Education and information are crucial factors in the management of infectious disease. The distinction between the disease and the epidemic is key.
More on COVID-19
We prepared a comprehensive list of our resources related to the novel Coronavirus:
- Is there a place for diet and lifestyle in the prevention of severe COVID-19 infection?
- Antibodies: what are they and why are the antibody studies so important?
- The dilemma of predictions based upon data from early in epidemics
What do we know about the disease?
We continue to get more data and information is evolving on a daily basis. I will continue to update this information in terms of:
The Disease: What will happen to me or my family if we catch this virus?
The Epidemic: How likely am I to catch this virus and is that risk changing?
We are rapidly developing an understanding of this disease. There is an evolving consensus of three potential stages. The first stage, the incubation period, patients typically have no symptoms, they may or may not have detectable virus. Many people recover without progressing. The second phase involves symptoms, typically not severe, the virus will be detectable. Most people will never get past this stage and will recover. In the third stage, there is the development of increasingly severe inflammatory or infective symptoms, especially involving the lungs. This leads to increasing cough and shortness of breath. This stage is associated with increasing viral load. In some patients this stage is associated with an excessive immune reaction which may lead to a ‘cytokine storm’. This is an overwhelming internal battle which can lead to damage not only to the lungs but other organs. Part of this immune response is targeted against blood vessels. The immune reaction of blood vessels leads to an increase in blood clotting which plays a role in the damage to organs within the body. One rare but important example of this tendency has been the increase in Kawasaki disease in children1.
In addition to older males with other illness we are increasingly appreciating that the disease is more serious in health care workers (because of viral load) and in association with obesity and poverty. Studies in the UK and US also show a significantly higher incidence in people from Black, Asian and Ethnic Minority (BAME) backgrounds. It is unclear yet the relative contribution of genetic, economic and social factors but it is already clear that COVID-19 will amplify social inequality. Poverty is associated with both higher infection rates and worse outcomes. One simple way of thinking about this process is that COVID-19 is generally worse as biological age increases. It is important to appreciate that individuals do not age biologically at the same rate. We can control some factors which influence the biological ageing process. Obesity, poor diet, lack of exercise and metabolic syndrome make people old before their time and these conditions significantly worsen outlook in COVID-19. A recent review in the Lancet looked at outcomes in critically ill patients. The mean age in this cohort was 60 with 67% males. The authors concluded that ‘the severity of SARS-CoV-2 pneumonia poses great strain on critical care resources in hospitals, especially if they are not adequately staffed or resourced2. This message was reinforced in a further paper in the Lancet looking at the Italian experience3. The mean age of death in Italy was 81 years. This varied from 83.4 years in females to 79.9 years in males. There was a 4 to 1 ratio of males to females. Two thirds of fatalities had other medical conditions or were smokers. Between 9-11% of patients admitted to hospital required intensive care treatment.
The early drug treatments are beginning to report. One of the first peer reviewed trials published in the Lancet was from Hong Kong4. This trial showed benefits for drug treatment in mild to moderate disease as defined by reduction in symptoms, viral shedding and hospital stay. Which patients need treatment, what will be the optimal treatment and whether suppressing the virus early will reduce more severe complications later needs further research. More high quality peer reviewed studies on intervention are needed.
There is a general consensus that testing will play a central role in the control of COVID-19 although how that plays out on the ground varies between countries. Hong Kong, China, Singapore, Taiwan and Korea have all, to different degrees, had success in containing the epidemic with the central strategies of case identification, contact tracing and quarantine (see the epidemic curves which will be updated regularly).
There is now good evidence that population based social distancing measures can help to control epidemic size in multiple locations.
