Miracle-dz/UniFAR-FTUs huggingface dataset:
Early estimates of COVID-19 infections in small, medium and large population clusters

Introduction Since its emergence in late December 2019, COVID-19 has rapidly developed into a pandemic in mid of March with many countries suffering heavy human loss and declaring emergency conditions to contain its spread. The impact of the disease, while it has been relatively low in the sub-Saharan Africa (SSA) as of May 2020, is feared to be potentially devastating given the less developed and fragmented healthcare system in the continent. In addition, most emergency measures practised may not be effective due to their limited affordability as well as the communal way people in SSA live in relative isolation in clusters of large as well as smaller population centres. Methods To address the acute need for estimates of the potential impacts of the disease once it sweeps through the African region, we developed a process-based model with key parameters obtained from recent studies, taking local context into consideration. We further used the model to estimate the number of infections within a year of sustained local transmissions under scenarios that cover different population sizes, urban status, effectiveness and coverage of social distancing, contact tracing and usage of cloth face mask. Results We showed that when implemented early, 50% coverage of contact tracing and face mask, with 33% effective social distancing policies can bringing the epidemic to a manageable level for all population sizes and settings we assessed. Relaxing of social distancing in urban settings from 33% to 25% could be matched by introduction and maintenance of face mask use at 43%. Conclusions In SSA countries with limited healthcare workforce, hospital resources and intensive care units, a robust system of social distancing, contact tracing and face mask use could yield in outcomes that prevent several millions of infections and thousands of deaths across the continent.
COVID-19, City Lockdown, and Air Pollution: Evidence from China

The rapid spread of COVID-19 is a global public health challenge. To prevent the escalation of its transmission, China locked down one-third of its cities and strictly restricted human mobility and economic activities. Using timely and comprehensive air quality data in China, we show that these counter-COVID-19 measures led to remarkable improvement in air quality. Within weeks, the Air Quality Index and PM2.5 concentrations were brought down by 25%. The effects are larger in colder, richer, and more industrialized cities. We estimate that such improvement would avert 24,000 to 36,000 premature deaths from air pollution on a monthly basis.
Using Early Data to Estimate the Actual Infection Fatality Ratio from COVID-19 in France

Background. The number of screening tests carried out in France and the methodology used to target the patients tested do not allow for a direct computation of the actual number of cases and the infection fatality ratio (IFR). The main objective was to estimate the actual number of people infected with COVID-19 during the observation window in France and to deduce the IFR. Methods. We develop a 'mechanistic-statistical' approach coupling a SIR epidemiological model describing the unobserved epidemiological dynamics, a probabilistic model describing the data acquisition process and a statistical inference method. Results. The actual number of infected cases in France is probably higher than the observations: we find here a factor x 8 (95%-CI: 5-12) which leads to an IFR in France of 0.5% (95%-CI: 0.3-0.8) based on hospital death counting data. Adjusting for the number of deaths in nursing homes, we obtain an IFR of 0.8% (95%-CI: 0.45-1.25). Conclusions. This IFR is consistent with previous findings in China (0.66%) and in the UK (0.9%) and lower than the value previously computed on the Diamond Princess cruse ship data (1.3%).
Why is chest CT important for early diagnosis of COVID-19? Prevalence matters

SARS-CoV-2 viral infection is a global pandemic disease (COVID-19). Reaching a swift, reliable diagnosis of COVID-19 in the emergency departments is imperative to direct patients to proper care and to prevent disease dissemination. COVID-19 diagnosis is based on the identification of viral RNA through RT-PCR from oral-nasopharyngeal swabs, which however presents suboptimal sensitivity and may require several hours in overstressed laboratories. These drawbacks have called for an additional, complementary first line approach. CT is the gold standard method for the detection of interstitial pneumonia, a hallmark feature of COVID-19, often present in the asymptomatic stage of the disease. Here, we show that CT scan presents a sensitivity of 95.48% (std.err=0.35%), vastly outperforming RT-PCR. Additionally, as diagnostic accuracy is influenced by disease prevalence, we argue that predictive values provide a more precise measure of CT reliability in the current pandemics. We generated a model showing that CT scan is endowed with a high negative predictive value (> 90%) and positive predictive value (69 - 84%), for the range of prevalence seen in countries with rampant dissemination. We conclude that CT is an expedite and reliable diagnostic tool to support first line triage of suspect COVID-19 patients in areas where the diffusion of the virus is widespread.
Anxiety, worry and perceived stress in the world due to the COVID-19 pandemic, March 2020. Preliminary results.

