Exact(8)
Fire blight warning systems are widely used to forecast disease outbreak and support treatment planning.
These models allow aggregating expert and clinical information to allow researchers and decision makers to forecast disease progression.
Lifetime risk estimates of chronic kidney disease (CKD) can motivate preventative behaviors at the individual level and forecast disease burden and health care use at the population level.
Not only that, but it comes with many caveats: it is impossible to forecast disease outbreaks, natural disasters, climate change, nuclear war, etc – all of which could have an impact on how long people live.
Some tuberculosis simulation models are constructed specifically to forecast disease burdens in the future.
Attempts to forecast disease outbreaks have focused on the impact of climate variability on infectious diseases (27).
Similar(52)
The performance of this approach is promising and recently was successful at forecasting disease activity in East-Central Africa (10).
Accurate assessment of lymph nodes in thoracic esophageal carcinoma is essential for selecting appropriate treatment and forecasting disease progression.
Since that initial system, researchers and practitioners have continued to search for determinants of spatial and temporal variability of malaria to improve systems for forecasting disease burden.
By accounting for the interaction of the physical (climate) and biologic (vector, pathogen, and host) systems, a more robust means of monitoring and forecasting disease should be attained.
Accurate assessment of locoregional lymph nodes in thoracic esophageal carcinoma is more complex but essential for selecting appropriate treatments and forecasting disease progression.
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