease, COVID-19. Various estimates of COVID-19 fatalities (based on the publicly and published information and data in Indian context of COVID-19): confirmed CFR, asymptomatic CFR, symptomatic CFR, and hospitalized CFR were 2.32% (2.05-2.59), 0.14% (0.12-0.16), 0.32% (0.27-0.36), 1.86% (1.64-2.07) respectively. The relative susceptibility of developing symptoms (RSODS) and relative susceptibility of developing infection (RSODI) of COVID-19 were ~33 times higher among people aged <45 years. The RSODS estimates were 1.97 (0.47-3.47), 0.62 (0.15-1.09), 0.29 (0.07-0.52), 0.06 (0.02-0.10) respectively, for patients <45 years, 45-60, 60-75, >75 years. Similar trend, for RSODI were found, with relatively higher value, compared to RSODS, which decreased with the increase of age. The 14-day lag estimate of CFR were 18.07 (15.67-20.47), and outcome (deaths plus recoveries)-based estimate of CFR were 16.57 (14.65-18.49). The growth rate, serial interval, reproduction number and average time from onset of COVID-19 infection to death were 6.12% (5.30%-6.99%), 11.4 days (9.91-12.85), 1.03 (1.01-1.05), and 11.85 days (10.55-13.15), respectively. Among 1,673,688 samples tested, 62,939 (3.76%) were positive for COVID-19 accounting 1,228 tests per million population of India, as of May 10, 2020. The average daily recovery was 19.45% (14.75- 24.15) and average cumulative recovery was 12.68% (10.70- 14.66) among COVID-19 patients. As per our belief and knowledge, this is the first study of its kind in reporting COVID-19 severity in Indian context during pandemic.

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

Computer Modeling in Engineering & Sciences ◽

10.32604/cmes.2020.012503 ◽

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

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Protection offered by COVID-19 vaccines in reducing SARS-CoV-2 infection frequency; severity and mortality, among Indian Healthcare Workers: Multicenter, pan-Fortis study

10.21203/rs.3.rs-1055978/v1 ◽

2021 ◽

Author(s):

Subrat Acharya ◽

Gaurav Mahindra ◽

Purushottam Nirala ◽

Sulabh Tripathi ◽

Bishnu Panigrahi ◽

...

Keyword(s):

General Population ◽

Healthcare Workers ◽

Case Fatality Rate ◽

Population Control ◽

Real Life ◽

Case Fatality ◽

Infection Frequency ◽

Fatality Rate ◽

Indian States ◽

Increased Risk

Abstract During COVID-19 pandemic, Healthcare Workers (HCWs) were at increased risk for exposure to SARS-CoV-2 virus and prioritized for early administration of COVID-19 vaccines in India. Real-life scenario information among vaccinated HCWs acquiring COVID-19 infection, is scarce. We retrospectively analyzed COVID-19 infection frequency, severity, and associated mortality among healthcare workers, immunized with either Covishield or Covaxin vaccines at 27 Fortis Hospitals across 11 Indian states. Positive cases were identified based on RT-PCR or rapid antigen tests for SARS-CoV-2 between 16th January 2021 till 15th May 2021. 20034 HCWs received vaccination. 3971 received 1 dose, 16063 received 2 doses. Post-vaccination, 1139 HCWs acquired COVID-19 infection, 180 (4.53%) and 959 (5.97%) among partially and fully vaccinated category, respectively. Breakthrough infection occurred among 913 (5.68%) HCWs. Concurrently, Case Positivity Rate was 11.9%, among general population (control). Among 1139 positive cases, mild, moderate, and severe infections were 1059 (93%), 71 (6.2%) and 9 (0.8%), respectively with Case Fatality Rate of 0.18%, compared to 0.92% among general population (p=0.0043). The Case Fatality Rate in vaccinated HCWs was found to be 80% less than that in general population (control). Hence, COVID-19 vaccines available in India seem to be effective against SARS-CoV-2 virus.

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

10.1101/2020.07.24.20161752 ◽

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.

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

10.1101/2022.01.09.22268969 ◽

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.

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COVID-19 pandemic scenario in India compared to China and rest of the world: a data driven and model analysis

10.1101/2020.04.20.20072744 ◽

2020 ◽

Cited By ~ 6

Author(s):

Manisha Mandal ◽

Shyamapada Mandal

Keyword(s):

Reproduction Number ◽

Case Fatality ◽

Respiratory Illness ◽

Data Driven ◽

Daily Growth Rate ◽

Daily Growth ◽

Epidemic Size ◽

Serial Interval ◽

The World ◽

Before And After

AbstractThe COVID-19 is a rapidly spreading respiratory illness caused with the infection of SARS-CoV-2. The COVID-19 data from India was compared with China and rest of the world. The average values of daily growth rate (DGR), case recovery rate (CRR), case fatality rate (CFR), serial interval (SI) of COVID-19 in India was 17%, 8.25%, and 1.87%, and 5.76 days respectively, as of April 9, 2020. The data driven estimates of basic reproduction number (R0), average reproduction number (R) and effective reproduction number (Re) were 1.03, 1.73, and 1.35, respectively. The results of exponential and SIR model showed higher estimates of R0, R and Re. The data driven as well as estimated COVID-19 cases reflect the growing nature of the epidemic in India and world excluding China, whereas the same in China reveal the involved population became infected with the disease and moved into the recovered stage. The epidemic size of India was estimated to be ∼30,284 (as of April 15, 2020 with 12,370 infectious cases) with an estimated end of the epidemic on June 9, 2020. The Re values in India before and after lockdown were 1.62 and 1.37 respectively, with SI 5.52 days and 5.98 days, respectively, as of April 17, 2020, reflecting the effectiveness of lockdown strategies. Beyond April 17, 2020, our estimate of 24,431 COVID-19 infected cases with lockdown is 78% lower compared to the 112,042 case estimates in absence of lockdown, on April 27, 2020. To early end of the COVID-19 epidemic, strong social distancing is important.

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

10.1101/2020.04.28.20083758 ◽

2020 ◽

Cited By ~ 3

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.

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