Sep 11, Digital Workplace 0 comments. As Microsoft ramps up its efforts around Windows 10, it is starting to wind down support for its older operating system OS. Beginning January , Microsoft will no longer provide security updates and support for Windows 7. To prepare for the Windows 7 sunset, we highly encourage you to upgrade to Windows All new PCs come standard with Windows 10, and while we recommend purchasing a new machine to upgrade, installing Windows 10 on current machines that meet required specs from Microsoft is possible.
Windows 10, which is already the standard OS at Northeastern, is the most secure and feature-rich option for PCs. Windows 10 also marks a major milestone in the way Microsoft supports operating systems: Instead of big releases in the future, Microsoft is continually rolling out smaller updates and features to improve performance—in fact, an upgrade to Windows 10 may be the last major OS upgrade you need.
Northeastern will be retiring myFiles, a personal web-based file storage site, at the end of November There are many Knowledge Base resources on how to begin using these file-storage services.
Forms Create surveys, quizzes, and polls in minutes. Power BI Create actionable, dynamic, and engaging data dashboards to share with your company or school. Stream Share videos of classes, meetings, presentations, training sessions, or other videos with people in your company or school. Training and Additional Information for Stream About Microsoft Stream is a video hosting platform to securely share and discover video within Northeastern University.
Smart searching and autocreated captioning for uploaded videos Create channels to showcase videos. Videos can be shared to all of campus or to just a smaller group at this time public access to videos is not supported.
Note : despite the name, you cannot Stream live events using Stream. Common Use Cases Create a channel for your department or division to showcase what your area is doing. Viewing previously recorded events, classes, lectures Host help videos for staff, faculty, and students Embed videos in Northeastern University internally focused websites Follow channels and watch videos shared to campus Training and More Information Microsoft Get Started with Microsoft Stream Learn more about Microsoft Stream Microsoft Stream FAQ Tips and Tricks Post your videos to a channel to make them easier to organize.
Mount your channel to your Team for easy viewing by your Team Let Stream create your auto caption files. After it takes a first pass, follow these instructions to download a copy of the auto-created caption file to make corrections, then re-upload the file.
Upload the video, click the autocaption. Go to my videos, then edit the video. Unclick the autocaption, and then follow the instructions to download the VTT file. Once you have the VTT file locally, edit it in a text editor, fix the captions, then upload them back to the server edit the video, then upload the caption file.
Enable a table of contents for your video In the description, on separate lines, type times M:SS format and a description of that point — Interesting comment here — Next speaker Add hashtags in your description to enable easier searching office, stream, topic, ssd Add a picture for your channel Customize the thumbnail for your video to show what you want.
If you are sharing videos solely to a Team, customize the permission to be only open to that Team. Add a second owner for your videos Customize your URL to add a timestamp if you want the video to start in a specific place. Use a watchlist to create a custom watchlist with videos from multiple channels.
Follow a channel that you are interested in. Video Share videos of classes, meetings, presentations, training sessions, or other videos with people in your company or school. Find and watch videos shared by other people in your organization.
Create and manage video channels. Training and Additional Information for Planner About Planner is a lightweight project management tool to visually assign and organize tasks for your team.
Drag and drop tasks between bucket columns as needed. Use the views — calendar view in particular can make planning much easier. Add backing documentation to the task itself. Use the labels to denote metadata importance, urgency, etc. Use the plan that was created with your Team.
Click the plus in a Team Channel to add a Planner Your plans are fully accessible to all team members, anyone can interact with any task, not just those they are assigned to. Planner cannot track dependencies between tasks. Try creating buckets to show dependency lines, or order the cards within a bucket to reflect the dependency track.
PowerApps Build mobile and web apps with the data your organization already uses. Training and Additional Information for PowerApps About Build apps fast with a point-and-click approach to app design. Choose from a large selection of templates or start from scratch. Easily connect your app to data and use Excel-like expressions to easily add logic. Publish your app to the web, iOS, Android, and Windows Use when you do not need custom branded applications.
