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  1. Last 7 days
  2. Jun 2020
    1. 2020-06-27

    2. Larremore, D. B., Wilder, B., Lester, E., Shehata, S., Burke, J. M., Hay, J. A., Tambe, M., Mina, M. J., & Parker, R. (2020). Test sensitivity is secondary to frequency and turnaround time for COVID-19 surveillance. MedRxiv, 2020.06.22.20136309. https://doi.org/10.1101/2020.06.22.20136309

    3. The COVID-19 pandemic has created a public health crisis. Because SARS-CoV-2 can spread from individuals with pre-symptomatic, symptomatic, and asymptomatic infections, the re-opening of societies and the control of virus spread will be facilitated by robust surveillance, for which virus testing will often be central. After infection, individuals undergo a period of incubation during which viral titers are usually too low to detect, followed by an exponential growth of virus, leading to a peak viral load and infectiousness, and ending with declining viral levels and clearance. Given the pattern of viral load kinetics, we model surveillance effectiveness considering test sensitivities, frequency, and sample-to-answer reporting time. These results demonstrate that effective surveillance, including time to first detection and outbreak control, depends largely on frequency of testing and the speed of reporting, and is only marginally improved by high test sensitivity. We therefore conclude that surveillance should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.
    4. 10.1101/2020.06.22.20136309
    5. Test sensitivity is secondary to frequency and turnaround time for COVID-19 surveillance
    1. 2020-06-23

    2. Fisman, D., Greer, A. L., & Tuite, A. (2020). Derivation and Validation of Clinical Prediction Rule for COVID-19 Mortality in Ontario, Canada. MedRxiv, 2020.06.21.20136929. https://doi.org/10.1101/2020.06.21.20136929

    3. Background: SARS-CoV-2 is currently causing a high mortality global pandemic. However, the clinical spectrum of disease caused by this virus is broad, ranging from asymptomatic infection to cytokine storm with organ failure and death. Risk stratification of individuals with COVID-19 would be desirable for management, prioritization for trial enrollment, and risk stratification. We sought to develop a prediction rule for mortality due to COVID-19 in individuals with diagnosed infection in Ontario, Canada. Methods: Data from the Ontario provincial iPHIS system were extracted for the period from January 23 to May 15, 2020. Both logistic regression-based prediction rules, and a rule derived using a Cox proportional hazards model, were developed in half the study and validated in remaining patients. Sensitivity analyses were performed with varying approaches to missing data. Results: 21,922 COVID-19 cases were reported. Individuals assigned to the derivation and validation sets were broadly similar. Age and comorbidities (notably diabetes, renal disease and immune compromise) were strong predictors of mortality. Four point-based prediction rules were derived (base case, smoking excluded as a predictor, long-term care excluded as a predictor, and Cox model based). All rules displayed excellent discrimination (AUC for all rules > 0.92 ) and calibration (both by graphical inspection and P > 0.50 by Hosmer-Lemeshow test) in the derivation set. All rules performed well in the validation set and were robust to random replacement of missing variables, and to the assumption that missing variables indicated absence of the comorbidity or characteristic in question. Conclusions: We were able to use a public health case-management data system to derive and internally validate four accurate, well-calibrated and robust clinical prediction rules for COVID-19 mortality in Ontario, Canada. While these rules need external validation, they may be a useful tool for clinical management, risk stratification, and clinical trials.
    4. 10.1101/2020.06.21.20136929
    5. Derivation and Validation of Clinical Prediction Rule for COVID-19 Mortality in Ontario, Canada
    1. 2020-06-26

    2. Times, T. N. Y. (n.d.). Coronavirus in the U.S.: Latest Map and Case Count. The New York Times. Retrieved June 26, 2020, from https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html

    3. More than 2,435,200 people in the United States have been infected with the coronavirus and at least 124,300 have died, according to a New York Times database. This map shows where the number of new cases is rising and where it is falling in the last 14 days.
    4. Coronavirus in the U.S.: Latest Map and Case Count
    1. 2020-06-25

    2. Islam, M. M., & Yunus, M. Y. (2020). Rohingya refugees at high risk of COVID-19 in Bangladesh. The Lancet Global Health, 0(0). https://doi.org/10.1016/S2214-109X(20)30282-5

    3. Several factors suggest that Bangladesh could be one of the next COVID-19 hotspots: it has a high population density; it has poor health infrastructure and resources; there has been poor adherence to physical distancing; complete lockdown has not been ensured at a national level; there is uncoordinated population mobility between rural and urban areas; there is little awareness of COVID-19 among the population; home quarantine has been used in place of institutional quarantine for returning overseas travellers; there are overcrowded urban areas with substandard housing; health institutions have limited capacities; and effective governance has been largely absent. In addition, the country is accommodating 1 118 576 forcibly displaced Myanmar nationals named as Rohingya, including 860 175 Rohingya people who are sheltering in the world's largest refugee camp in Cox's Bazar, a city in southeastern Bangladesh.1The Daily StarAll Rohingya refugees registered: Minister.https://www.thedailystar.net/rohingya-crisis/all-rohingya-refugeesregistered-minister-1603690Date: July 11, 2018Date accessed: June 8, 2020Google Scholar,  2
    4. 10.1016/S2214-109X(20)30282-5
    5. Rohingya refugees at high risk of COVID-19 in Bangladesh
    1. 2020-06-23

