A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model
The livestock industry has been economically affected by the emergence and reemergence of infectious diseases such as Lumpy Skin Disease (LSD). This has driven the interest to research efficient mitigating measures towards controlling the transmission of LSD. Mathematical models of real-life systems...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Computer Methods and Programs in Biomedicine Update |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666990025000023 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590015943671808 |
|---|---|
| author | Edwiga Renald Miracle Amadi Heikki Haario Joram Buza Jean M. Tchuenche Verdiana G. Masanja |
| author_facet | Edwiga Renald Miracle Amadi Heikki Haario Joram Buza Jean M. Tchuenche Verdiana G. Masanja |
| author_sort | Edwiga Renald |
| collection | DOAJ |
| description | The livestock industry has been economically affected by the emergence and reemergence of infectious diseases such as Lumpy Skin Disease (LSD). This has driven the interest to research efficient mitigating measures towards controlling the transmission of LSD. Mathematical models of real-life systems inherit loss of information, and consequently, accuracy of their results is often complicated by the presence of uncertainties in data used to estimate parameter values. There is a need for models with knowledge about the confidence of their long-term predictions. This study has introduced a novel yet simple technique for analyzing data uncertainties in compartmental models which is then used to examine the reliability of a deterministic model of the transmission dynamics of LSD in cattle which involves investigating scenarios related to data quality for which the model parameters can be well identified. The assessment of the uncertainties is determined with the help of Adaptive Metropolis Hastings algorithm, a Markov Chain Monte Carlo (MCMC) standard statistical method. Simulation results with synthetic cases show that the model parameters are identifiable with a reasonable amount of synthetic noise, and enough data points spanning through the model classes. MCMC outcomes derived from synthetic data, generated to mimic the characteristics of the real dataset, significantly surpassed those obtained from actual data in terms of uncertainties in identifying parameters and making predictions. This approach could serve as a guide for obtaining informative real data, and adapted to target key interventions when using routinely collected data to investigate long-term transmission dynamic of a disease. |
| format | Article |
| id | doaj-art-e55e4b630268494682fb619bca61b711 |
| institution | Kabale University |
| issn | 2666-9900 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computer Methods and Programs in Biomedicine Update |
| spelling | doaj-art-e55e4b630268494682fb619bca61b7112025-01-24T04:45:52ZengElsevierComputer Methods and Programs in Biomedicine Update2666-99002025-01-017100178A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease modelEdwiga Renald0Miracle Amadi1Heikki Haario2Joram Buza3Jean M. Tchuenche4Verdiana G. Masanja5School of Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania; Corresponding author.School of Engineering Science, Lappeenranta University of Technology, 53851, Lappeenranta, FinlandSchool of Engineering Science, Lappeenranta University of Technology, 53851, Lappeenranta, FinlandSchool of Life Science and Biomedical Engineering (LiSBE), Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, TanzaniaSchool of Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, TanzaniaSchool of Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, TanzaniaThe livestock industry has been economically affected by the emergence and reemergence of infectious diseases such as Lumpy Skin Disease (LSD). This has driven the interest to research efficient mitigating measures towards controlling the transmission of LSD. Mathematical models of real-life systems inherit loss of information, and consequently, accuracy of their results is often complicated by the presence of uncertainties in data used to estimate parameter values. There is a need for models with knowledge about the confidence of their long-term predictions. This study has introduced a novel yet simple technique for analyzing data uncertainties in compartmental models which is then used to examine the reliability of a deterministic model of the transmission dynamics of LSD in cattle which involves investigating scenarios related to data quality for which the model parameters can be well identified. The assessment of the uncertainties is determined with the help of Adaptive Metropolis Hastings algorithm, a Markov Chain Monte Carlo (MCMC) standard statistical method. Simulation results with synthetic cases show that the model parameters are identifiable with a reasonable amount of synthetic noise, and enough data points spanning through the model classes. MCMC outcomes derived from synthetic data, generated to mimic the characteristics of the real dataset, significantly surpassed those obtained from actual data in terms of uncertainties in identifying parameters and making predictions. This approach could serve as a guide for obtaining informative real data, and adapted to target key interventions when using routinely collected data to investigate long-term transmission dynamic of a disease.http://www.sciencedirect.com/science/article/pii/S2666990025000023Lumpy skin diseaseUncertainty assessmentParameter identificationMarkov Chain Monte Carlo |
| spellingShingle | Edwiga Renald Miracle Amadi Heikki Haario Joram Buza Jean M. Tchuenche Verdiana G. Masanja A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model Computer Methods and Programs in Biomedicine Update Lumpy skin disease Uncertainty assessment Parameter identification Markov Chain Monte Carlo |
| title | A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model |
| title_full | A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model |
| title_fullStr | A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model |
| title_full_unstemmed | A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model |
| title_short | A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model |
| title_sort | comparative approach of analyzing data uncertainty in parameter estimation for a lumpy skin disease model |
| topic | Lumpy skin disease Uncertainty assessment Parameter identification Markov Chain Monte Carlo |
| url | http://www.sciencedirect.com/science/article/pii/S2666990025000023 |
| work_keys_str_mv | AT edwigarenald acomparativeapproachofanalyzingdatauncertaintyinparameterestimationforalumpyskindiseasemodel AT miracleamadi acomparativeapproachofanalyzingdatauncertaintyinparameterestimationforalumpyskindiseasemodel AT heikkihaario acomparativeapproachofanalyzingdatauncertaintyinparameterestimationforalumpyskindiseasemodel AT jorambuza acomparativeapproachofanalyzingdatauncertaintyinparameterestimationforalumpyskindiseasemodel AT jeanmtchuenche acomparativeapproachofanalyzingdatauncertaintyinparameterestimationforalumpyskindiseasemodel AT verdianagmasanja acomparativeapproachofanalyzingdatauncertaintyinparameterestimationforalumpyskindiseasemodel AT edwigarenald comparativeapproachofanalyzingdatauncertaintyinparameterestimationforalumpyskindiseasemodel AT miracleamadi comparativeapproachofanalyzingdatauncertaintyinparameterestimationforalumpyskindiseasemodel AT heikkihaario comparativeapproachofanalyzingdatauncertaintyinparameterestimationforalumpyskindiseasemodel AT jorambuza comparativeapproachofanalyzingdatauncertaintyinparameterestimationforalumpyskindiseasemodel AT jeanmtchuenche comparativeapproachofanalyzingdatauncertaintyinparameterestimationforalumpyskindiseasemodel AT verdianagmasanja comparativeapproachofanalyzingdatauncertaintyinparameterestimationforalumpyskindiseasemodel |