scholarly journals Estimation of undetected COVID-19 infections in India

2020 ◽  
Author(s):  
Siuli Mukhopadhyay ◽  
Debraj Chakraborty
Keyword(s):  
Death Rate ◽  
Case Fatality Rate ◽  
Indian Population ◽  
Case Fatality ◽  
Infection Model ◽  
Fatality Rate ◽  
Single Digit ◽  
High Death Rate ◽  
True Number ◽  
High Death

Background and ObjectivesWhile the number of detected COVID-19 infections are widely available, an understanding of the extent of undetected COVID-19 cases is urgently needed for an effective tackling of the pandemic and as a guide to lifting the lockdown. The aim of this work is to estimate and predict the true number of COVID-19 (detected and undetected) infections in India for short to medium forecast horizons. In particular, using publicly available COVID-19 infection data up to 28th April 2020, we forecast the true number of infections in India till the end of lockdown (3rd May) and five days beyond (8th May).MethodsThe high death rate observed in most COVID-19 hit countries is suspected to be a function of the undetected infections existing in the population. An estimate of the age weighted infection fatality rate (IFR) of the disease of 0.41%, specifically calculated by taking into account the age structure of Indian population, is already available in the literature. In addition, the recorded case fatality rate (CFR= 1%) of Kerala, the first state in India to successfully flatten the curve by consistently reporting single digit new infections from 12-20 April, is used as a second estimate of the IFR. These estimates are used to formulate a relationship between deaths recorded and the true number of infections and recoveries. The estimated undetected and detected cases time series based on these two IFR estimates are then used to fit a discrete time multivariate infection model to predict the total infections at the end of the formal lockdown period.ResultsOver three consecutive fortnight periods during the lockdown, it was noted that the rise in detected infections has decreased by 8.2 times. For an IFR of 0.41%, the rise in undetected infections decreased 2.5 times, while for the higher IFR value of 1%, undetected cases decreased by 2.4 times. The predicted number of total infections in India on 3rd May for both IFRs varied from 2.8 - 6.8 lakhs.Interpretation and ConclusionsThe behaviour of the undetected cases over time effectively illustrates the effects of lockdown and increased testing. From our estimates, it is found that the lockdown has brought down the undetected to detected cases ratio, and has consequently dampened the increase in the number of total cases. However, even though the rate of rise in total infections has fallen, the lifting of the lockdown should be done keeping in mind that 2.3 to 6.4 lakhs undetected cases will already exist in the population by 3rd May.

scholarly journals Case Fatality Rate of COVID-19 and Association With Sociodemographic Characteristics of Populations in Ecuador, 2020

2021 ◽  
Author(s):  
Karla Flores Sacoto ◽  
Galo Sánchez Del Hierro ◽  
Xavier Jarrín Estupiñan ◽  
Felipe Moreno-Piedrahita Hernandez
Keyword(s):  
Death Rate ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Sociodemographic Factors ◽  
Cross Sectional Study ◽  
Secondary Outcome ◽  
Morbidity Rate ◽  
Fatality Rate ◽  
Cross Sectional ◽  
The Impact

Abstract Background COVID-19 has caused deaths worldwide affecting the most vulnerable population with different case fatality rates. Socioeconomic conditions have demonstrated a role regarding the spread of infections and mortality. Socioeconomic characteristics of Ecuador related to poverty, ethnicity and demographic characteristics increase the impact of COVID-19 in certain populations. Methods Objective To analyze the influence of demographic factors on the COVID-19 case fatality rate (CFR) in Ecuador. Design: cross sectional study. Setting 24 provinces in Ecuador-221 cantons. Population: data including 233.277 confirmed COVID-19 cases of Ecuador. Primary and secondary outcome measures COVID-19 CFR and crude cause-specific death rate weight calculated using province-country level data from health ministry of Ecuador in data website. Results Ecuadors CFR is 4,03%, analyzed by cantons the CFR increases to a median of 5,75%, with cantons like Playas with a CFR of 32,39%. The morbidity rate has a median of 795,31 per 100 000 hab. with the highest rate in Isabela-Galápagos (10185,49), Aguarico-Orellana (9506,75) and Baños-Tungurahua (4156,85). And the crude COVID-19 death rate has a median of 39,73 per 100 000 hab. with the highest rate in Penipe-Chimborazo (201,29), 24 de Mayo-Manabí (143,79) and San Pedro de Huaca-Carchi (134,36). The correlations show relations with sociodemographic factors like poverty, ethnicity and scholarity. Conclusion The CFR is the proxy indicator of COVID-19 impact in Ecuador and the analysis made by location give us new information about the specific impact of this disease.