Introduction: Since the beginning of the outbreak in China, ending 2019, the Novel Coronavirus (COVID-19) has spread subsequently to the rest of the world causing an on-going pandemic. The World Health Organisation (WHO) declared COVID-19: a public health emergency of international concern. Having into consideration the lockdown and quarantine situation, we decided to do evaluate the current emotional state on the general population with a web-based survey in English and in Spanish, which was considered a useful and fast method that could help us determine how people perceived stress and worry due to the COVID-19. Methods: The survey included a 22 items, gathering information in 3 sections: Sociodemographic data, the Perceived Stress Scale (PSS-10) by Cohen and additional queries that assessed the current worry and change of behaviours due to this pandemic. Results: The survey received 891 respondents from 25 countries, from March 17 to 23rd, 2020. The mean age of the respondents was 43.8 (14.2) years old, and more than two thirds were women. 12.8% were health personnel.The mean of the PSS-10 score was 17.4 (6.4). Significantly higher scores were observed among women, youth, students, and among those who expressed concern and those who perceived increased susceptibility to the COVID-19. In contrast, no significant differences were observed between the health professionals and the general population. A good correlation was observed between mean relative volume (RSV) of the las 14 days and the number of cases reported (rho = 0.68, p <0.001) and deaths (rho = 0.51, p <0.001). Discussion: With these results we describe an increase of affective symptoms due to the COVID-19. This pandemic is raising the anxiety levels. The findings of our study show the affective and cognitive alterations people are going through. This survey is the first attempt to measure the psychological consequences this pandemic is having, in order to be able to later be able to provide the support to confront this global issue, addressing the mental health care that will be needed.
Estimating the Risk of COVID-19 Death during the Course of the Outbreak in Korea, February–May 2020

Background: In Korea, a total of 10,840 confirmed cases of COVID-19 including 256 deaths have been recorded as of May 9, 2020. The time-delay adjusted case fatality risk (CFR) of COVID-19 in Korea is yet to be estimated. Methods: We obtained the daily series of confirmed cases and deaths in Korea reported prior to May 9, 2020. Using statistical methods, we estimated the time-delay adjusted risk for death from COVID-19 in Daegu, Gyeongsangbuk-do, other regions in Korea, as well as the entire country. Results: Our model-based crude CFR fitted the observed data well throughout the course of the epidemic except for the very early stage in Gyeongsangbuk-do; this was partially due to the reporting delay. Our estimates of the risk of death in Gyeongsangbuk-do reached 25.9% (95% Credible Interval (CrI): 19.6%–33.6%), 20.8% (95% CrI: 18.1%–24.0%) in Daegu, and 1.7% (95% CrI: 1.1%–2.5%) in other regions, whereas the national estimate was 10.2% (95% CrI: 9.0%–11.5%). Conclusions: The latest estimates of CFR of COVID-19 in Korea are considerably high, even with the early implementation of public health interventions including widespread testing, social distancing, and delayed school openings. Geographic differences in the CFR are likely influenced by clusters tied to hospitals and nursing homes.
Global convergence of COVID-19 basic reproduction number and estimation from early-time SIR dynamics

The SIR ('susceptible-infectious-recovered') formulation is used to uncover the generic spread mechanisms observed by COVID-19 dynamics globally, especially in the early phases of infectious spread. During this early period, potential controls were not effectively put in place or enforced in many countries. Hence, the early phases of COVID-19 spread in countries where controls were weak offer a unique perspective on the ensemble-behavior of COVID-19 basic reproduction number Ro. The work here shows that there is global convergence (i.e. across many nations) to an uncontrolled Ro=4.5 that describes the early time spread of COVID-19. This value is in agreement with independent estimates from other sources reviewed here and adds to the growing consensus that the early estimate of Ro=2.2 adopted by the World Health Organization is low. A reconciliation between power-law and exponential growth predictions is also featured within the confines of the SIR formulation. Implications for evaluating potential control strategies from this uncontrolled Ro are briefly discussed in the context of the maximum possible infected fraction of the population (needed for assessing health care capacity) and mortality (especially in the USA given diverging projections). Model results indicate that if intervention measures still result in Ro > 2.7 within 49 days after first infection, intervention is unlikely to be effective in general for COVID-19. Current optimistic projections place mortality figures in the USA in the range of 100,000 fatalities. For fatalities to be confined to 100,000 requires a reduction in Ro from 4.5 to 2.7 within 17 days of first infection assuming a mortality rate of 3.4%.
Risk factors for mortality of adult inpatients with Coronavirus disease 2019 (COVID-19): a systematic review and meta-analysis of retrospective studies

