This dataset includes three tables with the model-based projections and estimates as shown on CalCAT in 2025 (http://calcat.cdph.ca.gov) for California state, regions, and counties. (1) COVID-19 Nowcasts includes the R-effective estimates for COVID-19 from the different models available for the past 80 days from the archive date and the median ensemble thereof. (2) CalCAT Forecasts includes hospital census and admissions forecasts for COVID-19 and Influenza, and the corresponding ensemble metrics for a 4 week horizon from the archive date. (3) Variant Proportion Nowcasts contains the Integrated Genomic Epidemiology Dataset (IGED)-based and Terra-based estimates of COVID-19 variants circulating over the past 3 months as well as model-based predictions for the proportions of the variants of concern for dates leading up to the archive date. Prediction intervals are included when available. This dataset provides CalCAT users with programmatic access to the downloadable datasets on CalCAT. This dataset also includes a zipped file with the historical archives of the COVID-19 Nowcasts, CalCAT Forecasts and Variant Proportion Nowcasts through 2023.

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 global COVID-19 diagnosis market is projected to reach USD XXX million by 2033, with a CAGR of XX% during the forecast period 2025-2033. The market is driven by the increasing incidence of COVID-19, the rising demand for early and accurate diagnosis, and the growing adoption of molecular diagnostic tests. The market is segmented into two main types of tests: RT-PCR (Reverse Transcription Polymerase Chain Reaction) and isothermal nucleic acid amplification (INAAT). RT-PCR is the most commonly used test, as it is highly accurate and sensitive. However, it is also more expensive and time-consuming than INAAT. INAAT is a newer technology that is becoming increasingly popular, as it is faster and less expensive than RT-PCR. The market is also segmented by application, with hospitals and laboratories being the two main end-users. Hospitals are expected to account for the larger share of the market, as they are more likely to have the necessary equipment and expertise to perform COVID-19 tests. Laboratories are expected to play an increasingly important role in the market, as they are able to offer a wider range of testing services. With the global COVID-19 pandemic continuing to impact healthcare systems worldwide, the demand for accurate and reliable diagnostic tests has skyrocketed. The market for COVID-19 diagnosis has experienced significant growth, with advancements in technology and innovation driving the development of novel and efficient testing methods.

IHME has developed projections for multiple scenarios for total and daily deaths, daily infections and testing, hospital resource use, and social distancing due to COVID-19 for a number of countries. Forecasts at the subnational level are included for select countries.

Gives projections of COVID-19 cases, mortality, gathering risk when a person has an active COVID-19 infection, and what-if scenarios that include decisions on mobility restrictions, public health response, and testing that drives or impacts COVID-19 cases.

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.

The repository contains the code and the model output for county-level U.S. COVID-19 projections cases and deaths as states reopen back up. Projections are generated for daily new confirmed case, daily new infection (both reported and unreported), cumulative demand of hospital beds, ICU and ventilators as well as daily mortality (2.5, 25, 50, 75 and 97.5 percentiles).

This dataset contains forecasted weekly numbers of reported COVID-19 incident cases, incident deaths, and cumulative deaths in the United States, previously reported on COVID Data Tracker (https://covid.cdc.gov/covid-data-tracker/#datatracker-home). These forecasts were generated using mathematical models by CDC partners in the COVID-19 Forecast Hub (https://covid19forecasthub.org/doc/ensemble/). A CDC ensemble model was produced every week using the submitted models from that week at the national, and state/territory level.

This dataset is intended to mirror the observed and forecasted data, previously available for download on the CDC’s COVID Data Tracker. Mortality forecasts for both new and cumulative reported COVID-19 deaths were produced at the state and territory level and national level. Forecasts of new reported COVID-19 cases were produced at the county, state/territory, and national level. Please note that this dataset is not complete for every model, date, location or combination thereof. Specifically, county level submissions for COVID-19 incident cases were accepted, but not required, and are missing or incomplete for many models and dates. State and territory-level forecasts are more complete, but not all models submitted forecasts for all locations, dates, and targets (new reported deaths, new reported cases, and cumulative reported deaths). Forecasts for COVID-19 incident cases were discontinued in February 2022. Forecasts for COVID-19 cumulative and incident deaths were discontinued in March 2023.

In a survey on the impact of the coronavirus COVID-19 outbreak, 73 percent of respondents projected that there will be a major impact on the international economy. In terms of everyday life, 53 percent of Vietnamese respondents claimed that the virus outbreak had a major impact on their leisure travel plans while 41 percent of respondents stated that it had a major impact on their day to day lifestyle and working life, respectively.

