cord-000916-b22s00es	2013	This study estimates the effectiveness and total cost (from a societal perspective, with a lifespan time horizon) of a comprehensive range of social distancing and antiviral drug strategies, under a range of pandemic severity categories. For severe pandemics of category 3 (CFR 0.75%) and greater, a strategy combining antiviral treatment and prophylaxis, extended school closure and community contact reduction resulted in the lowest total cost of any strategy, costing $1,584 per person at category 5. For severe pandemics of category 3 (CFR 0.75%) and greater, a strategy combining antiviral treatment and prophylaxis, extended school closure and community contact reduction resulted in the lowest total cost of any strategy, costing $1,584 per person at category 5. Keywords: Pandemic influenza, Economic analysis, Antiviral medication, Social distancing, Pandemic severity, Case fatality ratio Background While the H1N1 2009 virus spread world-wide and was classed as a pandemic, the severity of resulting symptoms, as quantified by morbidity and mortality rates, was lower than that which had previously occurred in many seasonal epidemics [1] [2] [3] .
cord-021907-omruua6n	2009	
cord-127109-jdizyzbl	2020	Indeed, I show here that SIR type models -and others exhibiting similarly flexible growth dynamics -are non-identified with respect to the CFR and the fraction of observed infections. Figure 4 shows the country specific estimates of reporting delay, CFR and fraction of observed cases (assuming a true CFR of 1%) obtained in this fashion. In turn, Figure 5 shows the implied relative case counts when shifted by the estimated delays and scaled to reflect the unobserved fraction of cases for each country. A suitable reporting delay τ c can be estimated by visual inspection of the data, but again the fraction of observed cases α c and CFR cfr are not jointly identifiable if there exist sets of parameters such that a t−τ = αa t , as is the case for dynamic SIR type models. Assuming that only a fraction α of cases is observed, the model is estimated with the following Relative days since two death per mill.
cord-245047-d81cf3ms	2020	
cord-258235-khdyxiwe	2020	To solve the first problem, we presented a hybrid approach based on autoregressive integrated moving average model and Wavelet-based forecasting model that can generate short-term (ten days ahead) forecasts of the number of daily confirmed cases for Canada, France, India, South Korea, and the UK. In this section, we first briefly discuss these datasets, followed by the development of the proposed hybrid model, and finally, the application of the proposed model to generate short-term forecasts of the future COVID-19 cases for five different countries. Algorithm 1 Proposed Hybrid ARIMA-WBF Model 1 Given a time series of length n, input the in-sample (training) COVID-19 daily cases data. Thus, these real-time short-term forecasts based on the proposed hybrid ARIMA-WBF model for Canada, France, India, South Korea, and the UK will be helpful for government officials and policymakers to allocate adequate health care resources for the coming days.
cord-263044-o8aosx2q	2015	
cord-266989-n040i865	2020	
cord-269455-pkjov371	2020	
cord-279539-s2zv7hr4	2020	
cord-280672-6x968dwk	2020	
cord-281508-zl2url8z	2020	
cord-285546-5tjhdczt	2020	
cord-286958-e1ey31eo	2020	
cord-291400-o9skj94r	2004	Several retrospective reviews of bacteremic pneumococcal pneumonia suggest that dual therapy with a beta-lactam and a macrolide antimicrobial agent is associated with a lower case fatality rate than therapy with a beta-lactam alone. With the advent of modern microbiology, Streptococcus pneumoniae (pneumococcus) was identified as the cause of community-acquired pneumonia (CAP) in the most patients [1] . Changes that have been associated with improvements in CFR in some series of patients with CAP include more rapid antibiotic delivery [31] , combination therapy with a cephalosporin with good pneumococcal activity and macrolide (versus the cephalosporin alone), and therapy with a fluoroquinolone (ciprofloxacin; versus a cephalosporin alone) [32] . A previous study of patients with CAP, but not nonbacteremic pneumococcal pneumonia, found that treated with blactamase inhibitors and a macrolide were less effective than treatment with a cephalosporin alone [32] .