Background: Coronavirus disease 2019 (COVID-19) is an emerging disease that was first reported in Wuhan city, the capital of Hubei province in China, and has subsequently spread worldwide. Risk factors for mortality have not been well summarized. Current meta-analysis of retrospective cohort studies was done to summarize available findings on the association between age, gender, comorbidities and risk of death from COVID-19 infection. Methods: Online databases including Web of Science, PubMed, Scopus and Google scholar were searched to detect relevant publications up to 22 March 2020, using relevant keywords. To pool data, random-effects model was used. Furthermore, sensitivity analysis and publication bias test were also done. Results: In total, six retrospective studies with 22,350 COVID-19 infected patients and 741 cases of death were included in the current meta-analysis. A significant positive association was found between older age (≥65 years old) and COVID-19 mortality (combined effect size=2.39 (over twofold), 95% CIs=1.75-3.28, p<0.001). Such finding was also seen for hypertension (combined effect size=3.29 (over threefold), 95% CIs=1.54-7.05, p=0.002), diabetes (combined effect size=3.11 (over threefold), 95% CIs=1.10-8.80, p=0.032), chronic obstructive pulmonary disease (COPD) (combined effect size=7.69 (over sevenfold), 95% CIs=5.65-10.47, p<0.001) and cardiovascular diseases (CVDs) (combined effect size=7.39 (over sevenfold), 95% CIs=2.88-18.96, p<0.001). Conclusions: Older age, hypertension, diabetes, COPD and CVDs were associated with greater risk of death from COVID-19 infection. These findings could help clinicians to identify patients with poor prognosis at an early stage.
Critical Care for Severe COVID-19: A Population-based Study from a Province with Low Case-fatality Rate in China

Background Data regarding critical care for patients with severe COVID-19 are limited. We aimed to describe and analyse the clinical course, multi-strategy management, and respiratory support usage for the severe COVID-19 at the provincial level. Methods Using data from Sichuan Provincial Department of Health and the population-based multicentre cohort study, all microbiologically confirmed COVID-19 patients in Sichuan who met the national severe criteria were included from January 16 to March 15, 2020. All patients were followed-up from the day of inclusion, noted as Day one (D1), until discharge, death, or the end of the study. Findings Out of 539 COVID-19 patients, 81 severe cases (15.0%) were identified. The median (IQR) age was 50 (39-65) years, 37% were female, and 53.1% had chronic comorbidities. All severe cases were identified before requiring mechanical ventilation. Among the five predefined criteria for severe illness, low PaO2:FiO2 ratio (<300 mmHg) was the most commonly reported, accounting for 87.7% of the severe diagnosed. By establishing provisional ICUs, all 81 patients were admitted to hospitals capable of providing critical care, among whom 51 (63.0%) were treated in provisional ICUs. On D1, 77 patients (95.1%) were admitted, 76 (93.8%) were administered by respiratory support, including 51 (67.9%) by conventional oxygen therapy (COT). By D28, 53 (65.4%) were discharged, three (3.7%) were deceased, and 25 (30.9%) were still hospitalized. COT, administered to 95.1% of the patients, was the most commonly used respiratory support and met 62.7% of the respiratory support needed, followed by high-flow nasal cannula (19.5%) and non-invasive mechanical ventilation (10%). Interpretation The multi-strategy management for severe COVID-19 patients included early identification and timely critical care, including appropriate respiratory support in particular. Preparation of sufficient conventional oxygen equipment should be prioritized.
Use of siltuximab in patients with COVID-19 pneumonia requiring ventilatory support

COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SAR-CoV-2), resulting in symptoms, such as fever, cough, and shortness of breath. The SARS-CoV-2 virus has also been suggested to initiate a cytokine storm in patients with COVID-19 evidenced by elevated cytokines, such as interleukin-6 (IL-6) and C-reactive protein (CRP). We report preliminary data from 21 patients with COVID-19 who developed pneumonia/acute respiratory distress syndrome (ARDS) and participated in a compassionate-use program at Papa Giovanni XXIII hospital in Bergamo, Italy. All 21 patients received intravenous siltuximab, a chimeric mAb that binds to and blocks the effect of IL-6, at a dose ranging between 700 to 1,200 mg (median 900 mg). The median age of patients treated was 64 years, and all patients were followed for a median of eight days. Serum CRP levels reduced in all 16 patients with available data following treatment. An improvement in the clinical condition was observed in 33% (7/21) of patients, 43% (9/21) of patients stabilized as evidenced by no clinically relevant change in their condition, and 24% (5/21) experienced a worsening in their condition. Of those patients who experienced a worsening in their condition, one patient died, and one patient experienced a cerebrovascular event. This analysis is presented to inform the medical community of the potential role of siltuximab in treating patients with ARDS secondary to SARS-CoV-2 infection, and a cohort study with patients treated with standard therapy in our hospital is ongoing, and will report the 30-day mortality rates upon completion.
An effect assessment of Airborne particulate matter pollution on COVID-19: A multi-city Study in China

Objective: Coronavirus disease 2019 (COVID-19) is a serious infectious disease, which has caused great number of deaths and health problems worldwide. This study aims to examine the effects of airborne particulate matter (PM) pollution on COVID-19 across China. Methods: In this study, we obtained confirmed cases of COVID-19, the data of airborne ambient PM with aerodynamic diameter [≤] 2.5 m (PM2.5) and [≤] 10 m (PM10), ambient temperature (AT), absolute humidity (AH) and migration scale index (MSI) in 72 cities of China (excluded Wuhan city) on a daily basis, each of which confirmed more than 50 cases from January 20th to March 2nd, 2020. We applied a two-stage analysis. Generalized additive models with quasi-Poisson distribution was first fitted to estimate city-specific effects of PM10 and PM2.5 on daily confirmed COVID-19 cases while controlling AT, AH and MSI. Then, we used meta-analysis to generate the pooled effect estimates from city-specific results. Results: During the study period, there were a total of 24 939 COVID-19 cases, most of which were reported in Hubei Province. In our meta-analysis, we found each 10 g/m3 increase in concentration of PM2.5 and PM10 in single day lag (from lag 0 to lag 7 and lag 14) were positively associated with confirmed cases of COVID-19, not including PM10 at lag 5, lag 6 and lag 7, and PM2.5 at lag 5, lag 6. Similar trend was also found in different cumulative lag days (from lag 01 to lag 07 and lag 014). The effects of PM2.5 and PM10 on daily COVID-19 confirmed cases are statistically significant for three cumulative lag periods over 3, 7 and 14 days with the greatest effect over 14 days. The estimated RRs of which were 1.64 (95% CIs: 1.47, 1.82) and 1.47 (95% CIs: 1.34, 1.61) with each 10 g/m3 increase in concentrations of PM2.5 and PM10, respectively. In addition, we found that the effects of PM2.5 on daily confirmed cases were greater than PM10 in all included lag days. Conclusions: This nationwide study suggests that airborne PM pollution likely increases the risk of getting COVID-19 in China.
Further Evidence of a Possible Correlation Between the Severity of Covid-19 and BCG Immunization

In this work we observe a number of cases supporting the possible correlation between the administration of BCG tuberculosis vaccine and the severity of Covid-19 effects in the population proposed in the earlier works [1]. Based on the early prelimi-nary analysis of the publicly available data we propose a number of arguments and observations providing further support for the correlation hypothesis and make an observation that the effectiveness of the protection effect of BCG im-munization, if confirmed, may depend to a significant extent on the age of admin-istration, with the early age inoculation more effective for the lasting protection.
COVID-19 pandemic in the African continent: forecasts of cumulative cases, new infections, and mortality

