Abstract
The rapid detection of ongoing outbreak — and the identification of causative pathogen — is pivotal for the early recognition of public health threats. The emergence and re-emergence of infectious diseases are linked to several determinants, both human factors — such as population density, travel, and trade — and ecological factors — like climate change and agricultural practices. Several technologies are available for the rapid molecular identification of pathogens [e.g. real-time polymerase chain reaction (PCR)], and together with on line monitoring tools of infectious disease activity and behaviour, they contribute to the surveillance system for infectious diseases. Web-based surveillance tools, infectious diseases modelling and epidemic intelligence methods represent crucial components for timely outbreak detection and rapid risk assessment. The study aims to integrate the current prevention and control system with a prediction tool for infectious diseases, based on regression analysis, to support decision makers, health care workers, and first responders to quickly and properly recognise an outbreak. This study has the intention to develop an infectious disease regressive prediction tool working with an off-line database built with specific epidemiological parameters of a set of infectious diseases of high consequences. The tool has been developed as a first prototype of a software solution called Infectious Diseases Seeker (IDS) and it had been established in two main steps, the database building stage and the software implementation stage (MATLAB® environment). The IDS has been tested with the epidemiological data of three outbreaks occurred recently: severe acute respiratory syndrome epidemic in China (2002–2003), plague outbreak in Madagascar (2017) and the Ebola virus disease outbreak in the Democratic Republic of Congo (2018). The outcomes are promising and they reveal that the software has been able to recognize and characterize these outbreaks. The future perspective about this software regards the developing of that tool as a useful and user-friendly predictive tool appropriate for first responders, health care workers, and public health decision makers to help them in predicting, assessing and contrasting outbreaks.
Article PDF
Avoid common mistakes on your manuscript.
References
McNeill WH. Plagues and peoples. New York: Doubleday; 1976.
Pinner RW, Rebmann CA, Schuchat A, Hughes JM. Disease surveillance and the academic, clinical, and public health communities. Emerg Infect Dis 2003;9;781–7.
Chang M, Glynn MK, Groseclose SL. Endemic, notifiable bioterrorism-related diseases, United States, 1992–1999. Emerg Infect Dis 2003;9;556–64.
Damianos L, Ponte J, Wohlever S, Reeder F, Day D, Wilson G, Hirschman L. MiTAP for bio-security: a case study. AI Mag 2002;23;13–29.
Buehler JW, Hopkins RS, Overhage JM, Sosin DM, Tong V, CDC Working Group. Framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the CDC Working Group. MMWR Recomm Rep 2004;53;1–11.
Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, Daszak P. Global trends in emerging infectious diseases. Nature 2008;451;990–3.
Weiss RA, McMichael AJ. Social and environmental risk factors in the emergence of infectious diseases. Nat Med 2004;10;S70–S6.
Lipkin WI. The changing face of pathogen discovery and surveillance. Nat Rev Microbiol 2013;11;133–41.
Morse SS, Mazet JAK, Woolhouse M, Parrish CR, Carroll D, Karesh WB, et al. Prediction and prevention of the next pandemic zoonosis. Lancet 2012;380;1956–65.
Hyman JM, LaForce T. Modeling the spread of influenza among cities. In: Banks HT, Castillo-Chavez C, editors. Bioterrorism: mathematical modeling applications in homeland security. Philadelphia, PA, USA: Society for Industrial and Applied Mathematics; 2003, pp. 211–36.
Christaki E. New technologies in predicting, preventing and controlling emerging infectious diseases. Virulence 2015;6;558–65.
World Health Organization (WHO)/United Nations (UN). Available from: https://www.who.int/news-room/fact-sheets & https://www.un.org/en/sections/issues-depth/health/index.html.
U.S. Centers for Disease Control and Prevention (CDC). Available from: https://www.cdc.gov/DiseasesConditions/.
European Centre for Disease Prevention and Control (ECDC). Available from: https://www.ecdc.europa.eu/en/publications-data.
Global Burden of Disease (GBD). Available from: http://www.healthdata.org/gbd/publications.
Program for Monitoring Emerging Disease (ProMED). https://www.promedmail.org/.
