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Rift Valley fever ( RVF) is a vector-borne zoonotic disease that has caused widespread outbreaks throughout Africa and the Arabian Peninsula, with serious consequences for livestock-based economies and public health. Although there have never been any reports of RVF in Morocco, Algeria, Tunisia and Libya, it is a priority disease in the Maghreb, due to the threat of introduction of the virus through transboundary livestock movements or infected mosquito vectors. However, the implementation of surveillance activities and early warning contingency plans requires better knowledge of the epidemiological situation. We conducted a multicriteria decision analysis, integrating host distribution with a combination of important ecological factors that drive mosquito abundance, to identify hotspots and suitable time periods for RVF enzootic circulation (i.e. stable transmission at a low to moderate level for an extended period of time) and an RVF epizootic event (i.e. a sudden occurrence of a large number of infected animals over a large geographic area) in the Maghreb. We also modelled vector species distribution using available information on vector presence and habitat preference. We found that the northern regions of the Maghreb were moderately suitable for RVF enzootics, but highly suitable for RVF epizootics. The vector species distribution model identified these regions as the most favourable mosquito habitats. Due to the low density of animal hosts and arid conditions, the desert region showed low RVF suitability, except in oases. However, the presence of competent vectors in putative unsuitable areas underlines the need for further assessments of mosquito habitat preference. This study produced monthly RVF suitability maps useful for animal health managers and veterinary services involved in designing risk-based surveillance programmes. The suitability maps can be further enhanced using existing country-specific sources of information and by incorporating knowledge - as it becomes available - on the epidemiology of the disease and distribution of vectors in the Maghreb. [ABSTRACT FROM AUTHOR]

Epidemiological models have played a key role in informing national response strategies for the current COVID-19 pandemic. We aimed to identify how mathematical models were employed in the early phase of the pandemic, at a time of great epidemiological uncertainty, as well as to formally assess the quality of models used. Hence we aimed to identify areas for improvement in model-based decision-making for future unknown disease threats. A systematic review of mathematical modelling studies estimating the epidemiological impact of COVID-19 (risk of importation/spread) and non-pharmaceutical interventions (NPI) was conducted. We systematically searched PubMed, Embase, and preprints in ARxiv, MedRxiv and bioRxiv. We adopted two published quality assessment frameworks to formally assess the extent in which modelling studies met minimal requirements for incorporation of uncertainty and good modelling practice. In total, 166 articles met our eligibility criteria. The vast majority (129 studies, 78%) of models evaluated the effectiveness NPIs. NPI effectiveness was predominantly modelled in China and Italy, but varied by global region. Asian studies largely evaluated the impact of quarantine and isolation (64 studies), whereas European modelling studies modelled the impact of containment (15 studies), quarantine of travellers, and the isolation of cases. Early models primarily concerned compartmental, deterministic frameworks using SEIR or variants of SEIR compartments (93 studies, 56%) assuming homogenous, symptomatic transmission. Incorporation of parameter uncertainty through model calibration (inference of unknown parameters by fitting models to data) and sensitivity analyses were relatively common (66% and 56% of studies respectively), the former mainly using Chinese data. In contrast, inclusion of structural uncertainty (uncertainty in disease characteristics) was relatively uncommon, as was validation of model output to external data. This work allows for the identification of existing challenges in the mathematical modelling of emerging diseases, and emphasises minimal criteria for enhancing reliable model estimation and reporting. Limited availability of epidemiological data in the early phase of a new disease treat challenges model calibration to local, and validation to external data, emphasising the critical importance of enforcing standardised protocols for early epi-data collection, and raising awareness among modellers and decision-makers alike in handling uncertainty. [ABSTRACT FROM AUTHOR]

Epidemic intelligence (EI) has been adopted by several countries to reach fast detection of new and emerging infectious diseases. EI collects information from two types of sources: official sources (i.e. health reports from OIE or FAO) and unofficial sources (i.e. online media outlets, scientific publications, etc.). In France, the EI system PADI-web (Platform for Automated extraction of Disease Information from the Web) is used since 2014 to detect signals of animal health events with risk of introduction to France. The objective of this work was to understand how health information (signal) is disseminated from a primary source (transmitter) to a final source (EI system) through quantitative and qualitative network analysis methods. We analysed all English reports related to avian influenza detected by PADI-web between August 2018 and June 2019. We used the sources cited in the detected reports to trace the path of each signal. Signals were categorized as official and unofficial according to the source. We have built a directed network where the nodes represented the sources (characterized by type, location and geographical focus) and the edges represented the signal flow. To describe the network, we used network centrality measurements (degree, betweenness and eigenvector) to determine which nodes were important in the data dissemination. We also included the reactivity, calculated as the difference (in days) between the detection of an outbreak by PADI-web and its official notification by Empres-i (gold-standard) with a distinction between wild and domestic birds. PADI-web detected 202 official signals and 26 unofficial signals. The OIE occupies a central position in the PADI-web information network. National veterinary authorities were the major primary sources. Online news outlets followed by press agencies were the main secondary sources. A significant portion of the signals was detected early in wild birds (41%) and in domestic birds (13%). This work showed PADI-web's capacity to detect early signals and can be used to define priority sources to improve this tool in terms of reactivity and data quality. In the future, similar work will be conducted on other diseases and EI systems to improve these systems. [ABSTRACT FROM AUTHOR]

In the first quarter of 2020, World Health Organization (WHO) declared COVID-19 as a public health emergency around the globe. Therefore, different users from all over the world shared their thoughts about COVID-19 on social media platforms i.e., Twitter, Facebook etc. So, it is important to analyze public opinions about COVID-19 from different regions over different period of time. To fulfill the spatial analysis issue, a previous work called H-TF-IDF (Hierarchy-based measure for tweet analysis) for term extraction from tweet data has been proposed. In this work, we focus on the sentiment analysis performed on terms selected by H-TF-IDF for spatial tweets groups to know local situations during the ongoing epidemic COVID-19 over different time frames. The primary step is to extract terms from tweets using H-TF-IDF approach. Moreover, these terms are utilized in two ways i.e., 1) select tweets containing terms, 2) terms used as features for sentiment analysis. Thereafter, data preprocessing is performed to clean the text. Afterwards, Vectorization models i.e., bag-of-words (BOW) and term frequency-inverse document frequency (TF-IDF) are used to extract features with the help of n-gram techniques. These features are extracted to train the prediction models for sentiment analysis. Lastly, different statistical and machine learning models i.e., Logistic regression, support vector machine (SVM), etc. are applied to classify the spatial tweets groups. For preliminary results, experiments are conducted on H-TF-IDF tweets corpus having geocoded spatial information for the period of January, 2020. These tweets are extracted from the dataset collected by E.Chen (https://github.com/echen102/COVID-19-TweetIDs) that focuses on the early beginning of the outbreak. A uniform experiment setup of train-test (80% and 20%) split scheme is used for each prediction model. The results illustrate that specific terms highlighted by H-TF-IDF provide useful information that would not have been identified without this spatial analysis. The classification results spatial location tweet groups into positive, negative and neutral by subjectivity and polarity measures. The current work is applied on English language-based Twitter information. A following work is to incorporate other languages to perform sentiment analysis. Furthermore, BERT will be used to extend these features. [ABSTRACT FROM AUTHOR]