Prognose der Ausbreitung von pandemia Covid-19 in der Welt
Identifieur interne : 000121 ( Hal/Checkpoint ); précédent : 000120; suivant : 000122Prognose der Ausbreitung von pandemia Covid-19 in der Welt
Auteurs : Jonas Kibala Kuma [République démocratique du Congo]Source :
Abstract
We tried to determine the approximate date from which Corona Virus (Covid-19) pandemia could affect or touch all the world population, heard that this one would be evaluated around 7,759,559,800 according to our projections on the basis of estimate of UNO (see : https://coronavirus.politologue.com), under the assumption that the current rate/rhythm of the evolution of pandemia as well as the measurements taken by various authorities remain unchanged (all things remaining are equal). In such exercise of modelling, our concern is to draw the world authorities' attention about few time assigned to us for eradicating this pandemia, if not it would be difficult or even impossible to stop the haemorrhage because this pandemia could strike or infect all the world population as from May 25, 2020 to June 25, 2020. To carry out this analysis, or to forecast our study time series or the number of people infected by Corona Virus (Covid-19) pandemia, we made recourse to a functional form of exponential type, according to our time series structure, of which the essential parameter, which is the average growth rate number of infected people by covid19 pandemia in the world by day, is estimated according to a nonlinear approach which is the Gauss-Newton algorithm. Future studies could look further into the question with more succeeded specifications (for example, to resort to the nonlinear cointegration to escape the fallacious regressions) or functional forms much better than that retained in this analysis.
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="de">Prognose der Ausbreitung von pandemia Covid-19 in der Welt</title><title xml:lang="en">Forecast of the propagation of Covid-19 pandemia in the world</title><title xml:lang="fr">Prévision de la propagation de la pandémie Covid-19 dans le monde</title><author><name sortKey="Kibala Kuma, Jonas" sort="Kibala Kuma, Jonas" uniqKey="Kibala Kuma J" first="Jonas" last="Kibala Kuma">Jonas Kibala Kuma</name><affiliation wicri:level="1"><hal:affiliation type="institution" xml:id="struct-217378" status="VALID"> <orgName>University of Kinshasa</orgName> <orgName type="acronym">UNIKIN</orgName> <desc> <address> <addrLine>B.P. 127 Kinshasa</addrLine> <country key="CD"></country> </address> <ref type="url">http://www.unikin.cd/</ref> </desc> </hal:affiliation><country>République démocratique du Congo</country></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">HAL</idno><idno type="RBID">Hal:hal-02525455</idno><idno type="halId">hal-02525455</idno><idno type="halUri">https://hal.archives-ouvertes.fr/hal-02525455</idno><idno type="url">https://hal.archives-ouvertes.fr/hal-02525455</idno><date when="2020-03-31">2020-03-31</date><idno type="wicri:Area/Hal/Corpus">000002</idno><idno type="wicri:Area/Hal/Curation">000002</idno><idno type="wicri:Area/Hal/Checkpoint">000121</idno><idno type="wicri:explorRef" wicri:stream="Hal" wicri:step="Checkpoint">000121</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="de">Prognose der Ausbreitung von pandemia Covid-19 in der Welt</title><title xml:lang="en">Forecast of the propagation of Covid-19 pandemia in the world</title><title xml:lang="fr">Prévision de la propagation de la pandémie Covid-19 dans le monde</title><author><name sortKey="Kibala Kuma, Jonas" sort="Kibala Kuma, Jonas" uniqKey="Kibala Kuma J" first="Jonas" last="Kibala Kuma">Jonas Kibala Kuma</name><affiliation wicri:level="1"><hal:affiliation type="institution" xml:id="struct-217378" status="VALID"> <orgName>University of Kinshasa</orgName> <orgName type="acronym">UNIKIN</orgName> <desc> <address> <addrLine>B.P. 127 Kinshasa</addrLine> <country key="CD"></country> </address> <ref type="url">http://www.unikin.cd/</ref> </desc> </hal:affiliation><country>République démocratique du Congo</country></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"> <p>We tried to determine the approximate date from which Corona Virus (Covid-19) pandemia could affect or touch all the world population, heard