The crude international mortality figures are exaggerated as there are a lot of people who have mild infections who will not be tested. Whilst factors in national health care systems may be a factor, in particular the overwhelming of intensive care facilities, for reasons explained previously this is most likely to represent more widespread, undiagnosed illness in countries with either limited testing capacity or who have made an active decision not to widely test. The high apparent mortality figures are most likely to represent more widespread, undiagnosed illness in countries with either limited testing capacity or who have made an active decision not to widely test. The current crude fatality rate in Hong Kong of COVID-19 is 0.39%, in Singapore it is 0.01%. Hong Kong and Singapore both had excellent preparedness plans and instituted widespread testing, isolation and quarantine. They are both more likely to recognize asymptomatic or mildly symptomatic young people and it is logical to assume that they are closer to the true infection rate than countries which have allowed the epidemic to burn without testing. Disease severity is typically downgraded as epidemics evolve and this is happening with COVID-19. Final mortality rates are likely to vary across populations. This may be influenced by genetic, social and economic factors that influence the health of populations. The efficiency of health systems and the application of public health measures will also influence mortality on a national and regional level. Current evidence would suggest an infection mortality rate of COVID-19 of <0.5%.
A number of antibody tests (blood tests) are now being used. It is significantly easier to develop a test which measures the virus than it is to develop tests which measure antibody response to the virus. In new viral infections it is also difficult to know exactly what antibody tests mean. One of the first research papers on antibody testing from Shanghai suggested that significant numbers of patients who had proven infection did not develop antibodies5. A number of other studies have been reported showing antibody prevalence of 2-4% in China and 10-14% in Italy, Germany and the US. OT&P are currently undertaking a study in association with Hong Kong University in order to assess the prevalence of antibodies in our population.
As I have previously explained questions about the disease relates to the question what happens if something bad happens to me or my family? These are What If? questions. In order to understand the risk of this bad thing happening we need to replace these questions with What is the risk of catching COVID-19? To answer this question we need information. When we are dealing with uncertainties it helps to have an anchor for risk. In order to assess this risk we need to understand the epidemic.
The Epidemic Process
To understand the risk of catching COVID-19 we need to understand the epidemic.
In a previous article we explained the principles of the public health measures.
In any infectious virus the following factors influence the size of the epidemic:
- Mode of Spread
- Incubation Period
- Infectivity in the Incubation
- Individual and Population Immunity
These factors ultimately lead to a basic reproduction number R0 for each illness. This number is a measure of on average how many people are infected by an individual with the specific disease. If this number is <1 the epidemic will die (this means that on average each infected person infects slightly less than one other person). If this number is >1 the epidemic is likely to grow. Infections with higher R0 values are more likely to spread. Population measures such as isolation of infected individuals, masks, hand washing, school closures and social distancing all work by reducing the exposure risk with the intention of reducing the effective R0 below 1 but regardless as much as possible in order to give the best possible chance for the epidemic to die. We now have good evidence that public health controls based on the principles of social distancing can reduce R0 and control epidemic spread. The balance between the costs and benefits of public health controls will be key in controlling the epidemic. Hong Kong University measures the effective R0 in Hong Kong. It is available here6. This is a good measure of the impact of the current public health measures in Hong Kong in real time.
Understanding epidemics is maths and data. Try to look at the change in cases rather than cumulative cases. The number of cases and the number of deaths will get bigger every day. It is the rate of change which tells us whether the epidemic is getting bigger or smaller. Epidemic curves are useful to get a sense of where the fires are burning and most importantly whether that intensity is increasing or reducing as this will determine risk. It is important to realise that even within countries, we are not dealing with one big fire, but lots of little clusters which may be very distinct in the risk that they pose. Remember too that death is a lagging indicator. People must first get sick, they are then usually diagnosed and either recover or in some cases die. In the first series the mortality was 10-12 days post diagnosis. This may reasonably expected to be delayed with greater awareness and understanding of treatments so look for the mortality curve to lag the new cases by a number of weeks.
Figure 1 - Epidemic curve of confirmed COVID-19, by date of report and WHO region through 17 May 20207.
Figure 2 - Daily number of newly confirmed cases reported in Mainland China since January 10, 2020. Including the case revision on April 17, 2020.
Figure 3 is the latest data from the epidemic curve for Guangdong.
Figure 3 - Daily number of confirmed cases reported in Guangdong Province since Janaury 10, 2020.
