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      Exact calculation of end-of-outbreak probabilities using contact tracing data

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          Abstract

          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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          A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics

          Abstract The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses. Transmissibility can be measured by the reproduction number R, the average number of secondary cases caused by an infected individual. Several methods have been proposed to estimate R over the course of an epidemic; however, they are usually difficult to implement for people without a strong background in statistical modeling. Here, we present a ready-to-use tool for estimating R from incidence time series, which is implemented in popular software including Microsoft Excel (Microsoft Corporation, Redmond, Washington). This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. This tool should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data.
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            Superspreading and the effect of individual variation on disease emergence

            Coughs and sneezes... From Typhoid Mary to SARS, it has long been known that some people spread disease more than others. But for diseases transmitted via casual contact, contagiousness arises from a plethora of social and physiological factors, so epidemiologists have tended to rely on population averages to assess a disease's potential to spread. A new analysis of outbreak data shows that individual differences in infectiousness exert powerful influences on the epidemiology of ten deadly diseases. SARS and measles (and perhaps avian influenza) show strong tendencies towards ‘superspreading events’ that can ignite explosive epidemics — but this same volatility makes outbreaks more likely to fizzle out. Smallpox and pneumonic plague, two potential bioterrorism agents, show steadier growth but still differ markedly from the traditional average-based view. These findings are relevant to how emerging diseases are detected and controlled. Supplementary information The online version of this article (doi:10.1038/nature04153) contains supplementary material, which is available to authorized users.
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              Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods

              Background Efficient and reliable surveillance and notification systems are vital for monitoring public health and disease outbreaks. However, most surveillance and notification systems are affected by a degree of underestimation (UE) and therefore uncertainty surrounds the 'true’ incidence of disease affecting morbidity and mortality rates. Surveillance systems fail to capture cases at two distinct levels of the surveillance pyramid: from the community since not all cases seek healthcare (under-ascertainment), and at the healthcare-level, representing a failure to adequately report symptomatic cases that have sought medical advice (underreporting). There are several methods to estimate the extent of under-ascertainment and underreporting. Methods Within the context of the ECDC-funded Burden of Communicable Diseases in Europe (BCoDE)-project, an extensive literature review was conducted to identify studies that estimate ascertainment or reporting rates for salmonellosis and campylobacteriosis in European Union Member States (MS) plus European Free Trade Area (EFTA) countries Iceland, Norway and Switzerland and four other OECD countries (USA, Canada, Australia and Japan). Multiplication factors (MFs), a measure of the magnitude of underestimation, were taken directly from the literature or derived (where the proportion of underestimated, under-ascertained, or underreported cases was known) and compared for the two pathogens. Results MFs varied between and within diseases and countries, representing a need to carefully select the most appropriate MFs and methods for calculating them. The most appropriate MFs are often disease-, country-, age-, and sex-specific. Conclusions When routine data are used to make decisions on resource allocation or to estimate epidemiological parameters in populations, it becomes important to understand when, where and to what extent these data represent the true picture of disease, and in some instances (such as priority setting) it is necessary to adjust for underestimation. MFs can be used to adjust notification and surveillance data to provide more realistic estimates of incidence.
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                Author and article information

                Contributors
                Role: Formal analysisRole: InvestigationRole: SoftwareRole: ValidationRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: SoftwareRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Journal
                J R Soc Interface
                J R Soc Interface
                RSIF
                royinterface
                Journal of the Royal Society Interface
                The Royal Society
                1742-5689
                1742-5662
                December 13, 2023
                December 2023
                December 13, 2023
                : 20
                : 209
                : 20230374
                Affiliations
                [ 1 ] Mathematics Institute, University of Warwick, , Coventry CV4 7AL, UK
                [ 2 ] Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, , Coventry CV4 7AL, UK
                [ 3 ] Mathematical Institute, University of Oxford, , Oxford OX2 6GG, UK
                [ 4 ] Geneva Centre of Humanitarian Studies, University of Geneva, , Geneva 1205, Switzerland
                Author notes
                [ † ]

                Contributed equally to this work.

                Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.6960558.

                Author information
                http://orcid.org/0000-0002-2504-6860
                http://orcid.org/0000-0002-3039-1159
                http://orcid.org/0000-0001-8545-5212
                Article
                rsif20230374
                10.1098/rsif.2023.0374
                10715912
                38086402
                f712fef8-0505-4902-8a69-0832ba75358a
                © 2023 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : July 4, 2023
                : November 17, 2023
                Funding
                Funded by: UKRI;
                Award ID: EP/V053507/1
                Award ID: EP/W524311/1
                Categories
                1004
                24
                44
                Life Sciences–Mathematics interface
                Research Articles

                Life sciences
                mathematical modelling,infectious disease epidemiology,end-of-outbreak declaration,public health measures,resurgence,local extinction

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