PMC:7050133 / 23700-34413 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7050133","sourcedb":"PMC","sourceid":"7050133","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7050133","text":"Disscussion\nIn this study, we used a novel approach to distill information from the cumulative number of diagnosed cases of COVID-19 infection. Among various types of surveillance data, this data often reported the earliest and on a continuous basis with high completeness and are most widely available. In addition, patients with a diagnosed infection are those with high likelihoods to spread the virus to others. Findings from this study provided useful information in a real time manner to monitor, evaluate and forecast the COVID-19 epidemic in China. The methods used in this study although somewhat mathematical, are easy to follow while information extracted from the commonly used data with the methods are highly useful and more sensitive than the daily new and cumulative cases.\n\nNonlinear and chaotic nature of the COVID-19 outbreak\nAlthough an analytical demonstration of the COVID-19 outbreak as nonlinear, chaotic and catastrophic requires more time to wait till the epidemic ends, evidence in the first 2 months suggests that the COVID-19 outbreak in China is nonlinear and chaotic. The epidemic emerged suddenly after a long latent period without dramatic changes as revealed from the cumulative cases, and their first and second derivatives. The high responsiveness of the epidemic to interventions adds additional evidence supporting the chaotic and catastrophic nature, and demonstrating the selection of a good timing to start intervention. Many of these characters are similar to those observed in the 2003 SARS epidemic started in Hong Kong [2], the 2013–16 Ebola spread in the West Africa [4, 5], the 2009 pandemic of H1N1 started in the US [6–8], and the measles outbreaks over 80 cities in the US recently [9]. Even the seasonal common flu has been proved to have a nonlinear component [11, 12].\nThe significance of nonlinear and chaotic nature of COVID-19 means that no methods are available to predict exactly at what point in time the epidemic will emerge as an outbreak, just like volcanoes and earthquakes. Therefore, practically there is no so-called a best time or missed the best time to take actions. There will also no so-called rational analysis and rational responses. There is no silver bullet to use, no standard-operating-procedure (SOP) to follow, and no measures without negative consequences to control the epidemic [2]. For example, it took more than 6 months for both the US and the WHO to determine the 2009 H1N1 pandemic as an outbreak [13, 14]. Therefore, knowing the nonlinear and chaotic nature of an epidemic outbreak, like COVID-19, for all stockholders will be essential to the mobilization of resources, working together, taking all actions possible to control the epidemic, and minimizing the negative consequences.\nSpecifically, what we can do to deal with an outbreak like COVID-19 would be to (1) collect information as early as possible, (2) monitor the epidemic as close as possible just like we do for an earthquake and make preparations for a hurricane and (3) communicate with the society and use confirmed data appropriately reframed not causing or exacerbating fear and panic in the public, stress and distress among medical and public health professionals, as well as administrators to make right decisions and take the right strategies at the right time in the right places for the right people.\nKnowing the nonlinear and chaotic nature is also essential for taking actions to control the outbreak of an epidemic like the COVID-19 infection. As soon as an outbreak is confirmed, the follow measures should be in position immediately 1) closely and carefully monitor the epidemic; 2) take evidence-based interventions to control the epidemic, 3) actively assess responses of the epidemic to the interventions; 4) allow errors in the intervention, particularly during the early period of the epidemic, 5) always prepare for alternatives.\nAnother confusion is, when an epidemic starts, everyone asks what it is? How does it happen? How should I do to avoid infection? Is there any effective treatment? Answering these questions takes time, but there is no need to wait till all these questions are resolved before taking actions. We can take actions to prevent COVID-19 immediately while waiting for answers to these questions. This is because we have the evidence-based strategy for control and prevention of any infectious disease without complete understanding of an infection. That is so-called Tri-Component Strategy: locating and controlling the sources of infection, identifying and blocking the transmission paths, and protecting those who are susceptible [10].\nThis was just what China has done, is doing, and will continue to do this time. Typical examples of control and prevention measures include locking down of cities, communities, and villages with potential of large scale transmission, massive environment sterilization, promotion of mask use, efforts to locate, isolate and treat the infected. More importantly, most of these actions are initiated, mobilized, coordinated and supported by the government from central to local, and enhanced by volunteers and international support.