Manceps is enlisting its data scientists and machine learning experts in the fight against the pandemic.

Using BigQuery to Debunk Coronavirus Myths

A real-time data visualization project by Manceps

As misinformation and rumor continue to swirl around the spread of the COVID-19 pandemic, we decided to create a series of charts to see if it would be possible to identify any previously-unnoticed trends. By combining health data from the WHO, climate data from NOAA, and World Development Indicators from the World Bank, we were able to discover some fascinating correlations.

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International Impact

In this chart, we mapped the prevalence of confirmed cases, recovered patients, and deaths. In terms of raw numbers, you can see that America is clearly at the top. However, by looking at it per capita, we can see that the US's case rate is similar to that of Europe.

Confirmed Cases by Average National Temperature

Here, we attempted to validate whether average temperature is correlated to the spread of the virus. We grouped by latitude, longitude buckets so that we could look at temperature effects independent of countries. Many have theorized that warmer temperatures will lead to a reduction in confirmed cases; however, these charts seem to indicate that there is not a relationship.

Growth Rate By National Economies

We were curious to see whether national economies could be correlated to the number of corona virus cases. As these charts seem to indicate, wealthier nations tend to have higher numbers of cases (and deaths) per capita. Presumably, poorer nations aren't testing as often as wealthier ones, which may skew the information presented in these charts.

Growth Rate By National Health Indicators

One way to score overall healthcare systems is to look at each country's infant and overall mortality rates. In the first row of charts, we can see a clear pattern that seems to suggest that nations with better functioning healthcare systems have more cases of corona. Again, this could be related to the availability of testing. In the second row of charts, the correlation becomes less clearly defined.

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