Wednesday 22 April 2020

The good, the bad and ugly of Crisis response statistics

It's natural to tabulate data for use during a disaster or crisis as it unfolds. It is also useful to leaders that need facts and information about events as they occur and what, if any actions, should be taken in response. Too little information, then response options are likely to be ignored for further action. Too much information from limited sources, often leads to improper use and conclusions.

Statistics are a valuable tool that can be used to develop insights and situational awareness. We can use this data in multiple forms and visualization, allowing experts and government agencies to resolve significant issues as events occur. To do so requires critical understanding of what the data can influence, which if done incorrectly, can lead to miscalculations and perhaps, make problems worse rather than better.

Additionally, it is also natural to make comparisons using data collections from other regions or countries. It is an often used option in the scientific and social sciences communities when generating forecasting models including environmental conditions, disease, economic activity, etc.. The Canadian Broadcasting Corporation (CBC News) has attempted to compare different countries infection curves. It included source references and significant levels of data detail. But it would be unfair to calculate how well each nation's response capability has been without understanding local conditions as identified below.

Not all environments or population demographics are equal or possible to adjust to generate a median average. Nor should they be given cultural and governance differences that exist throughout the world. Canada's response and infection curve will not the same outcomes as other countries. Comparisons using medians such as per hundred thousand, is not an accurate reflection of governance options implemented. Such an analysis should not assume what happens in Country A will occur in Country B.

Data can be easily abused or misinterpreted with devastating consequences that generate doubt and conflict in both the scientific and political space. This easily induces mistrust in the public, if the data is not sufficiently scrutinized and debated or peer reviewed. Parsing data is becoming a common problem that intentionally generates divisiveness and is easily distributed on the internet. In itself, data abuse has spread like a pandemic and is the foundation of most conspiracy theories now spreading on internet blogs and websites.

Very few organizations generate statistical models that are peer reviewed prior to release. It's one of several reasons some models are open to debate and not taken seriously while others are given a trustworthy seal of approval based on historical reputation and accuracy. The level of detail of any statistical model, is only as good as its sources and its accuracy. This is particularly true of forecast models or those that attempt to predict future outcomes using a mathematical algorithms or logarithms. Some models do disclose all sources used in generating their statistical models as is the case with the John Hopkins University Coronavirus dashboard. They published an thorough article on how they generated their data to create a visualization of the pandemic, on its site, The Lancet. The result is a dashboard of the current Coronavirus pandemic displaying a global picture of the number of infections and fatalities. While it has some detail limitations, these are not because of how the data is being interpreted or manipulated but the availability limitations or format of how the data is exported from each host nation or organization.

Image Capture of John Hopkins Center for Systems Science and Engineering April 22, 2020

When attempting to blend baseline data with behaviour models (social activity, movement, employment demographics, culture, etc), the level of accuracy is significantly impacted if the level of evidence is hypothetical and itself based on a logarithmic formula. For example, let's create a hypothetical COVID-19 transmission model that attempts to explain how the virus can spread within a community. To do so, we will need baseline data to generate a profile of the impact zone and generate ground zero of what the event looks like now.

We can use the following datasets to generate this baseline:

  • Census Data (often 2 to 8 years old) 
  • Residential Property tax database
  • Voter Registration database
We then use medical information to determine potential forecast model of the virus:
  • Virus characteristics (What is it, who it can affect, why it spreads, where can it spread, when does it incubate / become active)
  • Corrective actions necessary (isolation, quarantine, level of medical care required)
We then input local infrastructure data:
  • Transportation system (bus / train terminals and routes, airports and airplanes, personal, commercial logistics vehicles)
  • Business composition (service, industrial, professional, business / corporate, entertainment)
  • Social services (hospitals, elderly care, locations of worship)
Almost all of these datasets exist and can generate activity models which can display different levels of saturation (i.e. peak hours, congestion levels, etc.). When blended with the behaviour model data, a picture emerges as to how the virus could spread. 

Census data is widely available in most countries. Some examples include:
The same is true of tax databases and voter registration counts along with the others identified. But many of the forecast models currently being published do not disclose what sources they are using or how they have generated their mathematically generated conclusions. Even when these datasets are properly filtered, the granular level of detail can generate models that have a degree of inaccuracy. For example, how old is the data. Simulation of dynamic data maybe required in advance when using transportation data (peak hours, to and from destinations, public facility usage, etc.). In almost all cases, simulations are rarely calculated to this level of granular detail. 

