Trends and Spatiotemporal Patterns of Avian Influenza Outbreaks in Italy: A Data-Driven Approach.

In recent years, the unprecedented spread of the Avian Influenza Viruses (AIVs) among birds and mammals has caused devastation in animal populations, including poultry, wild birds, and some mammals, damaging farmers' livelihoods and the food trade. Given the urgency of the situation, it is particularly important that scientists and the public can access research results and data as soon as possible. The main aim of this study is to present a global open-access dataset of Avian Influenza outbreaks to enable researchers and policymakers (i) to rapidly detect, and respond to animal outbreaks as the first line of defense; (ii) to conduct epidemiological and virological investigations around animal outbreaks and human infections; and (iii) to communicate the risk.

The challenges of open data for future epidemic preparedness: The experience of the 2022 Ebolavirus outbreak in Uganda.

On 20 September 2022, the Ministry of Health in Uganda, together with the World Health Organization-Regional Office for Africa (WHO AFRO) confirmed an outbreak of EVD due to Sudan ebolavirus in Mubende District, after one fatal case was confirmed. Real-time information are needed to provide crucial information to understand transmissibility, risk of geographical spread, routes of transmission, risk factors of infection, and provide the basis for epidemiological modelling that can inform response and containment planning to reduce the burden of disease. We made an effort to build a centralized repository of the Ebola virus cases from verified sources, providing information on dates of symptom onset, locations (aggregated to the district level), and when available, the gender and status of hospitals, reporting bed capacity and isolation unit occupancy rate according to the severity status of the patient.

Monkeypox: Early estimation of basic reproduction number R in Europe.

This world outbreak of Monkeypox (MPX) infections outside Africa emerged on May 2022 in Europe and spread worldwide with unique characteristics: inter-human contagion and infection in men without specific previous immunization, prevalently men-who-have-sex-with-men (MSM). Phylogenetic analysis confirmed a unique clade, the West African clade, subclade IIb. On August 30, WHO stated 48 895 laboratory confirmed cases from 101 different countries, of which 28 050 were in Europe.

Monkeypox: EpiMPX Surveillance System and Open Data with a Special Focus on European and Italian Epidemic.

Background: The current out-of-Africa 2022 outbreak of Monkeypox requires a coordinated, international response through the rapid sharing of data and research results, as we have seen with COVID-19 and the previous Ebola and Zika outbreaks, which demonstrated how important real-world data are to inform public health, to create surveillance systems, to determine policy decisions and to improve clinical trials.

Objectives: To support global response efforts by providing public access to real-time Monkeypox-related data for effective use of open data that could accelerate scientific knowledge and discoveries in terms of understanding, preventing, and treating the disease. In practice, to create a new surveillance system easy to consult and utilize.

Hepatitis of unknown origin in children: Why and how to create an open access database.

During the early stages of an epidemic, obtaining reliable data is a challenge, especially on a global scale. The COVID-19 pandemic has underlined the importance of having "open data" (i.e.

Predicting the Spread of SARS-CoV-2 in Italian Regions: The Calabria Case Study, February 2020-March 2022.

Despite the stunning speed with which highly effective and safe vaccines have been developed, the emergence of new variants of SARS-CoV-2 causes high rates of (re)infection, a major impact on health care services, and a slowdown to the socio-economic system. For COVID-19, accurate and timely forecasts are therefore essential to provide the opportunity to rapidly identify risk areas affected by the pandemic, reallocate the use of health resources, design countermeasures, and increase public awareness. This paper presents the design and implementation of an approach based on autoregressive models to reliably forecast the spread of COVID-19 in Italian regions.