Can we rely on COVID-19 data? An assessment of data from over 200 countries worldwide

Trusting the Health System and COVID 19 Restriction Compliance

We examine the extent to which exposure to higher relative COVID-19 mortality (RM), influences health system trust (HST), and whether changes in HST explain the perceived ease of compliance with pandemic restrictions during the COVID-19 pandemic. Drawing on evidence from two representative surveys covering all regions of 28 European countries before and after the first COVID-19 wave, and using a difference in differences strategy together with Coarsened Exact Matching (CEM), we document that living in a region with higher RM during the first wave of the pandemic increased HST. However, the positive effect of RM on HST is driven by individuals over 45 years of age, and the opposite effect is found among younger cohorts. Furthemore, we find that a higher HST reduces the costs of complying with COVID-19 restrictions, but only so long as excess mortality does not exceed the average by more than 20%, at which point the ease of complying with COVID-19 restrictions significantly declines, offsetting the positive effect of trust in the healthcare system. Our interpretation of these estimates is that a higher RM is interpreted as a risk signal among those over 45, and as a signal of health-care system failure among younger age individuals.

Desempeño del sistema de vigilancia COVID-19 en Honduras 2020: Análisis de la ley de Benford

El COVID-19 es una enfermedad infecciosa responsable hasta la fecha de 6,369,703 muertes a nivel mundial. Para poder controlarla es importante adaptar y reforzar los sistemas nacionales de salud y ampliar las capacidades en materia de vigilancia, siendo el reporte diario de nuevos casos y muertes por COVID-19, una de las principales herramientas para comprender y dar respuesta a la pandemia. El trabajo se centró en identificar inconsistencias en los datos reportados sobre casos y muertes confirmadas de COVID-19, por el sistema de salud hondureño durante el año 2020. Se analizaron los datos oficiales publicados entre el 11 de marzo y el 31 de diciembre de 2020, por el centro de recursos coronavirus de la Universidad Johns Hopkins, los datos fueron divididos en cuatro segmentos de análisis, el primero correspondió a los primeros 50 días de pandemia en el país, el segundo incluyó datos hasta el 24 junio, el tercero hasta el 1 diciembre y el cuarto incluyó todos los datos del 2020. Para el análisis se hizo uso de la ley de Benford, el cual es un fenómeno estadístico ampliamente usado que permite detectar datos anómalos en los sistemas de vigilancia de cada país. Hasta el 31 de diciembre el sistema de salud hondureño confirmó 121,827 casos positivos y 3,130 muertes por COVID-19 en Honduras. Respecto a la detección de datos anómalos el segundo segmento de análisis obtuvo un mejor resultado, sin embargo, se observo que el análisis acumulado correspondiente al 2020 no se ajustó a la Ley de Benford. En general, la vigilancia tuvo una mejoría en el desempeñó después de los primeros 50 días de pandemia, no obstante, los datos referentes al 2020 revelan una disconformidad con la ley de Benford, lo que se interpreta como un desempeño de notificación no adecuado. Palabras clave .COVID-19, distribuciones estadísticas, Honduras, monitoreo epidemiológico, notificación de enfermedades.

A highly powerful non-specific strategy to reduce COVID-19 deaths

The coronavirus disease 2019 (COVID‐19) pandemic caused by the coronavirus severe acute respiratory syndrome coronavirus 2 remains risky worldwide. We elucidate here that good IDM (isolation, disinfection, and maintenance of health) is powerful to reduce COVID‐19 deaths based on the striking differences in COVID‐19 case fatality rates among various scenarios. IDM means keeping COVID‐19 cases away from each other and from other people, disinfecting their living environments, and maintaining their health through good nutrition, rest, and treatment of symptoms and pre‐existing diseases (not through specific antiviral therapy). Good IDM could reduce COVID‐19 deaths by more than 85% in 2020 and more than 99% in 2022. This is consistent with the fact that good IDM can minimize co‐infections and maintain body functions and the fact that COVID‐19 has become less pathogenic (this fact was supported with three novel data in this report). Although IDM has been frequently implemented worldwide to some degree, IDM has not been highlighted sufficiently. Good IDM is relative, nonspecific, flexible, and feasible in many countries, and can reduce deaths of some other relatively mild infectious diseases. IDM, vaccines, and antivirals aid each other to reduce COVID‐19 deaths. The IDM concept and strategy can aid people to improve their health behavior and fight against COVID‐19 and future pandemics worldwide.

Multiple striking differences in COVID‐19 case fatality rate were calculated.

These differences support that a strategy could reduce COVID‐19 deaths by more than 85% in 2020 and more than 99% in 2022.

The strategy is based on isolation, disinfection, and health maintenance.

The strategy can reduce co‐infections and maintain body functions.

The strategy has been implemented frequently but insufficiently worldwide.

The strategy is non‐specific and can reduce deaths of other mild infectious diseases.

Is there a dose-response relationship? Investigating the functional form between COVID-19 incidence rates and life satisfaction in a multilevel framework

While there is plenty of research linking the effects of the global COVID-19 pandemic to a drastic reduction of life satisfaction in the population, there is little information on the functional form of this relationship. Until now, one could suspect that this association is linear and a higher number of COVID-19 infections in a region leads to a continuous decline of satisfaction. However, there are reasons to assume that this interrelation is indeed more complex and deserves further attention. To resolve this question, high-quality panel data of the first wave of COVID-19 from Germany are analysed in a fixed-effect multilevel framework. With information from more than 6,000 respondents (after imputation) nested in 339 federal districts, we estimate linear models with higher-order terms up to the fifth degree of median COVID-19 incidence rates and random intercepts for districts to describe the functional form. The results indicate that even regions with very low incidences are affected and a linear decline of satisfaction is only apparent for rather low incidence levels, quickly reaching a plateau, which is then quite constant, even for higher incidence levels. These findings indicate that at least in rich and industrialized countries like Germany, assuming a strictly linear relation between incidences and change of satisfaction is not appropriate.

Stylometry and Numerals Usage: Benford’s Law and Beyond

We suggest two approaches to the statistical analysis of texts, both based on the study of numerals occurrence in literary texts. The first approach is related to Benford’s Law and the analysis of the frequency distribution of various leading digits of numerals contained in the text. In coherent literary texts, the share of the leading digit 1 is even larger than prescribed by Benford’s Law and can reach 50 percent. The frequencies of occurrence of the digit 1, as well as, to a lesser extent, the digits 2 and 3, are usually a characteristic the author’s style feature, manifested in all (sufficiently long) literary texts of any author. This approach is convenient for testing whether a group of texts has common authorship: the latter is dubious if the frequency distributions are sufficiently different. The second approach is the extension of the first one and requires the study of the frequency distribution of numerals themselves (not their leading digits). The approach yields non-trivial information about the author, stylistic and genre peculiarities of the texts and is suited for the advanced stylometric analysis. The proposed approaches are illustrated by examples of computer analysis of the literary texts in English and Russian.