Nonetheless, the success of IHR (2005) and other global public health initiatives such as the Millennium Development Goals depends on strong national public health systems with competent, well-trained staff and well-equipped facilities. By targeting the core of public health systems, especially in low-resource countries that currently lag behind in public health capacity and infrastructure, IANPHI will play a key role in improving the capacity of countries to effectively detect, investigate, and respond to public health emergencies. The result will be better control of endemic diseases such as HIV/AIDS, acute lower respiratory tract infections, diarrheal diseases, measles, tuberculosis, malaria, and the neglected tropical diseases. These efforts will strengthen the practice of public health worldwide, yield global public health benefi ts of disease control and prevention, and ultimately accelerate social and economic development in the poorest countries of the world and progress toward achieving the Millenium Development Goals. Dr Rodier is director of International Health Regulations coordination at WHO in Geneva. Dynamics and Control of Diseases in Networks with Community Structure The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies. Mitigating or preventing the spread of infectious diseases is the ultimate goal of infectious disease epidemiology, and understanding the dynamics of epidemics is an important tool to achieve this goal. A rich body of research [1, 2, 3] has provided major insights into the processes that drive epidemics, and has been instrumental in developing strategies for control and eradication. The structure of contact networks is crucial in explaining epidemiological patterns seen in the spread of directly transmissible diseases such as HIV/AIDS [1, 4, 5] , SARS [6, 7] , influenza [8, 9, 10, 11] etc. For example, the basic reproductive number R 0 , a quantity central to developing intervention measures or immunization programs, depends crucially on the variance of the distribution of contacts [1, 12, 13] , known as the network degree distribution. Contact networks with fat-tailed degree distributions, for example, where a few individuals have an extraordinarily large number of contacts, result in a higher R 0 than one would expect from contact networks with a uniform degree distribution, and the existence of highly connected individuals makes them an ideal target for control measures [7, 14] .