A key challenge for public health policymakers is determining when an infectious disease outbreak has finished. Following a period without cases, an estimate of the probability that no further cases will occur in future (the end-of-outbreak probability) can be used to inform whether or not to declare an outbreak over. An existing quantitative approach (the Nishiura method), based on a branching process transmission model, allows the end-of-outbreak probability to be approximated from disease incidence time series, the offspring distribution and the serial interval distribution. Here, we show how the end-of-outbreak probability under the same transmission model can be calculated exactly if data describing who-infected-whom (the transmission tree) are also available (e.g. from contact tracing studies). In that scenario, our novel approach (the traced transmission method) is straightforward to use. We demonstrate this by applying the method to data from previous outbreaks of Ebola virus disease and Nipah virus infection. For both outbreaks, the traced transmission method would have determined that the outbreak was over earlier than the Nishiura method. This highlights that collection of contact tracing data and application of the traced transmission method may allow stringent control interventions to be relaxed quickly at the end of an outbreak, with only a limited risk of outbreak resurgence.
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http://dx.doi.org/10.1098/rsif.2023.0374 | DOI Listing |
J Hazard Mater
December 2024
School of Environment, Nanjing Normal University, Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, Nanjing 210023, China. Electronic address:
Indoor dust can adsorb various pollutants and long-term deposition can significantly impact air quality and human health. This study investigated the occurrence, source apportionment, and health risks associated with polycyclic aromatic hydrocarbons (PAHs) and their derivatives (d-PAHs) in indoor dust, by focusing on residential and public buildings in Nanjing, China. The concentration of 16 PAHs and 27 d-PAHs ranged from 511 to 5472 ng/g and from 422 to 2904 ng/g, with the most abundant compounds being fluoranthene and 1,2-benz[a]anthraquinone, respectively.
View Article and Find Full Text PDFBull Math Biol
January 2025
Department of Mathematics, University of Trento, Via Sommarive 14, Povo, 38123, Trento, Italy.
One of the strategies used in some countries to contain the COVID-19 epidemic has been the test-and-isolate policy, generally coupled with contact tracing. Such strategies have been examined in several simulation models, but a theoretical analysis of their effectiveness in simple epidemic model is, to our knowledge, missing. In this paper, we present four epidemic models of either SIR or SEIR type, in which it is assumed that at fixed times the whole population (or a part of the population) is tested and, if positive, isolated.
View Article and Find Full Text PDFDisaster Med Public Health Prep
January 2025
Faculty of Public Health, Universitas Islam Negeri Sumatera Utara, Medan, Indonesia.
Objective: Mpox, a zoonotic disease, has emerged as a significant international public health concern due to an increase in the number of cases diagnosed in non-endemic countries. To support public health response efforts to interrupt Mpox transmission in the community, this study aims to identify epidemiological and clinical aspects of Mpox in Jakarta, Indonesia.
Methods: The study collected Mpox data from the Provincial Health Department in Jakarta, Indonesia, from October 2023 to February 2024.
Health Commun
January 2025
Department of Communications and New Media, National University of Singapore.
This study applies protection motivation theory (PMT) to the COVID-19 contact-tracing context by including privacy concerns, collective efficacy, and a mediator (fear of COVID-19) and tests it in the US and South Korea. The study uses a structural equation modeling (SEM) approach and a sample of 418 Americans and 444 South Koreans. According to the results, fear was positively associated with adoption intentions in the US sample but not in the Korean sample.
View Article and Find Full Text PDFMol Biomed
January 2025
Department of Artificial Intelligence and Machine Learning, Faculty of Engineering and Technology, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, Maharashtra, 442001, India.
Integrating Artificial Intelligence (AI) across numerous disciplines has transformed the worldwide landscape of pandemic response. This review investigates the multidimensional role of AI in the pandemic, which arises as a global health crisis, and its role in preparedness and responses, ranging from enhanced epidemiological modelling to the acceleration of vaccine development. The confluence of AI technologies has guided us in a new era of data-driven decision-making, revolutionizing our ability to anticipate, mitigate, and treat infectious illnesses.
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