Is a more aggressive COVID-19 case detection approach mitigating the burden on ICUs? Some reflections from Italy

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Profiling patients by intensity of nursing care: An operative approach using machine learning

Physical function is a patient-oriented indicator and can be considered a proxy for the assignment of healthcare personnel. The study aims to create an algorithm that classifies patients into homogeneous groups according to physical function. A two-step machine-learning algorithm was applied to administrative data recorded between 2015 and 2018 at the University Hospital of Padova. A clustering-large-applications (CLARA) algorithm was used to partition patients into homogeneous groups. Then, machine learning technique (MLT) classifiers were used to categorize the doubtful records. Based on the results of the CLARA algorithm, records were divided into three groups according to the Barthel index: <45, >65, ≥45 and ≤65. The support vector machine was the MLT showing the best performance among doubtful records, reaching an accuracy of 66%. The two-step algorithm, since it splits patients into low and high resource consumption, could be a useful tool for organizing healthcare personnel allocation according to the patients’ assistance needs

Regional Differences in Mortality Rates during the COVID-19 Epidemic in Italy

Objective: The coronavirus disease 2019 (COVID-19) outbreak started in Italy on February 20, 2020, and has resulted in many deaths and intensive care unit (ICU) admissions. This study aimed to illustrate the epidemic COVID-19 growth pattern in Italy by considering the regional differences in disease diffusion during the first 3 mo of the epidemic. Methods: Official COVID-19 data were obtained from the Italian Civil Protection Department of the Council of Ministers Presidency. The mortality and ICU admission rates per 100,000 inhabitants were calculated at the regional level and summarized by means of a Bayesian multilevel meta-analysis. Data were retrieved until April 21, 2020. Results: The highest cumulative mortality rates per 100 000 inhabitants were observed in northern Italy, particularly in Lombardia (85.3; 95% credibility intervals [CI], 75.7-94.7). The difference in the mortality rates between northern and southern Italy increased over time, reaching a difference of 67.72 (95% CI, 66-67) cases on April 2, 2020. Conclusions: Northern Italy showed higher and increasing mortality rates during the first 3 mo of the epidemic. The uncontrolled virus circulation preceding the infection spreading in southern Italy had a considerable impact on system burnout. This experience demonstrates that preparedness against the pandemic is of crucial importance to contain its disruptive effects

Predicting lifetime production and longevity of organic dairy cows from 1st or 2nd lactation data

Longevity of dairy cows is a key trait combining all functional traits and is decisive for sustainability of dairy production at economic, environmental and ethical level. We used herdbook data of culled Swiss dairy cows to predict longevity (days) and average lifetime daily milk yield (LT_DMY, kg milk) of dairy cows in low-input dairy farms from data easily available for the farmer. First, we assessed the suitability of 1st vs 2nd lactation data to predict longevity and LT_DMY without (n=10,031 cows, 384 farms) and with information on number of inseminations (n=6,011 cows, 372 farms). Second, we tested if lactation curve parameter estimates (LCPs) derived from test day records can be successfully used to predict LT_DMY and longevity (n=1,632 cows, 321 farms). Finally, we investigated breed differences between local dual-purpose breeds and pronounced dairy type breeds in a subset of mixed herds (n=1,796 cows, 72 farms). Models based on 2nd lactation data were consistently better across all traits investigated. Although estimation of LCPs was only possible with sufficient reliability for about 16% of the cows, models including LCPs performed best in predicting LT_DMY with a mean predictability of 73.3%. By contrast, longevity models performed best when using insemination data, but mean predictability only reached 4.6%. Somatic cell count, breed, calving interval, age at first calving, lactation curve persistency, fat protein ratio and information on alpine pasturing were additional traits improving predictions of both traits. Investigation of breed differences in mixed herds revealed lower LT_DMY in the local breeds Simmental and Original Braunvieh compared to Swiss Fleckvieh and Holstein cows. Original Braunvieh lived longer than Holstein (1,949±70 SE vs 1,709±54 SE days, P=0.046), while the local Simmental cows (1,681±61 SE days) did not. We conclude that it seems possible to develop models for TL_DMY, while reliable prediction of longevity remains challenging using information at farmer’s hands

A Web-Based Application to Monitor and Inform about the COVID-19 Outbreak in Italy: The {COVID-19ita} Initiative

