Comparison of COVID-19 Growth Rates

The global coronavirus (COVID-19) outbreak has elicited widely varying responses by national governments and societies. In evaluating opportunities for mitigating the spread of COVID-19, Anderson, Heesterbeek, Klinkenberg, and Hollingsworth (2020) identified personal and governmental responsibilities toward slowing the transmission of the virus. Identifying successful strategies from transmission data is critical toward preventing further catastrophic outcomes.

Over the last several months of intensive surveillance, Johns Hopkins University’s Center for System Science and Engineering  (JHU CSSE) has provided a public data set curated from a number of international data sources, including the WHO, China CDC, and the US CDC. In addition to their online dashboard, the Center has published the data publicly on GitHub. This data has allowed for widespread education and investigation into COVID-19 activity.

For institutional preparedness plans, knowing the volume of cases over time is a critical factor in understanding staffing and equipment needs.  In addition, to have a framework to examine if local transmission rates are growing above or below expectations can help to understand timelines for surge capacity needs.  We used recent epidemic growth rate data to develop a predictive model for COVID-19 case counts.

COVID-19 Spread

As of March 7th, 2020, 105,836 people across 101+ countries have been confirmed as infected by COVID-19 (JHU CSSE, 2020). Unfortunately, this has already resulted in 3,558 deaths internationally and 17 in the United States. Further insights from the confirmed cases data can help identify countries where the spread of COVID-19 has been slowed. These countries’ responses to mitigating spread can serve as best practices.

Using the JHU CSSE data set of confirmed cases, we aggregated the daily case count at the national level. The cases count from each nation was aligned making time zero (t=0) the day with the first reported case. Only seven of the 101 countries started with confirmed cases on the first day of data reporting, and only Mainland China (547), Thailand (2), and Japan (2) had more than one case. While Mainland China will be included in the remainder of analysis, little is known about the early spread of the virus.

Next, utilizing principles from the physical sciences, the case counts were log transformed to better investigate the transmission growth factors. In Figure 1, we observed the log transformed rates of confirmed COVID-19 cases from the first infection for the five countries with the most cases and the United States (currently 9th). Mainland China, South Korea, Italy, and Iran all show strong signs of diminishing growth (bending of the curve), which in not yet seen in France or the US. Another interesting observation is a lag in explosive growth from the initial infections, which could be interpreted as the time before community spread of the virus. In Italy, the lag was around 20 days from the first confirmed case. In South Korea, France, and the US, the lag was approximately 28-32 days.

Figure 1. Confirmed Cases by Days Since First Case

Shows the growth of COVID-19 by select countries after the first case

Qualitatively, the observed epidemic curves appear to have two different phases.  In most countries, an initial phase in which cases were likely imported to local communities and testing was limited, is followed by a second phase characterized by community transmission. Of most interest is the comparison of rates of community transmission in this second phase because this may identify countries with the slowest rates of transmission, and may suggest the most efficient infection control strategies.

Assessing Community Spread Rates

To assess the growth of cases with community spread, each country’s data was aligned to time zero (t=0) identified by the last point of the lag period. Data was re-aligned for the 17 countries and regions with over 100 confirmed cases. Figure 2 displays the log transformed re-aligned confirmed cases curves for these countries. The curve for the United States was designed with each data point identified for ease of identification in the graph.

Figure 2. Confirmed Cases by Days Since Estimated Community Spread Start

Shows the growth of COVID-19 by country after the estimated start of community spread

The most important feature of the curves in Figure 2 is the slope of the curve, as this is an indicator of transmission growth rates. The curves for Mainland China, South Korea, Singapore, and Hong Kong show signs of diminishing growth meaning the rate of further infections is declining. Many of the other curves, including the United States, are more linear meaning that the infection rate has not started to slow. This data also shows differing slopes meaning the infection rate in the various countries is different. Some are experiencing faster spread and some have slower spread.

Ranking the COVID-19 growth by Country/Region

The rate of growth of COVID-19 in countries can be compared by estimating the transmission growth factor from the series of daily confirmed case count. The transmission growth factor indicates how fast COVID-19 will spread; larger numbers imply faster growth. Using linear approximation for the slope of the curve, we identify a wide range of transmission growth factors in each country, which are shown in Table 1. Additionally identified in the table are the implied days for the cases to double, days to go from the 1st to 100th case, and days to go from the 100th to the 1,000th case. As the growth factor increases, the measure of days decrease.

Of these identified countries, Belgium has the most aggressive growth factor followed by The Netherlands, Norway, and Iran. The United States has one of the slower growth rates, but is higher than the current rate in Japan, Mainland China, Singapore, and Hong Kong. It is worth reminding that a limitation of this data set is that the initial spread of COVID-19 in China is undocumented in the data set. However, the rate of continued spread in China is less than in the US.

Table 1. Growth Factor Estimate and Implied Growth Periods for COVID-19 Spread by Country

Table that compares rates of COVID-19 spread by country, includes calculation on days to number of 
infections

Conclusion

This analysis has relied on preliminary data of COVID-19 global confirmed cases to determine the rate of spread in various countries. In the United States, COVID-19 is currently spreading slower than in many other highly impacted countries, but this should not be a signal of complacency. The daily data is currently showing a consistent rate of growth, not a declining rate. Because we are still in the early days of this infection, it is imperative to continue domestic mitigation efforts to slow or stop the continued spread of this deadly virus.

Promising future research could continue to look at the form of the COVID-19 transmission rates as this epidemic continues to evolve. Additionally, more information and evaluation on the specific interventions employed by varying countries could help build best practices for the current situation and future episodes.

Written by Thomas A. Webb, MBA, and Bala Hota, MD, MPH

References:

Anderson, R. M., Heesterbeek, H., Klinkenberg, D., & Hollingsworth, T. D., (2020). How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet. Retrieved from https://www.thelancet.com/pb-assets/Lancet/pdfs/S0140673620305675.pdf

Johns Hopkins University Center for Systems Science and Engineering. (2020). Novel coronavirus (COVID-19) cases [Data file]. Retrieved from https://github.com/CSSEGISandData/COVID-19

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