L ive poultry markets (LPMs) can serve as hubs for avian influenza virus (AIV) amplification in poultry and pose a risk for human zoonotic infections (1-4). Adopting efficient sampling strategies to monitor AIVs with human zoonotic potential at LPMs is essential for zoonotic disease prevention and pandemic preparedness. Recommendations regarding routine surveillance that would robustly and efficiently inform AIV activity at LPMs have been limited (5). Handling of live poultry interrupts the vending process; moreover, such routine surveillance is difficult to implement. Environmental samples have been collected to monitor AIV activity at LPMs (5-9). There have been limited parallel comparisons of AIV detection rates among poultry and environmental samples (7,10). Without frequent cleaning, the environment often permits AIV accumulation; environmental samples may thus overestimate AIV prevalence in poultry. Subtype-specific detection rates among different environmental samples may also vary. To inform the development of effective sampling strategies for AIV surveillance, we compared monthly detection rates for AIV subtypes H5, H7, and H9 in chickens and various environmental samples at LPMs in Guangzhou, China. The Study During December 2015-July 2018, we performed sampling twice per month at 1 wholesale (52 stalls) and 1 retail (8 stalls) LPM, from 2 randomly selected stalls per sampling event. We collected paired oropharyngeal and cloacal swab samples (n = 3,119 chickens) and environmental samples (n = 3,008) in viral transport medium at the LPMs (Appendix Figure 1, https:// wwwnc.cdc.gov/EID/article/26/3/19-0888-App1. pdf). We randomly collected samples from all chickens at the selected stalls. We rarely observed sick chickens but we sampled them when identified. We also collected environmental samples from 3 key activity areas: poultry holding zones (fecal droppings, drinking water, and poultry feed), slaughtering zones (defeathering machines and surrounding defeathering working areas), and selling zones (chopping boards and display tables) near the selected chickens whenever possible (5-9). (Stalls sampled at the wholesale LPM [wLPM] have only poultry holding zones.) We sampled air using BC-251 cyclone-based NIOSH bioaerosol samplers that fractionate airborne particles into >4 µm, 1-4 µm, and <1 µm size fractions (11). We applied quantitative real-time reverse transcription PCR to detect the matrix gene segment of AIV; we analyzed positive samples by the hemagglutinin gene to determine the AIV subtype (H5, H7, or H9) using specific primers and probes (12,13). H5, H7, and H9 detection rates in environmental samples (median monthly difference 6.2% for H5,

Characterizing the spatial transmission pattern is critical for better surveillance and control of human influenza. Here, we propose a mutation network framework that utilizes network theory to study the transmission of human influenza H3N2. On the basis of the mutation network, the transmission analysis captured the circulation pattern from a global simulation of human influenza H3N2. Furthermore, this method was applied to explore, in detail, the transmission patterns within Europe, the United States, and China, revealing the regional spread of human influenza H3N2. The mutation network framework proposed here could facilitate the understanding, surveillance, and control of other infectious diseases.