that this one would be evaluated around 7,759,559,800 according to our projections on the basis of estimate of UNO (see : https://coronavirus.politologue.com), under the assumption that the current rate/rhythm of the evolution of pandemia as well as the measurements taken by various authorities remain unchanged (all things remaining are equal). In such exercise of modelling, our concern is to draw the world authorities' attention about few time assigned to us for eradicating this pandemia, if not it would be difficult or even impossible to stop the haemorrhage because this pandemia could strike or infect all the world population as from May 25, 2020 to June 25, 2020. To carry out this analysis, or to forecast our study time series or the number of people infected by Corona Virus (Covid-19) pandemia, we made recourse to a functional form of exponential type, according to our time series structure, of which the essential parameter, which is the average growth rate number of infected people by covid19 pandemia in the world by day, is estimated according to a nonlinear approach which is the Gauss-Newton algorithm. Future studies could look further into the question with more succeeded specifications (for example, to resort to the nonlinear cointegration to escape the fallacious regressions) or functional forms much better than that retained in this analysis.</p> </div></front></TEI><hal api="V3"> <titleStmt> <title xml:lang="de">Prognose der Ausbreitung von pandemia Covid-19 in der Welt</title> <title xml:lang="en">Forecast of the propagation of Covid-19 pandemia in the world</title> <title xml:lang="fr">Prévision de la propagation de la pandémie Covid-19 dans le monde</title> <author role="aut"> <persName> <forename type="first">Jonas</forename> <surname>KIBALA KUMA</surname> </persName> <email type="md5">55efa0e3f2614d73f7ca0216ed3efbce</email> <email type="domain">gmail.com</email> <idno type="halauthorid">11184309</idno> <orgName ref="#struct-217378"></orgName> <affiliation ref="#struct-217378"></affiliation> </author> <editor role="depositor"> <persName> <forename>Jonas</forename> <surname>KIBALA</surname> </persName> <email type="md5">55efa0e3f2614d73f7ca0216ed3efbce</email> <email type="domain">gmail.com</email> </editor> </titleStmt> <editionStmt> <edition n="v1" type="current"> <date type="whenSubmitted">2020-03-31 00:48:52</date> <date type="whenModified">2020-04-08 01:10:45</date> <date type="whenReleased">2020-04-07 10:53:32</date> <date type="whenProduced">2020-03-31</date> <date type="whenEndEmbargoed">2020-03-31</date> <ref type="file" target="https://hal.archives-ouvertes.fr/hal-02525455/document"> <date notBefore="2020-03-31"></date> </ref> <ref type="file" subtype="author" n="1" target="https://hal.archives-ouvertes.fr/hal-02525455/file/Jonas%20Kibala_Work%20Covid19.pdf"> <date notBefore="2020-03-31"></date> </ref> </edition> <respStmt> <resp>contributor</resp> <name key="588491"> <persName> <forename>Jonas</forename> <surname>KIBALA</surname> </persName> <email type="md5">55efa0e3f2614d73f7ca0216ed3efbce</email> <email type="domain">gmail.com</email> </name> </respStmt> </editionStmt> <publicationStmt> <distributor>CCSD</distributor> <idno type="halId">hal-02525455</idno> <idno type="halUri">https://hal.archives-ouvertes.fr/hal-02525455</idno> <idno type="halBibtex">kibalakuma:hal-02525455</idno> <idno type="halRefHtml">2020</idno> <idno type="halRef">2020</idno> </publicationStmt> <seriesStmt> <idno type="stamp" n="INSMI">CNRS-INSMI - INstitut des Sciences Mathématiques et de leurs Interactions</idno> <idno type="stamp" n="TDS-MACS">Réseau de recherche en Théorie des Systèmes Distribués, Modélisation, Analyse et Contrôle des Systèmes</idno> </seriesStmt> <notesStmt></notesStmt> <sourceDesc> <biblStruct> <analytic> <title xml:lang="de">Prognose der Ausbreitung von pandemia Covid-19 in der Welt</title> <title xml:lang="en">Forecast of the propagation of Covid-19 pandemia in the world</title> <title xml:lang="fr">Prévision de la propagation de la pandémie Covid-19 dans le monde</title> <author role="aut"> <persName> <forename type="first">Jonas</forename> <surname>KIBALA KUMA</surname> </persName> <email type="md5">55efa0e3f2614d73f7ca0216ed3efbce</email> <email type="domain">gmail.com</email> <idno