There are maps being generated using real-time data that attempt to explain how the virus is spreading between States. One example was published by the Daily Dot after the annual College spring break that often center in the State of Florida. Before and after Spring Break vacation, students head head to Fort Lauderdale, Orlando or Miami and then head back home or to their respective College or University. Mobile phone signal datasets were plotted and then placed on a Geo-spatial Information System (GIS) based map.

Source: www.dailydot.com


The logic of this heat map based on mobile phone activity, is a potentially good data source and can explain how COVID-19 is spreading across the midwest and other regions across the United States. Note that unnecessary travel between Canada and the State of Florida restriction was already put in place and is accurately reflected in this heat map. But what it does not do, is explain or calculate the level of transmission that actually occurs, yet several government leaders look at this map and began to make decisions on who can and cannot enter their State from Florida or elsewhere. The source of this data is not a sufficient scientifically or statistical model of evidence that can conclusively generate a forecast model. It can be a valuable source of data directing government organizations where to focus prevention measures such as virus screening and testing, sanitation protocols, crowd control, etc. When combined with transportation information (airport datasets, etc.), the prevention evidence begins to build and generate accurate data points for use in preventing the spread of the virus. But this is only true if agencies act upon the information it generates.

Source: BBC News, London Tube train, March 23, 2020 - Packed with no social distancing rules in place
Many Local and State level governments are not disclosing evidence based on scientific data when making policy decisions when to re-open or reduce lock-down / isolation policies. Nor are they  allowing peer review of any analysis they have projected. In some cases, officials have taken a different approach all together, believing the economy is more important than the crisis event the virus has created. This is the case for the State of Florida and Texas. Or in one case, the Governor of Georgia stated he was not aware that COVID-19 was not capable of human to human transmission by that are asymptomatic infected 4 months after the first case of the virus occurred. The State of Rhode Island Governor has banned all non-essential travel from any State by car. The question everyone has raised, is it based on science or fear? No statistical model was used in making this decision. The City Mayor of Las Vegas seems to believe in statistics - based her belief that the city can get back work using the city as a control test location. The city's statistician said no to the idea.

Statistics are valuable sources of information during a crisis, if collected and interpreted by experts are provided the right datasets and most importantly, are used correctly. Without this expertise and detailed analysis, any briefing leaders give, will be open to interpretation and potential discourse in what the data is informing the public. 

Thursday 16 April 2020

First the Crisis, then stabilization. Now onto the difficult phase, recovery. Are we there yet?

The global pandemic known as COVID-19 has spread throughout the world and impacted each continent at different intervals and velocity of transmission. It has brought with it, uncertain economic uncertainty and chain reactions that many government leaders are beginning to recognize as difficult to deal with. Some nations as of this writing are at the beginning of the pandemic (India) while others have decided to take a very low profile approach (Sweden) and escalate only as necessary. Epidemiologists have been thrust into the media spotlight, which for many is not where they want to be. They prefer their focus remain on tackling the virus itself, not attend press conferences that have critical eyes and ears looking for an opening to ask questions that many journalists do not even know what they are asking that enables several different conspiracy theories to run rampant. One example is the widespread distribution on the internet that is fueling anti-vaxxer followers to gain traction where none actually exists.

Experienced Crisis and Disaster management teams knew this scenario would unfold on a grand scale spanning different cultures, political systems, religions and doctrine. Pandemic simulation exercises have been run since the beginning of the 21st century. The initial scenarios and results were not frightening to analyze because most believed solutions could be found to the weaknesses discovered. But as technology and virology assessment tools improved, so has the accuracy of simulations how a pandemic would spread. Simulation analytical programs and scenario planning improved dramatically after several pandemics swept across the world between 2000 and 2016. As disaster management and medical knowledge improved, simulation accuracy and outcomes came into focus with sobering outcomes and conclusions.

From a scientific perspective, it has become clear that preparedness and early decision making, are critical to any outcome. But none of the scenarios or models could predict a critical element; political acceptance and initiative to follow  recommendations or expert consensus. Some governments have followed recommended guidelines while others have not. Crisis managers and academic circles will publish hundreds of reports analyzing the consequences as the crisis subsides.

All three phases of this pandemic have struggled with the scientific - political equation. No one region has got it completely right or wrong. To err is to be human. This is only portion of the explanation that reflects our current environment that we are all now facing.