The pandemic outbreak of COVID-19 has posed several questions about public health emergency risk communication. Due to the effort required for the population to adopt appropriate behaviors in response to the emergency, it is essential to inform the public of the epidemic situation with transparent data sources. The COVID-19ita project aimed to develop a public open-source tool to provide timely, updated information on the pandemic’s evolution in Italy. It is a web-based application, the front end for the eponymously named R package freely available on GitHub, deployed both in English and Italian. The web application pulls the data from the official repository of the Italian COVID-19 outbreak at the national, regional, and provincial levels. The app allows the user to select information to visualize data in an interactive environment and compare epidemic situations over time and across different Italian regions. At the same time, it provides insights about the outbreak that are explained and commented upon to yield reasoned, focused, timely, and updated information about the outbreak evolution

Joint Models to Predict Dairy Cow Survival from Sensor Data Recorded during the First Lactation.

Early predictions of cows' probability of survival to different lactations would help farmers in making successful management and breeding decisions. For this purpose, this research explored the adoption of joint models for longitudinal and survival data in the dairy field. An algorithm jointly modelled daily first-lactation sensor data (milk yield, body weight, rumination time) and survival data (i.e., time to culling) from 6 Holstein dairy farms. The algorithm was set to predict survival to the beginning of the second and third lactations (i.e., second and third calving) from sensor observations of the first 60, 150, and 240 days in milk of cows' first lactation. Using 3-time-repeated 3-fold cross-validation, the performance was evaluated in terms of Area Under the Curve and expected error of prediction. Across the different scenarios and farms, the former varied between 45% and 76%, while the latter was between 3.5% and 26%. Significant results were obtained in terms of expected error of prediction, meaning that the method provided survival probabilities in line with the observed events in the datasets (i.e., culling). Furthermore, the performances were stable among farms. These features may justify further research on the use of joint models to predict the survival of dairy cattle

Excess of all-cause mortality is only partially explained by COVID-19 in Veneto (Italy) during spring outbreak

Background: Italy has been the first European country to be affected by the COVID-19 epidemic which started out at the end of February. In this report, we focus our attention on the Veneto Region, in the North-East of Italy, which is one of the areas that were first affected by the rapid spread of SARS-CoV-2. We aim to evaluate the trend of all-cause mortality and to give a description of the characteristics of the studied population. Methods: Data used in the analyses were released by the majority of municipalities and cover the 93% of the total population living in the Veneto Region. We evaluated the trend of overall mortality from Jan.01 to Jun.30. 2020. Moreover we compared the COVID-19-related deaths to the overall deaths. Results: From March 2020, the overall mortality rate increased exponentially, affecting males and people aged > 76 the most. The confirmed COVID-19-related death rate in the Veneto region between Mar.01 and Apr.302020 is 30 per 100,000 inhabitants. In contrast, the all-cause mortality increase registered in the same months in the municipalities included in the study is 219 per 100,000 inhabitants. Conclusions: COVID-19 has a primary role in the increase in mortality but does not entirely explain such a high number of deaths. Strategies need to be developed to reduce this gap in case of future waves of the pandemic

To swab or not to swab? The lesson learned in italy in the early stage of the COVID-19 pandemic

none12noTesting for the SARS-CoV-2 infection is critical for tracking the spread of the virus and controlling the transmission dynamics. In the early phase of the pandemic in Italy, the decentralized healthcare system allowed regions to adopt different testing strategies. The objective of this paper is to assess the impact of the extensive testing of symptomatic individuals and their contacts on the number of hospitalizations against a more stringent testing strategy limited to suspected cases with severe respiratory illness and an epidemiological link to a COVID-19 case. A Poisson regression modelling approach was adopted. In the first model developed, the cumulative daily number of positive cases and a temporal trend were considered as explanatory variables. In the second, the cumulative daily number of swabs was further added. The explanatory variable, given by the number of swabs over time, explained most of the observed differences in the number of hospitalizations between the two strategies. The percentage of the expected error dropped from 70% of the first, simpler model to 15%. Increasing testing to detect and isolate infected individuals in the early phase of an outbreak improves the capability to reduce the spread of serious infections, lessening the burden of hospitals.noneBerchialla P.; Giraudo M.T.; Fava C.; Ricotti A.; Saglio G.; Lorenzoni G.; Sciannameo V.; Urru S.; Prosepe I.; Lanera C.; Azzolina D.; Gregori D.Berchialla, P.; Giraudo, M. T.; Fava, C.; Ricotti, A.; Saglio, G.; Lorenzoni, G.; Sciannameo, V.; Urru, S.; Prosepe, I.; Lanera, C.; Azzolina, D.; Gregori, D