tetano
Editor, Senior Moderator
Interdiscip Perspect Infect Dis
. 2025 Dec 11:2025:5555858.
doi: 10.1155/ipid/5555858. eCollection 2025. Spatiotemporal Dynamics of Avian Influenza: Understanding Avian Influenza Transmission via Mallard Migration Data
Sena Mursel[SUP] 1 2 [/SUP], Brian D Davison[SUP] 2 3 [/SUP], Thomas McAndrew[SUP] 2 4 [/SUP], Bilal Khan[SUP] 3 4 [/SUP], Paolo Bocchini[SUP] 1 2 [/SUP]
Affiliations
Influenza, categorized as one of the emergent infectious diseases, presents a substantial public health concern due to its capacity to trigger extensive epidemics and global pandemics. Every recent pandemic of human influenza has been attributed to avian influenza viruses (AIVs), underscoring their pandemic potential and the associated public health risks. Interestingly, there remains a significant knowledge gap concerning the mechanisms that sustain the survival and proliferation of these viruses within their natural avian reservoirs. Migratory waterfowl, in particular mallard (Anas platyrhynchos), plays a crucial role as potential reservoirs, facilitating the spread of the virus through their migratory patterns. In order to better understand the factors that contribute to AIV spillover and hotspots, we built a mechanistic transmission model and investigated the likelihood of an AIV hotspot for the animal-to-human spillover of the virus in a region in Kansas. Our findings challenge the notion that the size of the overall mallard population is a reliable predictor of spillover hazard. To study this aspect, we systematically evaluated weekly trends in both the total population density and the total infected population density. In more than 70% of the locations studied, these two indicators showed periods with opposite trends. This conclusion stresses the importance of developing and calibrating compartmental models that can capture the diffusion of the virus within the reservoir and its spatiotemporal distribution due to animal movement.
Keywords: SIR model; avian influenza; disease hotspot; mathematical epidemiology.
. 2025 Dec 11:2025:5555858.
doi: 10.1155/ipid/5555858. eCollection 2025. Spatiotemporal Dynamics of Avian Influenza: Understanding Avian Influenza Transmission via Mallard Migration Data
Sena Mursel[SUP] 1 2 [/SUP], Brian D Davison[SUP] 2 3 [/SUP], Thomas McAndrew[SUP] 2 4 [/SUP], Bilal Khan[SUP] 3 4 [/SUP], Paolo Bocchini[SUP] 1 2 [/SUP]
Affiliations
- PMID: 41477103
- PMCID: PMC12752836
- DOI: 10.1155/ipid/5555858
Influenza, categorized as one of the emergent infectious diseases, presents a substantial public health concern due to its capacity to trigger extensive epidemics and global pandemics. Every recent pandemic of human influenza has been attributed to avian influenza viruses (AIVs), underscoring their pandemic potential and the associated public health risks. Interestingly, there remains a significant knowledge gap concerning the mechanisms that sustain the survival and proliferation of these viruses within their natural avian reservoirs. Migratory waterfowl, in particular mallard (Anas platyrhynchos), plays a crucial role as potential reservoirs, facilitating the spread of the virus through their migratory patterns. In order to better understand the factors that contribute to AIV spillover and hotspots, we built a mechanistic transmission model and investigated the likelihood of an AIV hotspot for the animal-to-human spillover of the virus in a region in Kansas. Our findings challenge the notion that the size of the overall mallard population is a reliable predictor of spillover hazard. To study this aspect, we systematically evaluated weekly trends in both the total population density and the total infected population density. In more than 70% of the locations studied, these two indicators showed periods with opposite trends. This conclusion stresses the importance of developing and calibrating compartmental models that can capture the diffusion of the virus within the reservoir and its spatiotemporal distribution due to animal movement.
Keywords: SIR model; avian influenza; disease hotspot; mathematical epidemiology.