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Reverse epidemiology is a mathematical modelling tool used to ascertain information about the source of a pathogen, given the spatial and temporal distribution of cases, hospitalisations and deaths. In the context of a deliberately released pathogen, such as Bacillus anthracis (the disease-causing organism of anthrax), this can allow responders to quickly identify the location and timing of the release, as well as other factors such as the strength of the release, and the realized wind speed and direction at release. These estimates can then be used to parameterise a predictive mechanistic model, allowing for estimation of the potential scale of the release, and to optimise the distribution of prophylaxis. In this paper we present two novel approaches to reverse epidemiology, and demonstrate their utility in responding to a simulated deliberate release of B. anthracis in ten locations in the UK and compare these to the standard grid-search approach. The two methods—a modified MCMC and a Recurrent Convolutional Neural Network—are able to identify the source location and timing of the release with significantly better accuracy compared to the grid-search approach. Further, the neural network method is able to do inference on new data significantly quicker than either the grid-search or novel MCMC methods, allowing for rapid deployment in time-sensitive outbreaks. Author summary: In this paper we demonstrate three methods for estimating the source location and timing of a deliberate release of Bacillus anthracis based on the temporal and spatial distribution of cases. Two of our proposed methods, a modified MCMC approach and a neural network based approach, provide significant improvements over previous methods by directly addressing the problematic parameter-likelihood surface, and, in the case of the neural network approach, addressing the slow deployment speeds of existing methods. Our results represent a major step forward in the accuracy and speed of epidemiological back-calculation. [ABSTRACT FROM AUTHOR]

Introduction The World Health Organization (WHO) recommended that Member States implement their established First Few X enhanced surveillance protocol (1) to investigate the clinical and epidemiological characteristics of at least [...], Objective To describe the clinical presentation, course of disease and health-care seeking behaviour of the first few hundred cases of coronavirus disease 2019 (COVID-19) in the United Kingdom of Great Britain and Northern Ireland. Methods We implemented the World Health Organization's First Few X cases and contacts investigation protocol for COVID-19. Trained public health professionals collected information on 381 virologically confirmed COVID-19 cases from 31 January 2020 to 9 April 2020. We actively followed up cases to identify exposure to infection, symptoms and outcomes. We also collected limited data on 752 symptomatic people testing negative for COVID-19, as a control group for analyses of the sensitivity, specificity and predictive value of symptoms. Findings Approximately half of the COVID-19 cases were imported (196 cases; 51.4%), of whom the majority had recent travel to Italy (140 cases; 71.4%). Of the 94 (24.7%) secondary cases, almost all reported close contact with a confirmed case (93 cases; 98.9%), many through household contact (37 cases; 39.8%). By age, a lower proportion of children had COVID-19. Most cases presented with cough, fever and fatigue. The sensitivity and specificity of symptoms varied by age, with nonlinear relationships with age. Although the proportion of COVID-19 cases with fever increased with age, for those with other respiratory infections the occurrence of fever decreased with age. The occurrence of shortness of breath also increased with age in a greater proportion of COVID-19 cases. Conclusion The study has provided useful evidence for generating case definitions and has informed modelling studies of the likely burden of COVID-19. Objectif Decrire la manifestation clinique, l'evolution de la maladie et la propension a recourir aux services de sante des premieres centaines de cas de maladie a coronavirus 2019 (COVID-19) au Royaume-Uni de Grande-Bretagne et d'Irlande du Nord. Methodes Nous avons applique le Protocole d'enquete sur la maladie a coronavirus 2019 (COVID-19)--les X premiers cas et contacts (FFX) publie par l'Organisation mondiale de la Sante. Des professionnels de la sante formes a cet effet ont collecte des informations sur 381 cas virologiquement confirmes de COVID-19 entre le 31 janvier 2020 et le 9 avril 2020. Nous avons etroitement surveille ces cas afin d'identifier la source d'exposition a l'infection, les symptomes observes et l'issue de la maladie. Nous avons egalement recolte des donnees sur 752 personnes symptomatiques testees negatives a la COVID-19 qui ont servi de groupe de controle pour analyser la sensibilite, la specificite et la valeur predictive des symptomes. Resultats Environ la moitie des cas de COVID-19 ont ete importes (196 cas; 51,4%), la majorite s'etant recemment rendue en Italie (140 cas; 71,4%). Sur les 94 (24,7%) cas secondaires, presque tous ont signale un contact rapproche avec un cas confirme (93 cas; 98,9%), generalement au sein de leur foyer (37 cas; 39,8%). Au niveau de l'age, la proportion d'enfants ayant contracte la COVID-19 etait moindre. La plupart des cas avaient de la toux, de la fievre et de la fatigue. La sensibilite et la specificite des symptomes variaient en fonction de l'age, presentant une relation non lineaire avec cet element. Plus l'age des cas de COVID-19 avancait, plus ils etaient susceptibles de developper de la fievre, a l'exception de ceux touches par d'autres infections respiratoires. Le risque d'essoufflement augmentait lui aussi avec l'age chez une grande partie des cas de COVID-19. Conclusion Cette etude a apporte un eclairage utile dans l'etablissement d'une definition des cas. Elle a egalement fourni, pour diverses modelisations, des indications sur la charge que pourrait faire peser la COVID-19. Objetivo Describir la presentacion clinica, el curso de la enfermedad y el comportamiento de busqueda de atencion sanitaria de los primeros cientos de casos de la enfermedad del coronavirus de 2019 (COVID-19) en Reino Unido e Irlanda del Norte. Metodos Implementamos el protocolo de investigacion de los primeros casos y contactos de la Organizacion Mundial de la Salud para la COVID-19. Profesionales expertos en el area de la salud publica recopilaron informacion sobre 381 casos virologicamente confirmados de COVID-19 entre el 31 de enero de 2020 y el 9 de abril de 2020. Realizamos un seguimiento activo de los casos para identificar la exposicion a la infeccion, los sintomas y los resultados. Tambien recogimos datos limitados sobre 752 personas sintomaticas que dieron negativo en los resultados de la COVID-19, como grupo de control para el analisis de la sensibilidad, la especificidad y el valor predictivo de los sintomas. Resultados Casi la mitad de los casos de COVID-19 fueron importados (196 casos; 51,4%), de los cuales la mayoria habia hecho un viaje reciente a Italia (140 casos; 71,4%). De los 94 (24,7%) casos secundarios, casi todos informaron de un contacto cercano con un caso confirmado (93 casos; 98,9%), muchos de ellos a traves del contacto con el hogar (37 casos; 39,8%). Por edad, una proporcion menor de ninos tenia COVID-19. La mayoria de los casos se presentaban con tos, fiebre y fatiga. La sensibilidad y especificidad de los sintomas variaba segun la edad, con relaciones no lineales con la edad. Aunque la proporcion de casos de COVID-19 con fiebre aumentaba con la edad, en el caso de los que tenian otras infecciones respiratorias la aparicion de fiebre disminuia con la edad. La incidencia de la falta de aliento tambien aumento con la edad en una mayor proporcion de casos de COVID-19. Conclusion El estudio ha aportado pruebas utiles para generar definiciones de casos y tambien ha servido de base a los estudios de modelizacion del probable coste de la COVID-19.

