Analyzing the Covid-19 Vaccine Distribution Method and Experience

Analyzing the Covid-19 Vaccine Distribution Method and Experience

by | Jan 15, 2021 | Covid

Analyzing the Covid-19 Vaccine Distribution Method and Experience

Introduction

As the Covid-19 vaccine begins to roll out for those who qualify for the initial distribution, the country as a whole is still seeing spikes and surges of cases, as well as high occupancy levels at hospitals and ICUs. The LA Times reported that “one hospital in southwest Los Angeles, Memorial Hospital of Gardena, is at 320% occupancy [and that] the 172-bed medical center has been in various levels of “internal disaster status” since March.”  That said, as the vaccine becomes more available to the general public, it is important to understand what type of analytics can help us bring these shocking statistics down and how we can understand if distribution plans are effectively working. 

Discussion

In evaluating the best way to roll out the vaccine, Accenture put together a list of relevant information to be collected for analysis. So what are the relevant data and statistics that go into a successful roll out? The first step in planning is forecasting and this is done by collecting data on a given population, this can be conducted at the county, city, state or regional level; relevant data in terms of the vaccine will include age and level of disease present in that population. From that information, distributors can decide where to locate dispensing sites and the supply necessary at each dispensing site. Once distribution starts, the next step will be adjustments which can be aided through artificial intelligence and machine learning. This would simply analyze the forecasting method used in the initial step and improve upon it so that new distribution centers are better prepared than the first ones. The final step is to evaluate the success of distribution which will vary based on the distribution leader, however, because the goal is to vaccinate as many people as possible and as quickly as possible, this will look at tiers that had the most waste in terms of vaccines and tiers that did not have enough vaccines to meet their demand. 

One of the main concerns when it comes to vaccine distribution, on every level including the country level, is equality in distribution. An interesting point of data is that “as of 15 November 2020, several countries have made premarket purchase commitments totaling 7.48 billion doses, or 3.76 billion courses, of Covid-19 vaccines from 13 vaccine manufacturers. Just over half (51%) of these doses will go to high income countries, which represent 14% of the world’s population.” This brings up questions of income disparity as it relates to population health and is something that can also be tracked through analytics. 

Conclusion 

As accessibility to the Covid-19 vaccine increases, we highly encourage you to consider getting vaccinated. The policies and practices in place in this country make the vaccinations very safe, in fact,“the US has the best post-licensure surveillance system in the world making vaccines extremely safe and not getting vaccinated puts yourself and others at risk.”  There is a lot of hesitation and concern when it comes to the vaccine which will make people less likely to get it on their own. To encourage you to get it as soon as it is made available to you, we are sharing the experience of Adiba Mobin, a UT Southwestern employee who has received both doses of the vaccine:

“I received the first dose of the Pfizer COVID-19 vaccine on December 23, 2020, just 9 days after the initial vaccine rollout in Texas. I did not take any medication (Ibuprofen, Acetaminophen, etc) before getting the vaccine. A few hours after receiving the first dose my arm became sore at the injection site and the soreness soon spread to my entire upper arm (shoulder to elbow) so I took some Tylenol for the pain which helped with the pain. Within the next 24 hours the soreness had vanished and I had absolutely no other side effects. 

I received the 2nd dose on January 12, 2021, exactly 21 days later. This time I went prepared with a painkiller 30 minutes before getting vaccinated. The nurse administering my vaccine encouraged me to stay ahead of the possible side effects by taking Aleve or Tylenol specifically, as Ibuprofen might not be as effective. Once again, about 2-3 hours after receiving the second dose I had arm soreness at the site and at 5 hours post vaccination I could feel a headache coming on. At 8 hours post I took 1 extra strength Tylenol. The following day at 24 hours post-vaccination there was still arm soreness and a lingering headache but I did not take another Tylenol until 32 hours post vaccine, to help with some very minor muscle aches and arm soreness. All of my side effects were extremely manageable and nothing that took me away from my work for the day.” 

