Based on projections made on December 17, the number of new cases of COVID-19 per day, including those not tested, could range from 901 thousand to 3.4 million worldwide by March 31, 2021, depending on the scenario. The best case scenario being 95 percent mask usage universally and the worst case being continued easing of social distancing mandates. This statistic shows the projected number of new COVID-19 cases per day worldwide from December 1, 2020 to March 31, 2021 based on three different scenarios, as of December 17.

The outbreak of COVID-19, also known as novel coronavirus, has led to revised growth forecasts for global IT spending. The PC/Tablet segment is forecast to grow by almost 17 percent. This is likely due to an increase of hybrid work setups that allow people to work from different locations during the pandemic.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

The global COVID-19 vaccine market is driven by high R&D investments, expanded manufacturing, and new public-private partnerships.

The global COVID-19 diagnostics market size is valued at USD 13 billion in 2020 and is expected to reach USD 7.4 billion by 2026. Increased demand for mass testing is one of the major factors driving the COVID-19 diagnostics market growth.

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

Based on projections made on December 17, the number of deaths due to COVID-19 in the United States by the end of March 2021 could range from 505,894 to 713,674 depending on the scenario. The best case scenario being 95 percent mask usage universally and the worst case being continued easing of social distancing mandates. This statistic shows the projected number of deaths due to COVID-19 in the U.S. from December 1, 2020 to March 31, 2021 based on three different scenarios, as of December 17.

SPI-M-O consensus and individual model medium term projections created between October 2020 and the end of January 2021 for daily number of COVID-19 patients admitted to hospital, and deaths within 28 days of positive test by date of death, within England and English regions.

According to a forecast as of March, BioNTech's and Pfizer's vaccine against COVID-19 could generate sales revenues of nearly 22 billion U.S. dollars during 2021. The BioNTech/Pfizer vaccine was the first COVID-19 vaccine to be widely approved and used. German biotech company BioNTech saw a 156 percent growth in its shares in the last 12 months as of March 2021.

Will Moderna be the big winner? Moderna is expected to be the company with the largest sales revenues from a COVID-19 vaccine. Forecasts predict that the company will make around 43 billion U.S. dollars in sales through its vaccine. Interestingly, Moderna was established in 2010 and had never made profit before the pandemic. Thus, the development of the covid vaccine based on the latest mRNA technology will mark a definitive breakthrough for the Massachusetts-based biotech company. Moderna received significant funding through taxpayer money as well as help in research and development from the National Institutes of Health.

Vaccine pricing in a pandemic Drug pricing is always a big issue and this was also the case with COVID-19 vaccines. While some companies, like AstraZeneca, stated early on that prices for the vaccine will be on a non-profit base at least as long as the pandemic is ongoing, others took a more profit-oriented approach. However, even these companies state that their current prices are low special prices, taking into account urgent public health interests, which normally would be much higher. According to several projections, COVID-19 drugs and vaccines could establish a market worth some 40 billion U.S. dollars annually.

The complexity of COVID-19 and variations in control measures and containment efforts in different countries have caused difficulties in the prediction and modeling of the COVID-19 pandemic. We attempted to predict the scale of the latter half of the pandemic based on real data using the ratio between the early and latter halves from countries where the pandemic is largely over. We collected daily pandemic data from China, South Korea, and Switzerland and subtracted the ratio of pandemic days before and after the disease apex day of COVID-19. We obtained the ratio of pandemic data and created multiple regression models for the relationship between before and after the apex day. We then tested our models using data from the first wave of the disease from 14 countries in Europe and the US. We then tested the models using data from these countries from the entire pandemic up to March 30, 2021. Results indicate that the actual number of cases from these countries during the first wave mostly fall in the predicted ranges of liniar regression, excepting Spain and Russia. Similarly, the actual deaths in these countries mostly fall into the range of predicted data. Using the accumulated data up to the day of apex and total accumulated data up to March 30, 2021, the data of case numbers in these countries are falling into the range of predicted data, except for data from Brazil. The actual number of deaths in all the countries are at or below the predicted data. In conclusion, a linear regression model built with real data from countries or regions from early pandemics can predict pandemic scales of the countries where the pandemics occur late. Such a prediction with a high degree of accuracy provides valuable information for governments and the public.

This report contains a summary of the analyst consensus forecasts available in the GlobalData Pharma Intelligence Center Drug Sales and Analyst Consensus Database for the Indication COVID-19. Currently there are 37 drugs indicated for COVID-19 which have analyst consensus forecasts available. Read More

For more information, see the Google Cloud Blog . Developed on Google Cloud’s robust infrastructure with guidance from the Harvard Global Health Institute, the COVID-19 Public Forecasts offer a prediction of COVID-19's impact over the next 28 days. The forecasts are generated from a novel time series machine learning approach that combines AI with a robust epidemiological foundation and are trained on public data. The forecasts are maintained by Google Cloud to ensure they remain up-to-date in the changing landscape. For more detail on how the model works, see the White Paper . Forecasts are available for US state and county and Japan prefecture. US User Guide , Japan User Guide ( English and Japanese ). We encourage users who intend to make decisions in part based on these forecasts to closely review the Fairness Analysis . All bytes processed in queries against this dataset will be zeroed out making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 2021, queries over these datasets will revert to the normal billing rate. This dataset is hosted in BigQuery and included in BigQuery's 1TB/mo of free tier processing. Each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. What is BigQuery?