scholarly journals The role of population structure when measuring COVID-19 impact across countries

2020 ◽  
Author(s):  
Octavio Bramajo ◽  
Mauro Infantino ◽  
Rafael Unda ◽  
Walter D Cardona-Maya ◽  
Pablo Richly
Keyword(s):  
Death Rate ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Mortality Data ◽  
Significant Heterogeneity ◽  
Fatality Rate ◽  
Crude Death Rate ◽  
Using Data ◽  
Rate Case

AbstractThe search for accurate indicators to compare the pandemic impact between countries is still a challenge. The crude death rate, case fatality rate by country and sex, standardized fatality rate, and standardized death rate were calculated using data from Argentina and Colombia countries. We show that even when frequently used indicator as deaths per million are quite similar, 512 deaths per million in Argentina and 522 deaths per million in Colombia, a significant heterogeneity can be found when the mortality data is decomposed by sex or age.

scholarly journals COVID-19 case-fatality rate and demographic and socioeconomic influencers: worldwide spatial regression analysis based on country-level data

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e043560 ◽  
Author(s):  
Yang Cao ◽  
Ayako Hiyoshi ◽  
Scott Montgomery
Keyword(s):  
Death Rate ◽  
Case Fatality Rate ◽  
Spatial Regression ◽  
Temporal Trends ◽  
Case Fatality ◽  
Secondary Outcome ◽  
Fatality Rate ◽  
Country Level ◽  
Level Data ◽  
Testing Policies

ObjectiveTo investigate the influence of demographic and socioeconomic factors on the COVID-19 case-fatality rate (CFR) globally.DesignPublicly available register-based ecological study.SettingTwo hundred and nine countries/territories in the world.ParticipantsAggregated data including 10 445 656 confirmed COVID-19 cases.Primary and secondary outcome measuresCOVID-19 CFR and crude cause-specific death rate were calculated using country-level data from the Our World in Data website.ResultsThe average of country/territory-specific COVID-19 CFR is about 2%–3% worldwide and higher than previously reported at 0.7%–1.3%. A doubling in size of a population is associated with a 0.48% (95% CI 0.25% to 0.70%) increase in COVID-19 CFR, and a doubling in the proportion of female smokers is associated with a 0.55% (95% CI 0.09% to 1.02%) increase in COVID-19 CFR. The open testing policies are associated with a 2.23% (95% CI 0.21% to 4.25%) decrease in CFR. The strictness of anti-COVID-19 measures was not statistically significantly associated with CFR overall, but the higher Stringency Index was associated with higher CFR in higher-income countries with active testing policies (regression coefficient beta=0.14, 95% CI 0.01 to 0.27). Inverse associations were found between cardiovascular disease death rate and diabetes prevalence and CFR.ConclusionThe association between population size and COVID-19 CFR may imply the healthcare strain and lower treatment efficiency in countries with large populations. The observed association between smoking in women and COVID-19 CFR might be due to the finding that the proportion of female smokers reflected broadly the income level of a country. When testing is warranted and healthcare resources are sufficient, strict quarantine and/or lockdown measures might result in excess deaths in underprivileged populations. Spatial dependence and temporal trends in the data should be taken into account in global joint strategy and/or policy making against the COVID-19 pandemic.

