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Comunicación Breve Discussion Decisions based on biased surveillance data, without considering surveillance fatigue, would incorrectly inform the true magnitude of infections. Lipsitch et al. (2009), proposed an alternative way to account for case reporting during epidemics: identify a sample of the population to be strictly monitored by health institutions in sentinel localities, then develop a correction parameter for an accurate estimation of posterior cases10. Although this method needs robust evaluation, it can mitigate surveillance fatigue by correcting the number of disease reports during epidemics and could help to effectively allocate resources even in scenarios of surveillance fatigue10. Modern epidemiology is facing dramatic changes in the distribution and incidence of infectious diseases. These changes may be associated with high population density, increased connectivity among countries, and land cover change. In epidemiology, new concepts are necessary to represent disease patterns in a changing world, to maintain an updated conceptual framework among health professionals. Acknowledgements. Authors thank Dennis Guerra-Centeno for his advice in Latin and Huijie Qiao for discussion of the term. Abstract This manuscript presents the concept of “surveillance fatigue” (fatigatio vigilantiae), to describe an epidemiological scenario of an evident underreporting of cases during overwhelming epidemics. We revised past epidemics and found that surveillance fatigue is a common pattern, thus, it may be a useful concept in modern epidemiology. 292 www.sochinf.cl References 1.- Broutet N, Krauer F, Riesen M, Khalakdina A, Almiron M, Aldighieri S, et al. Zika virus as a cause of neurologic disorders. N Engl J Med 2016; 374: 1506-9. 2.- Nsubuga P, White M E, Thacker S B, Anderson M A, Blount S B, Broome C V, et al. Chapter 53: Public health surveillance: a tool for targeting and monitoring interventions. Jamison D T, Breman J G, Measham A R, et al. editors. Disease control priorities in developing countries, 2nd ed. Washington, DC, USA: World Bank and Oxford University Press; 2006, p. 997-1015. 3.- Hitchcock P, Chamberlain A, Van Wagoner M, Inglesby T V, O’Toole T. Challenges to global surveillance and response to infectious disease outbreaks of international importance. Biosecur Bioterror 2007; 5: 206-27. 4.- Escobar L E, Qiao H, Peterson A T. Forecasting Chikungunya spread in the Americas via data-driven empirical approaches. Parasit Vectors 2016; 9: 112. 5.- Frieden T R. Government’s role in protecting health and safety. N Engl J Med 2013; 368: 1857-9. 6.- Atkins K E, Wenzel N S, Ndeffo-Mbah M, Altice F L, Townsend J P, Galvani A P. Under-reporting and case fatality estimates for emerging epidemics. Br Med J 2015; 350: h1115. 7.- Briand S, Mounts A, Chamberland M. Challenges of global surveillance during an influenza pandemic. Public Health 2011; 125: 247-56. 8.- World Health Organization. WHO: Ebola Response Roadmap Situation Report-8 October 2014. WHO 2014: 1-10. http://apps.who.int/iris/ bitstream/10665/136020/1/roadmapsitrep_8Oct2014_eng.pdf (accessed May 7, 2016). 9.- Rossetto E V, Luna E J. Reporting delay during the yellow fever outbreak, Angola. Rev Inst Med Trop São Paulo 2016; 58: 91. 10.- Lipsitch M, Hayden F G, Cowling B J, Leung G M. How to maintain surveillance for novel influenza A H1N1 when there are too many cases to count. Lancet 2009; 374: 1209-11. Rev Chilena Infectol 2017; 34 (3): 291-292


Revista-Chilena-de-Infectologia-3-2017
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