Cenciarelli O, Pietropaoli S, Malizia A, Carestia M, D’Amico F, Sassolini A, et al. Ebola virus disease 2013–2014 outbreak in West Africa: an analysis of the epidemic spread and response. Int J Microbiol 2015;2015;769121.
Garske T, Cori A, Ariyarajah A, Blake IM, Dorigatti I, Eckmanns T, et al. Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013–2016. Philos Trans R Soc Lond B Biol Sci 2017;372;20160308.
Martorano Raimundo S, Amaku M, Massad E. Equilibrium analysis of a yellow fever dynamical model with vaccination. Comput Math Methods Med 2015;2015;482091.
Hui DSC, Chan MCH, Wu AK, Ng PC. Severe acute respiratory syndrome (SARS): epidemiology and clinical features. Postgrad Med J 2004;80;373–81.
Mehedi M, Groseth A, Feldmann H, Ebihara H. Clinical aspects of Marburg hemorrhagic fever. Future Virol 2011;6;1091–106.
Chua KB. Epidemiology, surveillance and control of Nipah virus infections in Malaysia. Malays J Pathol 2010;32;69–73.
Iowa State University Center for Food Security and Public Health, “Hendra Virus Infection”. Center for Food Security and Public Health Technical Factsheets.71; 2015. Available from: http://lib.dr.iastate.edu/cfsph_factsheets/71.
Agusto FB, Bewick S, Fagan WF. Mathematical model of Zika virus with vertical transmission. Infect Dis Model 2017;2;244–67.
Yun NE, Walker DH. Pathogenesis of Lassa fever. Viruses 2012;4;2031–48.
National Institute for Communicable Diseases (NICD) - Healthcare Workers Guidelines on Rift Valley fever. Available from: https://www.nicd.ac.za/wp-content/uploads/2018/05/RVF-2018-Guidelines-for-HCWs.pdf (updated January 13, 2020).
Bakach I, Braselton J. A survey of mathematical models of dengue fever. Comput Sci Syst Biol 2015;8;255–67.
Chevalier V, Tran A, Durand B. Predictive modeling of West Nile virus transmission risk in the mediterranean basin: how far from landing? Int J Environ Res Public Health 2014;11;67–90.
Al-Asuoad N, Rong L, Alaswad S, Shillor M. Mathematical model and simulations of MERS outbreak: predictions and implications for control measures, Biomath 2016;5;1612141.
Lai S, Qin Y, Cowling BJ, Ren X, Wardrop NA, Gilbert M, et al. Global epidemiology of avian influenza A H5N1 virus infection in humans, 1997–2015: a systematic review of individual case data. Lancet Infect Dis 2016;16;e108–e18.
Cohen JI. Epstein–Barr virus infection. N Engl J Med 2000;343;481–92.
Bonthius DJ. Lymphocytic choriomeningitis virus: an under-recognized cause of neurologic disease in the fetus, child, and adult. Semin Pediatr Neurol 2012;19;89–95.
Ganesan VK, Duan B, Reid SP. Chikungunya virus: pathophysiology, mechanism, and modeling. Viruses 2017;9;368.
Lambert AJ, Martin DA, Lanciotti RS. Detection of North American eastern and western equine encephalitis viruses by nucleic acid amplification assays. J Clin Microbiol 2003;41;379–85.
Pisano MB, Oria G, Beskow G, Aguilar J, Konigheim B, Cacace ML, et al. Venezuelan equine encephalitis viruses (VEEV) in Argentina: serological evidence of human infection. PLoS Negl Trop Dis 2013;7;e2551.
MacLachlan JH, Cowie BC. Hepatitis B virus epidemiology. Cold Spring Harb Perspect Med 2015;5;a021410.
Saito T, Ueno Y. Transmission of hepatitis C virus: self-limiting hepatitis or chronic hepatitis? World J Gastroenterol 2013;19;6957–61.
Mueller NH, Gilden DH, Cohrs RJ, Mahalingam R, Nagel MA. Varicella zoster virus infection: clinical features, molecular patho-genesis of disease, and latency. Neurol Clin 2008;26;675–97.