Create mobile apps quickly note: you will need PowerApps for mobile to see the applications. Training and Additional Information for PowerAutomate About Use Flow to create automated workflows between applications and cloud services Start from a template or create a workflow from scratch Visual builder for workflow development Common Use Cases Create approval workflows Notify your Team when other activity occurs Save your email attachments to OneDrive Many, many more.
Look at the templates for some ideas. Dynamics Break down the silos between your business processes and applications with Microsoft Dynamics Training and Additional Information for Dynamics Common Use Cases Customer Insights — Combines customer data from Dynamics , Office and third-party data sources, and helps users find actionable insights from that data.
Includes Power BI for analytics and visualization, and artificial intelligence tools to identify customer behaviors and provide predictive scoring. Customer Service — Omnichannel customer engagement tools, customer self-service and communities, and tools for support agents. Field Service — Scheduling resources, contract management, inventory management, insight into internet of things-connected products and customer communications tools.
Finance and Operations — Financial management with reporting and analytics; manufacturing tools for project management, production planning, scheduling, and cost management; and warehouse and inventory control tools for supply chain management. Kaizala A secure messaging and work management application that lets you collaborate with others in and outside of your organization. Training and Additional Information for Kaizala Microsoft Kaizala is a secure messaging and work management app that lets you collaborate with others in and outside of your organization.
Send and receive instant messages, coordinate tasks, submit invoices, and use special tools to interact with your team wherever you are. Bookings Online appointment scheduling for your small business. Training and Additional Information for Bookings Common Use Cases Microsoft Bookings is an online and mobile app for small organizations who provide services to customers on an appointment basis. Examples of organizations include academic advisors, co-op advisors, financial services providers, and consultants.
Circle size corresponds to the population size of each state and country. Colour and circle size are proportional to the probability. Source data. The model integrates real-time human mobility and population data with a mechanistic epidemic model at a global scale, incorporating changes in contact patterns and mobility according to the non-pharmaceutical interventions NPIs implemented in each region.
It is calibrated on international case introductions out of mainland China at the early stage of the pandemic using an approximate Bayesian computation ABC methodology The model returns an ensemble of stochastic realizations of the global epidemic spread including international and domestic infection importations, incidence of infections and deaths at a daily resolution see Methods.
In the following text, we provide a detailed discussion of the analyses and results concerning European countries and the US states; however, to further test and validate our approach, in the Supplementary Information , we report the modelling results for 24 additional countries that are globally representative, including countries of world regions such as Latin America, the Middle East, Africa, East Asia and Oceania. In Fig.
These values are much larger than the number of officially reported cases see Fig. The estimated ascertainment rates are in agreement with independent results based on different statistical methodologies 14 , 15 , We see that the progression of the virus through the USA and Europe was both temporally and spatially heterogeneous. While many cities had not yet experienced much community transmission by late February, a few areas such as New York City and London are very likely to have already had local outbreaks.
Rather than describing a specific, causal chain of events, we can estimate possible time windows pertaining to the initial chains of transmission in different geographical regions. We define the onset of local transmission for a country or state as the earliest date when at least 10 new infections are generated per day. This number is chosen because at this threshold the likelihood of stochastic extinction is extremely small 17 , As detailed in the Supplementary Information , further calibration on the US states and European countries suggests posterior values of R 0 ranging from 2.
These values are consistent with many other country-dependent estimates 19 , 20 , 21 , 22 , 23 , However, it is worth noting that each distribution, p t , has a support spanning several weeks. In Italy, the 5th and 95th percentiles of the p t distribution are the week of 6 January and the week of 30 January , respectively.
These dates also suggest that it is not possible to rule out introductions and transmission events as early as December , although the probability of this is very small. Countries and states are ordered by the median date of their posterior distribution. The week of this date corresponds to the dates reported on the vertical axis. For each state in the USA and each country in Europe, we compared the order in which they surpassed cumulative infections in the model and in the surveillance data gathered from the John Hopkins University Coronavirus Resource Center In Extended Data Fig.