An App for Understanding the Case Fatality Rate of Novel Coronavirus (COVID -19): Observation Study (Preprint)

2020 ◽  
Author(s):  
Tsair-Wei Chien ◽  
Wei-Chih Kan ◽  
Yu-Tsen Yeh ◽  
Shu-Chun Kuo
Keyword(s):  
Death Rate ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Daily Basis ◽  
World Health ◽  
Fatality Rate ◽  
Observation Study ◽  
The Mean ◽  
Novel Coronavirus ◽  
Health Organization

BACKGROUND When a new disease starts to spread, one of the commonly asked questions is (1) how deadly it is. World Health Organization (WHO) announced in a press conference on January 29th, 2020 and reported the death rate of COVID-19 was 2% on the case fatality rate(CFR). Whether the claim was underestimated is worthy of clarifications when considering the lag days from symptom onset to death. OBJECTIVE We developed an app for online displaying three types of computations of CFR and verifying the death rate of 2% substantially underestimated. METHODS We downloaded COVID-19 outbreak numbers from January 21 to February 25, 2020, in countries/regions on a daily basis from Github that contains daily information on confirmed cases, deaths, and the recovered in more than 30 Chinese locations and other worldwide countries/regions. Three CFRs on COVID-19 were compared, including (A) deaths/confirmed;(B) deaths/(deaths+recovered); and (C) deaths/(cases x days ago). The coefficients of variance (CV=the ratio of the standard deviation to the mean) were applied to measure the relative variability for each CFR. A dashboard was developed for daily display of the CFR on COVID-19 for each region. RESULTS We observed that the CVs were 0.07, 9.23, and 5.08 and the CFRs were 3.37%, 8.85%, and 3.58% for these three CFR computations, respectively, on Feb. 25, 2020. The death rate of COVID-19(=2%) announced by WHO using the formula of deaths/confirmed was substantially underestimated. A dashboard was created to present the provisional CFRs of COVID-19 on a daily basis. CONCLUSIONS We suggest examining these three CFRs as a reference to the final CFR. An app developed for displaying the provisional CFR with these three CFRs can modify the underestimated CFR reported by WHO and media. CLINICALTRIAL Not available

Letter To The Editor

PEDIATRICS ◽  
1978 ◽  
Vol 62 (3) ◽  
pp. 438-439
Author(s):  
Myron E. Wegman
Keyword(s):  
Death Rate ◽  
Neonatal Death ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Neonatal Care ◽  
The Other ◽  
Specific Rate ◽  
Fatality Rate ◽  
Considerable Influence ◽  
Neonatal Rate

Dr. Talbot is quite right that social and demographic factors have considerable influence on the neonatal death rate, as for almost any specific rate. As I stated, improved neonatal care seemed to me a factor because the neonatal rate had dropped much more rapidly than the postneonatal rate. I may well have given too much weight to this observation. On the other hand, I fear that, while Dr. Talbot's proposed case fatality rate could be useful and worth doing to analyze a particular hospital's experience, it would lead to more uncertainty than precision about the community.

scholarly journals Diphtheria in Glasgow 1934–42

1944 ◽  
Vol 43 (5) ◽  
pp. 341-348 ◽  
Author(s):  
H. S. Carter
Keyword(s):  
Death Rate ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Fatality Rate ◽  
General Survey ◽  
Average Case ◽  
Small Shift ◽  
Predominant Type

1. A general survey of diphtheria in Glasgow during 1934–42 has been made. Over 11,000 strains ofC. diphtheriaehave been typed and the incidence of the types is recorded.2. The period has been marked by a change in the predominant type of organism fromintermediustogravis, andgravisinfections are now responsible for most of the mortality, though the average case fatality rate due to them is not high.3. Diphtheria on the whole has been mild and complications relatively few, but some shift in the death-rate incidence to the higher age periods with increase in the corresponding case fatality rate has taken place. A small shift in the age incidence of diphtheria is noted.4. Some account of the effect of immunization is given.5. Comment is made upon colony variation among the types ofC. diphtheriae.

