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pp 1-16 | Cite as

Enhancing Situational Awareness to Prevent Infectious Disease Outbreaks from Becoming Catastrophic

  • Marc LipsitchEmail author
  • Mauricio Santillana
Chapter
Part of the Current Topics in Microbiology and Immunology book series

Abstract

Catastrophic epidemics, if they occur, will very likely start from localized and far smaller (non-catastrophic) outbreaks that grow into much greater threats. One key bulwark against this outcome is the ability of governments and the health sector more generally to make informed decisions about control measures based on accurate understanding of the current and future extent of the outbreak. Situation reporting is the activity of periodically summarizing the state of the outbreak in a (usually) public way. We delineate key classes of decisions whose quality depends on high-quality situation reporting, key quantities for which estimates are needed to inform these decisions, and the traditional and novel sources of data that can aid in estimating these quantities. We emphasize the important role of situation reports as providing public, shared planning assumptions that allow decision makers to harmonize the response while making explicit the uncertainties that underlie the scenarios outlined for planning. In this era of multiple data sources and complex factors informing the interpretation of these data sources, we describe four principles for situation reporting: (1) Situation reporting should be thematic, concentrating on essential areas of evidence needed for decisions. (2) Situation reports should adduce evidence from multiple sources to address each area of evidence, along with expert assessments of key parameters. (3) Situation reports should acknowledge uncertainty and attempt to estimate its magnitude for each assessment. (4) Situation reports should contain carefully curated visualizations along with text and tables.

Notes

Acknowledgments

ML was partially supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number U54GM088558. MS was partially supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R01GM130668. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