Figure 4 - Daily number of newly confirmed cases reported in Korea since January 10, 2020.
Figure 5 - Daily number of newly confirmed cases reported in Japan since January 10, 2020.
Figure 6 - Daily number of newly confirmed cases reported in Italy since January 10, 2020.
Figure 7 - Daily number of newly confirmed cases reported in Iran since January 10, 2020.
Figure 8 - Daily number of newly confirmed cases reported in France since January 10, 2020.
Figure 9 - Daily number of newly confirmed cases reported in Germany since January 10, 2020.
Figure 10 - Daily number of newly confirmed cases reported in Spain since January 10, 2020. As of April 26, 2020, Spain has revised its cases to only PCR-positive cases, reducing their numbers by 12,130.
Figure 11 - Daily number of newly confirmed cases reported in United Kingdom since January 10, 2020.
Figure 12 - Daily number of newly confirmed cases reported in United States since January 10, 2020. As of May 10, 2020, United States changed their standards of diagnosing COVID-19 and their cases were adjusted to 1,245,775.
Ultimately the impact of this disease on the population will be driven by the evolution of the epidemic. Excessive focus on the disease has the potential to distort real risk and significantly increase anxiety for the reasons previously discussed.
Information and education are key factors in the management of infectious disease. We have discussed the importance of having an anchor against which to measure risk. We have chosen influenza as a known entity against which to compare risks. COVID-19 is not a type of influenza and we are certainly not down playing its importance. Currently this disease seems to be more severe. Until we know the number of mild cases we cannot be certain about the relative severity on a case by case basis. Influenza has an infection fatality rate of approximately 0.1%. The crude fatality rate of COVID-19 in Hong Kong is currently 0.39%.
Influenza kills 650,000 people globally from respiratory disease. Although the numbers for COVID-19 are currently smaller they are accelerating. Whether COVID-19 has a greater or lesser disease burden than influenza will be determined by the ultimate size of the epidemic. This will depend in large part on the success of the public health interventions and comparison will only be possible in retrospect. Look at the changing numbers rather than cumulative COVID-19 numbers in isolation in order to get a sense of changing risk.
We will continue to update the article as data becomes available and information changes.
1. Verdoni, L., Mazza, A., Gervasoni, A., Martelli, L., Ruggeri, M., Ciuffreda, M., … Dantiga, L. (2020). An outbreak of severe Kawasaki-like disease at the Italian epicentre of the SARS-CoV-2 epidemic: an observational cohort study. The Lancet. doi: 10.1016/s0140-6736(20)31103-x
2. Yang, X., Yu, Y., Xu, J., Shu, H., Xia, J., Liu, H., … Shang, Y. (2020). Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. The Lancet Respiratory Medicine, 8(5), 475–481. doi: 10.1016/s2213-2600(20)30079-5
3. Remuzzi, A., & Remuzzi, G. (2020). COVID-19 and Italy: what next? The Lancet, 395(10231), 1225–1228. doi: 10.1016/s0140-6736(20)30627-9
4. Hung, I. F.-N., Lung, K.-C., Tso, E. Y.-K., Liu, R., Chung, T. W.-H., Chu, M.-Y., … Yuen, K.-Y. (2020). Triple combination of interferon beta-1b, lopinavir–ritonavir, and ribavirin in the treatment of patients admitted to hospital with COVID-19: an open-label, randomised, phase 2 trial. The Lancet. doi: 10.1016/s0140-6736(20)31042-4
5. Wu, Wang, A., Liu, M., Wang, Q., Chen, J., Xia, S., … Huang, J. (2020, January 1). Neutralizing antibody responses to SARS-CoV-2 in a COVID-19 recovered patient cohort and their implications. Retrieved May 15, 2020, from https://www.medrxiv.org/content/10.1101/2020.03.30.20047365v2
6. Real-time dashboard. (n.d.). Retrieved from https://covid19.sph.hku.hk/
7. World Health Organization. Novel Coronavirus (2019-nCoV) situation reports. Retrieved from https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports