Further estimations of the likely total infections and deaths due to COVID19 in select countries (version 2 dt. April 10, 2020)
Notice bibliographique
Résumé
We had earlier estimated the likely cases and deaths over the course of the pandemic for a number of countries This was an early attempt and gave somewhat tentative results With some 7 more days of data being now available, better estimates are possible which we bring out in this paper As in the previous paper we use a logistic model of cumulative cases and deaths, to estimate the zero growth level of cases and deaths We also provide an upper bound to these estimates The earlier estimates are further reinforced, and new estimates are made for a select set of countries where the growth rates in the numbers of cases, and in deaths have begun to decline We also give estimates of the current growth rates in cases and deaths that these countries are likely to witness The study as before presumes that the spread of infection is one-stage logistic process, once significant numbers of infections have taken place This may not be true of countries which witnessed low deaths and cases In countries that have witnessed much spread and deaths relative to their populations and with more sustainable approaches to containment may not witness significantly more deaths than what has happened thus far This would be the case of Iran, Italy China and Korea too with their rather highly coordinated approach despite low spread of cases and low number of deaths relative to their population would along with Iran, Italy and Denmark and Turkey would most likely not see a secondary wave of infections Argentina and South Africa show very high growth rate in deaths even the increase in cases have slowed down considerable Spain has stabilized its growth in deaths to nearly zero levels bit since the cases are continuing to grow at around 5 7% the death rates could again turn positive after a while Germany and Indonesia show continuing rise in deaths and cases at moderately high rates Japan, Malaysia, Brazil and Singapore show low to moderate death rates, but since the rise in cases continues to be between 5 and 8%, these low(Japan) moderate growth rate in deaths are likely to continue for a while before they fall to zero France, Sweden Australia and Thailand would see continuing growth in cases at moderate rates even though the growth in deaths continue to be at high rates The US most notably shows very high growth rates in both deaths and in cases indicating that the deaths at high rates are likely to continue for a while While estimates are made for Canada, India, Bangladesh, Russia, Mexico, UK and the Philippines, they are of limited value since it is too early for the logistic model to fit However, all of these except Russia show high death rates and high case rates These countries could all see continuing rise in cases before the decline in rates happen, so that their current decline in death rates even when statistically significant could change for the worse We have as in the previous paper used a logistic model to estimate the current growth rates, and made forecasts of the ultimate stable cases and deaths before these stop rising any further For 26 countries (with a combined population of 3 8 billion) the total cases as on date 9th/10th April was where the logistic trend has been realized for cases, was 1 36 million We expect the cases to rise to a maximum in the countries covered to 2 9 million The death trends in only 22 of the 29 countries considered had stabilized to a logistic model In these 22 countries (with a combined population of 3 7 billion) the deaths as on date were 87,472 These would surely rise to between 121,000 to 355,000 before stabilizing In the estimates above India most notably has not been included, since its trends have not yet stablised to a logistic unfoldment At present it is engaged in a titanic struggle through near complete lock downs to restrict the cases and deaths to low levels Whether this would work to quell the spread to very levels, or whether the problem explodes later is still an open question
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».