In the Fight Against Covid-19: What Do We Know and to Whom Can We Turn for Answers?
Written By Alyson Marks
Source: Johns Hopkins University
One could argue that during the current global pandemic, data has never played such an important role. It is relied on for life and death decisions that are impacting billions of people around the globe. In particular, the need for accurate and real-time data, which many in the data for sustainable development community have been advocating for years, has come to the forefront of mainstream conversations. Yet in the haze of the countless Covid-19 data dashboards and discussions on data, much of the data being reported (especially in the United States) is incomplete, confusing to the general public, and being miscommunicated by journalists and politicians alike. As the spread of Covid-19 continues to increase at an alarming rate, policymakers and citizens each face numerous limitations.
What Do Policymakers Need to Know?
Policymakers need to know more than just how many people are infected. They need to be able to answer critical questions: Where are these populations living and how can we reach them? Are men more vulnerable to the virus than women? How did individuals specifically contract the virus? What access do populations have to basic services, such as medical facilities? Where are quarantine measures working? What percentage of those who contract the virus are at risk of dying? What is the number of pending tests? These are just a handful of the key questions that policymakers are having to make decisions on without reliable data.
What’s the Current State of the Data Available?
The data that has been collected and reported so far on how many people are infected and how the Covid-19 epidemic is evolving are not reliable. Much of this is due to limited and botched testing. For instance, patients in the U.S. who did not “fit” the federal criteria to be tested for Covid-19 were not tested. Not to mention, there were steep lags in the diagnosis processing. With limited testing, cases and deaths are being missed, and it will be impossible to know if we are actually flattening the curve. The numbers also don’t take into consideration vulnerable populations, such as undocumented citizens, who may fear getting tested, those who can’t afford to miss work and a paycheck to get tested, or those who simply have milder cases and aren’t getting tested. Furthermore, infectious disease models are scarce and resources are constrained, which makes it even more difficult to predict the virus spread.
Policymakers also need to be careful about drawing conclusions from the reported numbers alone, as confirmed cases are just a function of the confirmed tests. For instance, not all U.S. states and countries are reporting hospitalizations, and the cause-of-death definitions vary significantly – with many deaths being misdiagnosed as the result of pneumonia. The U.S. is also not monitoring the sex breakdown of Covid-19 cases. This is significant because women and men are likely to experience the virus differently, which can impact the type of treatment needed, whether they are more likely to require hospitalization, and other key factors that affect decision making.
What Other Data Sources Should be Considered?
Without a doubt, Covid-19 has underscored the need to invest in better, timely, and more reliable data. In the interim, as policymakers demand for more aggressive testing, they should consult with academics and public health officials who are experts on epidemiology and infectious disease as much as possible to ensure that they are operating off of facts. They should also rely on multiple models to inform policies and decision-making, and seek out quality admin data, which can help improve the timeliness and granularity of their data. Additionally, now is not the time to try rash innovations or experimentation, but alternative data sources, such as Earth Observations can help fill data gaps. For example, members from the POPGRID Data Collaborative are using their satellite-based population estimates to identify where the most vulnerable people are, and whether they have physical access to healthcare services.
Who Should Citizens Trust?
For citizens, the perils of relying on unreliable data and misinformation are and will continue to preclude us from mitigating the spread and effects of Covid-19. Take the recent debate over the use of ibuprofen to treat Covid-19 symptoms. Last month, researchers in Greece published a highly speculative letter in the Lancet Respiratory Medicine journal that an ingredient in ibuprofen could potentially raise the risk of coronavirus infection. Just days after the letter was released, the French Ministry of Health released a warning for citizens to avoid all ibuprofen, and this was widely reported across the world media, in outlets ranging from the U.S. to Israel to Singapore. And it doesn’t help when the information and data governments are putting out there, which citizens rely on, doesn’t offer the full picture and is sometimes downright false. With many taking the theories and claims by the government at face-value, citizens need not only to understand the limitations of the data, but how to interpret the data.
What Questions Should We – the General Public – Keep in Mind?
For many of the models, they are only as good as the quality of the data provided. Some models are even incorrect. Additionally, not all dashboards are created equal, and not everyone has access to the same information. For example, U.S. sanctions prevent Iranians from accessing the widely referenced John Hopkins University coronavirus map. Further, the dashboards can be confusing, especially for those who lack the technical expertise. When viewing the dashboards, there are a number of factors citizens should keep in mind including:
• Is the data updated regularly, and does it include a time-stamp of the last update?
• What are the critical assumptions and inputs for each model?
• What is the model’s level of uncertainty?
• How was the data obtained?
• Who collected the data and produced the model?
• Does the model provide a history on cases for each location?
• Who is the model intended for – the lay person or the scientist?
• Does the model disaggregate the data? If so, by what? Gender, age, nationality, etc.?
• What type of data was used to produce the model? (e.g. health admin data vs. modelled estimates)
• Are there privacy issues with this data?
Additionally, to better understand the coronavirus data and models, there are many news and analysis sites to follow, podcasts to tune into, and experts to follow on social media. I’ve included a few of my favorites below:
News and Analysis
• Center for Global Development
• The Economist’s Coronavirus Coverage
• Nature’s Coronavirus Coverage
Podcasts
• Tableau’s If Data Could Talk: Covid-19 Edition
• STAT’s The Readout Loud
• Vox’s Your Phone Knows You are Staying at Home
• Esri’s Battling Covid-19 with Data, Location Intelligence, and Visualization
Data Experts on Social Media
As we continue to confront this crisis there will be more uncertainties, but we will only be able to cut through them if we remain informed, are cognizant of the data’s limitations, and demand for more accurate, real-time, and quality data to help stem this pandemic.