How Data Analysis Helps Nurses Understand the Risk of Infectious Diseases

The battle against infectious diseases has been present for the longest time in history. Yet, even today, we continue to witness outbreaks such as the recent COVID-19 pandemic. Unfortunately, infectious diseases tend to occur suddenly, causing havoc and uncertainty in communities due to their ability to spread fast. Moreover, growing globalization and interconnectedness have made outbreaks more lethal.

However, technological advancements boost traditional surveillance techniques, which offers a glimpse of hope in this fight. Data analytics and artificial intelligence are helping healthcare workers and critical decision-makers find timely insights and information that play a crucial role in predicting, understanding, preventing, and mitigating risks of infectious diseases.

Gathering Data to Understand Infectious Diseases

Data used by healthcare workers to understand the risk of infectious diseases has to be gathered from reliable sources. Big data analytics helps collect phone data from telecom companies. Telecoms hold data that can show patient behavior during an outbreak. A good example is how health workers worked with a telecom company to track people’s movement using mobile phone data in the 2010 earthquake in Haiti. The same data proved valuable ten months later when a cholera outbreak happened. Health workers could see population movement quickly, which gave them a leg up in containing the spread.

Another useful source is social media. With millions of people active daily on popular social platforms such as Twitter, Facebook, WeChat, and the like, these platforms offer a reliable stream of data. This data can be analyzed in real-time to give insights into infectious disease transmission in terms of time and geographical locations. Other sources include search engine activity, travel, tourism, hospitality companies, community follow-ups, and hospital and clinic data.

Data Analysis and Classification

Gathering data from multiple sources results in both structured and unstructured data. For healthcare workers to make sense of the data, it has to be analyzed and classified accordingly. When done successfully, big data analytics can help healthcare workers get insights that are valuable in the following ways:

Predicting infectious diseases outbreak – This helps hospitals take proactive preparations, such as ensuring enough patient beds and personal protective equipment for healthcare workers.
Predicting disease spread progression to show areas where infectious diseases are unfolding and where they are more likely headed.
Identifying most vulnerable communities – This gives insights on areas or populations where most or urgent care is needed to inform decisions on preventive measures, emergency funds allocation, and response efforts.
Understanding adverse drug reactions and antibiotic resistance – This is important in drug and vaccine development.

Limitations of Data Analytics When Drawing Conclusions

The role of data analytics in understanding infectious diseases is challenging. However, understanding the possible limitations can help healthcare workers find ways to overcome them. Here are a few of them.

-Use of Experts

Big data analytics in healthcare involves using AI, machine learning, deep learning, and other techniques to make sense of data for infectious diseases such as Zika, Ebola, SARS, seasonal flu, and more. In addition, correct analysis requires reliable and relevant data. This calls for high-level expertise, so it’s essential to consult with a data analyst. Less understood diseases require a team of experts ranging from data scientists, geographers, epidemiologists, vets, and ecologists.

-Human Biases

Data streaming from internet sources and phone companies are likely to miss critical demographic identifiers such as sex and age. In addition, people that don’t frequent the internet, such as the elderly, infants, and children, might be underrepresented in the data. This also includes populations from less developed countries. This can be overcome by having traditional data streams such as hospital and clinic data and insurance claim data in the analysis.

-Data Privacy

Data privacy can be a concern, so it is paramount to adhere to ethical data use. Healthcare organizations must set up data privacy regulations and policies to protect individual privacy. This can include coming up with clear guidelines regarding the use of personal data.

Conclusion

Infectious diseases have been prevalent globally for a long time. However, big data analytics provide a way to understand the risk of these diseases. Data gathered from various sources is essential in preventing outbreaks and the severity of the diseases. However, data must be reliable and accurate for it to make sense. Using the knowledge of experts and eliminating biases can go a long way. Moreover, data privacy and security have to be prioritized.

Author
Recent Posts

Maya Payne

Maya is a surgical nurse. She has been working for over five years now. She has been preparing the patients before surgery for a very long time now, and she says that it is her favorite part of the job. Apart from their everyday job, Maya enjoys writing. Mostly, she writes about health-related topics.