Submitted to The ISSI 2021 Conference. The conference is organised by KU Leuven in close collaboration with the university of Antwerp under the auspices of ISSI – the International Society for Informetrics and Scientometrics (http://www.issi-society.org/).

We present a forecasting analysis on the growth of scientific literature related to COVID-19 expected for 2021. Considering the paramount scientific and financial efforts made by the research community to find solutions to end the COVID-19 pandemic, an unprecedented volume of scientific outputs is being produced. This questions the capacity of scientists, politicians and citizens to maintain infrastructure, digest content and take scientifically informed decisions. A crucial aspect is to make predictions to prepare for such a large corpus of scientific literature. Here we base our predictions on the ARIMA model and use two different data sources: the Dimensions and World Health Organization COVID-19 databases. These two sources have the particularity of including in the metadata information on the date in which papers were indexed. We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by NLM source (PubMed and PMC), and by domain-specific repository (SSRN and MedRxiv). We conclude by discussing our findings.

For February 21, 2021, it was estimated that around 33 percent of the U.S. population was immune to COVID-19. According to the latest modeling, herd immunity - if estimated at around 70 percent of a population being immune to the virus - will probably not be reached in the course of 2021. Several reasons can be mentioned, among others: new and more infectious variants of the virus, vaccination hesitancy, and the lack of a vaccine for children. However, it is still not totally clear what percentage of a population needs to be immune to COVID-19 for there to be herd immunity against the disease. As the data shows, it is possible that from summer 2021 on something near herd immunity - or: a state closer to normal life - could be reached.

Forecasts are available from 2020-04-01 to 2021-10-20 for dozens to more than 200 geographic entities (GE) across the world (from 46 GE on 2020-04-01 to 246 GE on 2021-10-20).

Each forecast of cumulative mortality is grounded on a probabilistic mixture of mortality trajectories of ahead-of-time geographic entities playing the role of real-life predictors eventually complemented by a parametric model based on a SIR representation. The methodology is presented in Soubeyrand, Ribaud et al. (2020, https://doi.org/10.1371/journal.pone.0238410) and Soubeyrand, Demongeot et al. (2020, https://doi.org/10.1016/j.onehlt.2020.100187).

The forecast are daily implemented by a web application entitled "COVID-19 Visualization" available at https://shiny.biosp.inrae.fr/app_direct/mapCovid19/

The original code is available here: https://gitlab.paca.inrae.fr/biosp/shinyMapCovid19

Raw data for drawing the forecast are provided by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE; https://systems.jhu.edu/) available at https://github.com/CSSEGISandData/COVID-19/ (Dong et al., 2020, https://doi.org/10.1016/S1473-3099(20)30120-1).

Information about the data set and the code are provided in the readme.txt file.

Data are provided in the forecast_data.rds file produced originally with the saveRDS() function of the R Statistical Software (https://cran.r-project.org/).

A code for loading the data set and extracting some data corresponding to specific dates and geographic entities with the R Statistical Software is provided in the read_data.R file.

This dataset contains large files which can be used to reproduce the results in McDonald, D.J., Bien, J., Green, A., Hu, A.J., DeFries, N., Hyun, S., Oliveira, N.L., Sharpnack, J., Tang, J., Tibshirani, R., Ventura, V., Wasserman, L., and Tibshirani, R.J. “Can Auxiliary Indicators Improve COVID-19 Forecasting and Hotspot Prediction?,” Proceedings of the National Academy of Sciences, 2021. https://doi.org/10.1101/2021.06.22.21259346 Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the U.S. This paper studies the utility of five such indicators---derived from de-identified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity---from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that (a) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; (b) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; (c) one indicator, based on Google searches, seems to be particularly helpful during "up" trends. Complete descriptions as well as code are available from https://github.com/cmu-delphi/covidcast-pnas/ and are permanently accessible at https://doi.org/10.5281/zenodo.5639567. This material is based on work supported by gifts from Facebook, Google.org, the McCune Foundation, and Optum.

The COVID-19 rule out criteria (CORC score).

As of June 2020, the budget balance in Ethiopia was revised to a deficit of *** percent of the GDP in 2020 and *** percent of the GDP in 2021. The reviewed projection assumed a scenario where the COVID-19 pandemic persists through December 2020. Before the outbreak, a deficit of *** percent of the GDP was forecasted for 2020, while the projected deficit for 2021 was at *** percent of the GDP.

The impact of the coronavirus (COVID-19) lockdown in India slashed GDP growth forecasts for financial year 2021. Among the agencies that estimated growth, World Bank predicted a contraction of nearly ten percent, while the SBI (before the Maharashtra lockdown in April 2021) estimated a decline of seven percent.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

The Public Health Agency of Canada (PHAC) released new modelling projections of the number of opioid-related deaths that may occur over the course of the coming months. The results of the model suggest that, under some scenarios, the number of opioid-related deaths may remain high or may even increase through to December 31, 2021.

Prior to the coronavirus (COVID-19) pandemic, the inflation rate in Morocco in 2020 and 2021 was expected at 1 and 1.2 percent, respectively. On the contrary, under a baseline scenario, inflation was projected at 0.4 percent in 2020 and 1.1 percent in 2021. Moreover, under a worst case scenario, where the pandemic continued to the end of 2020, inflation rate was estimated at 0.4 and 1.3 percent for 2020 and 2021 respectively.