A Complex Systems Approach to Infectious Disease Surveillance and Response

The transmission of infectious diseases can be affected by various interactive factors at or across different scales, such as environmental factors (e.g., temperature) and physiological factors (e.g., immunity). In view of this, to effectively and efficiently monitor and response to an infectious disease, it would be necessary for us to systematically model these factors and their impacts on disease transmission. In this paper, we propose a complex systems approach to infectious disease surveillance and response that puts a special emphasis on complex systems modeling and policy-level decision making with consideration of multi-scale interactive factors and/or surveillance data of disease prevalence. We demonstrate the implementation of our approach by presenting two real-world studies, one on the air-borne influenza epidemic in Hong Kong and the other on the vector-borne malaria endemic in Yunnan, China.

[1]  Andrew J Tatem,et al.  International population movements and regional Plasmodium falciparum malaria elimination strategies , 2010, Proceedings of the National Academy of Sciences.

[2]  Benjamin J. Cowling,et al.  School Closure and Mitigation of Pandemic (H1N1) 2009, Hong Kong , 2010, Emerging infectious diseases.

[3]  Christelle Vancutsem,et al.  A Vectorial Capacity Product to Monitor Changing Malaria Transmission Potential in Epidemic Regions of Africa , 2012, Journal of tropical medicine.

[4]  Jiming Liu,et al.  Identifying the Relative Priorities of Subpopulations for Containing Infectious Disease Spread , 2013, PloS one.

[5]  S. Sinha,et al.  Mathematical models of malaria - a review , 2011, Malaria Journal.

[6]  P. Eckhoff A malaria transmission-directed model of mosquito life cycle and ecology , 2011, Malaria Journal.

[7]  Colin J. Sutherland,et al.  Determination of the Processes Driving the Acquisition of Immunity to Malaria Using a Mathematical Transmission Model , 2007, PLoS Comput. Biol..

[8]  Uriel Kitron,et al.  The Role of Human Movement in the Transmission of Vector-Borne Pathogens , 2009, PLoS neglected tropical diseases.

[9]  Eric H. Y. Lau,et al.  The Effective Reproduction Number of Pandemic Influenza: Prospective Estimation , 2010, Epidemiology.

[10]  J. Medlock,et al.  Optimizing Influenza Vaccine Distribution , 2009, Science.

[11]  Jiming Liu,et al.  Malaria transmission modelling: a network perspective , 2012, Infectious Diseases of Poverty.

[12]  Benjamin J Cowling,et al.  The infection attack rate and severity of 2009 pandemic H1N1 influenza in Hong Kong. , 2010, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[13]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[14]  S. Hay,et al.  The Malaria Atlas Project: Developing Global Maps of Malaria Risk , 2006, PLoS medicine.

[15]  David Schlossberg,et al.  Clinical Infectious Disease: Clinical Syndromes – Respiratory Tract , 2008 .

[16]  David L. Smith,et al.  Quantifying the Impact of Human Mobility on Malaria , 2012, Science.

[17]  Lai Ming Ho,et al.  Estimating Infection Attack Rates and Severity in Real Time during an Influenza Pandemic: Analysis of Serial Cross-Sectional Serologic Surveillance Data , 2011, PLoS medicine.

[18]  D. Mabey,et al.  Neglected tropical diseases. , 2010, British medical bulletin.

[19]  David L Smith,et al.  Statics and dynamics of malaria infection in Anopheles mosquitoes , 2004, Malaria Journal.