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We estimated the case-fatality risk for coronavirus disease cases in China (3.5%); China, excluding Hubei Province (0.8%); 82 countries, territories, and areas (4.2%); and on a cruise ship (0.6%). Lower estimates might be closest to the true value, but a broad range of 0.25%-3.0% probably should be considered.

Case-fatality risk (CFR) is an important indicator of disease severity for influenza infection and an input to estimates of influenza burden and vaccination impact. However, CRF estimates based on laboratory-confirmed cases (cCFR) are more-highly sensitive to features of the local health-care system and surveillance. Estimates based on diagnosed-symptomatic cases (sCFR) are likely to be more consistent across health systems but are less commonly reported. We present a systematised review of sCFR for seasonal influenza to determine the availability of studies, variation across their sCFR estimates, and factors driving this variation. We identified 10 studies reporting sCFR, or primary data for its direct estimation, resulting in 40 location and season-specific point estimates (range 0.3–908 per 100,000 cases). There is considerable variation in sCFR across geographies, which was not linearly related to key socio-economic factors, but the variation can be even larger across seasons in a geography. The wide variation across studies and the lack of studies in many world regions point to the need for standardised protocols and more data collection.

As of 28 February 2020, Italy had 888 cases of SARS-CoV-2 infections, with most cases in Northern Italy in the Lombardia and Veneto regions. Travel-related cases were the main source of COVID-19 cases during the early stages of the current epidemic in Italy. The month of February, however, has been dominated by two large clusters of outbreaks in Northern Italy, south of Milan, with mainly local transmission the source of infections. Contact tracing has failed to identify patient zero in one of the outbreaks. As of 28 February 2020, twenty-one cases of COVID-19 have died. Comparison between case fatality rates in China and Italy are identical at 2.3. Additionally, deaths are similar in both countries with fatalities in mostly the elderly with known comorbidities. It will be important to develop point-of-care devices to aid clinicians in stratifying elderly patients as early as possible to determine the potential level of care they will require to improve their chances of survival from COVID-19 disease.

We used 10 years of surveillance data to describe listeriosis frequency in Germany. Altogether, 5,576 cases were reported, 91% not pregnancy associated; case counts increased over time. Case-fatality rate was 13% in non–pregnancy-associated cases, most in adults ≥65 years of age. Detecting, investigating, and ending outbreaks might have the greatest effect on incidence

COVID-19 case fatalities surged during the month of March 2020 in Italy, reaching over 10,000 by 28 March 2020. This number exceeds the number of fatalities in China (3,301) recorded from January to March, even though the number of diagnosed cases was similar (85,000 Italy vs. 80,000 China). Case Fatality Rates (CFR) could be somewhat unreliable because the estimation of total case numbers is limited by several factors, including insufficient testing and limitations in test kits and materials, such as NP swabs and PPE for testers. Sero prevalence of SARS-CoV-2 antibodies may help in more accurate estimations of the total number of cases. Nevertheless, the disparity in the differences in the total number of fatalities between Italy and China suggests investigation into several factors, such as demographics, sociological interactions, availability of medical equipment (ICU beds and PPE), variants in immune proteins (e.g., HLA, IFNs), past immunity to related CoVs, and mutations in SARS-CoV-2, could impact survival of severe COVID-19 illness survival and the number of case fatalities.

The human cutaneous anthrax case-fatality rate is ≈1% when treated, 5%-20% when untreated. We report high case-fatality rates (median 35.0%; 95% CI 21.1%-66.7%) during 2005-2016 linked to livestock handling in northern Ghana, where veterinary resources are limited. Livestock vaccination and access to human treatment should be evaluated.

Inhospital death is commonly used as an outcome measure. However, it may be a biased measure of overall fatal outcome. The objective of this study was to evaluate inhospital death as a measure of all-cause 30-day case fatality in patients with bloodstream infection (BSI). A population-based surveillance cohort study was conducted, and patients who died in hospital within 30 days (30-day inhospital death) were compared with those who died in any location by day 30 post BSI diagnosis (30-day all-cause case fatality). A total of 1,773 residents had first incident episodes of BSI. Overall, 299 patients died for a 30-day all-cause case fatality rate of 16.9%. Most (1,587; 89.5%) of the patients were admitted to hospital, and ten (5.4%) of the 186 patients not admitted to hospital died. Of the 1,587 admitted patients, 242 died for a 30-day inhospital death rate of 15.2%. A further 47 patients admitted to hospital died after discharge but within 30 days of BSI diagnosis for a 30-day case fatality rate among admitted patients of 18.2%. Patients who died following discharge within 30 days were older and more likely to have dementia. The use of inhospital death is a biased measure of true case fatality.

