http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111968751-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-70 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-80 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F17-18 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-80 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-70 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F17-18 |
| filingDate | 2020-06-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2021-10-19-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2021-10-19-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-111968751-B |
| titleOfInvention | Infectious disease trend prediction method and system using multi-baseline correction model |
| abstract | The invention relates to a data acquisition system based on the Internet, which comprises the following steps: acquiring epidemic situation data of the infectious disease; preprocessing data; judging the epidemic situation development stage; predicting an interval value from an autoregressive baseline; an exponential baseline calibration upper limit value; growing a baseline calibration lower limit value; calculating and calibrating, wherein the main baseline, the pessimistic auxiliary baseline and the optimistic auxiliary baseline are mutually calibrated; and (6) outputting a prediction result. Compared with the infectious disease professional model analysis method, the method has the advantages that the requirement on data is low, only historical basic data such as time, country, accumulated confirmed diagnosis quantity and the like are needed, the data requirement threshold of infectious disease prediction is greatly reduced, and the difficulty of epidemic situation prediction is obviously reduced; compared with a time series model analysis method, the method has the advantages that the influence of time series instability caused by human factors can be reduced by adopting infectious disease trend prediction based on multi-baseline calibration, and accurate short-term prediction results can be obtained. |
| priorityDate | 2020-06-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID419493476 http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID4235 |
Total number of triples: 16.