More specifically, we record the cumulative number of introductions in each stochastic realization of the model until 30 April For both the USA and Europe, the contribution from mainland China is barely visible and the local share that is, sources within Europe and the USA becomes significantly higher across the board.
Hence, while introduction events in the early phases of the outbreak were key to start local spreading see details in the Supplementary Information , the cryptic transmission phase was sustained largely by internal flows.
In the Supplementary Information , we report the full breakdown for all states and countries. Importation flows are directed and weighted. We normalize links considering the total in-flow for each state so that the sum of importation flows, for each state, is 1.
In the Supplementary Information, we report the complete list of countries contributing as importation sources in each geographical region. It is also necessary to distinguish between the full volume of SARS-CoV-2 introductions and the introduction events that could be relevant to the early onset of local transmission in each stochastic realization of the model.
To this point, it is worth stressing that seeding introductions are different from the actual number of times the virus has been introduced to each location with subsequent onward transmission. Even after a local outbreak has started, future importation events may give rise to additional onward transmission forming independently introduced transmission lineages of the virus In the model, we can investigate seeding events by recording introduction events before the local transmission chains were established.
In our model, it is not possible to exclude increased contacts in highly populated places before social distancing interventions and disentangle this effect from increased seeding due to the correlation between travel volume and population size.
Starting in March , the establishment and timing of NPIs as well as other epidemiological drivers that is, population size and density, age structure and so on determined the disease burden in the USA and Europe 29 , 30 , 31 , We account for these features by calibrating the model results, individually, for each US state and European country. More precisely, we estimate the posterior distribution of the infection fatality ratio IFR and infection attack rate in each US state and European country.
To this end, we adopt the ABC approach using as evidence the number of new deaths reported from 22 March to 27 June We consider a uniform prior for the average IFR in the range from 0. We also consider a uniform prior for reporting delays between the date of death and reporting ranging from 2 to 22 days in both Europe and the USA Details are provided in the Supplementary Information. Additional model results for all investigated regions including a sensitivity analysis of different calibration methods can be found in the Supplementary Information.
We find a strong correlation between the weekly model-estimated deaths and the reported values with a Pearson correlation coefficient of 0. As the data suggest, many European countries and US states saw peaks in April and May with various decreasing trajectories that depend on the mitigation strategies in place. Additionally, we report the estimated posteriors for the cumulative infection attack rates and IFRs as of 4 July in European countries experiencing more than total deaths and the top 20 states ranked by infection attack rate in the USA.
Posterior distributions in e and j are the result of the ABC analysis of , independent model realizations. Within Europe, Belgium has the highest estimated infection attack rate of However, Italy is estimated to have the highest median IFR of 1. The US states with the highest infection attack rates are located within the northeast and experienced a significant first wave during March—April NY and NJ are the top two states with infection attack rates of These numbers are aligned with estimates from New York City reported in ref.
In the Supplementary Information , we report summary tables with estimated IFRs, infection attack rates and the reproductive number in the absence of mitigation measures for all calibrated US states and European countries. The seroprevalence estimates are compared to the model estimates during the same time window the studies were performed details on the seroprevalence data from this figure can be found in Supplementary Table 8 and Supplementary Section 9.
The model presented here captures the spatial and temporal heterogeneity of the early stage of the pandemic, going beyond the single-country-level reconstruction. Furthermore, rather than showing specific evidence for early infection in a few locations, our study aims at providing a statistical characterization and quantification of the initial transmission pathways at a global scale. Our results can be compared to and complement analyses based on gene sequencing and travel volumes.
Additionally, similar to our findings, ref. As our analysis is a statistical description of the possible introduction pathways, differences could arise due to our model design, and also from genomic sampling biases This caused reactive response strategies, such as issuing travel restrictions targeting countries only after local transmission is confirmed, ineffective at preventing local outbreaks.
Our results suggest that many regions in the USA and Europe experienced an onset of local transmission in January and February , during the time when testing capacity was limited. If testing had been more widespread and not restricted to individuals with a travel history from China, there would have been more opportunities for earlier detection and interventions.