The outbreak of COVID-19, also known as novel coronavirus, has led to revised growth forecasts for global IT spending. The current forecast shows global IT industry declining by 5.1 percent in 2020 compared to the previous year. This is a further decline compared to already adjusted forecasts from April 2020. The data from the March 2020 forecast provided two possible scenarios for the impact of the coronavirus pandemic on global IT spending. In the "probable" scenario the IT spending is projected to grow by 3.7 percent compared to 2019. The"pessimistic" scenario shows a growth of 1.3 percent in 2020. The newest release now even exceeds the pessimistic scenario from that forecast. Instead of a small growth the IT market is now set to shrink in 2020.

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

This file contains data of midterm projections for the Covid-19 epidemic in Luxembourg obtained by a stochastic agent based epidemiological model specified in the document https://storage.fnr.lu/index.php/s/UOZO8rQ9PJmzeEo/download. The data set refers to Exit Scenario F detailed in the document and contains predictions on the number of positively tested persons, assumed total number of Covid-19 cases, ICU demands and deaths cases. Data represent the average value of 40 individual simulations and the 90% confidence interval. Simulations consider available data until 10th of May.

Context

In partnership with the Harvard Global Health Institute, Google Cloud is releasing the COVID-19 Public Forecasts to serve as an additional resource for first responders in healthcare, the public sector, and other impacted organizations preparing for what lies ahead. These forecasts are available for free and provide a projection of COVID-19 cases, deaths, and other metrics over the next 14 days for US counties and states. For more info, see https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-is-releasing-the-covid-19-public-forecasts and https://storage.googleapis.com/covid-external/COVID-19ForecastWhitePaper.pdf

Content

A projection of COVID-19 cases, deaths, and other metrics over the next 14 days for US counties and states

Acknowledgements

Released on BigQuery by Google Cloud:

https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-is-releasing-the-covid-19-public-forecasts

https://pantheon.corp.google.com/marketplace/product/bigquery-public-datasets/covid19-public-forecasts

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 COVID-19 Scenario Hub contains a standardized set of data on scenario projections from teams making projections of cumulative and incident deaths and incident hospitalizations due to COVID-19 in the United States. The Scenario Hub harmonizes scenario projections in the United States to generate long-term COVID-19 projections combining insights from different models and in order to make them available to decision-makers, public health experts, and the general public.

Background: This study aims to estimate the total number of infected people, evaluate the effects of NPIs on the healthcare system, and predict the expected number of cases, deaths, hospitalizations due to COVID-19 in Turkey.Methods: This study was carried out according to three dimensions. In the first, the actual number of infected people was estimated. In the second, the expected total numbers of infected people, deaths, hospitalizations have been predicted in the case of no intervention. In the third, the distribution of the expected number of infected people and deaths, and ICU and non-ICU bed needs over time has been predicted via a SEIR-based simulator (TURKSAS) in four scenarios.Results: According to the number of deaths, the estimated number of infected people in Turkey on March 21 was 123,030. In the case of no intervention the expected number of infected people is 72,091,595 and deaths is 445,956, the attack rate is 88.1%, and the mortality ratio is 0.54%. The ICU bed capacity in Turkey is expected to be exceeded by 4.4-fold and non-ICU bed capacity by 3.21-fold. In the second and third scenarios compliance with NPIs makes a difference of 94,303 expected deaths. In both scenarios, the predicted peak value of occupied ICU and non-ICU beds remains below Turkey's capacity.Discussion: Predictions show that around 16 million people can be prevented from being infected and 94,000 deaths can be prevented by full compliance with the measures taken. Modeling epidemics and establishing decision support systems is an important requirement.

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.

All mfiles starting with 'Run_*' can be run on Matlab without any pre-loaded variables or data.

The three files in the root directory consist of the only input data to all program mfiles. Of which, SICM.m is the mathematical model, Input_Matched_InitialsAndParameters.mat contains initial conditions and parameter values for each day from day 50 to day 590, 30 best-fitted values for each day, ranked from the best to worst fit. The third csv file is the US cases and deaths data from CDC.

The order in which to generate the output data files is to run all Run_* mfiles in folders (1) Build_Real_Data (2) Build_Solution_Book (3) Build_Forecast_Book (4) Plot_Output

When you reach the Plot_Output folder, you have build all data files for plotting and annimation.

Run "Run_Plot_Output" will generate the first batch of output plots.

Note: It requires zero knowledge to verify the result. All one needs is the model SICM.m and the initial values and parameters provided to explore the dynamics of the model, and to analyze the fit of the model to the data, with the parameter and initial values provided from the data file Input_Matched_InitialsAndParameters.mat'. All the remaining folders contain Matlab programs to present the fit and the forecast presented in the manuscriptForecast U.S. Covid-19 Numbers by Open SIR Model with Testing'.

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?

The dataset contains observed and 4 weeks forecast new and total weekly COVID-19 deaths at national and state level until March 9, 2023. Forecasting teams predict numbers of deaths using different types of data (e.g., COVID-19 data, demographic data, mobility data), methods, and estimates of the impacts of interventions (e.g., social distancing, use of face coverings).

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.