New statistical RI index allow to better track the dynamics of COVID-19 outbreak in Italy

Association of anti-contagion policies with the spread of COVID-19 in United States

BACKGROUND

The outbreak of a novel coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, raised worldwide concern. The present study investigates the association between anti-contagion policies and the spread of COVID-19 across the United States.

DESIGN AND METHODS

We selected the most frequently implemented COVID-19 anti-contagion policies in all the U.S. states issued from 29 February 2020. Accordingly, we modified an epidemiological model and combined it with a comprehensive statistical analysis to evaluate the policies' individual and overall likely impact.

RESULTS

For the first time, a novel index, evaluates the associations between policy implementation and COVID-19 spread at both statewide and national levels. Our results indicate that governmental policies requiring mask use, businesses social distancing, and quarantining travelers may be most effective for controlling COVID-19 spread. Simultaneously, widespread orders like school closure and safer-at-home that can be particularly disruptive to the economy and social fabric of society may be unnecessary given their lack of association with reducing infection.

CONCLUSIONS

The absence of any COVID-19 vaccines during the first several months of its pandemic necessitated using governmental policies to help stop the spread of this disease. Our index showed the association between implemented policies and COVID-19 spread, highlighting the specific policies with the greatest association - mandatory quarantine upon entering a state, businesses implementing social distancing, and mandatory mask use - and those with less association like school closure and safer-at-home orders. This study provided evidence to inform policy choices for the current global crisis and future pandemics.

Analytical Performance of COVID-19 Detection Methods (RT-PCR): Scientific and Societal Concerns

Background. Health and social management of the SARS-CoV-2 epidemic, responsible for the COVID-19 disease, requires both screening tools and diagnostic procedures. Reliable screening tests aim at identifying (truely) infectious individuals that can spread the viral infection and therefore are essential for tracing and harnessing the epidemic diffusion. Instead, diagnostic tests should supplement clinical and radiological findings, thus helping in establishing the diagnosis. Several analytical assays, mostly using RT-PCR-based technologies, have become commercially available for healthcare workers and clinical laboratories. However, such tests showed some critical limitations, given that a relevant number of both false-positive and false-negative cases have been so far reported. Moreover, those analytical techniques demonstrated to be significantly influenced by pre-analytical biases, while the sensitivity showed a dramatic time dependency. Aim. Herein, we critically investigate limits and perspectives of currently available RT-PCR techniques, especially when referring to the required performances in providing reliable epidemiological and clinical information. Key Concepts. Current data cast doubt on the use of RT-PCR swabs as a screening procedure for tracing the evolution of the current SARS-COV-2 pandemic. Indeed, the huge number of both false-positive and false-negative results deprives the trustworthiness of decision making based on those data. Therefore, we should refine current available analytical tests to quickly identify individuals able to really transmit the virus, with the aim to control and prevent large outbreaks.

Repeated Exposure to Subinfectious Doses of SARS-CoV-2 May Promote T Cell Immunity and Protection against Severe COVID-19

Europe is experiencing a third wave of COVID-19 due to the spread of highly transmissible SARS-CoV-2 variants. A number of positive and negative factors constantly shape the rates of COVID-19 infections, hospitalization, and mortality. Among these factors, the rise in increasingly transmissible variants on one side and the effect of vaccinations on the other side create a picture deeply different from that of the first pandemic wave. Starting from the observation that in several European countries the number of COVID-19 infections in the second and third pandemic wave increased without a proportional rise in disease severity and mortality, we hypothesize the existence of an additional factor influencing SARS-CoV-2 dynamics. This factor consists of an immune defence against severe COVID-19, provided by SARS-CoV-2-specific T cells progressively developing upon natural exposure to low virus doses present in populated environments. As suggested by recent studies, low-dose viral particles entering the respiratory and intestinal tracts may be able to induce T cell memory in the absence of inflammation, potentially resulting in different degrees of immunization. In this scenario, non-pharmaceutical interventions would play a double role, one in the short term by reducing the detrimental spreading of SARS-CoV-2 particles, and one in the long term by allowing the development of a widespread (although heterogeneous and uncontrollable) form of immune protection.

A Framework for a Statistical Characterization of Epidemic Cycles: COVID-19 Case Study

Background

Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that drive its local transmission cycles to make better decisions regarding prevention and control measures. Different modeling approaches have been proposed in an attempt to predict the behavior of these local cycles.

Objective

This paper presents a framework to characterize the different variables that drive the local, or epidemic, cycles of the COVID-19 pandemic, in order to provide a set of relatively simple, yet efficient, statistical tools to be used by local health authorities to support decision making.

Methods

Virtually closed cycles were compared to cycles in progress from different locations that present similar patterns in the figures that describe them. With the aim to compare populations of different sizes at different periods of time and locations, the cycles were normalized, allowing an analysis based on the core behavior of the numerical series. A model for the reproduction number was derived from the experimental data, and its performance was presented, including the effect of subnotification (ie, underreporting). A variation of the logistic model was used together with an innovative inventory model to calculate the actual number of infected persons, analyze the incubation period, and determine the actual onset of local epidemic cycles.

Results

The similarities among cycles were demonstrated. A pattern between the cycles studied, which took on a triangular shape, was identified and used to make predictions about the duration of future cycles. Analyses on effective reproduction number (R_t) and subnotification effects for Germany, Italy, and Sweden were presented to show the performance of the framework introduced here. After comparing data from the three countries, it was possible to determine the probable dates of the actual onset of the epidemic cycles for each country, the typical duration of the incubation period for the disease, and the total number of infected persons during each cycle. In general terms, a probable average incubation time of 5 days was found, and the method used here was able to estimate the end of the cycles up to 34 days in advance, while demonstrating that the impact of the subnotification level (ie, error) on the effective reproduction number was <5%.

Conclusions

It was demonstrated that, with relatively simple mathematical tools, it is possible to obtain a reliable understanding of the behavior of COVID-19 local epidemic cycles, by introducing an integrated framework for identifying cycle patterns and calculating the variables that drive it, namely: the R_t, the subnotification effects on estimations, the most probable actual cycles start dates, the total number of infected, and the most likely incubation period for SARS-CoV-2.