 

New Developments in How Data Science is Helping in the Battle Against Covid-19

New Developments in How Data Science is Helping in the Battle Against Covid-19

by | Dec 4, 2020 | Analytics, Covid, Industry Trends, Technology

 

New Developments in How Data Science is Helping in the Battle Against Covid-19

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

 

Introduction

As we move into the winter months, we are seeing many states hit new and unprecedented records of Covid-19 cases. This is something that has been expected since lockdowns began in March, however, that does not make it any less concerning to healthcare professionals and researchers. In an effort to continue to prevent deaths and long-term side effects of Covid-19, researchers are always looking for new ways to apply artificial intelligence and machine learning to different areas. In today’s article we will take a look at some of these upcoming applications of data science to the battle against Covid-19. 

Discussion 

In a previous article, we detailed the ways in which machine learning can be applied to medical imaging. Due to the fact that machine learning has a great capacity for learning and understanding images and anomalies within them, the application of data science to imaging has potential to help identify Covid-19 cases in x-rays. Researchers at Northwestern Medicine Bluhm Cardiovascular Institute have been working with a machine learning algorithm that was trained by over 17,000 chest x-rays and found that the algorithm could “detect COVID-19 in x-ray images about ten times faster and one to six percent more accurately than specialized thoracic radiologists.” 

Though the algorithm is still in the research phase, it has great potential to be a significant tool and is an open source resource which means that other researchers can contribute and build on top of the existing algorithm.

Another application of data science in the battle against Covid-19 is actually in the development of a vaccine. The pharmaceutical giant, Pfizer, was recently approved in the U.K. to distribute an emergency vaccine and, with a 90% efficacy rate, the United States is likely not far behind. However, researchers at MIT’s Computer Science and Artificial Intelligence Lab have found results in their data that “suggest that the vaccines may not have the same impact among all patient populations” and may leave minority groups, specifically those of Black and Asian descent. This is incredibly important because it affects how minority groups will experience a “post-Covid” world in that they may have to continue to take precautions until a fully effective vaccine has been developed. As artificial intelligence algorithms continue to study these vaccines, they may find that these minority groups are more likely to have a gene or gene sequence that prevents the vaccine from being effective for everyone; if found, researchers can begin to create targeted gene therapies that allow the vaccine to perform its functions effectively. 

Conclusion 

As we continue to try to prevent the spread of Covid-19 across the United States, machine learning and artificial intelligence play an important role in improving our understanding of the virus and its effects on our bodies and society. 

 

Studying Our Genes to Understand the Impacts of Covid-19

Studying Our Genes to Understand the Impacts of Covid-19

by | Oct 14, 2020 | Covid, Industry Trends, Technology

Studying Our Genes to Understand the Impacts of Covid-19
Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Here at Altheia Predictive Health, we analyze several factors when making disease predictions; one of these factors is family history and DNA markers which are parts of looking at genetic predispositions. Some companies dive deeper into DNA markers as a primary indicator for other conditions, such as how the BRCA1 gene relates to breast cancer in women. Similarly, genetics is now being used to understand if certain people may be at a higher risk of suffering serious complications from Covid-19 due to certain genetic expressions. Studies are also being conducted to understand if and how Covid-19 may change or mutate genetic expressions. In this article, we will take a look at some of the research being performed in this area. 

Discussion

When it comes to looking at gene expression as it relates to Covid-19, researchers are not simply looking at who is most likely to test positive for the disease because many people are simply asymptomatic carriers. Rather, they are looking at gene expressions related to patients who have dealt with severe symptoms, or even passed away, as a result of Covid-19. A recent study published in the scientific journal, Nature Research, discussed a “genetic association study [that] identified a gene cluster on chromosome 3 as a risk locus for respiratory failure after infection with severe acute respiratory syndrome coronavirus 2.” The study went one step further and mapped this genetic expression to find that it was actually inherited from Neanderthals and “is carried by around 50% of people in south Asia and around 16% of people in Europe.” 