\n\nHighly effective of the national effort\nAnother important piece of findings is that we detected the effect of the national efforts taken by China from the beginning when they were in position till the end of this study. For example, from the second derivative, we observed increases in the infected through the action on January 22, 2020, the next day after the massive intervention started on January 21, 2020. This result was also picked up by the exponential modeling. From day one on January 21, 2020 when the massive intervention measures activated to February 4, 2020 is the latent period of COVID-19 infection. The second derivative precisely recorded the change in newly diagnosed cases in response to the massive measures, reflected as the rapid increase in detection rate, consistent with the result from the exponential modeling analysis.\nThe detected responsiveness of the epidemic to the intervention provided data to predict the occurrence of deceleration of the epidemic on February 4, 2020 if the same measures persist, which was exactly what we observed from the second derivative. Based on the findings from our analysis, the COVID-19 in China may end up soon. Despite a delay of 43 days from the first confirmed cases on December 8, 2019 to January 20, 2020, the COVID-19 epidemic was highly responsive to massive interventions, supporting the effectiveness of these interventions. It is our prediction that the outbreak of the COVID-19 infection will be brought under control by the end of February 2020, given the effective control measures known to everyone, increases in immune level in the total population due to latent infections, and most widely spread of knowledge and skills for infectious disease control and prevention among the 1.4 billion people in China.\n\nEffective methods for surveillance\nThere are a number of advantages of methods we developed and used in this study. First, framing the diagnosed cases as the cumulative, the first and the second derivative constructs a system to gauge the epidemic, with the cumulative cases showing the overall level of the epidemic, the first derivative to reflect the change of the epidemic, and the second derivative to monitor the speed of change. By inclusion of the mortality rate as a reference, results from our approach will be (1) comprehensive to inform the public to be prepared, not scared and not to blame others; (2) useful for administrators to make decisions; (3) valuable for medical and health professionals to take actions.\nSecond, we conceptually separated (1) the true number of infections, which will never be practically detected, from (2) the infections that are practically detectable if services are available and accessible and detection technologies are sensitive and reliable, and (3) the actually detected cases of infections. This classification greatly improved our understanding of the observed data as well as findings from the two derivatives, and aided us in assessing the responsiveness to the massive interventions, and predicting of the epidemic over time. The clarification also enhanced our analytical approach by adding an exponential model to evaluate the detection rate and to bring more data assessing the responsiveness of the epidemic to the massive interventions. We highly recommend the inclusion of the methods as a part of routine surveillance in disease control and prevention institutions.\n\nLimitations and future plan\nThere are limitations. First, this study covered only the first 2 months of the epidemic. We will continue to evaluate the utility of this method as we follow the development of the epidemic. Second, the methods used in this study was based on a close population. This hypothesis may not be true because of a large number of people with potential history of exposure in China traveled to other countries. Up to February 8, 2020, the total cases diagnosed were 37,552 worldwide (Worldometer on Coronavirus) with 37,198 in China, which accounted for 99.1% of the total number of the world. Therefore, the impact of close-population assumption would be rather limited. Third, there was a lack of individual patient-level data for detailed analysis. Fourth, our model can be further improved with other data, such as cases by severity, number of the suspected, number of those who received treatments and treatment results. We will follow the epidemic closely and prepare for further research on the topic when more data become available.\nDespite the limitations, this study provided new data to encourage those who are infected to better fight against the infections; to inform and encourage the general public, the medical and health professionals and the government to continue their current measures and to think of more measures that are innovative and effective to end the COVID-19 epidemic. One of the greatest motivations for this study is to attempt to provide right information at the population level in a real manner to complement the data from micro-organism centered and laboratory-based biological, molecular, pharmacological and clinical information in both the academic and the mass media that often scare rather than encourage people, even health professionals. Of the diagnosed COVID-19 cases, less than 20% are severe. Findings from our study indicated that there is no need to be panic from a public health population perspective. Although the total cases COVID-19 reached to big numbers, but the 2-month incidence rate was about a half of the natural death rate for Wuhan 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