In the Supplementary Information, we report a counterfactual scenario where we assume broader testing specifications not based on the individual travel history and find that the epidemic progression is considerably delayed see Supplementary Section 8.
As testing capacity increased and more cases were detected, many governments began to issue social distancing guidelines to mitigate the spread of SARS-CoV The first European country to implement a cordon sanitaire was Italy on 23 February , for a few northern cities Many other countries followed suit and implemented national lockdowns in March refs.
Overall, our results strengthen the case for preparedness plans with broader indication for testing that are able to detect local transmission earlier. As with all modelling analyses, results are subject to biases from the limitations and assumptions within the model as well as the data used in its calibration. Although the model is robust to variations in these parameters see the Supplementary Information for the sensitivity analysis , more information on the key characteristics of the disease would considerably reduce uncertainties.
The model calibration does not consider correlations among importations that is, family travel and assumes that travel probabilities are age specific across all individuals in the catchment area of each transportation hub. In light of the assumptions and limitations inherent to this modelling approach, the results are able to complement the SARS-CoV-2 genome sequencing analyses used to reconstruct the early epidemic history of the COVID pandemic The methods used in this analysis offer a blueprint to identify the most likely early spreading dynamics of emerging viruses, and they can be used as a real-time risk assessment tool.
Anticipating the locations where a virus is most likely to spread to next could be instrumental in guiding enhanced testing and surveillance activities. The estimated SARS-CoV-2 importation patterns and the cryptic transmission phase dynamics are of potential use when planning and developing public health policies in relation to international travelling, and they could provide important insights into assessing the potential risk and impact of emerging SARS-CoV-2 variants in regions of the world with limited testing and genomic surveillance resources.
Previously this model was used to characterize the early stage of the COVID epidemic in mainland China to estimate the effectiveness of travel bans and restrictions The GLEAM model divides the global population into more than 3, subpopulations in roughly different countries and territories interconnected by realistic air-travel and commuting mobility networks.
A subpopulation is defined as the catchment area around major transportation hubs. The airline transportation data encompass daily travel data in the origin—destination format from the Official Aviation Guide database 50 reflecting actual traffic changes that occurred during the pandemic.
Ground mobility and commuting flows are derived from the analysis and modelling of data collected from the statistics offices of 30 countries on 5 continents 11 , The international travel data account for travel restrictions and government-issued policies.
Furthermore, the model accounts for the reduction of internal, country-wide mobility and changes in contact patterns in each country and state in Specific model details are reported in the Supplementary Information. The transmission dynamics take place within each subpopulation and assume a classic compartmentalization scheme for disease progression similar to those used in several large-scale models of SARS-CoV-2 transmission 15 , 51 , 52 , 53 , 54 , Each individual, at any given point in time, is assigned to a compartment corresponding to their particular disease-related state specifically, one could be susceptible, latent, infectious or removed Individuals transition between compartments through stochastic chain binomial processes.
Susceptible individuals can acquire the virus through contact with individuals in the infectious category and can subsequently become latent that is, infected but not yet able to transmit the infection. The process of infection is modelled using age-stratified contact patterns at the state and country level 56 , Latent individuals progress to the infectious stage at a rate inversely proportional to the latent period, and infectious individuals progress to the removed stage at a rate inversely proportional to the infectious period.
The sum of the mean latent and infectious periods defines the generation time. Removed individuals are those who can no longer infect others.
To estimate the number of deaths, we consider a uniformly distributed prior of the IFRs ranging from 0. The transmission model does not assume heterogeneities due to age differences in susceptibility to the SARS-CoV-2 infection for younger children 1—10 years old. This is an intense area of discussion 58 , 59 , The transmission dynamic and the offspring distribution of infectious individuals in the model will depend on the specific details of each population, local and global mobility, NPIs and so on.
Additional simulations considering a fixed level of dispersion, informed by past studies, result in differences of less than 3 days in onset times Supplementary Fig.
We assume a start date of the epidemic in Wuhan, China, that falls between 15 November and 1 December , with 20 initial infections 49 , 51 , 61 , 62 ,
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