Background: Africa is the last major region to capitulate to the SARS-CoV-2 (COVID-19) pandemic. The first confirmed COVID-19 case in the region was reported on February 14, but what lies ahead in terms of the course and magnitude of infection remains speculative. To the best of our knowledge, no study, using a robust methodology, provides the immediate and long-term trajectory of COVID-19 for the entire region or accounts for its local context. This paper is the first systematic attempt to provide estimates on how many people would contract the virus and how many would die in the coming few months across Africa. Methods: The forecasts on caseloads and incidences are from a co-variate-based instrumental variable regression model. Fatality rates from Italy and China were further applied to generate mortality estimates after adjustments were made for differences in age-structure, health service quality, and living standards between each of the African countries and those of the reference population. We cover all countries that reported a confirmed case as of March 31, 2020. Results: By the end of June, 16,283,085 people will contract COVID-19 (95% CI 718,403 to 98, 358, 799). With a cumulative caseload of 5,413,4517 (95% CI 1,332,953 to 8,489,940) and 906,625 (95% CI 173, 821 to 4,742,917) Northern and Eastern Africa will respectively be the most and least affected sub-regions in the continent. Cumulative COVID-19 cases on June 30, 2020 are expected to reach 2,912, 864 (95% CI 465,028 to 18,286,358) in Southern Africa, 2,787, 913 (95% CI 517, 489 to 15,056,314) in Western Africa, and 1,185,742 (95% CI 229, 111 to 6,138,692) in Central Africa. New infections (incidence) for the month of April are expected to be the highest in Djibouti, 32.8 per 1000 (95% CI 6.25 to 171.77), while Morocco 1045 (95% CI 167 to 6,547) will register the highest number of deaths. Conclusion: Our study shows that countries that are least urbanized and have a low level of socio-economic development, hence least connected to the outside world, are likely to register lower and slower transmissions, at least at the early stage of the epidemic. However, the same set of enabling factors that worked for their benefit are likely to go against them in implementing interventions that have lessened the impact of the disease elsewhere.
Covid-19 Testing, Hospital Admission, and Intensive Care Among 2,026,227 United States Veterans Aged 54–75 Years

Importance: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes coronavirus disease 2019 (Covid-19), an evolving pandemic. Limited data are available characterizing SARS-Cov-2 infection in the United States. Objective: To determine associations between demographic and clinical factors and testing positive for coronavirus 2019 (Covid-19+), and among Covid-19+ subsequent hospitalization and intensive care. Design, Setting, and Participants: Retrospective cohort study including all patients tested for Covid-19 between February 8 and March 30, 2020, inclusive. We extracted electronic health record data from the national Veterans Affairs Healthcare System, the largest integrated healthcare system in the United States, on 2,026,227 patients born between 1945 and 1965 and active in care. Exposures: Demographic data, comorbidities, medication history, substance use, vital signs, and laboratory measures. Laboratory tests were analyzed first individually and then grouped into a validated summary measure of physiologic injury (VACS Index). Main Outcomes and Measures: We evaluated which factors were associated with Covid-19+ among all who tested. Among Covid-19+ we identified factors associated with hospitalization or intensive care. We identified independent associations using multivariable and conditional multivariable logistic regression with multiple imputation of missing values. Results: Among Veterans aged 54-75 years, 585/3,789 (15.4%) tested Covid-19+. In adjusted analysis (C-statistic=0.806) black race was associated with Covid-19+ (OR 4.68, 95% CI 3.79-5.78) and the association remained in analyses conditional on site (OR 2.56, 95% CI 1.89-3.46). In adjusted models, laboratory abnormalities (especially fibrosis-4 score [FIB-4] >3.25 OR 8.73, 95% CI 4.11-18.56), and VACS Index (per 5-point increase OR 1.62, 95% CI 1.43-1.84) were strongly associated with hospitalization. Associations were similar for intensive care. Although significant in unadjusted analyses, associations with comorbid conditions and medications were substantially reduced and, in most cases, no longer significant after adjustment. Conclusions and Relevance: Black race was strongly associated with Covid-19+, but not with hospitalization or intensive care. Among Covid-19+, risk of hospitalization and intensive care may be better characterized by laboratory measures and vital signs than by comorbid conditions or prior medication exposure.