How Far Must a Country Go To Flatten Its Curve?
/By: Tony Jiang, MD
Figure 1. Models to characterize pandemic disease growth. During exponential growth (left), the number of new cases is directly proportional to the existing number of infected individuals, giving rise to a J shape curve. For logistic growth (right), growth is initially exponential but eventually decelerates and pandemic disease burden approaches a limit shown by the dashed line, giving rise to an S shape curve.
Figure 2. Modeling COVID-19 case growth in Ohio. The cumulative cases of COVID-19 reported by the State Department of Health are modeled as exponential (black line, r2 = 0.95) or logistic (magenta line, r2 = 1.0) growth.
In a pandemic when disease spread is not controlled, new cases grow exponentially (Fig. 1). Hospital resources become overtaxed as the rate of infection easily outpaces the rate of recovery. However, with the implementation of appropriate interventions, the pandemic can transition from exponential to logistic growth as the emergence of new disease cases decelerate, allowing existing cases to recover (Fig 1). So which growth model best reflects our current situation?
Using data reported by the Ohio Department of Health (Ohio Department of Health, 2020), we found the curve is indeed flattening at a local level. New COVID-19 cases appear to be decelerating in Ohio as the logistic curve begins to fit the data better than an exponential curve (Fig. 2). In other words, uncontrolled growth is slowing and the curve is beginning to flatten.
As of April 14, forty-two states have strict stay at home orders in place, with non-essential businesses shut down (Mervosh et al., 2020). Clearly these restrictions come with severe penalties to the economy - the Dow Jones Industrial Average had dipped over 30% in the past month. Many of us are ready to relieve these restrictions, but if we return to normal life too soon, COVID-19 spread will likely sharply rebound.
With vaccine development estimated to take more than a year from now, our best hope is finding a currently FDA approved drug that helps with this infection. There has been immense interest in azithromycin and hydroxychloroquine from preliminary studies showing decreased viral shedding in patients treated with this regimen (Gautret et al., 2020). Indeed until these findings are replicated in more rigorous studies, or until we see advancements in other COVID-19 treatments, lifting shelter in place policies could still lead to a rebounding in disease spread and preventable deaths. Since some other coronaviruses show seasonality, there is also hope that SARS-CoV-2 transmission will naturally slow as we enter the summer season as well (Payne et al., 1986; Dowell & Ho, 2004).
Figure 3. Cumulative cases of COVID19 in OH reported by the Department of Health and Johns Hopkins University.
Improvements in our ability to track COVID-19 cases may also alter our perspective on the trajectory of disease burden. On a global level, the Coronavirus Resource Center powered by Johns Hopkins University has taken the lead in monitoring COVID-19 cases (Lau, 2020). This resource is one of the most comprehensive COVID-19 trackers for the general public; however, the data here tells a different story than that from NYC Department of Health, with discrepancies as high as 50% (Fig. 3). These differences likely stem from the Department of Health receiving more comprehensive data for their region that may not be retrievable by public databases.
Although differences in local versus global databases may seem trivial at first glance, when it comes to determining which phase of pandemic growth we are in, accurate reporting of disease spread is fundamental. Indeed, better communication between databases would surely help determine if our social policies are working, but there is growing concern that our privacy may be violated in order to achieve such goals.
As early as January 2020, South Korea had quickly launched a contact-tracing and testing regimen to identify and isolate infected individuals, then alert nearby people. The thoroughness is well described by Zastrow in a news article published in Nature:
“A typical alert can contain the infected person’s age and gender, and a detailed log of their movements down to the minute — in some cases traced using closed-circuit television and credit-card transactions, with the time and names of businesses they visited. In some districts, public information includes which rooms of a building the person was in, when they visited a toilet and whether or not they wore a mask. Even overnight stays at ‘love motels’ have been noted.”
These measures, if adopted by the United States, would promote awareness and help reduce the virus’s spread as evidenced by South Korea’s success. The technology exists to take this to another level, where health data such as heart rate and temperature from wearable smart devices, when combined with footage obtained from closed-circuit televisions, could be analyzed by artificial intelligence to predict and isolate individuals before symptoms manifest. But what if these powers introduced to combat COVID-19 remain after the pandemic subsides? This could have long-lasting effects on civil liberties – an idea championed by Edward Snowden during a video-conference interview for the Copenhagen Documentary Film Festival.
Alternatively, what if the government did not abuse these new capabilities? Perhaps allowing authorities to establish this new disease tracking infrastructure may help prepare us for the next infectious crisis. After all, South Korea’s ability to act so quickly for COVID-19 stemmed from the laws passed to handle the 2015 MERS outbreak. The South Korean government has even stated that the public is more likely to be more trusting if accurate and transparent information is released about the virus. It seems unlikely that national policies for handling the virus will be implemented in the United States: we are many months into this pandemic and rather than have a unified front, the onus for handling the virus has been relegated to the state level. Perhaps this will change if the socioeconomic situation does not improve soon.
References
Lau, R. (2020, April 1). GitHub - CSSEGISandData/COVID-19: Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE. Retrieved April 14, 2020, from https://github.com/CSSEGISandData/COVID-19
Ohio Department of Health. (2020, April 1). State of Ohio COVID-19 Dashboard. Retrieved April 14, 2020, from https://coronavirus.ohio.gov/wps/portal/gov/covid-19/home/dashboard
Mervosh, S., Lu, D., & Swales, V. (2020, April 2). See Which States and Cities Have Told Residents to Stay at Home - The New York Times. Retrieved April 14, 2020, from The New York Times website: https://www.nytimes.com/interactive/2020/us/coronavirus-stay-at-home-order.html
Gautret, Philippe, et al. "Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial." International journal of antimicrobial agents (2020): 105949.
Payne, Claire M., et al. "An eight-year study of the viral agents of acute gastroenteritis in humans: ultrastructural observations and seasonal distribution with a major emphasis on coronavirus-like particles." Diagnostic microbiology and infectious disease 5.1 (1986): 39-54.
Dowell, Scott F., and Mei Shang Ho. "Seasonality of infectious diseases and severe acute respiratory syndrome–what we don't know can hurt us." The Lancet infectious diseases 4.11 (2004): 704-708.
Zastrow, M. "South Korea is reporting intimate details of COVID-19 cases: has it helped?." Nature (2020).
