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. It has therefore become important to define new epidemiological indices. Starting from our new surveillance system EpiMPX open data, we defined an early R0 measure, using European ECDC confirmed cases from the epidemic start to the end of August 2022; our early R0 pooled median is 2.44, with high variability between countries. We observed the higher R0 in Portugal and Germany, followed by Italy, Spain, and France. Anyway, these high estimates refer to the MSM group rather than to the general population. Early estimation of R0 can be used to support the epidemiological understanding of transmission dynamics and contain MPX from spreading in naive populations and core groups with risk factors. MPX is in an evolving situation with much to learn and to do to combat the current epidemic outbreak.

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).

No abstract

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. The proposed data repository provides researchers and policymakers timely, complete, and easy-accessible data to monitor the most recent trends of the Ebola outbreak in Ugandan districts with informative graphical outputs. This favors a rapid global response to the disease, enabling governments to prioritize and adjust their decisions quickly and effectively in response to the rapidly evolving emergency, with a solid data basis.