Another study, out of the University of Edinburgh, went a step further to better understand the strength of impact this marker and found that “because 74% of patients [with the marker] were so sick that they needed invasive ventilation, it had the statistical strength to reveal other markers, elsewhere in the genome, linked to severe COVID-19; and that a single copy of the associated variant more than doubles an infected person’s odds of developing severe COVID-19.” 

For those who survive a tough battle against Covid-19, as well as for those who test positive but are asymptomatic, the question remains of whether or not the disease can cause long term damage to our genes. Google recently granted researchers at the University of North Carolina – Chapel Hill $500,000 to study if and how Covid-19 alters gene expression. To conduct this research, researchers will “ compare RNA, a marker of gene expression, from the blood collected over years from the same individuals before and after COVID-19 infection [and]… use artificial intelligence tools to scan the genome for changes in gene expression that may be due to COVID-19 infection.”  

Conclusion 

Similar to many other health issues, our genetics can clearly play a huge role in how successfully we battle a disease such as Covid-19. What is promising about the research being conducted in this area is that once we better understand who is at the highest risk, we can better protect them and even create gene therapies to prevent severe symptoms or death due to Covid-19 for these people. However, just because you are not at risk of suffering from severe symptoms is no sign to put away your mask – Covid-19 has already been shown to have long term effects on those who test positive while asymptomatic and further studies will help us understand the severity of how the disease alters our genetic expression.

 

 

Prescriptive Analytics

Prescriptive Analytics

by | Aug 5, 2020 | Analytics, Covid, Industry Trends

Prescriptive Analytics

 

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

 

Introduction

Here at Altheia Predictive Health, we are constantly improving techniques for disease prediction and diagnosis to improve the lives of people around the world. Where we leave off, companies like IBM pick up and begin applying analytics to the next step in the disease management process which is a treatment plan. Prescriptive analytics in healthcare can refer to several different things but in this article, we will look at prescriptive analytics as it related to the study of prescribed steps for those with chronic conditions to better understand how to manage disease conditions at various stages of a given condition. This can be an extremely useful tool for doctors who are looking at a borderline case and deciding whether or not to begin medication treatments by giving them the ability to see compiled and analyzed data for similar cases.

Discussion

After being diagnosed with a disease, the next question patients and providers have is what steps can be taken to prevent progression of the disease and manage the negative effects of a disease; for some people this is achieved through diet and exercise, for others it may be a prescribed medication and it is highly dependent on several patient factors. Prescriptive analytics is rooted in how to best solve a problem – if the problem is diabetes, a prescriptive model would look at variables in a patient’s file to determine if the problem can be mitigated through diet and exercise. If not, the model would then provide a game plan for how to begin prescribing medicines in the most effective way for any given patient. In diabetes patients, insulin therapy is a common form of treatment where adjustments to a specific individual can be tricky but prescriptive analytics can use the data of similar patients to provide a tried and true treatment plan.  Prescriptive analytics can also be used in radiation therapy by weighing various favors, such as treatment location and surrounding organs, to provide a highly targeted treatment plan that treats an area with a calibrated dosage while leaving the rest of the body as unharmed as possible. This is something that we as humans can make educated guesses on but the use of prescriptive analytics is likely much more accurate which means better care for patients. 

This field of study also has an incredibly relevant application amidst the Covid-19 Pandemic. Throughout the pandemic data has been collected on patients such as their age, BMI, gender, ethnicity, presence of chronic conditions and more. All of this information combined with their response to treatment plans provides a unique opportunity to apply analytics – by looking at these sets of data together, we can use artificial intelligence to understand which patients respond better to certain treatment plans. This provides a great opportunity to improve Covid-19 survival rates and lower the number of patients suffering from long-term complications as a result of Covid-19. 

Conclusion

Prescriptive analytics in healthcare is essentially a way for analytics to troubleshoot disease conditions by providing a data-based treatment plan. By providing physicians with analytical tools, such as prescriptive analytics, we can significantly improve their success rate in treatment plans and as a result improve the quality of life for hundreds of thousands of people.

