Malaria Modeling at Northwestern

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Malaria Modeling at Northwestern

Malaria Modeling at Northwestern

@nu_modeling

We use dynamical models, statistical models, and data analysis to support decision-making around policies to manage malaria (and COVID-19 in Illinois).

Chicago, IL 가입일 Nisan 2021
66 팔로잉130 팔로워
Malaria Modeling at Northwestern 리트윗함
Nature Communications
Nature Communications@NatureComms·
Tracking changes in #SARSCoV2 transmission with an outpatient sentinel surveillance system This method has fewer operational delays than methods based on hospital data, and may be subject to fewer biases @nu_modeling go.nature.com/3VCsiSd
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Malaria Modeling at Northwestern 리트윗함
Jaline Gerardin
Jaline Gerardin@jalinegerardin·
Thanks especially to Emile Jorgensen at @ChiPublicHealth as well as @IsaacGhinai and Massimo Pacilli, who were interested in new surveillance methods; and Shannon Lightner at @IDPH who made the first data collection happen! What a pleasure working with all of you ❤️
Malaria Modeling at Northwestern@nu_modeling

🎉✨Out in Nature Communications!✨🎉 doi.org/10.1038/s41467… Alongside partners from @ChiPublicHealth and @IDPH, and led by Reese Richardson with @PhilAArevalo, Tobias Holden, @KatieMG, @sarahcobey, et al. we tested a novel method for SARS-CoV-2 sentinel surveillance.🧵(1/9)

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Malaria Modeling at Northwestern
These findings suggest that a deliberate outpatient sentinel surveillance system, with high testing volume and routine reporting of date of symptom onset, would provide robust early warning of increases in transmission. (8/9)
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Malaria Modeling at Northwestern
Huge congratulations to team member @tobiasmholden for being awarded an F31 research fellowship from NIAID on "Quantifying the potential contribution of asymptomatic screening and treatment to malaria control and elimination" in collaboration with INDIE 🤩
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Malaria Modeling at Northwestern
These findings suggest that a deliberate outpatient sentinel surveillance system, with high testing volume and routine reporting of date of symptom onset, would provide robust early warning of increases in transmission. (7/8)
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