Listed at the bottom of this blog, are links to regular updates by scientific institutions, and government health organizations from all over the world. By no means is the list complete. Post a comment with a link to any government health organization that uses RSS and we will add them to our list. These feeds are regularly updated using RSS code, that allows a user to be informed with updated information from various sources. We have collected RSS feeds relevant to the Coronavirus pandemic wherever possible. Over the coming weeks we will also begin to collect scientific papers and make them available using our file server as a central repository for analysis and public distribution.

No one single protocol is going to stop COVID19 from spreading person to person. It’s a combination of steps that includes simply following the standard protocol for COVID19 protection, then you and your family will be fine and in the process, allow doctors and nurses to take care of those infected.
  • Wash your hands after touching any unknown surfaces such as door handles, knobs, counter-tops, glass windows, etc. Above all, do not touch your face when you come into contact with these surfaces. Once you wash and dry your hands, then proceed to thoroughly wash your face and dry, then repeat washing your hands again.
  • When you go shopping, wash your hands for 20 seconds or use a alcohol based hand gel cleaner after every trip. Then wipe down exterior wrapping of products you buy when you get home. Then wash your hands again, all the while, avoid touching your face.
  • Do not go out of you have a pre-existing condition or over the age of 60.
  • If required to work in close proximity to others, wear approved N-95 respirator mask, to reduce your exposure risk to the virus. Know how to put on, use and dispose of the respirator properly or its effectiveness will be eliminated.
  • Adhere to social distancing of 6 feet or greater. Stay away from people who do not adhere to social distance requirements.
  • Stay away from work if required to be within 6′ of a coworker.
  • Avoid buses, airliners and cruise ships, shopping malls, movie theatres, theme parks, etc.
  • Do not attend gatherings of more than 5 people. Especially if you do not know them.
  • Do not flush sanitary products, masks or gloves down your toilet, they will clog your cities sewage system!
  • Stay safe and stay at home to protect yourself and your family by following all local, state and federal health orders. 

Friday 10 April 2020

How the maker space are using 3D printers to make PPE healthcare equipment

There's a shortage of N95 masks, respirators and face shields all over the world as the Coronavirus pandemic spreads to almost every nation around the world. Some hospitals are forced to reuse the same masks for more than one shift as supplies run out or are being rationed because the supply chain was not prepared for the surge in demand on a global basis all at once.

Many in the maker space using 3D printers to build prototypes of products or consumer products have proactively engaged in supporting their local hospitals and front line medical staff that need Personal Protective equipment or PPE. When a patient enters an Intensive Care Unit (ICU), staff have to take full precautions to prevent being infected themselves. They wear disposable gowns, rubber gloves, N95 masks and face shields. The masks, gowns and gloves should be discarded after every shift and shields disinfected at the minimum or ideally after every patient close quarters visit.

Face shields are in very short supply because their use is rarely needed in normal day healthcare service. But because the Coronavirus known as COVID-19 (or novel-CoV 2019) can remain airborne after an infected patient coughs, the droplets could land on a healthcare technicians face and ultimately infect them. The face shield is a mandatory item in the ICU ward and becoming a requirement for those working in the non-ICU section. Demand has outstripped supply in many regions around the world. The same is true of ventilators that assist a patients breathing when the lungs become filled with liquid as the virus opens up the pours of the lung walls.

Some hospitals have begun to share ventilator pumps using a manifold that can support multiple patients at the same time with a single pump due to the lack of availability.

Enter the 3D community that are now making face shields and respirator pump manifold valves. Some 3D printer companies have shared designs that any 3D printer owner can make and then distribute to their local healthcare facility or to healthcare volunteers that offer assistance at senior citizen assisted home care facilities.

There are problems including certification and quality control problems that normally arise when a product is home made. But some regions are desperate for supply of these vital items, regardless of where they are made. But the manufacturers of some 3D printers have began to directly work with the healthcare experts to ensure their designs follow best practices and received approval in some countries.

Here are some links to the 3D printer community.

U.S. Department of Health & Human Services - National Institutes of Health 3D Printing

Prusa Research Czech Republic - 3D printing face shields

AnyCubic - 3D Printed Face shields

3D Printing Media Network - Emergency Respirator 

Facebook Community 3D Printing Group - COVID-19 Makers Collaborative 

BBC News Report - Can we 3D print our way out of PPE shortage