    2. Cheng, C., Barceló, J., Hartnett, A. S., Kubinec, R., & Messerschmidt, L. (2020). COVID-19 Government Response Event Dataset (CoronaNet v.1.0). Nature Human Behaviour, 1–13. https://doi.org/10.1038/s41562-020-0909-7

    3. 10.1038/s41562-020-0909-7
    4. Governments worldwide have implemented countless policies in response to the COVID-19 pandemic. We present an initial public release of a large hand-coded dataset of over 13,000 such policy announcements across more than 195 countries. The dataset is updated daily, with a 5-day lag for validity checking. We document policies across numerous dimensions, including the type of policy, national versus subnational enforcement, the specific human group and geographical region targeted by the policy, and the time frame within which each policy is implemented. We further analyse the dataset using a Bayesian measurement model, which shows the quick acceleration of the adoption of costly policies across countries beginning in mid-March 2020 through 24 May 2020. We believe that these data will be instrumental for helping policymakers and researchers assess, among other objectives, how effective different policies are in addressing the spread and health outcomes of COVID-19.
    5. COVID-19 Government Response Event Dataset (CoronaNet v.1.0)
    1. 10.36190/2020.14
    2. 2020-06-19

    3. Shahi, G. K., & Nandini, D. (2020). FakeCovid—A Multilingual Cross-domain Fact Check News Dataset for COVID-19. ArXiv:2006.11343 [Cs]. https://doi.org/10.36190/2020.14

    4. In this paper, we present a first multilingual cross-domain dataset of 5182 fact-checked news articles for COVID-19, collected from 04/01/2020 to 15/05/2020. We have collected the fact-checked articles from 92 different fact-checking websites after obtaining references from Poynter and Snopes. We have manually annotated articles into 11 different categories of the fact-checked news according to their content. The dataset is in 40 languages from 105 countries. We have built a classifier to detect fake news and present results for the automatic fake news detection and its class. Our model achieves an F1 score of 0.76 to detect the false class and other fact check articles. The FakeCovid dataset is available at Github.
    5. FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19
    1. 2020-06-15

    2. Leffler, C., Ing, E., Lykins, J., Hogan, M., McKeown, C., & Grzybowski, A. (2020). Association of country-wide coronavirus mortality with demographics, testing, lockdowns, and public wearing of masks (Update June 15, 2020).

    3. Background. Wide variation between countries has been noted in per-capita mortality from the disease (COVID-19) caused by the SARS-CoV-2 virus. Determinants of this variation are not fully understood. Methods. Potential predictors of per-capita coronavirus-related mortality in 198 countries were examined, including age, sex ratio, obesity prevalence, temperature, urbanization, smoking, duration of infection, lockdowns, viral testing, contact tracing policies, and public mask-wearing norms and policies. Multivariable linear regression analysis was performed. Results. In univariate analyses, the prevalence of smoking, per-capita gross domestic product, urbanization, and colder average country temperature were positively associated with coronavirus-related mortality. In a multivariable analysis of 194 countries, the duration of infection in the country, and the proportion of the population 60 years of age or older were positively associated with per-capita mortality, while duration of mask-wearing by the public was negatively associated with mortality (all p<0.001). The prevalence of obesity was independently associated with mortality in models which controlled for testing levels or policy. International travel restrictions were independently associated with lower per-capita mortality, but other containment measures and viral testing and tracing policies were not. In countries with cultural norms or government policies supporting public mask-wearing, per-capita coronavirus mortality increased on average by just 8.0% each week, as compared with 54% each week in remaining countries. On multivariable analysis, lockdowns tended to be associated with less mortality (p=0.43), and increased per-capita testing with higher reported mortality (p=0.70), though neither association was statistically significant. Conclusions. Societal norms and government policies supporting the wearing of masks by the public, as well as international travel controls, are independently associated with lower per-capita mortality from COVID-19.
    4. Association of country-wide coronavirus mortality with demographics, testing, lockdowns, and public wearing of masks
    1. 2020-06-22

    2. Silverman, J. D., Hupert, N., & Washburne, A. D. (2020). Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States. Science Translational Medicine. https://doi.org/10.1126/scitranslmed.abc1126

    3. 10.1126/scitranslmed.abc1126
    4. Detection of SARS-CoV-2 infections to date has relied heavily on RT-PCR testing. However, limited test availability, high false-negative rates, and the existence of asymptomatic or sub-clinical infections have resulted in an under-counting of the true prevalence of SARS-CoV-2. Here, we show how influenza-like illness (ILI) outpatient surveillance data can be used to estimate the prevalence of SARS-CoV-2. We found a surge of non-influenza ILI above the seasonal average in March 2020 and showed that this surge correlated with COVID-19 case counts across states. If 1/3 of patients infected with SARS-CoV-2 in the US sought care, this ILI surge would have corresponded to more than 8.7 million new SARS-CoV-2 infections across the US during the three-week period from March 8 to March 28, 2020. Combining excess ILI counts with the date of onset of community transmission in the US, we also show that the early epidemic in the US was unlikely to have been doubling slower than every 4 days. Together these results suggest a conceptual model for the COVID-19 epidemic in the US characterized by rapid spread across the US with over 80% infected patients remaining undetected. We emphasize the importance of testing these findings with seroprevalence data and discuss the broader potential to use syndromic surveillance for early detection and understanding of emerging infectious diseases.
    5. Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States