Silk BJ, Foltz JL, Ngamsnga K, Brown E, Muñoz MG, Hampton LM, et al. Legionnaires’ disease case-finding algorithm, attack rates, and risk factors during a residential outbreak among older adults: an environmental and cohort study. BMC Infect Dis 2013;13;291.
Respicio-Kingry LB, Yockey BM, Acayo S, Kaggwa J, Apangu T, Kugeler KJ, et al. Two distinct yersinia pestis populations causing plague among humans in the West Nile region of Uganda. PLoS Negl Trop Dis 2016;10;e0004360.
Gürtler L, Bauerfeind U, Blümel J, Burger R, Drosten C, Gröner A, et al. Coxiella burnetii — pathogenic agent of Q (Query) fever. Transfus Med Hemother 2014;41;60–72.
Sulis G, Roggi A, Matteelli A, Raviglione MC. Tuberculosis: epidemiology and control. Mediterr J Hematol Infect Dis 2014;6;e2014070.
Gürcan Ş. Epidemiology of tularemia. Balkan Med J 2014;31;3–10.
Chiyaka ET, Muyendesi T, Nyamugure P, Mutasa FK. Theoretical assessment of the transmission dynamics of leprosy. Appl Math 2013;4;387–401.
Karkey A, Thompson CN, Tran Vu Thieu N, Dongol S, Phuong TLT, Vinh P V, et al. Differential epidemiology of Salmonella Typhi and Paratyphi A in Kathmandu, Nepal: a matched case control investigation in a highly endemic enteric fever setting. PLoS Negl Trop Dis 2013;7;e2391.
Costa F, Hagan JE, Calcagno J, Kane M, Torgerson P, Martinez-Silveira MS, et al. Global morbidity and mortality of leptospirosis: a systematic review. PLoS Negl Trop Dis 2015;9;e0003898.
Rouphael NG, Stephens DS. Neisseria meningitidis: biology, microbiology, and epidemiology. Methods Mol Biol 2012;799;1–20.
Xu R, He J, Evans MR, Peng G, Field HE, Yu D, et al. Epidemiologic clues to SARS origin in China. Emerg Infect Dis 2004;10;1031–7.
Nguyen VK, Parra-Rojas C, Hernandez-Vargas EA. The 2017 plague outbreak in Madagascar: data descriptions and epidemic modelling. Epidemics 2018;25;20–5.
Ebola Outbreak Epidemiology Team. Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April-May, 2018: an epidemiological study. Lancet 2018;392;213–21.
Wang W, Ruan S. Simulating the SARS outbreak in Beijing with limited data. J Theor Biol 2004;227;369–79.
Zhou Y, Ma Z, Brauer F. A discrete epidemic model for SARS transmission and control in China. Math Comput Model 2004;40;1491–506.
Tsuzuki S, Lee H, Miura F, Chan YH, Jung S, Akhmetzhanov AR, et al. Dynamics of the pneumonic plague epidemic in Madagascar, August to October 2017. Euro Surveill 2017;22;17-00710.
Meakin SR, Tildesley MJ, Davis E, Keeling MJ. A metapopulation model for the 2018 Ebola virus disease outbreak in Equateur province in the Democratic Republic of the Congo. bioRxiv 2018;465062.
Sule A, Lawal J. Mathematical modeling and optimal control of Ebola virus disease (EVD). Annu Res Rev Biol 2018;22;1–11.
Erraguntla M, Zapletal J, Lawley M. Framework for infectious disease analysis: a comprehensive and integrative multi-modeling approach to disease prediction and management. Health Informatics J 2019;25;1170–87.
George DB, Taylor W, Shaman J, Rivers C, Paul B, O’Toole T, et al. Technology to advance infectious disease forecasting for outbreak management. Nat Commun 2019;10;3932.
Author information
Authors and Affiliations
Corresponding author
Additional information
Data availability statement: The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.
Rights and permissions
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
About this article
Cite this article
Baldassi, F., Cenciarelli, O., Malizia, A. et al. First Prototype of the Infectious Diseases Seeker (IDS) Software for Prompt Identification of Infectious Diseases. J Epidemiol Glob Health 10, 367–377 (2020). https://doi.org/10.2991/jegh.k.200714.001
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.2991/jegh.k.200714.001