4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Improved susceptible–infectious–susceptible epidemic equations based on uncertainties and autocorrelation functions

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Compartmental equations are primary tools in the study of disease spreading processes. They provide accurate predictions for large populations but poor results whenever the integer nature of the number of agents is evident. In the latter instance, uncertainties are relevant factors for pathogen transmission. Starting from the agent-based approach, we investigate the role of uncertainties and autocorrelation functions in the susceptible–infectious–susceptible (SIS) epidemic model, including their relationship with epidemiological variables. We find new differential equations that take uncertainties into account. The findings provide improved equations, offering new insights on disease spreading processes.

          Related collections

          Most cited references26

          • Record: found
          • Abstract: found
          • Article: not found

          Epidemic Spreading in Scale-Free Networks

          The Internet has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from computer virus infections and find the average lifetime and persistence of viral strains on the Internet. We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior. This new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Epidemic dynamics and endemic states in complex networks.

            We study by analytical methods and large scale simulations a dynamical model for the spreading of epidemics in complex networks. In networks with exponentially bounded connectivity we recover the usual epidemic behavior with a threshold defining a critical point below that the infection prevalence is null. On the contrary, on a wide range of scale-free networks we observe the absence of an epidemic threshold and its associated critical behavior. This implies that scale-free networks are prone to the spreading and the persistence of infections whatever spreading rate the epidemic agents might possess. These results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic

              Social media have been proposed as a data source for influenza surveillance because they have the potential to offer real-time access to millions of short, geographically localized messages containing information regarding personal well-being. However, accuracy of social media surveillance systems declines with media attention because media attention increases “chatter” – messages that are about influenza but that do not pertain to an actual infection – masking signs of true influenza prevalence. This paper summarizes our recently developed influenza infection detection algorithm that automatically distinguishes relevant tweets from other chatter, and we describe our current influenza surveillance system which was actively deployed during the full 2012-2013 influenza season. Our objective was to analyze the performance of this system during the most recent 2012–2013 influenza season and to analyze the performance at multiple levels of geographic granularity, unlike past studies that focused on national or regional surveillance. Our system’s influenza prevalence estimates were strongly correlated with surveillance data from the Centers for Disease Control and Prevention for the United States (r = 0.93, p < 0.001) as well as surveillance data from the Department of Health and Mental Hygiene of New York City (r = 0.88, p < 0.001). Our system detected the weekly change in direction (increasing or decreasing) of influenza prevalence with 85% accuracy, a nearly twofold increase over a simpler model, demonstrating the utility of explicitly distinguishing infection tweets from other chatter.
                Bookmark

                Author and article information

                Journal
                R Soc Open Sci
                R Soc Open Sci
                RSOS
                royopensci
                Royal Society Open Science
                The Royal Society
                2054-5703
                February 2020
                19 February 2020
                19 February 2020
                : 7
                : 2
                : 191504
                Affiliations
                [1 ]Université Paris-Saclay, CNRS/IN2P3, and Université de Paris, IJCLab, 91405 Orsay, France
                [2 ]Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), Universidade de São Paulo (USP) , Ribeirão Preto 14040-901, Brazil
                [3 ]Instituto Nacional de Ciência e Tecnologia – Sistemas Complexos (INCT-SC) , Rio de Janeiro, Brazil
                Author notes
                Author for correspondence: Alexandre S. Martinez e-mail: asmartinez@ 123456usp.br
                Author information
                http://orcid.org/0000-0001-7803-1331
                http://orcid.org/0000-0001-8459-9812
                http://orcid.org/0000-0002-4395-0511
                Article
                rsos191504
                10.1098/rsos.191504
                7062106
                32257317
                6e6a93ee-3cef-421d-9bb7-fca68851e966
                © 2020 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
                : 29 August 2019
                : 27 January 2020
                Funding
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico, http://dx.doi.org/10.13039/501100003593;
                Award ID: 307948/2014-5
                Funded by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, http://dx.doi.org/10.13039/501100002322;
                Award ID: 88887.136416/2017-00
                Categories
                1001
                87
                203
                1008
                119
                Mathematics
                Research Article
                Custom metadata
                February, 2020

                stochastic process,epidemic models,monte carlo,fluctuations

                Comments

                Comment on this article