tetano
Editor, Senior Moderator
Infect Dis Model
. 2024 Sep 16;10(1):139-149.
doi: 10.1016/j.idm.2024.09.007. eCollection 2025 Mar. Predicting influenza in China from October 1, 2023, to February 5, 2024: A transmission dynamics model based on population migration
Huimin Qu[SUP] 1 [/SUP], Yichao Guo[SUP] 1 [/SUP], Xiaohao Guo[SUP] 1 [/SUP], Kang Fang[SUP] 1 [/SUP], Jiadong Wu[SUP] 1 [/SUP], Tao Li[SUP] 1 [/SUP], Jia Rui[SUP] 1 [/SUP], Hongjie Wei[SUP] 1 [/SUP], Kun Su[SUP] 2 [/SUP], Tianmu Chen[SUP] 1 [/SUP]
Affiliations
Introduction: Since November 2023, influenza has ranked first in reported cases of infectious diseases in China, with the outbreak in both northern and southern provinces exceeding the levels observed during the same period in 2022. This poses a serious health risk to the population. Therefore, short to medium-term influenza predictions are beneficial for epidemic assessment and can reduce the disease burden.
Methods: A transmission dynamics model considering population migration, encompassing susceptible-exposed-infectious-asymptomatic-recovered (SEIAR) was used to predict the dynamics of influenza before the Spring Festival travel rush.
Results: The overall epidemic shows a declining trend, with the peak expected to occur from week 47 in 2023 to week 1 in 2024. The number of cases of A (H3N2) is greater than that of influenza B, and the influenza situation is more severe in the southern provinces compared to the northern ones.
Conclusion: Our method is applicable for short-term and medium-term influenza predictions. As the spring festival travel rush approaches. Therefore, it is advisable to advocate for nonpharmaceutical interventions (NPIs), influenza vaccination, and other measures to reduce healthcare and public health burden.
Keywords: Influenza; Migration; Prediction; Transmission dynamics model.
. 2024 Sep 16;10(1):139-149.
doi: 10.1016/j.idm.2024.09.007. eCollection 2025 Mar. Predicting influenza in China from October 1, 2023, to February 5, 2024: A transmission dynamics model based on population migration
Huimin Qu[SUP] 1 [/SUP], Yichao Guo[SUP] 1 [/SUP], Xiaohao Guo[SUP] 1 [/SUP], Kang Fang[SUP] 1 [/SUP], Jiadong Wu[SUP] 1 [/SUP], Tao Li[SUP] 1 [/SUP], Jia Rui[SUP] 1 [/SUP], Hongjie Wei[SUP] 1 [/SUP], Kun Su[SUP] 2 [/SUP], Tianmu Chen[SUP] 1 [/SUP]
Affiliations
- PMID: 39380723
- PMCID: PMC11459688
- DOI: 10.1016/j.idm.2024.09.007
Introduction: Since November 2023, influenza has ranked first in reported cases of infectious diseases in China, with the outbreak in both northern and southern provinces exceeding the levels observed during the same period in 2022. This poses a serious health risk to the population. Therefore, short to medium-term influenza predictions are beneficial for epidemic assessment and can reduce the disease burden.
Methods: A transmission dynamics model considering population migration, encompassing susceptible-exposed-infectious-asymptomatic-recovered (SEIAR) was used to predict the dynamics of influenza before the Spring Festival travel rush.
Results: The overall epidemic shows a declining trend, with the peak expected to occur from week 47 in 2023 to week 1 in 2024. The number of cases of A (H3N2) is greater than that of influenza B, and the influenza situation is more severe in the southern provinces compared to the northern ones.
Conclusion: Our method is applicable for short-term and medium-term influenza predictions. As the spring festival travel rush approaches. Therefore, it is advisable to advocate for nonpharmaceutical interventions (NPIs), influenza vaccination, and other measures to reduce healthcare and public health burden.
Keywords: Influenza; Migration; Prediction; Transmission dynamics model.