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Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges
- Source :
- Epidemics, Vol 32, Iss, Pp 100393-(2020)
- Publication Year :
- 2020
- Publisher :
- Elsevier, 2020.
-
Abstract
- Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models' usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.
- Subjects :
- Policy and communication
Exploit
Computational methodology
Epidemiology
Computer science
media_common.quotation_subject
030231 tropical medicine
Bayesian analysis
Inference
Bayesian inference
Microbiology
Communicable Diseases
Models, Biological
lcsh:Infectious and parasitic diseases
1117 Public Health and Health Services
03 medical and health sciences
Bayes' theorem
0302 clinical medicine
Virology
Humans
Quality (business)
lcsh:RC109-216
030212 general & internal medicine
Health policy
media_common
Mathematical model
Parameter identifiability
Health Policy
Data challenges
Public Health, Environmental and Occupational Health
Bayes Theorem
1103 Clinical Sciences
Models, Theoretical
Prior knowledge
Infectious Diseases
Risk analysis (engineering)
Identifiability
Parasitology
Public Health
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Epidemics, Vol 32, Iss, Pp 100393-(2020)
- Accession number :
- edsair.doi.dedup.....3d08bd118cef9769c6ad9c81d9ca17ac