UNASSIGNED: Lumpy skin disease is a viral disease that affects cattle belonging to genus Capripoxvirus (Poxviridae) and lead to significant economic losses.
UNASSIGNED: The objective of this study was to evaluate the distribution of lumpy skin disease (LSD) outbreaks and predict future patterns based on retrospective outbreak reports in Ethiopia.
UNASSIGNED: Data were collected through direct communication with regional laboratories and a hierarchical reporting system from the Peasant Associations to Ministry of Agriculture. Time-series data for the LSD outbreaks were analyzed using classical additive time-series decomposition and STL decomposition. Four models (ARIMA, SARIMA, ETS, STLF) were also used to forecast the number of LSD outbreaks that occurred each month for the years (2021-2025) after the models\' accuracy test was performed. Additionally, the space-time permutation model (STP) were also used to study retrospective space-time cluster analysis of LSD outbreaks in Ethiopia.
UNASSIGNED: This study examined the geographical and temporal distribution of LSD outbreaks in Ethiopia from 2008 to 2020, reporting a total of 3,256 LSD outbreaks, 14,754 LSD-positive cases, 7,758 deaths, and 289 slaughters. It also covered approximately 68% of Ethiopia\'s districts, with Oromia reporting the highest LSD outbreaks. In the LSD\'s temporal distribution, the highest peak was reported following the rainy season in September to December and its lowest peak in the dry months of April and May. Out of the four models tested for forecasting, the SARIMA (3, 0, 0) (2, 1, 0) [12] model performed well for the validation data, while the STLF+Random Walk had a robust prediction for the training data. Thus, the SARIMA and STLF+Random Walk models produced a more accurate forecast of LSD outbreaks between 2020 and 2025. From retrospective Space-Time Cluster Analysis of LSD, eight possible clusters were also identified, with five of them located in central part of Ethiopia.
UNASSIGNED: The study\'s time series and ST-cluster analysis of LSD outbreak data provide valuable insights into the spatial and temporal dynamics of the disease in Ethiopia. These insights can aid in the development of effective strategies to control and prevent the spread of the disease and holds great potential for improving efforts to combat LSD in the country.

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The United States (U.S.) has the highest number of reported COVID-19 infections and related deaths in the world, accounting for 17.8% of total global confirmed cases as of August 2021. As COVID-19 spread throughout communities across the U.S., it became clear that inequities would arise among differing demographics. Several researchers have suggested that certain racial and ethnic minority groups may have been disproportionately impacted by the spread of COVID-19. In the present study, we used the daily data of COVID-19 cases in Kansas City, Missouri, to observe differences in COVID-19 clusters with respect to gender, race, and ethnicity. Specifically, we utilized a retrospective Poisson spatial scan statistic with respect to demographic factors to detect daily clusters of COVID-19 in Kansas City at the zip code level from March to November 2020. Our statistical results indicated that clusters of the male population were more widely scattered than clusters of the female population. Clusters of the Hispanic population had the highest prevalence and were also more widely scattered. This demographic cluster analysis can provide guidance for reducing the social inequalities associated with the COVID-19 pandemic. Moreover, applying stronger preventive and control measures to emerging clusters can reduce the likelihood of another epidemic wave of infection.

The Quebec Public Health Institute (INSPQ) was mandated to develop an automated tool for detecting space-time COVID-19 case clusters to assist regional public health authorities in identifying situations that require public health interventions. This article aims to describe the methodology used and to document the main outcomes achieved.
New COVID-19 cases are supplied by the \"Trajectoire de santé publique\" information system, geolocated to civic addresses and then aggregated by day and dissemination area. To target community-level clusters, cases identified as residents of congregate living settings are excluded from the cluster detection analysis. Detection is performed using the space-time scan statistic and Poisson statistical model, and implemented in the SaTScan software. Information on detected clusters is disseminated daily via an online interactive mapping interface.
The number of clusters detected tracked with the number of new cases. Slightly more than 4900 statistically significant (p ≤ 0.01) space-time clusters were detected over 14 health regions from May to October 2020. The Montréal region was the most affected.
Considering the objective of timely cluster detection, the use of near-real-time health surveillance data of varying quality over time and by region constitutes an acceptable compromise between timeliness and data quality. This tool serves to supplement the epidemiologic investigations carried out by regional public health authorities for purposes of COVID-19 management and prevention.
RéSUMé: OBJECTIFS: L’Institut national de santé publique du Québec (INSPQ) a reçu le mandat d’élaborer un outil de détection automatisé des agrégats spatio-temporels des cas de COVID-19 afin d’aider les régions à détecter des situations nécessitant des interventions de santé publique. Cet article vise à décrire la méthodologie utilisée et à présenter les principaux résultats obtenus. MéTHODE: Les nouveaux cas de COVID-19 proviennent du Système d’information Trajectoire de santé publique, ils sont géolocalisés à l’adresse civique, puis agrégés par jour et par aire de diffusion. Afin d’isoler la transmission communautaire, les cas identifiés comme résidents d’un milieu de vie fermé sont exclus des analyses de détection des agrégats. La méthode de détection est la statistique de balayage spatio-temporel basée sur le modèle de Poisson et implantée dans le logiciel SaTScan . Les agrégats détectés sont diffusés quotidiennement dans une interface cartographique web interactive. RéSULTATS: Le nombre d’agrégats détectés varie en fonction du nombre de nouveaux cas. Un peu plus de 4 900 agrégats spatio-temporels statistiquement significatifs (p ≤ 0,01) ont été détectés dans 14 régions sociosanitaires entre mai et octobre 2020. La région de Montréal est la plus touchée. CONCLUSION: Considérant l’objectif d’une détection d’agrégats en temps opportun, l’utilisation des données de vigie sanitaire en temps quasi réel, dont la qualité est variable dans le temps et selon les régions, constitue un compromis acceptable. Il s’agit d’un outil complémentaire aux enquêtes épidémiologiques menées par les autorités régionales de santé publique dans la gestion et la prévention des impacts populationnels de la COVID-19.