Top Tweets for #casetimeseries
Just started our 5-day course “Modern time series methods for public health and epidemiology” at #eepe in Florence. We teach #DLNM, #CaseTimeSeries, #climatechange, #ITS, #TwoStage and other TS methods in epidemiology. Registration soon open for next year: https://t.co/gMsbUlczRu
Only few places left for the 5-day course “Modern time series methods for public health and epidemiology” in Florence on 10-14 July 2023. Come and learn #DLNM, #CaseTimeSeries, #climatechange, #ITS, #TwoStage methods in epidemiology. Info&registration: https://t.co/BHzHXNx66e
New tutorial on #CaseTimeSeries design for small-area analysis just published, with a case study on heat-mortality in London. With R code to fully reproduce GIS and modelling steps from original data. Article and material: https://t.co/VKkpPqdgzJ
Today at 16:30 UK time I will present the #CaseTimeSeries at the @RSSMedSection event 'Time-series analysis of routine health data: challenges and opportunities'.
To attend, register here:
https://t.co/PvM20L70Uk
@RSSMedSection @RoyalStatSoc @RuteVVieira I will present the new #CaseTimeSeries at the next @RSSMedSection on 27th Oct. Nice chance to discuss applications of this design for routine health data
💥💥💥 finally out! Don’t miss the opportunity to have a look at the paper and code. @AGasparrini75 explains everything in this threat - the #CaseTimeSeries is here🙌🏻🙌🏻🙌🏻
And it’s out! The #CaseTimeSeries Design, just published. Full text and material at: https://t.co/EyAPegUXTj
Read the thread to learn on:
-The design structure
-Case studies in different epidemiological areas
-Assumptions and simulation results
-R code and reproducible examples
Case studies and simulations are completely replicable through tutorials & fully documented R code available at https://t.co/iX890g0Bfa
The #CaseTimeSeries design: apply it and spread the word!
The article includes a thorough simulation study that assesses the #CaseTimeSeries method in increasingly complex scenarios and explores the design assumptions. See eAppendix 3 at (https://t.co/EyAPegUXTj)
The #CaseTimeSeries is applicable in different areas: see eAppendices 1-2 (https://t.co/EyAPegUXTj) with case studies on:
1) Clinical epi: influenza and MI risk using linked clinical datasets
2) Environmental epi: PM/temperature/pollen and allergic reactions
The #CaseTimeSeries is based on an adaptable framework that combines:
1) Time series: longitudinal structure & modelling flexibility for time-varying confounders and temporal dependencies
2) Self-matched setting: individual-level and control by design of time-invariant factors
And it’s out! The #CaseTimeSeries Design, just published. Full text and material at: https://t.co/EyAPegUXTj
Read the thread to learn on:
-The design structure
-Case studies in different epidemiological areas
-Assumptions and simulation results
-R code and reproducible examples
Great work with @AirRaterUTAS on pollen and allergy using smartphone tech. Read the full text: https://t.co/lb3rmHKQHC.
It's also a teaser: first application of the new #CaseTimeSeries design. Stay tuned for the publication on the method, to appear soon...
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