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In their policy response to the COVID-19 crisis, world governments reached a wide agreement on the priority of citizens’ health. When initially implementing various forms of lockdown measures, countries followed the motto “health first, economy second”. For example, when deciding on whether a quarantine should be established or prolonged, governments prioritised the potential health implications of inaction over economic stability.

Having been adopted as the compass for policymakers during the pandemic - the effect of a virus on citizens’ health needs to be estimated with a high degree of completeness and accuracy. For instance, a broader picture of the health impacts associated with an additional day of a lockdown could save countries thousands of lives.

The COVID-19 crisis shows that world leaders were primarily focussed on the direct short-term effect of the virus on population health, when developing policies to respond to the pandemic. This was typically measured by the number of confirmed COVID-19 cases (absolute and per million people) and their seriousness as well as the number of deaths caused by the infection.

However, there are indications of the broader health implications caused by the COVID-19 crisis, which should be considered by policymakers (also shown in Figure 1😞

  • Direct health effect of COVID-19: deaths resulted from being infected by COVID-19

  • Indirect health effect of COVID-19: deaths from other health conditions, emerging or worsening as a result of the lockdown measures

  • Substitution effect of COVID-19: deaths from other health conditions, resulting from the shift of healthcare resources towards helping COVID-19 patients and citizens’ reluctance to visit hospitals due to the fear of catching the infection

Figure 1. Modelling health effects of COVID-19

Other Indirect Short-term Effects: Anxiety and Depression 

Another adverse short-term effect of the pandemic is declining mental health. In the US, which has the largest number of confirmed COVID-19 cases worldwide (; 15.05.2020), a poll conducted by the Kaiser Family Foundation suggested that the pandemic has already had a deep psychological effect on the society. 45% of the adult respondents said the pandemic has negatively affected their emotional wellbeing, and for a further 19% it has had a “major impact”.

Analysis of the poll results shows that the major reasons for the decline in mental health can be attributed to:

  • Feelings of loneliness and isolation: due to lockdown measures and social distancing

  • Financial stress and uncertainty: due to potential job loss or income insecurity

  • Burnout and stress among healthcare workers: due to strain and extra hours at the frontline

The psychological meltdown caused by the pandemic could accelerate worldwide rates of anxiety and depression. Depression alone already affects 264 million people across the globe and significantly contributes to 800,000 suicides annually. Many other countries, such as Canada and Australia, have recognized risks associated with increased anxiety and depression, and are already providing additional support services via government online portals (; help portal of the Australian Department of Health).

Other Indirect Middle-term Effects: Obesity and Overweight

Emerging research shows that the pandemic may also have a delayed negative effect on the development of other chronic diseases, such as obesity. A recent study by Columbia University predicts the pandemic could exacerbate childhood obesity in the US.

A study by the University of Sussex in the UK names the following ways that the pandemic may lead to becoming overweight or obese:

  • Lack of physical activity: due to the lockdown measures

  • Increased consumption of shelf-stable and frozen foods: due to food supply instability

  • Increased frequency of snacking on convenience foods: due to more stress and anxiety

Increased obesity that might be observed after the pandemic could cost thousands of lives. Obesity is currently the fifth leading cause of global deaths, costing 2.8 million adult lives annually (facts of the European Association for the Study of Obesity).

Substitution Effect: Cancer

Cancer could be considered “the forgotten C” of the pandemic. Since fighting COVID-19 became the primary focus of national healthcare systems, oncology patients have become a secondary priority. Moreover, many of them were reluctant to visit a doctor due to the fear of being infected.

Data from the UK shows a reduced number of daily appointments due to social distancing measures and busy hospital lines. This has led to the following worrying consequences:

  • Urgent referrals for cancer tests have fallen by 76%

  • The number of chemotherapy appointments has fallen by 60%

In England, it is estimated that 6,270 people might die within the next 12 months due to late cancer diagnosis and delayed treatment.

The Need for a Public Health Crisis Dashboard

Governments worldwide can benefit from using business intelligence to understand the broader health effects of COVID-19 and future pandemic threats.

Real-time analytics can help monitoring, not only the direct indicators of a crisis (total number of infections, active infections, deaths, etc.), but also exacerbation of other health conditions. A fuller picture of the broader healthcare implications could better inform crisis policymaking.

To bring policymaking insights to the next level, governments can harness the power of Predictive Analytics. With the help of machine learning models (classification, regression, time series, etc.) public officials can go beyond reporting on the current situation, but also simulate health outcomes of various crisis policies and measures.

Figure 2. “Black Box” of health causalities (on the example of depression, obesity and cancer)

As shown in Figure 2, analysing health implications is complicated by comorbidities and the systemic nature of the human body. Even so, machine learning algorithms, e.g. Artificial Neural Networks, can yield predictions even in “black box” scenarios where the inner workings of the system are unknown.

To optimize the value of reporting and predictive capabilities for potential future pandemics (or epidemics), governments should:

  • Decide on the key health KPIs to include into their digital crisis dashboard. Get inspired by publicly available dashboards (for example, info). Include statistics on other major chronic diseases.

  • Use the power of machine learning to predict the health effect (direct, indirect and substitution effects) of policy measures. Use simulation to define best policy design.

  • Collect data from other countries/regions with similar health profiles and crisis measures, and proactively train machine learning models to improve accuracy.

  • Collect behavioural insights, which can be indicators of adverse health trends, e.g. statistics on eating and exercising habits. These insights can inform policymakers about the chains of negative behaviours that should be broken to avoid fatal outcomes. For example, if the rising obesity rate can be largely attributed to poor eating habits, governments could encourage supermarkets to offer healthy food packages during the crisis.

Informed and Agile Governments

For governments worldwide, the true benefit of using Business Intelligence in times of a public health crisis is the ability to create monitoring dashboards and predictive models that combine data on a large variety of important health implications (direct, indirect and substitution). Thanks to the inbuilt Machine Learning, to produce accurate impact simulations public officials do not need to fully understand highly complicated net of health causalities – they should only agree on the set of county-specific chronic diseases that need to be considered.

Thus, data-driven approach to crisis-policy making allows governments to be both insightful and agile – they no longer need to compromise information completeness for speed and urgency.

Ultimately, combining the broader insights into health impacts and indexes of current economic activity can help world leaders make more informed decisions when compromising between health and economic stability.
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