cord-291975-y8ck4lo8	2020	The estimation of an IFR is based on two different and -regarding the influence of selection biasdivergent procedures to calculate a CFR from infection-related population data. This formula is not relying anymore on cases reported in the official databases of JH or ECDC and it served as a cross-validation figure for the IFR and the CFRs, which are solely based on these data and the population data of Iceland in the validation part of the results section. The IFRdeCode is the figure derived from testing the general population of Iceland and served to cross validate the mortality figures CFR and classic CFR that have been calculated from the data repositories of JH and the IFR that used this repository in conjunction with the test data published by Iceland''s Department of Public Health.
cord-296992-2vp35fwv	2018	
cord-300750-huyl21vz	2020	
cord-303991-pjycxlse	2020	
cord-315536-fgjhli0p	2020	
cord-330338-i6ozygkp	2020	
cord-330742-m5xx8861	2020	METHODS: We used the national surveillance database of COVID-19 in mainland China to compared gender differences in attack rate (AR), proportion of severe and critical cases (PSCC) and case fatality rate (CFR) in relation to age, affected province, and onset-to-diagnosis interval. After adjusting for age, affected province and onset-to-diagnosis interval, the female-to-male difference in AR, PSCC and CFR remained significant in multivariate logistic regression analyses. In this study, we used the surveillance data containing all confirmed cases in mainland China as of April 28, 2020 to evaluated gender-specific differences in attack rate, proportion of severe and critical cases, and case fatality in relation to age, affected province and onset-to-diagnosis interval, in order to provide evidence-based guidance for more effective and equitable interventions and treatments.
cord-333827-zpdnzwle	2020	title: Potential risk factors for case fatality rate of novel coronavirus (COVID-19) in China: A pooled analysis of individual patient data This study aims to perform the meta-analysis of risk factors for the case fatality rate (CFR) of the 2019 novel coronavirus (COVID-19). After comparing the patients between fatal cases and non-fatal cases, several important factors are found to significantly increase the CFR in patients with COVID-19, and include the age ranging 60–70 (OR = 1.85; 95% CI = 1.62 to 2.11; P < .00001) and especially≥70 (OR = 8.45; 95% CI = 7.47 to 9.55; P < .00001), sex of male (OR = 1.88; 95% CI = 1.30 to 2.73; P = .0008), occupation of retirees (OR = 4.27; 95% CI = 2.50 to 7.28; P < .00001), and severe cases (OR = 691.76; 95% CI = 4.82 to 99,265.63; P = .01).
cord-336115-7ykvl3u6	2020	
cord-338184-899km704	2020	
cord-338462-muetf7l1	2020	
cord-342996-honeavwj	2015	
cord-343449-4uxwojzo	2020	
cord-347182-oj3v1x99	2020	
cord-347353-ll2pnl81	2020	
cord-348056-kx9wvw8c	2020	
cord-349921-v1tewoi0	2020	
cord-349978-zklwovba	2020	We developed a model to use CFR alongside other epidemiological factors underpinning disease transmission to infer the likely number of cases in a population from newly reported deaths. This model combines data on the reproduction number (R) and serial interval distribution to simulate new cases ''y t '' on day ''t'' from a Poisson distribution: Our approach is implemented in the R software 13 and publicly available as R scripts (see Extended data) 14 , as well as in a user-friendly, interactive web-interface available at: https://cmmid.github.io/visualisations/ inferring-covid19-cases-from-deaths 2 . We first used our model to assess likely epidemic sizes when an initial COVID-19 death is reported in a new location. Extended data for: Inferring the number of COVID-19 cases from recently reported deaths This article describes a statistical modeling method for estimating the number of COVID-19 cases from the first reported deaths in a defined location.
cord-354073-tn76muv6	2020	
cord-354133-11b0d499	2020