 

 

Florida Covid-19 Predictions

Florida Covid-19 Predictions

by | Jul 16, 2020 | Analytics, Covid, News

Revisiting Our Covid-19 Predictions in Florida: What We Got Right, What We

Missed and What’s Next

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

In our last article we reflected on our predictions regarding the spread of Covid-19. We made observations and predictions in March using linear regression and found that our data had an error rate of less than 10% when looking at the American population as a whole. However, many states had different rules and regulations for social distancing, mask wearing and other precautions, so we also made predictions based on different levels of regulations to predict the impact Covid-19 on hospitals and ICU beds. In this article, we will explore the accuracy of those predictions for the state of Florida and tools that can help us going forward as many states begin to see their number of Covid-19 cases spiking again. 

 

Analysis and Interpretation 

In our analysis we made the following predictions for the state of Florida based on different levels of precautions taken:

Predicted Hospitalizations with All Precautions Taken

April

May

June

Mid-June

July

August

2,208

2,028

18, 251 

19, 264

10, 139

2,208

 

Predicted Hospitalizations with Some Precautions Taken 

April

May

June

July

August

2,208

10,139

35,487

10,139

2,028

 

Predicted Hospitalizations with No Precautions Taken

April

May

Mid-May

June

Mid-June

July

August

2,208

20, 278

55, 766

40, 557

10, 139

3,042

2,028

 

While looking at this data, keep in mind that the number of hospital beds available in the state of Florida is 55,727. Looking back on our data, it’s clear that our model was very conservative. On one hand, we did accurately predict peaks and the fact that Florida would be overwhelmed with Covid-19 cases, however, we predicted that even with no precautions we would begin to see this come to an end in August. As we come to mid-July, Florida is the latest state to break American records with 15,000 new Covid-19 cases in one day which indicates that the end of the battle against Covid-19 in Florida is nowhere near over (Linton). In fact, as of July 7th, 2020, “at least 56 intensive care units in Florida hospitals reached capacity” with another 35 showing less than 10% availability (Chavez). 

 

The natural question to ask when making models such as these is how to improve them. In some ways, our model was conservative – in a no precautions setting, it was almost impossible that Florida could be in back in the same place it was in April. However, some error can be attributed to the lack of knowledge regarding the spread of coronavirus in March. We created these predictions based on Imperial College London’s pandemic model, however, the pandemic model could not account for the various changes, such as when and how certain things would reopen and was also based on the fact that we had a predicted end date. It was also limited in the fact that it was an agent-based model and not a stochastic model, which accounts for randomness such as the random meeting of two people and the impact they would have on their community if they were infected with Covid-19. The most accurate prediction models take in a lot more information about a more concentrated population. For example, it would not try to make every studied population follow the same multipliers because it would consider much more specified information about that population such as the occupations of a population, their ages and activities, their political engagement and beliefs, their methods of transportation, their attendance of religious functions and even more. 

 

Conclusion

What we can take away from this interpretation of data is that there is not a one size fits all model for pandemic predictions. Different counties, cities, states and countries all follow different schedules and habits and while most hospitals in Florida are overwhelmed, there are also likely some that barely see any or limited Covid-19 related issues. For this reason, it is extremely important to consider a lot more information about a more specified population than to use broad, blanketing equations; especially when the data is used for resource allocation. As we continue to battle Covid-19, it is important to take precautions such as wearing a mask in public places, practicing social distancing and maintaining good hygiene. 

 

Prevention

Take a look at the image below to see low to high-risk situations and understand how you can limit your exposure to Covid-19.

 

  

Florida Covid-19 Predictions

Reflecting on Our Covid-19 Predictions and How Analytics Can Continue to Help

by | Jul 9, 2020 | Analytics, Covid

Reflecting on Our Covid-19 Predictions and How Analytics Can Continue to Help

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

As we climbed towards Covid-19 peaks across the country in March and April, we at Altheia Predictive Health looked at linear and exponential regression models of predicted case counts. We also used that data to make predictions regarding where hospitals would be overwhelmed. In recent weeks many states are seeing those peaks come back, so it seemed like the right time to look back at and reflect on our past Covid-19 predictions and look at how analytics can help us prepare for the second wave that many states are seeing come their way.