Geoparsing, the process of linking locations within text to sets of geographic coordinates, plays an important role in the extraction and analysis of information from unstructured textual data. With the rapid growth in availability of user-generated data from online sources, there is increasing demand for reliable geoparsing methods. Central to many of these methods is the accurate identification of toponyms within text. For some applications, however, simple identification of toponyms is insufficient. Problems which require the association of a piece of text containing multiple toponyms to a singular location require a more nuanced approach. In this paper, we show that a transformer based deep learning model, is able to identify the subject toponym within a given text, and classify other toponyms in terms of their spatial relationship with the subject. We curate a dataset of text taken from Wikipedia pages representing 5252 locations, and use OpenStreetMap data to classify toponyms within the text in terms of their spatial relationship with the subject of each article. This dataset is then used to train a transformer based deep-learning model. On a human labelled test set, our model achieves an F1 score of 0.916 when identifying the subject toponym, and 0.884 and 0.793 when identifying toponyms representing parent and child locations of the subject, respectively. We also consider the more complex adjacent and crossing relationships - with the model achieving F1 scores of 0.548 and 0.704 in these categories, respectively. [ABSTRACT FROM AUTHOR]

Air travel plays an important role in the cross-border spread of infectious diseases. During the SARS-CoV-2 pandemic many countries introduced strict border testing protocols to monitor the incursion of the virus. However, high implementation costs and significant inconvenience to passengers have led public health authorities to consider alternative methods of disease surveillance at borders. Aircraft wastewater monitoring has been proposed as one such alternative. In this paper we assess the theoretical limits of aircraft wastewater monitoring and compare its performance to post-arrival border screening approaches. Using an infectious disease model, we simulate an unmitigated SARS-CoV-2 epidemic originating in a seed country and spreading to the United Kingdom (UK) through daily flights. We use a probabilistic approach to estimate the time of first detection in the UK in aircraft wastewater and respiratory swab screening. Across a broad range of model parameters, our analysis indicates that the median time between the first incursion and detection in wastewater would be approximately 17 days (IQR: 7-28 days), resulting in a median of 25 cumulative cases (IQR: 6-84 cases) in the UK at the point of detection. Comparisons to respiratory swab screening suggest that aircraft wastewater monitoring is as effective as random screening of 20% of passengers at the border, using a test with 95% sensitivity. For testing regimes with sensitivity of 85% or less, the required coverage to outperform wastewater monitoring increases to 30%. Analysis of other model parameters suggests that wastewater monitoring is most effective when used on long-haul flights where probability of defecation is above 30%, and when the target pathogen has high faecal shedding rates and reasonable detectability in wastewater. These results demonstrate the potential use cases of aircraft wastewater monitoring and its utility in a wider system of public health surveillance., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Shingleton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)