tetano
Editor, Senior Moderator
Sci Rep
. 2025 Dec 16.
doi: 10.1038/s41598-025-12377-w. Online ahead of print. A systems approach for anticipating post-pandemic forecast challenges for pediatric influenza and hospital burden
Beth Ellinport[SUP] 1 [/SUP], Emily Wells[SUP] 1 [/SUP], Dinesh Rai[SUP] 2 [/SUP], Benjamin Rader[SUP] 2 [/SUP], Christopher L Cummings[SUP] 1 [/SUP], Benjamin D Trump[SUP] 3 4 [/SUP], Igor Linkov[SUP] 1 [/SUP]
Affiliations
The COVID-19 pandemic has significantly disrupted influenza forecasting, making it challenging for hospitals to anticipate the severity of upcoming flu seasons relative to typical annual respiratory virus patterns. Even for influenza, health facilities often lack precise information on potential influenza surges, which hinders hospital management's ability to anticipate necessary changes with respect to hospital staffing and resource allocation for an influx of patients. This study addresses this critical gap by developing an enhanced predictive model for pediatric influenza hospitalizations in Massachusetts. By integrating data from the Health and Human Services (HHS) Protect Public Data Hub, Centers for Disease Control and Prevention (CDC) FluSurv-NET, U.S. Department of Transportation (DOT) mobility data, and regional hospitalization rates, we demonstrate how recent improvements in data analytics and population tracking can amplify disease forecasts, and more precisely anticipate hospital burden relative to historical patient intake and hospital utilization trends.
. 2025 Dec 16.
doi: 10.1038/s41598-025-12377-w. Online ahead of print. A systems approach for anticipating post-pandemic forecast challenges for pediatric influenza and hospital burden
Beth Ellinport[SUP] 1 [/SUP], Emily Wells[SUP] 1 [/SUP], Dinesh Rai[SUP] 2 [/SUP], Benjamin Rader[SUP] 2 [/SUP], Christopher L Cummings[SUP] 1 [/SUP], Benjamin D Trump[SUP] 3 4 [/SUP], Igor Linkov[SUP] 1 [/SUP]
Affiliations
- PMID: 41402321
- DOI: 10.1038/s41598-025-12377-w
The COVID-19 pandemic has significantly disrupted influenza forecasting, making it challenging for hospitals to anticipate the severity of upcoming flu seasons relative to typical annual respiratory virus patterns. Even for influenza, health facilities often lack precise information on potential influenza surges, which hinders hospital management's ability to anticipate necessary changes with respect to hospital staffing and resource allocation for an influx of patients. This study addresses this critical gap by developing an enhanced predictive model for pediatric influenza hospitalizations in Massachusetts. By integrating data from the Health and Human Services (HHS) Protect Public Data Hub, Centers for Disease Control and Prevention (CDC) FluSurv-NET, U.S. Department of Transportation (DOT) mobility data, and regional hospitalization rates, we demonstrate how recent improvements in data analytics and population tracking can amplify disease forecasts, and more precisely anticipate hospital burden relative to historical patient intake and hospital utilization trends.