Epidemiological, socio-demographic and clinical features of the early phase of the COVID-19 epidemic in Ecuador

The SARS-CoV-2 virus has spread rapidly around the globe. Nevertheless, there is limited information describing the characteristics and outcomes of COVID-19 patients in Latin America. We conducted a cross-sectional analysis of 9,468 confirmed COVID-19 cases reported in Ecuador. We calculated overall incidence, mortality, case fatality rates, disability adjusted life years, attack and crude mortality rates, as well as relative risk and relative odds of death, adjusted for age, sex and presence of comorbidities. A total of 9,468 positive COVID-19 cases and 474 deaths were included in the analysis. Men accounted for 55.4% (n = 5, 247) of cases and women for 44.6% (n = 4, 221). We found the presence of comorbidities, being male and older than 65 years were important determinants of mortality. Coastal regions were most affected by COVID-19, with higher mortality rates than the highlands. Fatigue was reported in 53.2% of the patients, followed by headache (43%), dry cough (41.7%), ageusia (37.1%) and anosmia (36.1%). We present an analysis of the burden of COVID-19 in Ecuador. Our findings show that men are at higher risk of dying from COVID-19 than women, and risk increases with age and the presence of comorbidities. We also found that blue-collar workers and the unemployed are at greater risk of dying. These early observations offer clinical insights for the medical community to help improve patient care and for public health officials to strengthen Ecuador’s response to the outbreak

Epidemiological, socio-demographic and clinical features of the early phase of the COVID-19 epidemic in Ecuador.

The SARS-CoV-2 virus has spread rapidly around the globe. Nevertheless, there is limited information describing the characteristics and outcomes of COVID-19 patients in Latin America. We conducted a cross-sectional analysis of 9,468 confirmed COVID-19 cases reported in Ecuador. We calculated overall incidence, mortality, case fatality rates, disability adjusted life years, attack and crude mortality rates, as well as relative risk and relative odds of death, adjusted for age, sex and presence of comorbidities. A total of 9,468 positive COVID-19 cases and 474 deaths were included in the analysis. Men accounted for 55.4% (n = 5, 247) of cases and women for 44.6% (n = 4, 221). We found the presence of comorbidities, being male and older than 65 years were important determinants of mortality. Coastal regions were most affected by COVID-19, with higher mortality rates than the highlands. Fatigue was reported in 53.2% of the patients, followed by headache (43%), dry cough (41.7%), ageusia (37.1%) and anosmia (36.1%). We present an analysis of the burden of COVID-19 in Ecuador. Our findings show that men are at higher risk of dying from COVID-19 than women, and risk increases with age and the presence of comorbidities. We also found that blue-collar workers and the unemployed are at greater risk of dying. These early observations offer clinical insights for the medical community to help improve patient care and for public health officials to strengthen Ecuador's response to the outbreak

Blockwise pp-Tampering Attacks on Cryptographic Primitives, Extractors, and Learners

Austrin, Chung, Mahmoody, Pass and Seth (Crypto\u2714) studied the notion of bitwise $p$-tampering attacks over randomized algorithms in which an efficient `virus\u27 gets to control each bit of the randomness with independent probability $p$ in an online way. The work of Austrin et al. showed how to break certain `privacy primitives\u27 (e.g., encryption, commitments, etc.) through bitwise $p$-tampering, by giving a bitwise $p$-tampering biasing attack for increasing the average $E[f(U_n)]$ of any efficient function $f \colon \{0,1\}^n \to [-1,+1]$ by $\Omega(p \cdot Var[f(U_n)])$ where $Var[f(U_n)]$ is the variance of $f(U_n)$. In this work, we revisit and extend the bitwise tampering model of Austrin et al. to blockwise setting, where blocks of randomness becomes tamperable with independent probability $p$. Our main result is an efficient blockwise $p$-tampering attack to bias the average $E[f(X)]$ of any efficient function $f$ mapping arbitrary $X$ to $[-1,+1]$ by $\Omega(p \cdot Var[f(X)])$ regardless of how $X$ is partitioned into individually tamperable blocks $X=(X_1,\dots,X_n)$. Relying on previous works, our main biasing attack immediately implies efficient attacks against the privacy primitives as well as seedless multi-source extractors, in a model where the attacker gets to tamper with each block (or source) of the randomness with independent probability $p$. Further, we show how to increase the classification error of deterministic learners in the so called `targeted poisoning\u27 attack model under Valiant\u27s adversarial noise. In this model, an attacker has a `target\u27 test data $d$ in mind and wishes to increase the error of classifying $d$ while she gets to tamper with each training example with independent probability $p$ an in an online way