type="halauthorid">11184309</idno> <orgName ref="#struct-217378"></orgName> <affiliation ref="#struct-217378"></affiliation> </author> </analytic> <monogr> <imprint></imprint> </monogr> </biblStruct> </sourceDesc> <profileDesc> <langUsage> <language ident="fr">French</language> </langUsage> <textClass> <classCode scheme="halDomain" n="math.math-ap">Mathematics [math]/Analysis of PDEs [math.AP]</classCode> <classCode scheme="halDomain" n="math.math-st">Mathematics [math]/Statistics [math.ST]</classCode> <classCode scheme="halDomain" n="math.math-gm">Mathematics [math]/General Mathematics [math.GM]</classCode> <classCode scheme="halTypology" n="UNDEFINED">Preprints, Working Papers, ...</classCode> </textClass> <abstract xml:lang="en"> <p>We tried to determine the approximate date from which Corona Virus (Covid-19) pandemia could affect or touch all the world population, heard that this one would be evaluated around 7,759,559,800 according to our projections on the basis of estimate of UNO (see : https://coronavirus.politologue.com), under the assumption that the current rate/rhythm of the evolution of pandemia as well as the measurements taken by various authorities remain unchanged (all things remaining are equal). In such exercise of modelling, our concern is to draw the world authorities' attention about few time assigned to us for eradicating this pandemia, if not it would be difficult or even impossible to stop the haemorrhage because this pandemia could strike or infect all the world population as from May 25, 2020 to June 25, 2020. To carry out this analysis, or to forecast our study time series or the number of people infected by Corona Virus (Covid-19) pandemia, we made recourse to a functional form of exponential type, according to our time series structure, of which the essential parameter, which is the average growth rate number of infected people by covid19 pandemia in the world by day, is estimated according to a nonlinear approach which is the Gauss-Newton algorithm. Future studies could look further into the question with more succeeded specifications (for example, to resort to the nonlinear cointegration to escape the fallacious regressions) or functional forms much better than that retained in this analysis.</p> </abstract> <abstract xml:lang="fr"> <p>Nous avons cherché à déterminer la date approximative à partir de laquelle la pandémie ou maladie à Corona Virus (Covid-19) pourrait affecter ou toucher toute la population mondiale, entendue que celle-ci se situerait autour de 7.759.559.800 suivant nos projections sur base des estimations de l'ONU (en ligne sur : https://coronavirus.politologue.com), sous l'hypothèse que le rythme actuel de l'évolution de la pandémie ainsi que les mesures prises par différentes autorités demeurent inchangés (toutes choses restant égales par ailleurs). En se lançant dans un tel exercice de modélisation, notre souci est d'attirer l'attention des autorités dans le monde sur le temps qui nous est imparti pour éradiquer cette pandémie, à défaut de quoi il serait difficile ou même impossible d'arrêter l'hémorragie dans le sens où ladite pandémie pourrait frapper ou infecter toute la population mondiale à partir du 25 mai 2020 jusqu'au 25 juin 2020. Pour réaliser cette analyse, c'est dire prévoir notre série d'étude ou le nombre de personnes infectées par la pandémie ou maladie à Corona Virus (Covid-19), nous avons fait recours à une forme fonctionnelle de type exponentiel (au regard de la structure de notre série) dont le paramètre essentiel, traduisant le taux de croissance moyen journaliser du nombre d'infectés au covid-19 dans le monde, est estimé suivant une approche non linéaire qui est l'algorithme de Gauss-Newton. Des études ultérieures pourraient approfondir la question avec des spécifications plus abouties (par exemple, recourir à la cointégration non linéaire pour échapper aux régressions fallacieuses) ou des formes fonctionnelles bien plus meilleures que celle retenue dans cette analyse. Codes JEL : C13, C52, C53, C61. Mots-clés : Modélisation économétrique, analyse dynamique, différentiel et intégral.</p> </abstract> </profileDesc> </hal></record>Pour manipuler ce document sous Unix (Dilib)
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