Discussion

When writing our first articles about Covid-19 we made two types of predictions for the U.S.– one used exponential regression and the other used linear regression.  As we noted in our previous articles, the curve had begun to flatten by the time we made our exponential regression observations so here we will focus on linear regression. For linear regression of total cases in the United States, our prediction table, as well as the actual case numbers and percent error are as follows:

Date

Predicted Value

Actual Value

Percent Error

April 10th, 2020

469,140

512,010

8.37%

April 11th, 2020

495,604

542,498

8.64%

April 12th, 2020

522,068

570,358

8.46%

April 13th, 2020

548,532

597,452

8.18%

April 14th, 2020

574,996

624,893

7.98%

April 15th, 2020

601,460

655,569

8.25%

April 16th, 2020 

627,923

685,712

8.42%

 

Though it is dependent on the scenario, a percent error of less than 10% is generally accepted as a fair prediction which bodes well, not only for validation of our predictions, but also for validation of linear regression as a tool to use in the planning, analysis and allocation of hospital resources. 

New Technology

Big data has been used at nearly every step in the battle of Covid-19. The first step is, of course, prevention. The most important part in prevention is to practice social distancing and other preventative measures and to maintain good hygiene and health. However, in terms of analytics, community tracking of Covid-19 cases can use contact networks to help mitigate risk in some ways. Think of someone who tested positive for Covid-19 as a member of a social network, such as Facebook. If you are friends with that person, you are in their network and your friends, even if they are not directly “friends” with the original positive case, are at risk because of their connection to you. Disease tracking works in a similar way by creating a network of everyone who came in contact with a positive case patient and who came in contact with those people, and so on.  

The next step is diagnosis and condition management and to help this effort, John McDevitt and his team at New York University have used artificial intelligence and big data to predict which Covid-19 patients are likely to experience severe cases. They did so by identifying biomarkers in blood tests of patients who died and patients who survived their battle with Covid-19. The research team found that there was a difference in the levels of C-reactive proteins, myoglobin, procalcitonin and cardiac troponin I. The patients who died of Covid-19 had elevated levels of these measurements; the researchers factored this into their risk equations (Kent). 

The next step in the battle against Covid-19 is the creation of a vaccine. While this is still very much “in the works,” scientists at 15 universities, including Johns Hopkins University, University of Wisconsin, University of Alabama, Pennsylvania State University and others, have partnered to share data samples of electronic health records to aid in the creation of a vaccine. The motivation behind this collaboration is to gather as much data about Covid-19 patients as possible in order to quickly identify patient responses to antiviral and anti-inflammatory treatments (Shephard).

 

Conclusion

As many states are hit with a second wave of Covid-19 cases, it is reassuring to know that analytics can be an extremely helpful tool in every stage of the disease. Analytics can identify at-risk groups that may need to take extra precautions in protecting themselves due either to exposure or preexisting conditions. Analytics tools are also useful at the care management stage where doctors can identify patients who need ventilators more than others if, as demonstrated during Italy’s first wave, there comes a time when decisions need to be made about where resources should be focused. Finally, these predictive tools will be helpful in the creation of a vaccine, especially when collaboration across research institutions is encouraged and beneficial. Ultimately, the existence and widespread use of analytics in disease prevention and management is an encouraging fact as it greatly accelerates our ability as a society to bounce back from the struggles caused by Covid-19.

 

Prevention

Take a look at the image below to see low to high-risk situations and understand how you can limit your exposure to Covid-19.

 

  

Works Cited

Kent, Jessica. “How Artificial Intelligence, Big Data Can Determine COVID-19 Severity.” HealthITAnalytics, 15 June 2020, healthitanalytics.com/news/how-artificial-intelligence-big-data-can-determine-covid-19-severity.

Shephard, Bob. “Enlisting Big Data to Accelerate the COVID-19 Fight – News.” UAB News, 2020, www.uab.edu/news/research/item/11371-enlisting-big-data-to-accelerate-the-covid-19-fight.