Further estimations of the likely total infections and deaths due to COVID19 in select countries (version 2 dt. April 10, 2020)
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".