We aimed to analyze the situation of the first two epidemic waves in Myanmar using the publicly available daily situation of COVID-19 and whole-genome sequencing data of SARS-CoV-2. From March 23 to December 31, 2020, there were 33,917 confirmed cases and 741 deaths in Myanmar (case fatality rate of 2.18%). The first wave in Myanmar from March to July was linked to overseas travel, and then a second wave started from Rakhine State, a western border state, leading to the second wave spreading countrywide in Myanmar from August to December 2020. The estimated effective reproductive number (Rt) nationwide reached 6–8 at the beginning of each wave and gradually decreased as the epidemic spread to the community. The whole-genome analysis of 10 Myanmar SARS-CoV-2 strains together with 31 previously registered strains showed that the first wave was caused by GISAID clade O or PANGOLIN lineage B.6 and the second wave was changed to clade GH or lineage B.1.36.16 with a close genetic relationship with other South Asian strains. Constant monitoring of epidemiological situations combined with SARS-CoV-2 genome analysis is important for adjusting public health measures to mitigate the community transmissions of COVID-19.

Aims We aim to monitor and improve the quality of the heart failure (HF) integrated assistance model defined by national and regional guidelines and implemented in the province of Reggio Emilia, Italy. Specific aims of the audit were to estimate the prevalence of HF, describe the characteristics of patients with HF and the rate of patients enrolled in the integrated care treated in primary care, and identify socioeconomic and geographic determinants of the 4‐year survival of these patients. Methods and results Retrospective analysis of a cohort of prevalent cases of HF, diagnosed before 31 December 2015 in Reggio Emilia, Italy, alive on 1 January 2016, and residing at the time of diagnosis on the provincial territory. Age and sex‐adjusted prevalence of HF by area of residence were calculated according to the standard European population 2013. Patients were followed until death or 31 December 2019, whatever came first. The outcome measure of the study was four‐year case fatality. Cox proportional hazards models, adjusted for age, sex, and duration of disease were used to determine the association between socio‐geographic factors and death. The 4‐year case‐fatality rate was 36.7%, and it was the highest in the mountains (50.8%) compared with hills (34.6%), lowland (35.4%) and city (37.7%). The prevalence of HF was the lowest in the mountain [149.9, 95% confidence interval (CI) 112.1–187.7] and the highest in the lowland (340.8, 95% CI 308.7–372.9) and city (308, 95% CI 276.0–321.2). Patients living in the mountains had a lower deprivation index, and fewer hospitalizations prior to official diagnosis, although these characteristics were not statistically significant determinants of HF death in multivariate analysis. Behavioural (smoking and obesity) and socio‐geographic characteristics (educational level, deprivation index and area of residence) were not significantly associated with mortality in both univariable and multivariable analysis; however, patients who live in mountains (hazard ratio 1.10, 95% CI 0.73–1.66) or hills (hazard ratio 1.11, 95% CI 0.90–1.37) had a slightly higher risk of death than those living in the city. Only 197 (12.1%) of patients in the cohort were enrolled in the integrated care pathway over the course of 4 years. Conclusions Although clinical determinants outweigh the geographic and behavioural disparities in the survival of patients with CHF treated in primary care, effective prevention strategies are needed to address environmental and socio‐geographic inequalities in access to primary care and to hasten equitable linkage to integrated care.

Lassa fever (LF) is a viral hemorrhagic disease found in Sub-Saharan Africa and is responsible for up to 300,000 cases and 5000 deaths annually. LF is highly endemic in Sierra Leone, particularly in its Eastern Province. Kenema Government Hospital (KGH) maintains one of only a few LF isolation facilities in the world with year-round diagnostic testing. Here we focus on space-time trends for LF occurring in Sierra Leone between 2012 and 2019 to provide a current account of LF in the wake of the 2014–2016 Ebola epidemic. Data were analyzed for 3277 suspected LF cases and classified as acute, recent, and non-LF or prior LF exposure using enzyme-linked immunosorbent assays (ELISAs). Presentation rates for acute, recent, and non-LF or prior LF exposure were 6.0% (195/3277), 25.6% (838/3277), and 68.4% (2244/3277), respectively. Among 2051 non-LF or prior LF exposures, 33.2% (682/2051) tested positive for convalescent LF exposure. The overall LF case-fatality rate (CFR) was 78.5% (106/135). Both clinical presentations and confirmed LF cases declined following the Ebola epidemic. These declines coincided with an increased duration between illness onset and clinical presentation, perhaps suggesting more severe disease or presentation at later stages of illness. Acute LF cases and their corresponding CFRs peaked during the dry season (November to April). Subjects with recent (but not acute) LF exposure were more likely to present during the rainy season (May to October) than the dry season (p < 0.001). The findings here suggest that LF remains endemic in Sierra Leone and that caseloads are likely to resume at levels observed prior to the Ebola epidemic. The results provide insight on the current epidemiological profile of LF in Sierra Leone to facilitate LF vaccine studies and accentuate the need for LF cohort studies and continued advancements in LF diagnostics.