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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., data which are made accessible and available in a standardized machine-readable format and under a license that allows it to be re-used and reshared) to inform health policy decisions and improve clinical trials. The main goal of our work is to provide effective, timely and comprehensive data to investigate this emerging virus, i.e., the acute hepatitis of unknown origin in children. These data can be used: 1) to conduct real-time situation analysis, and early and timely diagnosis for effective containment; 2) to facilitate coordination and collaboration between national and local governments; 3) to inform citizens on the spread of the disease in the world; and 4) to support governments in the future prevention decisions.
RESUMEN
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. Study design: This work aims to build a surveillance system, namely EpiMPX, that allows researchers and policymakers to monitor the impact of Monkeypox in Europe, with a special focus on the epidemic trends in the Italian regions, based on an open-access database containing information on the laboratory confirmed Monkeypox cases reported by EU/EEA countries and updated once a week. In addition, users will be provided open-access R codes to estimate key epidemiological parameters such as the reproduction number (updating the Serial Interval distribution when new estimates will be published) and produce real-time results on their devices. Results: EpiMPX monitors the space-time distribution of cases and their characteristics, such as age, gender, symptoms, clinical status, and sexual orientation, when available. Even if it is currently too early for reliable calculation of epidemiological parameters, we estimated reproduction number R t in European countries with more than 28 days of observed incidence, assuming that the Serial Interval (SI) early estimate in Italy is valid for other countries too. This provides a direct visual assessment of the geographic distribution of risk areas as well as insights into the evolution of the outbreak over time. Italian data were evaluated concerning gender, region prevalence and cumulative data. Conclusions: The proposed EpiMPX surveillance system provides an overview of the European and Italian Monkeypox epidemiological situation with an open-access database to support epidemiological understanding of the origins and transmission dynamics of the disease with informative graphical outputs. These data confirmed the prevalent expression of Monkeypox within males, both in Europe and Italy. European MSM patients were affected by Monkeypox in a high percentage, confirming close sexual contact and possible sexual transmission. For the first time, Italian data on the regional distribution of cases and gender distribution were graphically evaluated. The data and research results are freely available and can be easily enriched to provide a prompt response to the scientific community and accelerate global efforts to contain the Monkeypox virus.
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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. Starting from the database of the Italian Civil Protection Department (DPC), the experimental evaluation was performed on real-world data collected from February 2020 to March 2022, focusing on Calabria, a region of Southern Italy. This evaluation shows that the proposed approach achieves a good predictive power for out-of-sample predictions within one week (R-squared > 0.9 at 1 day, R-squared > 0.7 at 7 days), although it decreases with increasing forecasted days (R-squared > 0.5 at 14 days).
RESUMEN
The rapid development of effective vaccines against COVID-19 is an extraordinary achievement. However, no medical product can ever be considered risk-free. Several countries have a pharmacovigilance system that detects, assesses, understands, and prevents possible adverse effects of a drug. To benefit from such huge data sources, specialists and researchers need advanced big data analysis tools able to extract value and find valuable insights. This paper defines a general framework for a pharmaceutical data analysis application that provides a predefined (but extensible) set of functions for each data processing step (i.e., data collection, filtering, enriching, analysis, and visualization). As a case study, we present here an analysis of the potential side effects observed following the administration of the COVID-19 vaccines. The experimental evaluation shows that: (i) most adverse events can be classified as non-serious and concern muscle/joint pain, chills and nausea, headache, and fatigue;(ii) the notification rate is higher in the age group 20–39 years and decreases in older age groups and in very young people.