References

  1. Bastos L, Economou T, Gomes M, Villela D, Bailey T, Codeço C (2017) Modelling reporting delays for disease surveillance data [Internet]. arXiv [stat.AP]. Available: http://arxiv.org/abs/1709.09150
  2. Chao DL, Halloran ME, Longini IM (2010) School opening dates predict pandemic influenza A (H1N1) outbreaks in the United States. J Infect Dis 202(6):877–880Google Scholar
  3. Cori A, Donnelly CA, Dorigatti I, Ferguson NM, Fraser C, Garske T et al (2017) Key data for outbreak evaluation: building on the Ebola experience. Philos Trans R Soc Lond B Biol Sci 372(1721).  https://doi.org/10.1098/rstb.2016.0371 Google Scholar
  4. Dimitrov NB, Goll S, Hupert N, Pourbohloul B, Meyers LA (2011) Optimizing tactics for use of the U.S. antiviral strategic national stockpile for pandemic influenza. PLoS One 6(1):e16094Google Scholar
  5. Executive Office of the President’s Council of Advisors on Science and Technology (2009) Report to the President on US Preparations for 2009-H1N1 Influenza. Aug 2009Google Scholar
  6. Finnie TJR, South A, Bento A, Sherrard-Smith E, Jombart T (2016) EpiJSON: a unified data-format for epidemiology. Epidemics 15(Jun):20–26Google Scholar
  7. Fraser C, Donnelly CA, Cauchemez S, Hanage WP, Van Kerkhove MD, Hollingsworth TD et al (2009) Pandemic potential of a strain of influenza A (H1N1): early findings. Science 324(5934):1557–1561Google Scholar
  8. Garske T, Legrand J, Donnelly CA, Ward H, Cauchemez S, Fraser C et al (2009) Assessing the severity of the novel influenza A/H1N1 pandemic. BMJ 339(Jul):b2840Google Scholar
  9. Generous N, Fairchild G, Deshpande A, Del Valle SY, Priedhorsky R (2014) Global disease monitoring and forecasting with Wikipedia. PLoS Comput Biol 10(11):e1003892Google Scholar
  10. Grad YH, Lipsitch M (2014) Epidemiologic data and pathogen genome sequences: a powerful synergy for public health. Genome Biol 15(11):538Google Scholar
  11. Hatchett RJ, Mecher CE, Lipsitch M (2007) Public health interventions and epidemic intensity during the 1918 influenza pandemic. Proc Natl Acad Sci USA. 104(18):7582–7587Google Scholar
  12. Höhle M, an der Heiden M (2014) Bayesian nowcasting during the STEC O104: H4 outbreak in Germany, 2011. Biometrics 70(4):993–1002Google Scholar
  13. Huang KE, Lipsitch M, Shaman J, Goldstein E (2014) The US 2009 A(H1N1) influenza epidemic: quantifying the impact of school openings on the reproductive number. Epidemiology 25(2):203–206Google Scholar
  14. Iuliano AD, Reed C, Guh A, Desai M, Dee DL, Kutty P et al (2009) Notes from the field: outbreak of 2009 pandemic influenza A (H1N1) virus at a large public university in Delaware. Clin Infect Dis 49(12):1811–1820Google Scholar
  15. Jain S, Kamimoto L, Bramley AM, Schmitz AM, Benoit SR, Louie J et al (2009) Hospitalized patients with 2009 H1N1 influenza in the United States, April–June 2009. N Engl J Med 361(20):1935–1944Google Scholar
  16. Jombart T, Aanensen DM, Baguelin M, Birrell P, Cauchemez S, Camacho A et al (2014) Outbreak tools: a new platform for disease outbreak analysis using the R software. Epidemics 7(Jun):28–34Google Scholar
  17. Koppeschaar CE, Colizza V, Guerrisi C, Turbelin C, Duggan J, Edmunds WJ et al (2017) Influenzanet: citizens among 10 countries collaborating to monitor influenza in Europe. JMIR Publ Health Surveill 3(3):e66Google Scholar
  18. Kyle JL, Harris E (2008) Global spread and persistence of dengue. Annu Rev Microbiol 62:71–92Google Scholar
  19. Leung K, Lipsitch M, Yuen KY, Wu JT (2017) Monitoring the fitness of antiviral-resistant influenza strains during an epidemic: a mathematical modelling study. Lancet Infect Dis 17(3):339–347Google Scholar
  20. Lipsitch M (2017) If a global catastrophic biological risk materializes, at what stage will we recognize it? Health Secur 15(4):331–334Google Scholar
  21. Lipsitch M, Eyal N (2017) Improving vaccine trials in infectious disease emergencies. Science 357(6347):153–156Google Scholar
  22. Lipsitch M, Finelli L, Heffernan RT, Leung GM, Redd SC, 2009 H1n1 Surveillance Group (2011) Improving the evidence base for decision making during a pandemic: the example of 2009 influenza A/H1N1. Biosecur Bioterror 9(2):89–115Google Scholar
  23. Lipsitch M, Hayden FG, Cowling BJ, Leung GM (2009a) How to maintain surveillance for novel influenza A H1N1 when there are too many cases to count. Lancet 374(9696):1209–1211Google Scholar
  24. Lipsitch M, Riley S, Cauchemez S, Ghani AC, Ferguson NM (2009b) Managing and reducing uncertainty in an emerging influenza pandemic [Internet]. New Engl J Med 112–115.  https://doi.org/10.1056/nejmp0904380 Google Scholar
  25. Lipsitch M, Donnelly CA, Fraser C, Blake IM, Cori A, Dorigatti I et al (2015) Potential biases in estimating absolute and relative case-fatality risks during outbreaks. PLoS Negl Trop Dis 9(7):e0003846Google Scholar
  26. Lowen AC, Mubareka S, Steel J, Palese P (2007) Influenza virus transmission is dependent on relative humidity and temperature. PLoS Pathog 3(10):1470–1476Google Scholar
  27. Lu FS, Hattab MW, Clemente CL, Biggerstaff M, Santillana M (2019) Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches [Internet]. Nature Commun 10.  https://doi.org/10.1038/s41467-018-08082-0
  28. Magpantay FMG, Rohani P (2015) Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola. R Soc B. Available from: http://rspb.royalsocietypublishing.org/content/282/1806/20150347.short
  29. McGough SF, Brownstein JS, Hawkins JB, Santillana M (2017) Forecasting Zika incidence in the 2016 Latin America outbreak combining traditional disease surveillance with search, social media, and news report data. PLoS Negl Trop Dis 11(1):e0005295Google Scholar
  30. McIver DJ, Brownstein JS (2014) Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time. PLoS Comput Biol 10(4):e1003581Google Scholar
