[Rt or RDt, that is the question!] / Rt or RDt, that is the question!

The article compares two of the most followed indices in the monitoring of COVID-19 epidemic cases: the Rt and the RDt indices. The first was disseminated by the Italian National Institute of Health (ISS) and the second, which is more usable due to the lower difficulty of calculation and the availability of data, was adopted by various regional and local institutions.The rationale for the Rt index refers to that for the R0 index, the basic reproduction number, which is used by infectivologists as a measure of contagiousness of a given infectious agent in a completely susceptible population. The RDt index, on the other hand, is borrowed from the techniques of time series analysis for the trend of an event measurement that develops as a function of time. The RDt index does not take into account the time of infection, but the date of the diagnosis of positivity and for this reason it is defined as diagnostic replication index, as it aims to describe the intensity of the development of frequency for cases recognized as positive in the population.The comparison between different possible applications of the methods and the use of different types of monitoring data was limited to four areas for which complete individual data were available in March and April 2020. The main problems in the use of Rt, which is based on the date of symptoms onset, arise from the lack of completeness of this information due both to the difficulty in the recording and to the absence in asymptomatic subjects.The general trend of RDt, at least at an intermediate lag of 6 or 7 days, is very similar to that of Rt, as confirmed by the very high value of the correlation index between the two indices. The maximum correlation between Rt and RDt is reached at lag 7 with a value of R exceeding 0.97 (R2=0.944).The two indices, albeit formally distinct, are both valid; they show specific aspects of the phenomenon, but provide basically similar information to the public health decision-maker. Their distinction lies not so much in the method of calculation, rather in the use of different information, i.e., the beginning of symptoms and the swabs outcome.Therefore, it is not appropriate to make a judgment of preference for one of the two indices, but only to invite people to understand their different potentials so that they can choose the one they consider the most appropriate for the purpose they want to use it for.

Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19.

Background: The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. In the early stage of the outbreak, the most important question concerns some meaningful milepost moments, including the time when the number of daily confirmed cases decreases, the time when the number of daily confirmed cases becomes smaller than that of the daily removed (recovered and death), and the time when the number of daily confirmed cases and patients treated in hospital becomes zero. Unfortunately, it is extremely difficult to make right and precise prediction due to the limited amount of available data at the early stage of the outbreak. To address it, in this paper, we propose a flexible framework incorporating the effectiveness of the government control to forecast the whole process of a new unknown infectious disease in its early-outbreak. Methods: We first establish the iconic indicators to characterize the extent of epidemic spread. Then we develop the tracking and forecasting procedure with mild and reasonable assumption. Finally we apply it to analyze and evaluate the COVID-19 using the public available data for mainland China beyond Hubei Province from the China Centers for Disease Control (CDC) during the period of Jan 29th, 2020, to Feb 29th, 2020, which shows the effectiveness of the proposed procedure. Results: Forecasting results indicate that the number of newly confirmed cases will become zero in the mid-early March, and the number of patients treated in the hospital will become zero between mid-March and mid-April in mainland China beyond Hubei Province. Conclusions: The framework proposed in this paper can help people get a general understanding of the epidemic trends in counties where COVID-19 are raging as well as any other outbreaks of new and unknown infectious diseases in the future.

Density-dependent mortality in Pinus sylvestris caused by the snow blight pathogen Phacidium infestans.

Relationships between disease incidence and the density of host plant populations were investigated in the Pinus sylvestris-Phacidium infestans host-fungal pathogen association, in which the season of death of plants killed up to 3 years previously could be accurately determined. Significant (P<0.05), positive density-dependent relationships between the proportion of plants dying in the winters of 1987-1988, 1988-1989 or 1989-1990 and the original stand density were detected in 12 of 26 comparisons. Of the remaining comparisons, all but three had positive regression coefficients for the same association. Plants killed up to 2 years previously contributed to inoculum production. The use of "standing dead" as a predictor in the analyses showed that the proportion of plants dying in the winters of 1988-1989 or 1989-1990 was generally better correlated with standing dead in the previous summer than with the density of the original population. Significant (P<0.05), positive density-dependent associations were also found between the proportion of living plants in 1990 infected with P. infestans and the number of standing dead plants in all nine comparisons. In contrast, only four of the nine associations between these proportions of infected plants and population density were significant. The strength of the density-dependent relationships varied substantially within and between sites. Much of this variation appears to be due to differences in the stage of development of the epidemics occurring at different sites.