The proportion of all models (black) and best-in-class models (red) sMOA outperforms in MAE (top) and WIS (bottom) if the validation window ranged from August 2020 through the x-axis date.
<p>sMOA outperforms the majority of all models and best-in-class models if the validation date cut off is between October 2020 and March 2023. Directly before October 2020, there was a dip in incidence case counts that sMOA failed to forecast accurately that caused the initial lower performanc...
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2025
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| _version_ | 1852019118753972224 |
|---|---|
| author | Alexander C. Murph (21587737) |
| author2 | G. Casey Gibson (21587740) Elizabeth B. Amona (21587743) Lauren J. Beesley (6836693) Lauren A. Castro (8463561) Sara Y. Del Valle (7465541) Dave Osthus (3216351) |
| author2_role | author author author author author author |
| author_facet | Alexander C. Murph (21587737) G. Casey Gibson (21587740) Elizabeth B. Amona (21587743) Lauren J. Beesley (6836693) Lauren A. Castro (8463561) Sara Y. Del Valle (7465541) Dave Osthus (3216351) |
| author_role | author |
| dc.creator.none.fl_str_mv | Alexander C. Murph (21587737) G. Casey Gibson (21587740) Elizabeth B. Amona (21587743) Lauren J. Beesley (6836693) Lauren A. Castro (8463561) Sara Y. Del Valle (7465541) Dave Osthus (3216351) |
| dc.date.none.fl_str_mv | 2025-06-23T17:49:46Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013203.g005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_proportion_of_all_models_black_and_best-in-class_models_red_sMOA_outperforms_in_MAE_top_and_WIS_bottom_if_the_validation_window_ranged_from_August_2020_through_the_x-axis_date_/29386141 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Ecology Inorganic Chemistry Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified synthetically generated segments maintain high accuracy instead matching segments developing versatile approaches observed time series local behavior observed emerging global epidemic 19 forecasting hub new method inspired historical time series div >< p time series data time series local behaviors historical data emerging epidemics 19 pandemic synthetic method synthetic </ strengthening preparedness possible </ performing 78 past decade particularly evident parametric nature novel pandemics model circumvents known values highly adaptable gained popularity early stages disease trends directly follow competitive performance broad range best match |
| dc.title.none.fl_str_mv | The proportion of all models (black) and best-in-class models (red) sMOA outperforms in MAE (top) and WIS (bottom) if the validation window ranged from August 2020 through the x-axis date. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>sMOA outperforms the majority of all models and best-in-class models if the validation date cut off is between October 2020 and March 2023. Directly before October 2020, there was a dip in incidence case counts that sMOA failed to forecast accurately that caused the initial lower performance.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_6d8dd2f9b496ba8c0dfd29ebf361fc3b |
| identifier_str_mv | 10.1371/journal.pcbi.1013203.g005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29386141 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The proportion of all models (black) and best-in-class models (red) sMOA outperforms in MAE (top) and WIS (bottom) if the validation window ranged from August 2020 through the x-axis date.Alexander C. Murph (21587737)G. Casey Gibson (21587740)Elizabeth B. Amona (21587743)Lauren J. Beesley (6836693)Lauren A. Castro (8463561)Sara Y. Del Valle (7465541)Dave Osthus (3216351)MedicineEcologyInorganic ChemistryInfectious DiseasesBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsynthetically generated segmentsmaintain high accuracyinstead matching segmentsdeveloping versatile approachesobserved time serieslocal behavior observedemerging global epidemic19 forecasting hubnew method inspiredhistorical time seriesdiv >< ptime series datatime serieslocal behaviorshistorical dataemerging epidemics19 pandemicsynthetic methodsynthetic </strengthening preparednesspossible </performing 78past decadeparticularly evidentparametric naturenovel pandemicsmodel circumventsknown valueshighly adaptablegained popularityearly stagesdisease trendsdirectly followcompetitive performancebroad rangebest match<p>sMOA outperforms the majority of all models and best-in-class models if the validation date cut off is between October 2020 and March 2023. Directly before October 2020, there was a dip in incidence case counts that sMOA failed to forecast accurately that caused the initial lower performance.</p>2025-06-23T17:49:46ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013203.g005https://figshare.com/articles/figure/The_proportion_of_all_models_black_and_best-in-class_models_red_sMOA_outperforms_in_MAE_top_and_WIS_bottom_if_the_validation_window_ranged_from_August_2020_through_the_x-axis_date_/29386141CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/293861412025-06-23T17:49:46Z |
| spellingShingle | The proportion of all models (black) and best-in-class models (red) sMOA outperforms in MAE (top) and WIS (bottom) if the validation window ranged from August 2020 through the x-axis date. Alexander C. Murph (21587737) Medicine Ecology Inorganic Chemistry Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified synthetically generated segments maintain high accuracy instead matching segments developing versatile approaches observed time series local behavior observed emerging global epidemic 19 forecasting hub new method inspired historical time series div >< p time series data time series local behaviors historical data emerging epidemics 19 pandemic synthetic method synthetic </ strengthening preparedness possible </ performing 78 past decade particularly evident parametric nature novel pandemics model circumvents known values highly adaptable gained popularity early stages disease trends directly follow competitive performance broad range best match |
| status_str | publishedVersion |
| title | The proportion of all models (black) and best-in-class models (red) sMOA outperforms in MAE (top) and WIS (bottom) if the validation window ranged from August 2020 through the x-axis date. |
| title_full | The proportion of all models (black) and best-in-class models (red) sMOA outperforms in MAE (top) and WIS (bottom) if the validation window ranged from August 2020 through the x-axis date. |
| title_fullStr | The proportion of all models (black) and best-in-class models (red) sMOA outperforms in MAE (top) and WIS (bottom) if the validation window ranged from August 2020 through the x-axis date. |
| title_full_unstemmed | The proportion of all models (black) and best-in-class models (red) sMOA outperforms in MAE (top) and WIS (bottom) if the validation window ranged from August 2020 through the x-axis date. |
| title_short | The proportion of all models (black) and best-in-class models (red) sMOA outperforms in MAE (top) and WIS (bottom) if the validation window ranged from August 2020 through the x-axis date. |
| title_sort | The proportion of all models (black) and best-in-class models (red) sMOA outperforms in MAE (top) and WIS (bottom) if the validation window ranged from August 2020 through the x-axis date. |
| topic | Medicine Ecology Inorganic Chemistry Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified synthetically generated segments maintain high accuracy instead matching segments developing versatile approaches observed time series local behavior observed emerging global epidemic 19 forecasting hub new method inspired historical time series div >< p time series data time series local behaviors historical data emerging epidemics 19 pandemic synthetic method synthetic </ strengthening preparedness possible </ performing 78 past decade particularly evident parametric nature novel pandemics model circumvents known values highly adaptable gained popularity early stages disease trends directly follow competitive performance broad range best match |