Availability of open data related to COVID-19 epidemic in Italy.

Background: Since the beginning of the COVID-19 outbreak in Italy, health authorities have released epidemiologic data about this disease. These data were the most important sources of information which were periodically updated and analyzed by researchers to predict the spread of the epidemic. However, comprehensive and timely data on the evolution of COVID-19 have not always been made available to researchers and physicians. Method: The aim of our work is to investigate quality, availability and format of epidemiologic data about COVID-19 in Italy in different territorial and temporal areas. We tried to access the online resources made available by each of the 19 Italian Regions and the two autonomous Provinces, and in more detail by the Local Health Authorities of one of them, the Emilia-Romagna Region. We analyzed the main sources and flows of data (namely new and cumulative cases of infection, total swabs, new and cumulative COVID-19 deaths, overall and divided by sex), describing their characteristics such as accessibility, format and completeness. We eventually reviewed the data published by the Italian Ministry of Health, the National Institute of Health (ISS) and the Civil Protection Department. The Tim Berners-Lee scale was used to evaluate the open data format. Results: The flow of COVID-19 epidemiologic data in Italy originated from the Local Health Authorities that transmitted the data - on a daily basis - to the regional authorities, which in turn transferred them to the national authorities. We found a rather high heterogeneity in both the content and the format of the released data, both at the local and the regional level. Few Regions were releasing data in open format. ISS was the only national source of data that provided the number of COVID-19 health outcomes divided by sex and age groups since Spring 2020. Conclusions: Despite multiple potential useful sources for COVID-19 epidemiology are present in Italy, very few open format data were available both at a macro geographical level (e.g. per Region) and at the provincial level. The access to open format epidemiologic data should be eased, to allow researchers to adequately assess future epidemics and therefore favor timely and effective public health interventions.

Estimating SARS-CoV-2 infections from deaths, confirmed cases, tests, and random surveys.

There are multiple sources of data giving information about the number of SARS-CoV-2 infections in the population, but all have major drawbacks, including biases and delayed reporting. For example, the number of confirmed cases largely underestimates the number of infections, and deaths lag infections substantially, while test positivity rates tend to greatly overestimate prevalence. Representative random prevalence surveys, the only putatively unbiased source, are sparse in time and space, and the results can come with big delays. Reliable estimates of population prevalence are necessary for understanding the spread of the virus and the effectiveness of mitigation strategies. We develop a simple Bayesian framework to estimate viral prevalence by combining several of the main available data sources. It is based on a discrete-time Susceptible-Infected-Removed (SIR) model with time-varying reproductive parameter. Our model includes likelihood components that incorporate data on deaths due to the virus, confirmed cases, and the number of tests administered on each day. We anchor our inference with data from random-sample testing surveys in Indiana and Ohio. We use the results from these two states to calibrate the model on positive test counts and proceed to estimate the infection fatality rate and the number of new infections on each day in each state in the United States. We estimate the extent to which reported COVID cases have underestimated true infection counts, which was large, especially in the first months of the pandemic. We explore the implications of our results for progress toward herd immunity.

Universal Screening for SARS-CoV-2 in Asymptomatic Pregnant Women: A Multi-center Experience.

BACKGROUND: Novel coronavirus (SARS-CoV-2) is responsible for the current global pandemic and understandably, Obstetrics is not spared. Private maternity hospitals have a unique challenge of reassuring unaffected patients of uneventful delivery with the lowest possible rate of coronavirus infection while consequently offering compassionate and state of art services to women who turn out to be positive for SARS-CoV-2. This has led to a routine SARS-CoV-2 testing of all patients before admission in many of the private hospitals in India. The current study was undertaken to determine the incidence of SARS-COV-2 among asymptomatic pregnant women and to ascertain the utility of universal screening in these women. METHODOLOGY: A retrospective observational multi-center study was conducted over a period of approximately 5 months (1-May-2020 to 10-September-2020) in a chain of privately run maternity hospitals with presence in multiple cities across India. All asymptomatic pregnant women were tested for SARS-CoV-2 prior to elective/emergency hospital admission. RESULTS: Among 4158 women tested, 54 (1.3%) were positive for SARS-CoV-2 and intra partum and postnatal period was uneventful for all of them. CONCLUSION: Universal screening should be continued as preferred approach to ensure low anxiety levels of delivering women and safety of frontline workers. Further, universal screening helps avoid emergence of maternity centers as virus clusters by effective isolation of identified positive cases and minimizing points of contact.