scholarly journals Study of COVID-19 epidemic in India and Andhra Pradesh with comparative global figures

2021 ◽  
Vol 6 (2) ◽  
pp. 90-93
Author(s):  
Ramakrishna Rachakonda ◽  
Kiranmavi Abburi ◽  
Sai Ramya Gonuguntla ◽  
Bhavanarayana Jannela ◽  
Chakradhar Bolleddu ◽  
...  
Keyword(s):  
United States Of America ◽  
Death Rate ◽  
Case Fatality Rate ◽  
Andhra Pradesh ◽  
Tertiary Care ◽  
Case Fatality ◽  
National Average ◽  
Family Welfare ◽  
Fatality Rate ◽  
Hospital Statistics

We have studied the pattern of COVID-19 epidemic in Andhra Pradesh and compared with other high burden states in India utilizing Government of India statistics. We have compared the Indian figures with the statistics in other countries. We have analyzed the data published by Ministry of Health and family Welfare Government of India, Government of Andhra Pradesh and WHO statistics as well as worldometer statistics. We have studied the hospital statistics of our tertiary care COVID center and analyzed the results.The statistics revealed highest number of cases are seen in United States of America with case fatality rate of 1.74%.Mexico has highest case fatality rate of 8.5%. Italy has 3.5% and United Kingdom 2.8%.In India Maharashtra has highest number of COVID-19 casualties with case fatality of 2.52%. Indian national average of case fatality is 1.47%. Andhra Pradesh has a case fatality of 0.80%. In Andhra Pradesh the pandemic of COVID-19 peaked in the months of August and September both in terms of number of cases and deaths and then decline started. Hospital based records showed a death rate of 3.92%.

Systematic review and meta-analysis of rates of clozapine-associated myocarditis and cardiomyopathy

2020 ◽  
Vol 54 (5) ◽  
pp. 467-481 ◽  
Author(s):  
Dan Siskind ◽  
Ashneet Sidhu ◽  
John Cross ◽  
Yee-Tat Chua ◽  
Nicholas Myles ◽  
...  
Keyword(s):  
Diagnostic Criteria ◽  
Adverse Drug Reactions ◽  
Death Rate ◽  
Case Fatality Rate ◽  
Event Rate ◽  
Case Fatality ◽  
Fatality Rate ◽  
Drug Reactions ◽  
Treatment Refractory ◽  
Event Rates

Background: Clozapine is the most effective medication for treatment refractory schizophrenia, but is associated with cardiac adverse drug reactions. Myocarditis and cardiomyopathy are the most serious cardiac adverse drug reactions although reported rates of these conditions vary in the literature. We systematically reviewed and meta-analysed the event rates, the absolute death rates and case fatality rates of myocarditis and cardiomyopathy associated with clozapine. Methods: PubMed, EMBASE and PsycINFO were searched for studies that reported on the incidence of cardiomyopathy or myocarditis in people exposed to clozapine. Data were meta-analysed using a random effects model, with subgroup analysis on study size, time frame, region, quality, retrospective vs prospective, and diagnostic criteria of myocarditis or cardiomyopathy. Results: 28 studies of 258,961 people exposed to clozapine were included. The event rate of myocarditis was 0.007 (95% confidence interval [CI] = [0.003, 0.016]), absolute death rate was 0.0004 (95% CI = [0.0002, 0.0009]) and case fatality rate was 0.127 (95% CI = [0.034, 0.377]). The cardiomyopathy event rate was 0.006 (95% CI = [0.002, 0.023]), absolute death rate was 0.0003 (95% CI = [0.0001, 0.0012]) and case fatality rate was 0.078 (95% CI = [0.018, 0.285]). Few included studies provided information on criteria for diagnosis of myocarditis and cardiomyopathy. Event rates of cardiomyopathy and myocarditis were higher in Australia. Conclusion: Clarity of diagnostic criteria for myocarditis remains a challenge. Observation bias may, in part, influence higher reported rates in Australia. Monitoring for myocarditis is warranted in the first 4 weeks, and treatment of comorbid metabolic syndrome and diabetes may reduce the risk of cardiomyopathy. The risks of myocarditis and cardiomyopathy are low and should not present a barrier to people with treatment refractory schizophrenia being offered a monitored trial of clozapine.