  31. Meltzer MI, Damon I, LeDuc JW, Millar JD (2001) Modeling potential responses to smallpox as a bioterrorist weapon. Emerg Infect Dis 7(6):959–969Google Scholar
  32. Meltzer MI, Atkins CY, Santibanez S, Knust B, Petersen BW, Ervin ED et al (2014) Estimating the future number of cases in the Ebola epidemic–Liberia and Sierra Leone, 2014–2015. Available from: https://stacks.cdc.gov/view/cdc/24901
  33. Messina JP, Brady OJ, Pigott DM, Golding N, Kraemer MUG, Scott TW et al (2015) The many projected futures of dengue. Nat Rev Microbiol 13(4):230–239Google Scholar
  34. of Health USD, Services H et al (2007) Community strategy for pandemic influenza mitigation. US Department of Health and Human Services Google Scholar
  35. Osterholm MT, Kelley NS, Sommer A, Belongia EA (2012) Efficacy and effectiveness of influenza vaccines: a systematic review and meta-analysis. Lancet Infect Dis 12(1):36–44Google Scholar
  36. Paul MJ, Dredze M, Broniatowski D (2014) Twitter improves influenza forecasting. PLoS Curr 6.  https://doi.org/10.1371/currents.outbreaks.90b9ed0f59bae4ccaa683a39865d9117
  37. Peak CM, Wesolowski A, Zu Erbach-Schoenberg E, Tatem AJ, Wetter E, Lu X et al (2018) Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data. Int J Epidemiol 47(5):1562–1570Google Scholar
  38. Reed C, Angulo F, Swerdlow D, Lipsitch M, Meltzer M et al (2009) Estimating the burden of pandemic influenza A/H1N1–United States, April–July 2009. Emerg Infect DisGoogle Scholar
  39. Rudolf F, Damkjær M, Lunding S, Dornonville de la Cour K, Young A, Brooks T et al (2017) Influence of referral pathway on ebola virus disease case-fatality rate and effect of survival selection bias. Emerg Infect Dis 23(4):597–600Google Scholar
  40. Santillana M, Nsoesie EO, Mekaru SR, Scales D, Brownstein JS (2014) Using clinicians’ search query data to monitor influenza epidemics. Clin Infect Dis 59(10):1446–1450Google Scholar
  41. Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS (2015) Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Comput Biol 11(10):e1004513Google Scholar
  42. Santillana M, Nguyen AT, Louie T, Zink A, Gray J, Sung I et al (2016) Cloud-based electronic health records for real-time, region-specific influenza surveillance. Sci Rep 6(May):25732Google Scholar
  43. Shaman J, Kohn M (2009) Absolute humidity modulates influenza survival, transmission, and seasonality. Proc Natl Acad Sci USA. 106(9):3243–3248Google Scholar
  44. Shaman J, Goldstein E, Lipsitch M (2011) Absolute humidity and pandemic versus epidemic influenza. Am J Epidemiol 173(2):127–135Google Scholar
  45. Signorini A, Segre AM, Polgreen PM (2011) The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS One 6(5):e19467Google Scholar
  46. Smolinski MS, Crawley AW, Baltrusaitis K, Chunara R, Olsen JM, Wójcik O et al (2015) Flu Near You: crowdsourced symptom reporting spanning 2 influenza seasons. Am J Publ Health 105(10):2124–2130Google Scholar
  47. Tiffany A, Dalziel BD, Kagume Njenge H, Johnson G, Nugba Ballah R, James D et al (2017) Estimating the number of secondary Ebola cases resulting from an unsafe burial and risk factors for transmission during the West Africa Ebola epidemic. PLoS Negl Trop Dis 11(6):e0005491Google Scholar
  48. van de Kasteele J, Elers P, Wallinga J (2019) Nowcasting the number of new symptomatic cases during infectious disease outbreaks using constrained P‐spline smoothing. Epidemiology (in press)Google Scholar
  49. Van Kerkhove MD, Asikainen T, Becker NG, Bjorge S, Desenclos J-C, dos Santos T et al (2010) Studies needed to address public health challenges of the 2009 H1N1 influenza pandemic: insights from modeling. PLoS Med 7(6):e1000275Google Scholar
  50. Voelker R (2018) Vulnerability to pandemic flu could be greater today than a century ago. JAMA 320(15):1523–1525Google Scholar
  51. Wallinga J, Lipsitch M (2007) How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 274(1609):599–604Google Scholar
  52. Wallinga J, Teunis P (2004) Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am J Epidemiol 160(6):509–516Google Scholar
  53. Wesolowski A, Qureshi T, Boni MF, Sundsøy PR, Johansson MA, Rasheed SB et al (2015) Impact of human mobility on the emergence of dengue epidemics in Pakistan. Proc Natl Acad Sci 112(38):11887–11892Google Scholar
  54. White LF, Pagano M (2008) A likelihood-based method for real-time estimation of the serial interval and reproductive number of an epidemic. Stat Med 27(16):2999–3016Google Scholar
  55. White LF, Wallinga J, Finelli L, Reed C, Riley S, Lipsitch M et al (2009) Estimation of the reproductive number and the serial interval in early phase of the 2009 influenza A/H1N1 pandemic in the USA. Influenza Other Respi Viruses 3(6):267–276Google Scholar
  56. Wilson N, Baker MG (2009) The emerging influenza pandemic: estimating the case fatality ratio. Euro Surveill 14(26). Available: https://www.ncbi.nlm.nih.gov/pubmed/19573509
  57. Wolkewitz M, Schumacher M (2017) Survival biases lead to flawed conclusions in observational treatment studies of influenza patients. J Clin Epidemiol 84(Apr):121–129Google Scholar
  58. Yang S, Santillana M, Kou SC (2015) Accurate estimation of influenza epidemics using Google search data via ARGO. Proc Natl Acad Sci USA. 112(47):14473–14478Google Scholar
  59. Yang S, Santillana M, Brownstein JS, Gray J, Richardson S, Kou SC (2017) Using electronic health records and Internet search information for accurate influenza forecasting. BMC Infect Dis 17(1):332Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Epidemiology and Department of Immunology and Infectious Diseases, Center for Communicable Disease DynamicsHarvard T.H. Chan School of Public HealthBostonUSA
  2. 2.Computational Health Informatics ProgramBoston Children’s HospitalBostonUSA
  3. 3.Department of PediatricsHarvard Medical SchoolBostonUSA

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