Outbreaks of emerging pathogens are among the most unpredictable threats to health and personal welfare. The causes of pathogen emergence are variable and seemingly idiosyncratic. Although forecasting infectious disease emergence (and re-emergence) therefore seems intractable, new developments in epidemic modeling show that emergence events are united by patterns common among dynamical systems that cross tipping points. Particularly, it has recently been shown that tipping points in the transmission of infectious diseases influence epidemiological patterns in a way that leaves signatures of their proximity. For instance, a statistical phenomenon called critical slowing down influences the variance and autocorrelation of data streams in characteristic ways. The long term objective of this study is to develop a theory for forecasting epidemic transitions before they occur, focusing on critical slowing down and other near-critical statistical patterns.
Its specific aims are: (1) T develop theories of forecastability for emerging and eliminable infectious diseases; (2) to develop model-independent statistical methods for forecasting disease emergence based on this theory; (3) to document critical slowing down and other near-critical phenomena by comparing data on re-emerging and non-emerging pathogens; and (4) to produce software packages implementing these methods. The research strategy adopted by this project combines theoretical studies with data analysis in an empirically- driven feedback loop. The research design requires first the development of mathematical models of intermediate complexity allowing both mathematical solution and dynamic realism to represent contemporary emerging and re-emerging infectious diseases. Solutions of these models provide the basis for statistical algorithms for detecting early warning patterns in epidemiological data. These algorithms will be stress-tested using computer simulations of emergence events, including both simple and complex scenarios for the conditions under which emergence occurs and the imperfect surveillance and reporting systems that generate data. The algorithms will then be applied to data on recent emergence and re-emergence of measles, pertussis, and rubella in populations from which these pathogens had been nearly eliminated; lessons learned from this empirical study will inform subsequent rounds of model development, simulation and validation. Finally, the results of these studies will be brought together in a range of software applications for use i research, policy making, and education.

Public Health Relevance

Despite the continued growth of medical science, novel pathogens continue to emerge while historically con- trolled pathogens are re-emerging due to changes in demography, individual health choices, and ecological and evolutionary shifts. In many cases emergence occurs after the pathogen reaches a tipping point. This study will develop new theoretical models to study the transmission of infectious diseases near tipping points. We will then develop algorithms and software for online detection of epidemiological patterns indicating the approach to emergence. Such software will form the basis for early warning systems for infectious disease emergence.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01GM110744-02
Application #
8927032
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Janes, Daniel E
Project Start
2014-09-12
Project End
2019-05-31
Budget Start
2015-06-01
Budget End
2016-05-31
Support Year
2
Fiscal Year
2015
Total Cost
Indirect Cost
Name
University of Georgia
Department
Type
Sch of Home Econ/Human Ecology
DUNS #
004315578
City
Athens
State
GA
Country
United States
Zip Code
30602
Evans, Michelle V; Murdock, Courtney C; Drake, John M (2018) Anticipating Emerging Mosquito-borne Flaviviruses in the USA: What Comes after Zika? Trends Parasitol 34:544-547
Kundrick, Avery; Huang, Zhuojie; Carran, Spencer et al. (2018) Sub-national variation in measles vaccine coverage and outbreak risk: a case study from a 2010 outbreak in Malawi. BMC Public Health 18:741
Drake, John M; Hay, Simon I (2017) Monitoring the Path to the Elimination of Infectious Diseases. Trop Med Infect Dis 2:
Miller, Paige B; O'Dea, Eamon B; Rohani, Pejman et al. (2017) Forecasting infectious disease emergence subject to seasonal forcing. Theor Biol Med Model 14:17
Chen, Shiyang; Epureanu, Bogdan (2017) Regular biennial cycles in epidemics caused by parametric resonance. J Theor Biol 415:137-144
Li, Sheng; Ma, Chao; Hao, Lixin et al. (2017) Demographic transition and the dynamics of measles in six provinces in China: A modeling study. PLoS Med 14:e1002255
Evans, Michelle V; Dallas, Tad A; Han, Barbara A et al. (2017) Data-driven identification of potential Zika virus vectors. Elife 6:
Schmidt, John Paul; Park, Andrew W; Kramer, Andrew M et al. (2017) Spatiotemporal Fluctuations and Triggers of Ebola Virus Spillover. Emerg Infect Dis 23:415-422
Brett, Tobias S; Drake, John M; Rohani, Pejman (2017) Anticipating the emergence of infectious diseases. J R Soc Interface 14:
Han, Barbara A; Drake, John M (2016) Future directions in analytics for infectious disease intelligence: Toward an integrated warning system for emerging pathogens. EMBO Rep 17:785-9

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