scholarly journals The effectiveness of public health interventions against COVID-19: Lessons from the Singapore experience

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248742
Author(s):  
John P. Ansah ◽  
David Bruce Matchar ◽  
Sean Lam Shao Wei ◽  
Jenny G. Low ◽  
Ahmad Reza Pourghaderi ◽  
...  
Keyword(s):  
Public Health ◽  
Case Fatality Rate ◽  
Total Population ◽  
Case Fatality ◽  
Contact Tracing ◽  
Infection Model ◽  
Fatality Rate ◽  
Health States ◽  
Actual Case ◽  
The Government

Background In dealing with community spread of COVID-19, two active interventions have been attempted or advocated—containment, and mitigation. Given the extensive impact of COVID-19 globally, there is international interest to learn from best practices that have been shown to work in controlling community spread to inform future outbreaks. This study explores the trajectory of COVID-19 infection in Singapore had the government intervention not focused on containment, but rather on mitigation. In addition, we estimate the actual COVID-19 infection cases in Singapore, given that confirmed cases are publicly available. Methods and findings We developed a COVID-19 infection model, which is a modified SIR model that differentiate between detected (diagnosed) and undetected (undiagnosed) individuals and segments total population into seven health states: susceptible (S), infected asymptomatic undiagnosed (A), infected asymptomatic diagnosed (I), infected symptomatic undiagnosed (U), infected symptomatic diagnosed (E), recovered (R), and dead (D). To account for the infection stages of the asymptomatic and symptomatic infected individuals, the asymptomatic infected individuals were further disaggregated into three infection stages: (a) latent (b) infectious and (c) non-infectious; while the symptomatic infected were disaggregated into two stages: (a) infectious and (b) non-infectious. The simulation result shows that by the end of the current epidemic cycle without considering the possibility of a second wave, under the containment intervention implemented in Singapore, the confirmed number of Singaporeans infected with COVID-19 (diagnosed asymptomatic and symptomatic cases) is projected to be 52,053 (with 95% confidence range of 49,370–54,735) representing 0.87% (0.83%-0.92%) of the total population; while the actual number of Singaporeans infected with COVID-19 (diagnosed and undiagnosed asymptomatic and symptomatic infected cases) is projected to be 86,041 (81,097–90,986), which is 1.65 times the confirmed cases and represents 1.45% (1.36%-1.53%) of the total population. A peak in infected cases is projected to have occurred on around day 125 (27/05/2020) for the confirmed infected cases and around day 115 (17/05/2020) for the actual infected cases. The number of deaths is estimated to be 37 (34–39) among those infected with COVID-19 by the end of the epidemic cycle; consequently, the perceived case fatality rate is projected to be 0.07%, while the actual case fatality rate is estimated to be 0.043%. Importantly, our simulation model results suggest that there about 65% more COVID-19 infection cases in Singapore that have not been captured in the official reported numbers which could be uncovered via a serological study. Compared to the containment intervention, a mitigation intervention would have resulted in early peak infection, and increase both the cumulative confirmed and actual infection cases and deaths. Conclusion Early public health measures in the context of targeted, aggressive containment including swift and effective contact tracing and quarantine, was likely responsible for suppressing the number of COVID-19 infections in Singapore.

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